ABSTRACT
The electrocardiogram (ECG), a record of
the biopotentials is associated with the
contractions of the heart muscle. A description of
some of the common types of signals that can be
derived from the ECG is also presented, together
with the motivation behind the analysis of these
signals.
The variation in the time period separating
consecutive heartbeats has come to be
conventionally described as heart rate variability
(HRV). These inter-beat intervals can be
measured conveniently as the separation between
the R peaks of adjacent QRS complexes in a
continuous electrocardiogram recording. The
sympathetic and parasympathetic branches of the
autonomic nervous system (ANS) regulate the
activity of the sinoatrial node, the cardiac
pacemaker. The beat-to-beat variation in the heart
rate therefore reflects the time varying influences
of the ANS and its components, on cardiac
function.
Heart rate variability, the variation over time of
the period between consecutive heartbeats, is
predominantly dependent on the extrinsic
regulation of the heart rate. HRV is thought to
reflect the heart’s ability to adapt to changing
circumstances by detecting and quickly
responding to unpredictable stimuli. HRV
analysis ability to assess overall cardiac health
and state of the autonomic nervous system
responsible for regulating cardiac activity
underlies its promise as a major new tool in the
diagnostics.
Basically , the support vector machine is a
linear machine. The main idea of a support vector
machine is to construct a hyperplane as the
decision surface in such a way that the margin of
separation between positive and negative
examples is maximized.
The support vectors consist of a small subset of
the training data extracted by the algorithm.
Depending on how this inner-product kernel is
generated, we may construct different learning
machines characterized by nonlinear decision
surfaces of their own. In particular, we may use
the support vector learning algorithm to construct
the following three types of learning machines:
• Linear machines
• Polynomial learning machines
• Radial-basis function networks
The steps involved in SVM classification are:
1. Training Phase
2. Testing Phase
We use the support vector learning algorithm
to implement the learning process using a given
set of training data and finally classifying the
testing data into respective classes. In many
applications, support vector machine (SVM) has
outperformed many other machine learning
methods and has established itself as a powerful
tool for classification problems.
KEYWORDS
ECG - Electro Cardio Gram
HRV - Heart Rate Variability
SVM - Support Vector Machines
OBJECTIVE
The objective of our thesis is to classify
arrhythmias using Support Vector Machines based
on features of HRV. HRV analysis is a powerful
tool to assess autonomic function in both health
and disease.
This study was conducted with a goal to:
1. Obtain time and frequency parameters of
HRV for the data taken from MIT-BIH
arrhythmia database using LABVIEW.
2. Test the significance of the obtained
results using analysis of variance.
3. Train the SVM using the obtained data.
4. Predict the respective classes for the
testing data.
PROPOSED SYSTEM
This thesis explores techniques for signal
processing of HRV from motivation to application
in an attempt to develop robust methods for HRV
analysis.
The study was conducted to validate and to
generate the measures in time/frequency domains
of HRV studies. This program will be utilized to
derive HRV parameters from ECG signals. All
time/frequency domain parameters were chosen
and programs were successfully implemented in
classifying the features into respective classes.
The data is collected from the MIT-BIH
arrhythmia database. The tachograms is extracted
from the ECG signal which is further divided into
small segments of 32 segments. They are
characterized by MIT-BIH arrhythmia database
annotations. Using XVIEW these annotated
segments are made readable into LABVIEW. This
gives the HRV parameters in time and frequency
domain. Using analysis of variance, the features
are reduced based on p-value obtained. These
features are given to a classifier called SVM, for
training and the remaining features for testing.
This classifies all the features into the respective
classes.
The proposed SVM classifier shows
satisfactory performances in discriminating four
types of arrhythmia. The discrimination of NSR,
VF, PVC and VF classes was found to have very
high accuracy of 96.5% .
Support Vector Machines (SVM) are
powerful kernel based learning machines which
combine essentially two strong ideas: maximum
margin classifiers with low capacity and implicit
features spaces defined by kernel functions. The
main idea of a support vector machine is to
construct a hyperplane in such a way that the
margin of separation between positive and
negative examples is maximized. More precisely
the support vector machine is an approximate
implementation of the method of structural risk
minimization. The support vector machine can
provide a good generalization performance on
pattern classification problems.
The basic pattern recognition problem is:
given a dataset with elements having certain class
labels, how can one design a machine that will
classify a new data element (whose class is
unknown), i.e., assign it one of the class labels? A
Support Vector Machine (SVM) is one such
machine that can perform pattern recognition; its
use, though, is not limited to this one application.
While most classifiers work on designing
rules that will place decision boundaries between
data belonging to different classes, SVMs go a
step ahead and design what are called “Support
Vectors” such that the data belonging to different
classes is now separated by a region rather than
just a hyperplane. Thus, the distinction between
classes is made more obvious, in an intuitive
sense. The following figure makes this clear:
Figure 1: Hyperplane
In the context of the figure 4.1.1, illustrated
for 2-class linearly separable data, the design of
the conventional classifier would be just to
identify the decision boundary w between the two
classes. However, SVMs identify support vectors
(SVs) on the planes-H1 and H2. They will create a
margin between the two classes, thus ensuring
that the data is “more separable” than in the case
of the conventional classifier.
Formally put, if we let {xi, yi} be the training
data x with class label y, yi {-1, +1}, then
any point lying on the hyperplane separating the
two classes (i.e., the decision boundary) will
satisfy w.x + b = 0, with w being the normal to the
hyperplane and |b|/||w|| being the perpendicular
distance of the hyperplane from the origin. Then if
d+ and d- are the distances of the points lying
nearest to the hyperplane in the classes (+1) and (-
1) respectively, then the support vectors are
designed such that these points lie on them, and
all other training points lie on the side different
from the other support vector. Thus, for training
data not lying on the support vectors, we have yi (
w.xi + b ) 0 for all i. Then, the support vectors
are separated by a distance equal to the minimum
distance between the training data for different
classes, i.e., the points lying on the support
vectors. That is from figure(1) :
The distance between H and H1 is:
|w•x+b|/||w||=1/||w||
{Because: the distance from a point (x0,y0) to a
line is given by: Ax+By+c = 0 is |A x0 +B y0
+c|/sqrt (A2+B2)}.
The distance between H1 and H2 is: 2/||w||
In order to maximize the margin, we need to
minimize ||w||. With the condition that there are
no datapoints between H1 and H2:
xi•w+b +1 when yi =+1
xi•w+b -1 when yi =-1
Can be combined into yi(xi•w) 1
Problem is: minimize ||w||, s.t. discrimination
boundary is obeyed, i.e., min f(x) s.t.
g(x)=0,where
f: ½ ||w||2 and
g: yi(xi•w)-b = 1 or [yi(xi•w)-b] - 1 =0
This is a constrained optimization problem
which is solved by Lagrangian multiplier
method.+
When the data is not linearly separable, we
introduce what are called Slack Variables to put a
penalty on the number and extent of outliers that
are not linearly separable. These slack variables,
represented by i, allow the constraints to be
relaxed in order to accommodate the non-linearly
separable outliers during SV design, but introduce
a further cost while doing so. Thus, the
mathematics involved now restructures the
optimization problem now: minimize ||w||2/2 + C
( i)k, where C is a Cost parameter that can be
introduced by the designer. for various values of
k.
It may be noted that mathematically, this
problem is equivalent to searching for a higher
dimension where the data are linearly separable,
and then designing a linear classifier in that higher
dimension. The kernel function (K {xi, xj}) is used
to map the data to higher dimension.
HEART RATE VARIABILITY IN THE
TIME DOMAIN
The simplest and oldest measure would be
the evaluation of the heart rate with respect to its
variation in time. This is called the time domain
analysis of HRV. In this method, the heart rate at
any moment or the intervals between successive
normal complexes are determined and analyzed.
In such methods the instantaneous heart rate or
the inter-beat intervals are determined and
concurrently its variations over time are studied.
All the studies of HRV originally begin from a
raw ECG signals. The interval between two
consecutive QRS intervals is termed the inter-beat
interval or IBI. Various statistical parameters are
derived from inter-beat variations. These form the
core of the time domain analysis.
Time domain measures are divided into:
• Simple time domain measures
• Complex time domain measures
1. Segmented measures
2. Interval differences
TIME DOMAIN MEASURES
1. The simple time domain measures are mean
RR, RR std , mean HR and HR std.
• RR Mean
It is the mean of the time interval between
consecutive heart beats that is the RR intervals.
It is measured in seconds.
• RR std
Standard deviation is defined as the rootmean-
square (RMS) deviation of the values from
their mean, or as the square root of the variance.
The standard deviation of RR intervals is called
RR std. It is measured in seconds.
• Mean HR
It is the no of contractions of the heart in one
min. It is expressed as “beats per min” (bpm).
• HR std
It is standard deviation of heart rates. It is
expressed as “beats per min” (bpm).
2. The most commonly used measure derived
from interval differences include
NN50, pNN50 and RMSSD
• NN50
NN50 is the number of intervals successive NN
intervals greater than 50ms.
• pNN50
This measure of the time domain analysis of
HRV studies is an interval difference parameter. It
is defined as the percentage of interval differences
of successive RR intervals greater than 50ms by
the total number of NN intervals. This measure
estimates high frequency variations in heart rate
and correlates to HF measures of the frequency
domain. pNN50 is a percentage and hence no unit.
pNN50 is given by the following equation.
• RMSSD
RMSSD is the square root of the mean
squared difference of successive NN intervals.
This is given by following equation.
This measure of time domain HRV analysis
may be defined as the square root of the mean of
the sum of the squares of the differences between
adjacent RR intervals. This is another measure of
short term variations of HRV and estimates high
frequency variations in heart rate. It is highly
correlated to pNN50 and corresponds to the HF
pNN50=NN50/ Total number of NN intervals
RMSSD=SQRT(MEAN([D1²,D2²,……….
DN²]))
measures in frequency domain. The unit of
RMSSD is ms.
HEART RATE VARIABILITY USING
GEOMETRIC METHODS
The series of NN intervals can also be
converted into geometric pattern, such as sample
density distribution of NN durations, sample
density distribution of difference between
adjacent NN intervals. This method judges
variability based on the geometric and/or graphic
properties of the resulting pattern.
Three general approaches are used in geometric
methods:
• A basic measurement of the geometric
pattern (for example, the width of the
distribution histogram at the specified
level) is converted into the measure of
HRV.
• The geometric pattern is interpolated by a
mathematically defined shape (for
example, approximation of the distribution
histogram by a triangle or approximation
of the differential histogram by an
exponential curve) and then the parameters
of this mathematical shape are used, and
• The geometric shape is classified into
several pattern-based categories that
represent different classes of HRV.
Most geometric methods require the RR (or
NN) interval sequence to be measured on or
converted to a discrete scale that is not too fine or
too coarse and permits the construction of
smoothed histograms.
Geometric measures include:
HRV Triangular Index, TINN
• HRV Triangular Index
The HRV triangular index measurement is
the integral of the density distribution (that is, the
number of all NN intervals) divided by the
maximum of the density distribution. Using a
measurement of NN intervals on a discrete scale,
the measure is approximated by the value
This is dependent on the length of the bin, that is,
on the precision of the discrete scale of
measurement. Thus, if the discrete approximation
of the measure is used with NN interval
measurement on a scale different from the most
frequent sampling of 128 Hz, the size of the bins
should be quoted.
Figure 2: Triangular index
• TINN
TINN stands for Triangular interpolation of
NN interval histogram is the baseline width of the
distribution measured as a base of a triangle
approximating the NN interval distribution (the
minimum square difference is used to find such a
triangle). Details of computing HRV triangular
index and TINN are shown. Both these measures
express overall HRV measured over 24 hours and
are more influenced by the lower than by the
higher frequencies. Other geometric methods are
still in the phase of exploration and explanation.
HEART RATE VARIABILITY IN
FREQUENCY DOMAIN
Various spectral methods for the analysis of
the tachogram have been applied since the late
1960s. Power spectral density (PSD) analysis
provides the basic information of how power
(variance) distributes as a function of frequency.
HRV Triangular index = total number of all
NN intervals) / ( Max. number of NN
intervals in the modal bin)
Independent of the method used, only an estimate
of the true PSD of the signal can be obtained by
proper mathematical algorithms.
SHORT-TERM RECORDINGS
Three main spectral components are
distinguished in a spectrum calculated from shortterm
recordings of 2 to 5 minutes, these are
• LF( Low Frequency)
• HF(High Frequency)
• VLF(Very Low Frequency)
The distribution of the power and the
central frequency of LF and HF are not fixed but
may vary in relation to changes in autonomic
modulations of heart period. In theory the VLF
component is also present but the physiological
explanation of the VLF component is much less
defined. VLF assessed from short term recordings
must be avoided when the power spectral
densities of the short term ECG are interpreted.
This is because the physiological explanation of
the VLF component is much less defined, and the
existence of a specific physiological process
attributable to these heart period changes might
even be questioned. The non harmonic
component, which does not have coherent
properties and is affected by algorithms of
baseline or trend removal, is commonly accepted
as a major constituent of VLF. Thus, VLF
assessed from short-term recordings ( 5 minutes)
is a dubious measure and should be avoided when
the PSD of short-term ECGs is interpreted.
The measurement of VLF, LF, and HF
power components is usually made in absolute
values of power (milliseconds squared). LF and
HF may also be measured in normalized units,
which represent the relative value of each power
component in proportion to the total power minus
the VLF component. The representation of LF and
HF in normalized units emphasizes the controlled
and balanced behavior of the two branches of the
autonomic nervous system. Moreover, the
normalization tends to minimize the effect of the
changes in total power on the values of LF and
HF components. Nevertheless, normalized units
should always be quoted with absolute values of
the LF and HF power in order to describe
completely the distribution of power in spectral
components.
RESULT
1. Feature vectors : RR mean and RR std
Test data
Actual
class
Predicted
class
0.66 0.24 4 4
0.57 0.18 4 4
0.56 0.27 4 4
0.83 0.09 3 3
0.84 0.04 3 3
0.83 0.08 3 3
0.68 0.16 2 2
0.65 0.16 2 2
1.11 0.06 1 1
1.01 0.13 1 1
0.94 0.1 1 1
Result Table
CLASSIFIER PERFORMANCE:
Class Sensitivity
(%)
Specificity
(%)
Accuracy
(%)
NSR 100 100 100
VF 100 100 100
PVC 100 100 100
AF 100 100 100
Performance Evaluation Table
The overall accuracy was found to be 100%.
2. Feature vectors: HR mean and HR std
Test data
Actual
class
Predicted
class
135.52 45.32 4 4
151.37 86.55 4 4
131.87 94.28 4 1
125.98 44.44 4 4
70.71 2.27 3 1
77.93 41.22 3 3
71.35 4.3 3 3
74.66 20.27 3 3
94.81 29.92 2 2
99.19 30.46 2 2
89.22 16.15 1 1
61.15 16.13 1 1
54.51 4.23 1 1
64.11 30.73 1 1
66.89 22.18 1 1
Result Table 2
CLASSIFIER PERFORMANCE:
Class Sensitivity
(%)
Specificity
(%)
Accuracy
(%)
NSR 100 80 86
VF 100 100 100
PVC 75 100 93
AF 75 100 93
Performance Evaluation Table 3
The overall accuracy was found to be 93%.
CONCLUSION
• In this thesis, we proposed a SVM based
arrhythmia classification algorithm which
provided a new approach to the problem of
pattern recognition.
• With respect to our thesis, the pattern-
Heart Rate Variability along with SVM
provided a unique method to classify
Arrhythmias.
• Our work has achieved an overall
accuracy of 96.5% for the total of four
features in the time domain.
• The results showed that the proposed
method is effective for classification of
cardiac arrhythmia, with acceptable high
accuracy. It is evident that the combination
of the linear and nonlinear features
together with the employed classifier is
very effective.
• The main advantage of this method
compared to other approaches in the
literature is that it is completely based on
RR-interval signal which can be extracted
with high accuracy even for noisy or
complicated ECG recordings, while the
extraction of all other type of ECG
analysis is seriously affected by noise.
BIBLIOGRAPHY
1. A Tutorial on Support Vector Machines
for Pattern Recognition by
CHRISTOPHER J.C. BURGES.
2. www.physionet.org/physiobank/database/
mitdb/ - 14k
3. Support Vector Machines for
Classification and Regression by Steve R.
Gunn.
4. Kernel Machines and Classifier Ensemble
Learning for Biomedical Applications by
Peng Li.
5. Support Vector Machinesen.
wikipedia.org/wiki/Support_vector_ma
chine.
6. Kernel –machines .org-kernel machineswww.
kernel-machines.org.
7. Support Vector machines for classification
and regression
www.ecs.soton.ac.uk/~srg/publications/pd
f/SVM.pdf.
8. National Instruments Test and
Measurements-www.ni.com.
9. Support Vector Machine Based
Arrhythmia Classification Using Reduced
Features of Heart Rate Variability Signal-
Babak Mohammadzadeh-Asl, Seyed
Kamaledin Setarehdan.
10. http://asi.insarouen.
fr/enseignants/~arakotom/toolbox/in
dex.html.
11. Project- RR interval.htm.
12. www.actapress.com/PDFViewer.aspx?pap
erId=33038
13. ieeexplore.ieee.org/iel5/10755/33900/0161
5671.pdf
Thursday, February 19, 2009
Support Vector Machine (SVM) as Post Classifier for Epilepsy Risk Level Classifications from Fuzzy based EEG Signal Parameters
Benefits of using Virtual Instrumentation
Virtual instrumentation allows us to use mainstream computer technologies combining it
with innovative software and flexible hardware. This makes it possible for us to develop
computer based instrumentation solutions. the concept of virtual instrumentation enables
students, engineers and scientists to build powerful applications for increasing productivity and
performance by Reduces programming complexity. Features such as virtual instrument
simulation makes it user friendly in a way to identified and rectify errors.
Products Used
Lab view 8.2
DAQ: NI 6259
[Bus P,Pc ,Express; Analog Input:32, Analog Output:4; Sollution:16 bit; Maxupdate Rate:2.8
Ms/s; Digital Input:48]
Problem to be solved
Epilepsy is caused due to Cumulative firing of neurons in brain. Once a diagnosis of
epilepsy is established, it is important to begin treatment right away. The longer treatment is
delayed, the more difficult the epilepsy is to treat. In this paper, the software and hardware
details of a prototype PC based monitoring unit for diagnosis of risk level of epilepsy allowing
the physician to monitor the patient’s epilepsy risk level to decide appropriate therapeutic
measures
Solution to the problem
Support Vector Machine (SVM) is used for pattern classification and non linear
regression like multilayer perceptrons and Radial Basis Function networks. SVM is now
regarded as important example of ‘Kernel Methods’. The main idea of SVM is to construct a
hyper plane as the decision surface in such a way that the margin of separation between positive
and negative examples is minimized. The SVM is an approximate implementation of method of
structural minimization. In SVM we investigate the optimization of fuzzy outputs in the
classification of Epilepsy Risk Levels from EEG (Electroencephalogram) signals. The fuzzy
techniques are applied as a first level classifier to classify the risk levels of epilepsy based on
extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and
covariance from the EEG signals of the patient.
Introduction
Support Vector Machine (SVM) is used as a post classifier to obtain the
optimized risk level characteristics of a epileptic patient. People attacked by epilepsy are
unnoticed and this leads to other events such as a stroke, which also causes falls or migraines. In
India number of persons suffering from epilepsy is increasing per year. The complexity involved
in the diagnosis and therapy is to be cost effective in nature. Airports, amusement parks, and
shopping malls are just a few of the places where computers are used to diagnosis a person’s
Epilepsy risk levels if a life threatening condition occurs. In some situation there is not always a
trained doctor’s and neuro scientists on hand. This project work is intended to synthesis a cost
effective SVM mechanism to classify the epilepsy risk level of the patients and to mimic a
doctor’s and neuro scientist’s diagnosis.
The EEG (Electroencephalogram) signals of 20 patients are collected from Sri
Ramakrishna Hospitals at Coimbatore and their risk level of epilepsy is identified after
converting the EEG signals to code patterns by fuzzy systems. This type of classification helped
doctor’s and neuro surgeons in giving appropriate therapeutic measures to the patients. This
project helps to save a patient’s life when a life threatening condition occurs. This scientific
project is carried in order to save a patient’s life and also to create public awareness among
people about the risk ness of epilepsy.
The project can be further improved by collecting EEG signals of another 10 patients and
diagnosis can be made in classifying the patient’s risk level and to design a cost effective therapy
device for the doctors. Since it is done in off line methodology further improvement is needed to
diagnose in on line. In on line method minute to minute diagnosis can be obtained for higher risk
level epilepsy patients and mass screening of epilepsy disease is possible.
The block diagram of epilepsy classifier is shown in figure1. This is accomplished as:
1. Fuzzy classification for epilepsy risk level at each channel from EEG signals and its
parameters.
2. Each channel results are optimized, since they are at different risk levels.
3. Performance of fuzzy classification before and after the SVM optimization methods is
analyzed.
Figure 1 SVM- Fuzzy Classification System
EEG Signal
Parameter
Fuzzy
System
Code
Patterns
SVM
Risk level
output
Hardware description
The Electroencephalogram signals from epileptic patients are to be collected from
hospitals. Then the EEG signals are then converted to code patterns by fuzzy systems. The
figure2 below shows how EEG signals are converted for processing;
Figure 2 EEG Signal Conversion
The output of a fuzzy system represents a wide space of risk levels. This is due to sixteen
different channels of input to the system in three epochs. This yields a total of forty-eight input
output pairs. Since we deal with known cases of epileptic patients, it is indispensable to find the
exact level of risk the patient. SVM optimization will also aid in the development of automated
systems that can precisely classify the risk level of the epileptic patient under observation. Hence
an optimization of the outputs of the fuzzy system is initiated. This will improvise the
classification of the patient’s state and can provide the EEGer with a clear picture.
Application description
The output of a fuzzy system represents a wide space of risk levels. This is due to sixteen
different channels of input to the system in three epochs. This yields a total of forty-eight input
output pairs. Since we deal with known cases of epileptic patients, it is indispensable to find the
exact level of risk the patient. Due to the low value of performance index (40%), quality value
(6.25) it is necessary to optimize the output of the fuzzy systems. Hence we are moving to SVM
classification which gives a performance index of 98% and a quality value of 22.94.
The following tasks are carried out to classify the risk levels by SVM which are,
1. First a simplest case is analyzed with hyper plane as decision function with the known linear
data.
2. A non linear classification is done for the codes obtained from a particular patient by using
quadratic discrimination.
3. Then the k-means clustering is performed for large data with different sets of clusters with
centroid for each.
4. The centroid obtained is mapped by the kernel function for obtaining a proper shape.
5. A linear separation is obtained by using SVM with kernel and k-means clustering
The parameters derived from the EEG signal are stored as data sets. Then the fuzzy
technique is used to obtain the risk level at every EEG channel. The objective was to classify
perfect risk levels with high rate of classification. Though it is impossible to obtain a perfect
performance in all these conditions, some compromises have been made. The classification rate
of epilepsy risk level of above 98% is possible in our method. The number of cases from the
present twenty patients has to be increased for better testing of the system. From this method we
can infer the occurrence of High-risk level frequency and the possible medication to the patients.
Also optimizing each region’s data separately can solve the focal epilepsy problem. The future
research is in the direction of a comparison between heuristic optimization models with SVM.
In fuzzy techniques more suboptimal solutions are arrived. These solutions are to be
optimized to arrive a better solution for identifying patient’s epilepsy risk level. For optimization
of fuzzy outputs the Support Vector Machine (SVM) method is identified.
The following solution constrains steps are followed:
Step 1: The linearization and convergence is done using Quadratic Optimization. The primal
minimization problem is transformed into its dual optimization problem of maximizing the dual
lagrangian LD with respect to :
Max LD =
(1)
Subject to
(2)
(3)
Step 2: The optimal separating hyper plane is constructed by solving the quadratic programming
problem defined by (1)-(3). In this solution, those points have non-zero Lagrangian multipliers (
) are termed support vectors.
Step 3: Support vectors lie closest to the decision boundary. Consequently, the optimal hyper
plane is only determined by the support vectors in the training data.
Step 4: The k-means clustering is done for the given set of data. The k-means function will form
a group of clusters according to the condition given in step2 and step3. Suppose for a group of 3
clusters, k-means function will randomly choose 3 centre points from the given set. Each centre
point will acquire the values that are present around them.
Step 5: Now there will be six centre points three from each epochs and then the SVM training
process is done by the Kernel methods. Thus, only the kernel function is used in the training
algorithm, and one does not need to know the explicit form of . Some of the commonly used
kernel functions are:
Polynomial function:
Radial Basis Function:
Sigmoid function:
The hyper plane and support vectors are used to separate linearly separable and non-linearly
separable data. The figure3, 4below shows the VI simulation of our project,
Figure 3 VI Simulation
Figure 4 VI Simulation
Kernel Functions
One of the major tricks of SVM learning is the use of kernel functions to extend the class
of decision functions to the non-linear case. This is done by applying the data from the input
space into a high dimensional feature space by a function and solving the linear
learning problem in . The actual function does not need to be known, it suffices to have a
kernel function which calculates the inner product in the feature space.
It was noticed by Sch¨olkopf in that the kernel function defines a distance measure d on the input
space by
(4)
(5)
This shows the kernel function can be interpreted as a measure of similarity between the
examples of and .
Linear kernel
The linear kernel is the most simple kernel function. The decision function
takes the form . When one uses the linear kernel to predict time series, i. e.
, this means the resulting model is an statistical
autoregressive model of the order k (AR[k]). With this kernel, time series are taken to be similar,
if they are generated by the same AR-model.
RBF kernels
Radial basis kernels take the form clearly, the similarity of two
examples is simply judged by their Euclidian distance. In terms of time series, this has a parallel
in the so called phase space representation. Assume the time series is generated by a function
such that . If one takes the time series and plots the (k+1)-
dimensional vectors, the resulting plot is a part of the graph of g, so the function g can be
estimated from the time series. Especially, assuming that the function is linear and the data is
generated by
where is a Gaussian noise (i. e. the time series model is AR[1]), it can
be shown that most of the data lies in an ellipsoid defined by the mean of the time series and the
variance of . In this is used in the phase space procedure for finding outliers in the time series.
This shows that information about a window of a time series can be gotten from other windows
of the time series that are similar in means of the euclidian distance, which makes the RBF
kernel promising for time series analysis.
Fourier Kernel
A common transformation for the analysis of time series data is to use the Fourier
transform (see Figure4). This representation is useful if the information of the time series does
not lie in the individual values at each time point but in the frequency of some events. It was
noted by Vapnik that the inner product of the Fourier expansion of two time series can be
directly calculated by the regularized kernel function
METHODOLOGY
The hyper plane and support vectors are used to separate linearly separable and nonlinearly
separable data. In this project we used, Radial Basis Kernel function (RBF) [4] for this
non-linear classification. RBF is a curve fitting approximation in higher dimensional space.
According to this learning it is equivalent to finding a surface in multi dimensional space that
provides a best fit by utilizing the training data and generalization is equivalent to use of this
multidimensional surface to interpolate the test data. It draws up on a traditional strict
interpolation in multidimensional space. Thus RBF provides a set of the testing data which acts
as a “basis” for input patterns when expanded into hidden space. From the set of RBF testing
values the Mean Square Error (MSE) and Average MSE is performed and error values are
calculated. The tool used in this study is mat lab v7.2 and Lab View 8.2.
An important factor for the choice of a classification method for a given problem is the
available a-priori knowledge. During the last few years support vector machines (SVM) have
shown to be widely applicable and successful particular in cases where a-priori knowledge
consists of labeled learning data. If more knowledge is available, it is reasonable to incorporate
and model this knowledge within the classification results or to require less training data.
Therefore, much active research is dealing with adapting the general SVM methodology to cases
where additional a-priori knowledge is available. We have focused on the common case where
variability of data can be modeled by transformations which leave the class membership
unchanged. If these transformations can be modeled by mathematical groups of transformations
one can incorporate this knowledge independently of the classifier during the feature extraction
stage by group integration, normalization etc. this leads to variant features, on which any
classification algorithm can be applied.
It is noted that one of main assumptions of SVM is that all samples in the training set are
independent and identically distributed (i.i.d), however, in many practical engineering
applications, the obtained training data is often contaminated by noise. Further, some samples in
the training data set are misplaced on the wrong side by accident. These known as outliers. In
this case, the standard SVM training algorithm will make decision boundary deviate severely
from the optimal hyper plane, such that, the SVM is very sensitive to noise, and especially those
outliers that are close to decision boundary. This makes the standard SVM no longer sparse, that
is, the number of support vectors increases significantly due to outliers. In this project, we
present a general method that follows the main idea of SVM using adaptive margin for each data
point to formulate the minimization problem, which uses the RBF kernel trick. It is noted that the
classification functions obtained by minimizing MSE are not sensitive to outliers in the training
set. The reason that classical MSE is immune to outliers is that it is an average algorithm. A
particular sample in the training set only contributes little to the final result. The effect of outliers
can be eliminated by taking average on samples. That is why the average technique is a simple
yet effective tool to tackle outliers.
In order to avoid outliers we utilized the RBF kernel functions and also decision
functions for determining the margin of each classes. Since we are analyzing twenty epilepsy
patients through leave one out methods and ten fold cross validation. Based on the MSE value
and Average MSE values of SVM models the classifications of epilepsy risk levels are validated.
The following fig 5 depicts the training and testing MSE of SVM models. The outliers problem
is solved through Average MSE method which is shown in figure 6.
MSE of Training and testing SVM Models
0
0.001
0.002
0.003
0.004
0.005
0.006
1
3
5
7
9
11
13
15
17
19
Patients
MSE of SVM Models
Series1
testing
Figure.5 MSE of Training and Testing of SVM Models
Average MSE under Testing
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
1
3
5
7
9
11
13
15
17
19
Patients
Average MSE
Average MSE under
Testing
Figure .6 Average MSE under Testing of SVM Models
Figure 7 shows the details of training data with Perfect Classification (PC) from which up
to 20% of training data set the perfect classification of 100% is obtained. When the training done
by the outliers the PC of epilepsy risk level is slipped to 95% level and finally all the sets of data
are trained the PC is settled at 98% only.
Training of Data with Perfect Classification
92
93
94
95
96
97
98
99
100
101
10 20 30 40 50 60 70 80 90 100
Percentage of Training Data
Perfect classification
Figure .7 Average MSE under Testing of SVM Models
Test Results
In SVM the performance classification is about 97.39% which is very high when
compared with Fuzzy logic which is 50% only. The sensitivity and selectivity of SVM is also
more when compared to the latter. The missed classification of SVM is 1.458% but it is about
20% in Fuzzy Network and the value of PI in SVM is 97.07 and 40 in Fuzzy. Table I indicates
the result details of Fuzzy and SVM methods.
TABLE I. PERFORMANCE INDEX
The PI calculated for the aforesaid classification methods using (8) for SVM optimization
is 97.07 which are higher than Fuzzy technique. It is evident that the optimizations give a
better performance than the Fuzzy techniques due to its lower false alarms and missed
classifications. This optimization model is evaluated in terms of its receiver operating
characteristics (ROC) curve for test data sets. This enables the user to evaluate a model in
terms of the trade-off between sensitivity and specificity. ROC matrices are used to show
how changing detection threshold affects detection versus false alarms. If the threshold is set
too high then the system will miss too much detection. Conversely, if the threshold is very
Methods
Perfect
Classification
Missed
Classification
False
Alarm
Performance
Index
Fuzzy logic 50 20 10 40
SVM Optimization 97.39 1.458 1.385 97.07
low then there will be heavy false alarms. The percentage of detections classified correctly is
plotted against the percentage of non -detections in correctly classified as detections (i.e.
false alarms) as a function of the detection threshold. ROC is the best way to evaluate a
detector.
The performance of classification for test data set is assessed by calculating the area
under the ROC curve of AZ . It is noticed that the values of AZ from range of 0.5 to 1 for a
perfect classifier. A good trade-off is observed between detections and false alarms. ROC
curve for the Fuzzy classifier with SVM optimization are shown in figure 8.
ROC of SVM Post Classifier
86
88
90
92
94
96
98
100
102
100
100
100
100
100
100
100
93.75
100
93.75
Specificty
Sensitivity
Figure 8. ROC of Fuzzy and SVM Classifiers
In Order to compare different classifier we need a measure that reflects the overall
quality of the classifier. Their quality is determined by three factors. Classification rate,
Classification delay and False Alarm rate. The quality value QV is defined as
( fa ) ( dly dct msd )
V R T P P
C
Q
+ 0.2 * * + 6*
= (6)
Where, C is the scaling constant
Rfa is the number of false alarm per set
Tdly is the average delay of the on set classification in seconds
Pdct is the percentage of perfect classification and
Pmsd is the percentage of perfect risk level missed
A constant C is empirically set to 10 because this scale is the value of QV to an easy
reading range. The higher value of QV, the better the classifier among the different classifier,
the classifier with the highest QV should be the best. Figure 9 depicts the details of quality
values for each patient. Table V shows the Comparison of the fuzzy and SVM optimization
techniques. It is observed from Table II, that SVM method is performing well with the
highest performance index and quality values.
Quality Value
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Patient
Quality Value
Figure 9: Quality value for Data set
TABLE II: COMPARISON RESULTS OF CLASSIFIERS TAKEN AS AVERAGE OF ALL TEN
PATIENTS
Parameters Fuzzy
Techniques
Without
Optimization
Optimization
With SVM
Technique
Perfect Classification (%) 50 97.39
Missed Classification (%) 20 1.458
False Alarm 10 1.389
Weighted Delay in secs 4 2.031
Performance Index (%) 40 97.07
Sensitivity 83.33 98.59
Specificity 71.42 98.52
Quality Value 6.25 22.94
CONCLUSION
This Project investigates the performance of SVM in optimizing the epilepsy risk level of
epileptic patients from EEG signals. The parameters derived from the EEG signal are stored as
data sets. Then the fuzzy technique is used to obtain the risk level from each epoch at every EEG
channel. The objective was to classify perfect risk levels with high rate of classification, a short
delay from onset, and a low false alarm rate. Though it is impossible to obtain a perfect
performance in all these conditions, some compromises have been made. As a high false alarm
rate ruins the effectiveness of the system, a low false-alarm rate is most important. SVM
optimization techniques are used to optimize the risk level by incorporating the above goals. The
classification rate of epilepsy risk level of above 98% is possible in our method. The missed
classification is almost 1.458 for a short delay of 2.031 seconds. The number of cases from the
present twenty patients has to be increased for better testing of the system. From this method we
can infer the occurrence of High-risk level frequency and the possible medication to the patients.
Also optimizing each region’s data separately can solve the focal epilepsy problem.
Sources:
1. Leon D.Iasemidis etal., Adaptive Epileptic SeizurePrediction System, IEEE Transactions on
Biomedical Engineering, May 2003,50(5): 616-627.
2. K P Adlassnig, Fuzzy Set Theory in Medical diagnosis, IEEE Transactions on Systems Man
Cybernetics, March 1986,16: 260-265.
3. Alison A Dingle et al, A Multistage system to Detect epileptic form activity in the EEG,IEEE
Transactions on Biomedical Engineering,1993, 40(12):1260-1268.
4. Haoqu and Jean Gotman, A patient specific algorithm for detection onset in long-term EEG
monitoring possible use as warning device, IEEE Transactions on Biomedical Engineering,
February 1997,44(2): 115-122.
5. Arthur C Gayton, Text Book of Medical Physiology, Prism Books Pvt. Ltd., Bangalore, 9th
Edition, 1996.
6. J.Seunghan Park et al, TDAT Domain Analysis Tool for EEG Analysis, IEEE Transactions on
Biomedical Engineering, August 1990,37(8): 803-811.
7. Donna L Hudson, Fuzzy logic in Medical Expert Systems, IEEE EMB Magazine,
November/December 1994,13(6): 693-698.
8. R.Harikumar and B.Sabarish Narayanan, Fuzzy Techniques for Classification of Epilepsy risk
level from EEG Signals, Proceedings of IEEE Tencon – 2003, 14-17 October 2003,Bangalore,
India, 209-213.
9. Mark van Gils, Signal processing in prolonged EEG recordings during intensive care, IEEE
EMB Magazine November/December 1997,16(6): 56-63.
10. Celement.C etal, A Comparison of Algorithms for Detection of Spikes in the
Electroencephalogram,IEEE Transaction on Bio Medical Engineering, April 2003, 50 (4):
521-26.
11. Pamela McCauley-Bell and Adedeji B.Badiru, Fuzzy Modeling and Analytic Hierarchy
Processing to Quantify Risk levels Associated with Occupational Injuries- Part I: The
Development of Fuzzy- Linguistic Risk Levels, IEEE Transaction on Fuzzy Systems, 1996,4
( 2): 124-31.
12. Joel.J etal, Detection of seizure precursors from depth EEG using a sign periodogram
transform, IEEE Transactions on Bio Medical Engineering, April 2004,51 (4):449-458.
13. S.Haykin, Neural networks a Comprehensive Foundation, Prentice- Hall Inc. 2nd Ed. 1999.
14. Mu-chun Su, Chien –Hsing Chou, A modified version of the k-means clustering algorithm with
a distance based on cluster symmetry, IEEE Transactions on Pattern Analysis and Machine
Intelligence June 2001, 23 (6): 674-680.
15. Rangaraj M. Rangayyan, Bio- Medical Signal Analysis A Case Study Approach, IEEE Press-
John Wiley &sons Inc New York 2002.
16. Sathish Kumar-Neural Networks, A Classroom Approach, McGraw-Hill New York, 2004.
17. Richard O. Duda, David G. Stroke, Peter E. Hart-Pattern Classification, second edition, A
Wiley-Interscience Publication, John Wiley and Sons, Inc, 2003.
18. Jehan Zeb Shah, Naomie bt Salim- Neural Networks and Support Vector Machines Based Bio-
Activity Classification, Proceedings of the 1st Conference on Natural Resources Engineering &
Technology 2006, 24-25th July 2006: Putra Jaya, Malaysia, 484-491.
19. Qing song, Wenjie Hu, and Wenfang Xie, Robust Support Vector Machine With Bullet Hole
Image Classification, IEEE Transaction on SMC Part C, 2002,32 ( 4):440-448.
20.V.Vapnik, Statistical Learning Theory, Wiely Chichester, GB,19
Virtual instrumentation allows us to use mainstream computer technologies combining it
with innovative software and flexible hardware. This makes it possible for us to develop
computer based instrumentation solutions. the concept of virtual instrumentation enables
students, engineers and scientists to build powerful applications for increasing productivity and
performance by Reduces programming complexity. Features such as virtual instrument
simulation makes it user friendly in a way to identified and rectify errors.
Products Used
Lab view 8.2
DAQ: NI 6259
[Bus P,Pc ,Express; Analog Input:32, Analog Output:4; Sollution:16 bit; Maxupdate Rate:2.8
Ms/s; Digital Input:48]
Problem to be solved
Epilepsy is caused due to Cumulative firing of neurons in brain. Once a diagnosis of
epilepsy is established, it is important to begin treatment right away. The longer treatment is
delayed, the more difficult the epilepsy is to treat. In this paper, the software and hardware
details of a prototype PC based monitoring unit for diagnosis of risk level of epilepsy allowing
the physician to monitor the patient’s epilepsy risk level to decide appropriate therapeutic
measures
Solution to the problem
Support Vector Machine (SVM) is used for pattern classification and non linear
regression like multilayer perceptrons and Radial Basis Function networks. SVM is now
regarded as important example of ‘Kernel Methods’. The main idea of SVM is to construct a
hyper plane as the decision surface in such a way that the margin of separation between positive
and negative examples is minimized. The SVM is an approximate implementation of method of
structural minimization. In SVM we investigate the optimization of fuzzy outputs in the
classification of Epilepsy Risk Levels from EEG (Electroencephalogram) signals. The fuzzy
techniques are applied as a first level classifier to classify the risk levels of epilepsy based on
extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and
covariance from the EEG signals of the patient.
Introduction
Support Vector Machine (SVM) is used as a post classifier to obtain the
optimized risk level characteristics of a epileptic patient. People attacked by epilepsy are
unnoticed and this leads to other events such as a stroke, which also causes falls or migraines. In
India number of persons suffering from epilepsy is increasing per year. The complexity involved
in the diagnosis and therapy is to be cost effective in nature. Airports, amusement parks, and
shopping malls are just a few of the places where computers are used to diagnosis a person’s
Epilepsy risk levels if a life threatening condition occurs. In some situation there is not always a
trained doctor’s and neuro scientists on hand. This project work is intended to synthesis a cost
effective SVM mechanism to classify the epilepsy risk level of the patients and to mimic a
doctor’s and neuro scientist’s diagnosis.
The EEG (Electroencephalogram) signals of 20 patients are collected from Sri
Ramakrishna Hospitals at Coimbatore and their risk level of epilepsy is identified after
converting the EEG signals to code patterns by fuzzy systems. This type of classification helped
doctor’s and neuro surgeons in giving appropriate therapeutic measures to the patients. This
project helps to save a patient’s life when a life threatening condition occurs. This scientific
project is carried in order to save a patient’s life and also to create public awareness among
people about the risk ness of epilepsy.
The project can be further improved by collecting EEG signals of another 10 patients and
diagnosis can be made in classifying the patient’s risk level and to design a cost effective therapy
device for the doctors. Since it is done in off line methodology further improvement is needed to
diagnose in on line. In on line method minute to minute diagnosis can be obtained for higher risk
level epilepsy patients and mass screening of epilepsy disease is possible.
The block diagram of epilepsy classifier is shown in figure1. This is accomplished as:
1. Fuzzy classification for epilepsy risk level at each channel from EEG signals and its
parameters.
2. Each channel results are optimized, since they are at different risk levels.
3. Performance of fuzzy classification before and after the SVM optimization methods is
analyzed.
Figure 1 SVM- Fuzzy Classification System
EEG Signal
Parameter
Fuzzy
System
Code
Patterns
SVM
Risk level
output
Hardware description
The Electroencephalogram signals from epileptic patients are to be collected from
hospitals. Then the EEG signals are then converted to code patterns by fuzzy systems. The
figure2 below shows how EEG signals are converted for processing;
Figure 2 EEG Signal Conversion
The output of a fuzzy system represents a wide space of risk levels. This is due to sixteen
different channels of input to the system in three epochs. This yields a total of forty-eight input
output pairs. Since we deal with known cases of epileptic patients, it is indispensable to find the
exact level of risk the patient. SVM optimization will also aid in the development of automated
systems that can precisely classify the risk level of the epileptic patient under observation. Hence
an optimization of the outputs of the fuzzy system is initiated. This will improvise the
classification of the patient’s state and can provide the EEGer with a clear picture.
Application description
The output of a fuzzy system represents a wide space of risk levels. This is due to sixteen
different channels of input to the system in three epochs. This yields a total of forty-eight input
output pairs. Since we deal with known cases of epileptic patients, it is indispensable to find the
exact level of risk the patient. Due to the low value of performance index (40%), quality value
(6.25) it is necessary to optimize the output of the fuzzy systems. Hence we are moving to SVM
classification which gives a performance index of 98% and a quality value of 22.94.
The following tasks are carried out to classify the risk levels by SVM which are,
1. First a simplest case is analyzed with hyper plane as decision function with the known linear
data.
2. A non linear classification is done for the codes obtained from a particular patient by using
quadratic discrimination.
3. Then the k-means clustering is performed for large data with different sets of clusters with
centroid for each.
4. The centroid obtained is mapped by the kernel function for obtaining a proper shape.
5. A linear separation is obtained by using SVM with kernel and k-means clustering
The parameters derived from the EEG signal are stored as data sets. Then the fuzzy
technique is used to obtain the risk level at every EEG channel. The objective was to classify
perfect risk levels with high rate of classification. Though it is impossible to obtain a perfect
performance in all these conditions, some compromises have been made. The classification rate
of epilepsy risk level of above 98% is possible in our method. The number of cases from the
present twenty patients has to be increased for better testing of the system. From this method we
can infer the occurrence of High-risk level frequency and the possible medication to the patients.
Also optimizing each region’s data separately can solve the focal epilepsy problem. The future
research is in the direction of a comparison between heuristic optimization models with SVM.
In fuzzy techniques more suboptimal solutions are arrived. These solutions are to be
optimized to arrive a better solution for identifying patient’s epilepsy risk level. For optimization
of fuzzy outputs the Support Vector Machine (SVM) method is identified.
The following solution constrains steps are followed:
Step 1: The linearization and convergence is done using Quadratic Optimization. The primal
minimization problem is transformed into its dual optimization problem of maximizing the dual
lagrangian LD with respect to :
Max LD =
(1)
Subject to
(2)
(3)
Step 2: The optimal separating hyper plane is constructed by solving the quadratic programming
problem defined by (1)-(3). In this solution, those points have non-zero Lagrangian multipliers (
) are termed support vectors.
Step 3: Support vectors lie closest to the decision boundary. Consequently, the optimal hyper
plane is only determined by the support vectors in the training data.
Step 4: The k-means clustering is done for the given set of data. The k-means function will form
a group of clusters according to the condition given in step2 and step3. Suppose for a group of 3
clusters, k-means function will randomly choose 3 centre points from the given set. Each centre
point will acquire the values that are present around them.
Step 5: Now there will be six centre points three from each epochs and then the SVM training
process is done by the Kernel methods. Thus, only the kernel function is used in the training
algorithm, and one does not need to know the explicit form of . Some of the commonly used
kernel functions are:
Polynomial function:
Radial Basis Function:
Sigmoid function:
The hyper plane and support vectors are used to separate linearly separable and non-linearly
separable data. The figure3, 4below shows the VI simulation of our project,
Figure 3 VI Simulation
Figure 4 VI Simulation
Kernel Functions
One of the major tricks of SVM learning is the use of kernel functions to extend the class
of decision functions to the non-linear case. This is done by applying the data from the input
space into a high dimensional feature space by a function and solving the linear
learning problem in . The actual function does not need to be known, it suffices to have a
kernel function which calculates the inner product in the feature space.
It was noticed by Sch¨olkopf in that the kernel function defines a distance measure d on the input
space by
(4)
(5)
This shows the kernel function can be interpreted as a measure of similarity between the
examples of and .
Linear kernel
The linear kernel is the most simple kernel function. The decision function
takes the form . When one uses the linear kernel to predict time series, i. e.
, this means the resulting model is an statistical
autoregressive model of the order k (AR[k]). With this kernel, time series are taken to be similar,
if they are generated by the same AR-model.
RBF kernels
Radial basis kernels take the form clearly, the similarity of two
examples is simply judged by their Euclidian distance. In terms of time series, this has a parallel
in the so called phase space representation. Assume the time series is generated by a function
such that . If one takes the time series and plots the (k+1)-
dimensional vectors, the resulting plot is a part of the graph of g, so the function g can be
estimated from the time series. Especially, assuming that the function is linear and the data is
generated by
where is a Gaussian noise (i. e. the time series model is AR[1]), it can
be shown that most of the data lies in an ellipsoid defined by the mean of the time series and the
variance of . In this is used in the phase space procedure for finding outliers in the time series.
This shows that information about a window of a time series can be gotten from other windows
of the time series that are similar in means of the euclidian distance, which makes the RBF
kernel promising for time series analysis.
Fourier Kernel
A common transformation for the analysis of time series data is to use the Fourier
transform (see Figure4). This representation is useful if the information of the time series does
not lie in the individual values at each time point but in the frequency of some events. It was
noted by Vapnik that the inner product of the Fourier expansion of two time series can be
directly calculated by the regularized kernel function
METHODOLOGY
The hyper plane and support vectors are used to separate linearly separable and nonlinearly
separable data. In this project we used, Radial Basis Kernel function (RBF) [4] for this
non-linear classification. RBF is a curve fitting approximation in higher dimensional space.
According to this learning it is equivalent to finding a surface in multi dimensional space that
provides a best fit by utilizing the training data and generalization is equivalent to use of this
multidimensional surface to interpolate the test data. It draws up on a traditional strict
interpolation in multidimensional space. Thus RBF provides a set of the testing data which acts
as a “basis” for input patterns when expanded into hidden space. From the set of RBF testing
values the Mean Square Error (MSE) and Average MSE is performed and error values are
calculated. The tool used in this study is mat lab v7.2 and Lab View 8.2.
An important factor for the choice of a classification method for a given problem is the
available a-priori knowledge. During the last few years support vector machines (SVM) have
shown to be widely applicable and successful particular in cases where a-priori knowledge
consists of labeled learning data. If more knowledge is available, it is reasonable to incorporate
and model this knowledge within the classification results or to require less training data.
Therefore, much active research is dealing with adapting the general SVM methodology to cases
where additional a-priori knowledge is available. We have focused on the common case where
variability of data can be modeled by transformations which leave the class membership
unchanged. If these transformations can be modeled by mathematical groups of transformations
one can incorporate this knowledge independently of the classifier during the feature extraction
stage by group integration, normalization etc. this leads to variant features, on which any
classification algorithm can be applied.
It is noted that one of main assumptions of SVM is that all samples in the training set are
independent and identically distributed (i.i.d), however, in many practical engineering
applications, the obtained training data is often contaminated by noise. Further, some samples in
the training data set are misplaced on the wrong side by accident. These known as outliers. In
this case, the standard SVM training algorithm will make decision boundary deviate severely
from the optimal hyper plane, such that, the SVM is very sensitive to noise, and especially those
outliers that are close to decision boundary. This makes the standard SVM no longer sparse, that
is, the number of support vectors increases significantly due to outliers. In this project, we
present a general method that follows the main idea of SVM using adaptive margin for each data
point to formulate the minimization problem, which uses the RBF kernel trick. It is noted that the
classification functions obtained by minimizing MSE are not sensitive to outliers in the training
set. The reason that classical MSE is immune to outliers is that it is an average algorithm. A
particular sample in the training set only contributes little to the final result. The effect of outliers
can be eliminated by taking average on samples. That is why the average technique is a simple
yet effective tool to tackle outliers.
In order to avoid outliers we utilized the RBF kernel functions and also decision
functions for determining the margin of each classes. Since we are analyzing twenty epilepsy
patients through leave one out methods and ten fold cross validation. Based on the MSE value
and Average MSE values of SVM models the classifications of epilepsy risk levels are validated.
The following fig 5 depicts the training and testing MSE of SVM models. The outliers problem
is solved through Average MSE method which is shown in figure 6.
MSE of Training and testing SVM Models
0
0.001
0.002
0.003
0.004
0.005
0.006
1
3
5
7
9
11
13
15
17
19
Patients
MSE of SVM Models
Series1
testing
Figure.5 MSE of Training and Testing of SVM Models
Average MSE under Testing
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
1
3
5
7
9
11
13
15
17
19
Patients
Average MSE
Average MSE under
Testing
Figure .6 Average MSE under Testing of SVM Models
Figure 7 shows the details of training data with Perfect Classification (PC) from which up
to 20% of training data set the perfect classification of 100% is obtained. When the training done
by the outliers the PC of epilepsy risk level is slipped to 95% level and finally all the sets of data
are trained the PC is settled at 98% only.
Training of Data with Perfect Classification
92
93
94
95
96
97
98
99
100
101
10 20 30 40 50 60 70 80 90 100
Percentage of Training Data
Perfect classification
Figure .7 Average MSE under Testing of SVM Models
Test Results
In SVM the performance classification is about 97.39% which is very high when
compared with Fuzzy logic which is 50% only. The sensitivity and selectivity of SVM is also
more when compared to the latter. The missed classification of SVM is 1.458% but it is about
20% in Fuzzy Network and the value of PI in SVM is 97.07 and 40 in Fuzzy. Table I indicates
the result details of Fuzzy and SVM methods.
TABLE I. PERFORMANCE INDEX
The PI calculated for the aforesaid classification methods using (8) for SVM optimization
is 97.07 which are higher than Fuzzy technique. It is evident that the optimizations give a
better performance than the Fuzzy techniques due to its lower false alarms and missed
classifications. This optimization model is evaluated in terms of its receiver operating
characteristics (ROC) curve for test data sets. This enables the user to evaluate a model in
terms of the trade-off between sensitivity and specificity. ROC matrices are used to show
how changing detection threshold affects detection versus false alarms. If the threshold is set
too high then the system will miss too much detection. Conversely, if the threshold is very
Methods
Perfect
Classification
Missed
Classification
False
Alarm
Performance
Index
Fuzzy logic 50 20 10 40
SVM Optimization 97.39 1.458 1.385 97.07
low then there will be heavy false alarms. The percentage of detections classified correctly is
plotted against the percentage of non -detections in correctly classified as detections (i.e.
false alarms) as a function of the detection threshold. ROC is the best way to evaluate a
detector.
The performance of classification for test data set is assessed by calculating the area
under the ROC curve of AZ . It is noticed that the values of AZ from range of 0.5 to 1 for a
perfect classifier. A good trade-off is observed between detections and false alarms. ROC
curve for the Fuzzy classifier with SVM optimization are shown in figure 8.
ROC of SVM Post Classifier
86
88
90
92
94
96
98
100
102
100
100
100
100
100
100
100
93.75
100
93.75
Specificty
Sensitivity
Figure 8. ROC of Fuzzy and SVM Classifiers
In Order to compare different classifier we need a measure that reflects the overall
quality of the classifier. Their quality is determined by three factors. Classification rate,
Classification delay and False Alarm rate. The quality value QV is defined as
( fa ) ( dly dct msd )
V R T P P
C
Q
+ 0.2 * * + 6*
= (6)
Where, C is the scaling constant
Rfa is the number of false alarm per set
Tdly is the average delay of the on set classification in seconds
Pdct is the percentage of perfect classification and
Pmsd is the percentage of perfect risk level missed
A constant C is empirically set to 10 because this scale is the value of QV to an easy
reading range. The higher value of QV, the better the classifier among the different classifier,
the classifier with the highest QV should be the best. Figure 9 depicts the details of quality
values for each patient. Table V shows the Comparison of the fuzzy and SVM optimization
techniques. It is observed from Table II, that SVM method is performing well with the
highest performance index and quality values.
Quality Value
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Patient
Quality Value
Figure 9: Quality value for Data set
TABLE II: COMPARISON RESULTS OF CLASSIFIERS TAKEN AS AVERAGE OF ALL TEN
PATIENTS
Parameters Fuzzy
Techniques
Without
Optimization
Optimization
With SVM
Technique
Perfect Classification (%) 50 97.39
Missed Classification (%) 20 1.458
False Alarm 10 1.389
Weighted Delay in secs 4 2.031
Performance Index (%) 40 97.07
Sensitivity 83.33 98.59
Specificity 71.42 98.52
Quality Value 6.25 22.94
CONCLUSION
This Project investigates the performance of SVM in optimizing the epilepsy risk level of
epileptic patients from EEG signals. The parameters derived from the EEG signal are stored as
data sets. Then the fuzzy technique is used to obtain the risk level from each epoch at every EEG
channel. The objective was to classify perfect risk levels with high rate of classification, a short
delay from onset, and a low false alarm rate. Though it is impossible to obtain a perfect
performance in all these conditions, some compromises have been made. As a high false alarm
rate ruins the effectiveness of the system, a low false-alarm rate is most important. SVM
optimization techniques are used to optimize the risk level by incorporating the above goals. The
classification rate of epilepsy risk level of above 98% is possible in our method. The missed
classification is almost 1.458 for a short delay of 2.031 seconds. The number of cases from the
present twenty patients has to be increased for better testing of the system. From this method we
can infer the occurrence of High-risk level frequency and the possible medication to the patients.
Also optimizing each region’s data separately can solve the focal epilepsy problem.
Sources:
1. Leon D.Iasemidis etal., Adaptive Epileptic SeizurePrediction System, IEEE Transactions on
Biomedical Engineering, May 2003,50(5): 616-627.
2. K P Adlassnig, Fuzzy Set Theory in Medical diagnosis, IEEE Transactions on Systems Man
Cybernetics, March 1986,16: 260-265.
3. Alison A Dingle et al, A Multistage system to Detect epileptic form activity in the EEG,IEEE
Transactions on Biomedical Engineering,1993, 40(12):1260-1268.
4. Haoqu and Jean Gotman, A patient specific algorithm for detection onset in long-term EEG
monitoring possible use as warning device, IEEE Transactions on Biomedical Engineering,
February 1997,44(2): 115-122.
5. Arthur C Gayton, Text Book of Medical Physiology, Prism Books Pvt. Ltd., Bangalore, 9th
Edition, 1996.
6. J.Seunghan Park et al, TDAT Domain Analysis Tool for EEG Analysis, IEEE Transactions on
Biomedical Engineering, August 1990,37(8): 803-811.
7. Donna L Hudson, Fuzzy logic in Medical Expert Systems, IEEE EMB Magazine,
November/December 1994,13(6): 693-698.
8. R.Harikumar and B.Sabarish Narayanan, Fuzzy Techniques for Classification of Epilepsy risk
level from EEG Signals, Proceedings of IEEE Tencon – 2003, 14-17 October 2003,Bangalore,
India, 209-213.
9. Mark van Gils, Signal processing in prolonged EEG recordings during intensive care, IEEE
EMB Magazine November/December 1997,16(6): 56-63.
10. Celement.C etal, A Comparison of Algorithms for Detection of Spikes in the
Electroencephalogram,IEEE Transaction on Bio Medical Engineering, April 2003, 50 (4):
521-26.
11. Pamela McCauley-Bell and Adedeji B.Badiru, Fuzzy Modeling and Analytic Hierarchy
Processing to Quantify Risk levels Associated with Occupational Injuries- Part I: The
Development of Fuzzy- Linguistic Risk Levels, IEEE Transaction on Fuzzy Systems, 1996,4
( 2): 124-31.
12. Joel.J etal, Detection of seizure precursors from depth EEG using a sign periodogram
transform, IEEE Transactions on Bio Medical Engineering, April 2004,51 (4):449-458.
13. S.Haykin, Neural networks a Comprehensive Foundation, Prentice- Hall Inc. 2nd Ed. 1999.
14. Mu-chun Su, Chien –Hsing Chou, A modified version of the k-means clustering algorithm with
a distance based on cluster symmetry, IEEE Transactions on Pattern Analysis and Machine
Intelligence June 2001, 23 (6): 674-680.
15. Rangaraj M. Rangayyan, Bio- Medical Signal Analysis A Case Study Approach, IEEE Press-
John Wiley &sons Inc New York 2002.
16. Sathish Kumar-Neural Networks, A Classroom Approach, McGraw-Hill New York, 2004.
17. Richard O. Duda, David G. Stroke, Peter E. Hart-Pattern Classification, second edition, A
Wiley-Interscience Publication, John Wiley and Sons, Inc, 2003.
18. Jehan Zeb Shah, Naomie bt Salim- Neural Networks and Support Vector Machines Based Bio-
Activity Classification, Proceedings of the 1st Conference on Natural Resources Engineering &
Technology 2006, 24-25th July 2006: Putra Jaya, Malaysia, 484-491.
19. Qing song, Wenjie Hu, and Wenfang Xie, Robust Support Vector Machine With Bullet Hole
Image Classification, IEEE Transaction on SMC Part C, 2002,32 ( 4):440-448.
20.V.Vapnik, Statistical Learning Theory, Wiely Chichester, GB,19
Tuesday, February 10, 2009
Shape and size recognition of complex pictures
The Challenge
Shape is an important cue as it captures a prominent element of an object. While humans
can effortlessly recognize thousands of object shapes despite changes in illumination,
pose, articulation, etc., it has so far not been possible to mimic similar competence in a
computer. This is mainly because the amount of visual information present in the real
world is clearly immense and recognizing objects from visual information is challenging.
Recognition remains challenging in large part due to the significant variations exhibited
by real-world images like Partial occlusions, viewpoint changes, varying illumination and
cluttered backgrounds .Human beings are equipped with a vision system that captures the
images of objects and sends them to the brain. Based on the signals received from the
brain, human beings do path planning effectively according to the nature and size of the
obstacles that are present in the way. But then a similar method has to be devised for A
Self navigating robotic vision that enables it to recognize the shape and size of objects for
effective path planning. That is the main Challenge
The Solution
To devise a method to identify the shape and o calculate the relative sizes of objects in a
given complex picture using National Instruments Lab View software
Introduction
Self navigating robot is a robot which is endowed with the capability for automatic
discovery of high-level perceptual features and hence performs large-scale actions for
learning to navigate in continuous environments In general all kinds of robots have a
vision system equipped with camera which acts as electronic eye for the robot. Vision is
one of the main parameter for effective functioning of any kind of robot. More so for a
self navigating robot because it is expected to do a lot of path planning beforehand so
That it can navigate on its own very effectively without any manual programs
System Implementation
The project is mainly divided into two modules namely shape recognition and size calculation
Shape recognition module
For shape recognition, first of all two databases one containing images of complex
pictures and the other containing images of different shapes is created This is done using
image extract method which involves specifying the source image window and target
image window and then extracting the image from the source and storing it in the target
image path in which the databases need to be created.
Then given an input image, the edges of all the objects in the image are enhanced
and all other parts of the image are made black. Using Thresholding VI.. The edge
information of the objects in the given picture is given as input to the pattern matching
VI.. The pattern matching first learns the edge pattern using learn pattern VI and then
compares it with the pattern of all the shapes present in the database. If it finds a suitable
match, it displays the name of the shape along with the number of such shapes present in
the given input picture.
Figure 1: Results for image extraction from image database
Figure 2: Result for image extraction from shape database
Figure 3: Appearance of output screen for shape recognition
Figure 4: Input image given for shape recognition
Figure 5: Final results for Shape recognition
Size calculation module
For size calculation, first of all pictures of objects at three fixed distances from the field
of view of the camera are taken. Then the objects are separated from the background
using Thresholding VI .Then the total number of pixels of every object at each of these
distances is calculated. Then a database containing the images of objects at different
distances along with the values of total number of pixels present in them is created.
Given an input picture, objects in the picture are separated form the background and are
compared with the objects in the image size database using the pattern matching VI. If a
match is found, the distance of the object from the camera is checked. If the distance is
any value other than the distance stored in the database, values of total number of pixels
versus distance are calculated using Interpolation VI.
The value of total number of pixels is converted to real world units using a spatial
calibration mechanism which is based on mapping of total number of pixels to real world
units using the formula derived by me. I derived the formula by calculating the total
number of pixels and the real world dimensions of a large number of objects and then
deriving a correspondence between the two which will henceforth stand valid for all other
objects also. The correspondence that is derived is
Physical Area = Total number of pixels/4 pixels per sq.mm.
This formula can be used to calculate the real world measurement of any given object in
terms of square millimeters.
Figure 6: Initial screen for Size calculation
Figure 7: Final output screen for size calculation
Conclusion
Testing has been carried out extensively using different target images. Image database
has been created with extensive care to include all probable minimal sets of images.
Results are highly satisfactory as testing has been carried out with a large number of
complex pictures each containing different shapes. The different shapes in each picture
were displayed along with the number of such shapes present in the given picture. It was
observed that even if the sizes of the image in the database was different form the size of
the object images present in the database, the shapes were identified correctly.
The size recognition module also gave very good results during testing. Real time
measurements of large number of objects were taken and their values were compared
with the values computed by the program. The values were found to be approximately
equal
Shape is an important cue as it captures a prominent element of an object. While humans
can effortlessly recognize thousands of object shapes despite changes in illumination,
pose, articulation, etc., it has so far not been possible to mimic similar competence in a
computer. This is mainly because the amount of visual information present in the real
world is clearly immense and recognizing objects from visual information is challenging.
Recognition remains challenging in large part due to the significant variations exhibited
by real-world images like Partial occlusions, viewpoint changes, varying illumination and
cluttered backgrounds .Human beings are equipped with a vision system that captures the
images of objects and sends them to the brain. Based on the signals received from the
brain, human beings do path planning effectively according to the nature and size of the
obstacles that are present in the way. But then a similar method has to be devised for A
Self navigating robotic vision that enables it to recognize the shape and size of objects for
effective path planning. That is the main Challenge
The Solution
To devise a method to identify the shape and o calculate the relative sizes of objects in a
given complex picture using National Instruments Lab View software
Introduction
Self navigating robot is a robot which is endowed with the capability for automatic
discovery of high-level perceptual features and hence performs large-scale actions for
learning to navigate in continuous environments In general all kinds of robots have a
vision system equipped with camera which acts as electronic eye for the robot. Vision is
one of the main parameter for effective functioning of any kind of robot. More so for a
self navigating robot because it is expected to do a lot of path planning beforehand so
That it can navigate on its own very effectively without any manual programs
System Implementation
The project is mainly divided into two modules namely shape recognition and size calculation
Shape recognition module
For shape recognition, first of all two databases one containing images of complex
pictures and the other containing images of different shapes is created This is done using
image extract method which involves specifying the source image window and target
image window and then extracting the image from the source and storing it in the target
image path in which the databases need to be created.
Then given an input image, the edges of all the objects in the image are enhanced
and all other parts of the image are made black. Using Thresholding VI.. The edge
information of the objects in the given picture is given as input to the pattern matching
VI.. The pattern matching first learns the edge pattern using learn pattern VI and then
compares it with the pattern of all the shapes present in the database. If it finds a suitable
match, it displays the name of the shape along with the number of such shapes present in
the given input picture.
Figure 1: Results for image extraction from image database
Figure 2: Result for image extraction from shape database
Figure 3: Appearance of output screen for shape recognition
Figure 4: Input image given for shape recognition
Figure 5: Final results for Shape recognition
Size calculation module
For size calculation, first of all pictures of objects at three fixed distances from the field
of view of the camera are taken. Then the objects are separated from the background
using Thresholding VI .Then the total number of pixels of every object at each of these
distances is calculated. Then a database containing the images of objects at different
distances along with the values of total number of pixels present in them is created.
Given an input picture, objects in the picture are separated form the background and are
compared with the objects in the image size database using the pattern matching VI. If a
match is found, the distance of the object from the camera is checked. If the distance is
any value other than the distance stored in the database, values of total number of pixels
versus distance are calculated using Interpolation VI.
The value of total number of pixels is converted to real world units using a spatial
calibration mechanism which is based on mapping of total number of pixels to real world
units using the formula derived by me. I derived the formula by calculating the total
number of pixels and the real world dimensions of a large number of objects and then
deriving a correspondence between the two which will henceforth stand valid for all other
objects also. The correspondence that is derived is
Physical Area = Total number of pixels/4 pixels per sq.mm.
This formula can be used to calculate the real world measurement of any given object in
terms of square millimeters.
Figure 6: Initial screen for Size calculation
Figure 7: Final output screen for size calculation
Conclusion
Testing has been carried out extensively using different target images. Image database
has been created with extensive care to include all probable minimal sets of images.
Results are highly satisfactory as testing has been carried out with a large number of
complex pictures each containing different shapes. The different shapes in each picture
were displayed along with the number of such shapes present in the given picture. It was
observed that even if the sizes of the image in the database was different form the size of
the object images present in the database, the shapes were identified correctly.
The size recognition module also gave very good results during testing. Real time
measurements of large number of objects were taken and their values were compared
with the values computed by the program. The values were found to be approximately
equal
Tuesday, February 3, 2009
Speech enhancement for hearing aids
THE CHALLENGE:
In single channel system, the clean speech cannot be processed prior to being affected by the
noise. This is one of the most difficult situations in speech enhancement, since no reference
signal of the noise is available. The major thrust in hearing aid development today is changing
from further miniaturization to developing improved forms of signal processing for speech
enhancement. This change in emphasis comes at a fortuitous time in that recent advances in
digital technology provide the means for implementing substantially more advanced forms of
signal processing in modern hearing aids.
THE SOLUTION:
Developing an efficient and reliable single channel speech enhancement algorithm(with out
reference input) for modern digital hearing aids using Advanced Signal Processing Toolkit, DSP
Module and Adaptive Filter Toolkit with LABVIEW 8.6 real time software.
INTRODUCTION:
Hearing-aid users have great difficulty of understanding speech in a noisy and/or
reverberant acoustic environment. Speech enhancement means the improvement in intelligibility
and/or quality of degraded speech signal to reduce the listener’s fatigue by using signal
processing tools. Speech enhancement is a very difficult problem for two reasons. First, the
nature and characteristics of the noise signal can change dramatically in time and application to
application. It is therefore laborious to find versatile algorithms that really work in different
practical environments. Second, the performance measure can also be defined differently for
each application. The speech signal is a highly robust redundant signal so that if the loss of more
cues due to noise or other distortions will reduce speech intelligibility. As a consequence, people
with hearing loss are particularly susceptible to the damaging effects of background noise on
speech intelligibility.
COMMON SOURCES OF NOISE:
There are many factors that affect the speech signal during transmission at various
stages. In Fig given below the effect of various noise sources is shown. In the transmission side,
the effect of background noise is added with the desired signal and the signal from other speakers
are treated as noise for the desired speaker. The signal with background noise is transmitted
through the channel where the transmission noise is added with the desired signal.
PROBLEMS IN THE EXISTING TECHNIQUES:
The noise spectrum is estimated during pauses in the speech and then subtracted from the
speech-plus-noise spectrum when speech is present. Although this technique is effective in
Desired
Speaker
Commn.
Channel
Enhancement
Process
+ Listener
Background
Noise
Other
Speakers
Transmission Noise
reducing the background noise level, speech intelligibility remains essentially unchanged, or
reduced to some extent as a result of audible signal-processing distortions. Even with a small
decrement in intelligibility, listeners with hearing loss who are especially sensitive to
background noise have indicated a preference for the processed, slightly distorted signals over
the noisy unprocessed signals.
PROPOSED METHOD:
In this proposed system speech signal enhancement is done using adaptive LMS filter without
noise reference. It further reduces the residual effect usually exist in the most commonly used
algorithms like spectral subtraction, sub space method etc.,
SYSTEM IMPLEMENTATION:
The system basically divided in to three modules LMS filter,Delay loop,SNR calculation.
LMS FILTER:
LMS filter has been given with two inputs.One is the noisy signal input which is taken from the
CSLU (Center for Spoken Language Understanding) database.Another input is from the delay
loop output.The filter length has been a fixed one.The step size for the LMS filter will be varied
automatically by calculating the step size for the given input.
DELAY LOOP:
The second input for the LMS filter is given from this module.Thus the input for this module is
noisy speech , the sample delay and the maximum delay is fixed for all the samples of speeches.
SIGNAL TO NOISE RATIO (SNR):
The global SNR values are determined by the following equation,
( )
( ) ( ) [ ]
−
=
n
n
dB
s n s n
s n
SNR
2
2
10
ˆ
10 log --- (4.1)
s(n) = clean speech.
Sˆ(n) = Enhanced speech.
If the summation is performed over the whole signal length, the operation is called as
global SNR. As SNR decreases, the observation signal becomes noisier.
POWER SPECTRAL DENSITY (PSD) AND SPECTROGRAM PLOT:
In addition to the above validation parameter frequency domain analysis has to be done as far as
speech is concerned. In this work the PSD and Spectrogram (time -frequency) plot of the
enhanced speech and clean speech are more comparable.
MEAN OPINION SCORE (MOS):
The mean opinion score (MOS) provides a numerical measure of the quality of human speech
and it is necessary to make it distinguishable to the listener. The scheme uses subjective tests
(opinionated scores) that are mathematically averaged to obtain a quantitative indicator of the
system performance. To determine MOS, a number of listeners rate the quality of test sentences
by hearing test. Based on the perceived speech more comparable with clean speech. Quality of
enhanced speech listener gives a rating for each sentence as follows: (1) Bad (2) Poor (3) Fair
(4) Good (5) Excellent. The MOS is the arithmetic mean of all the individual scores, and can
range from 1 (worst) to 5 (best). In this work opinion score was collected from 10 listeners. The
average MOS obtained for this proposed method is between 3.5 to 4.
0
0.5
1
1.5
2
2.5
3
3.5
4
EMF Wiener
Filtering
BWT WPT SS Proposed
Method
Avg MOS
VARIOUS NOISY SAMPLES TAKEN FOR COMPARISON:
The clean input for reference & noisy test samples have been taken from SPEAR (Speech
Enhancement Assessment Resource) database of CSLU (with various input SNR) dB values.
Table-1 shows the SNR improvement obtained in the proposed is compared with the reported
results of other techniques.
TABLE- 1: SNR VALUES OF VARIOUS METHODS COMPARED WITH PROPOSED
METHOD
Noisy input
speech
PSS
(dB)
Wiener
filtering
(dB)
EMF
(dB)
WPT
(dB)
BWT
(dB)
DEK
F
(dB)
NRA
F
(dB)
Proposed
method(d
B)
Pink noisy
(0dB)
0.5
2.5
7
3.2
7.3
5.5
-
9.87
Pink noisy
(6dB)
-
-
-
-
-
3.99
-
10.5722
Cell noisy
(0dB)
-
-
8.95
-
-
-
- 11.05
White
stationary
noisy (0dB)
1.5
2.5
4.99
6.5
8
7.60
-
11.55
White
stationary
noisy (7dB)
3.7
3.8
13
10
12.5
4.76
-
16.57
White
Bursting
noisy (0dB)
-
-
-
-
-
9.95
-
11.9
White
Bursting
noisy (3dB)
-
-
-
-
-
-
-
15.16
Figure 1&3. shows the PSD , Time domin and Spectrogram plot of clean reference for
database,noisy speech and enhanced speech obtained using the proposed method. It is understood
from that the results of proposed method is more comparable with the clean reference signal.
Figure 2 shows the results of the same approach in matlab environment and in time domain itself
lot of speech activity portion were lost.
Figure 1: PSD, Time domain and spectrogram plot of the enhanced speech from white stationary
noise (0dB)
Figure 2: Enhanced speech obtained in Matlab environment
Figure 3: PSD, Time domain and spectrogram plot of the enhanced speech from white
bursting noise (3dB)
MATLAB VS LABVIEW:
In Matlab environment the execution time for the proposed method takes about two minutes. But
in labview environment the execution time is only in the range of milliseconds.
Social Impact:
This method have fast processing speed , low computational time and high efficiency which
results in efficient hearing even in noisy environment and it reduces the listener’s fatigue.
CONCLUSION:
The system developed is highly reliable and efficient for the above-mentioned problems in
digital hearing aids. We were able to develop the algorithm for speech enhancement and
evaluated its performance by calculating SNR improvement and studying its power spectral
density and spectrogram plot of the enhanced speech.
In single channel system, the clean speech cannot be processed prior to being affected by the
noise. This is one of the most difficult situations in speech enhancement, since no reference
signal of the noise is available. The major thrust in hearing aid development today is changing
from further miniaturization to developing improved forms of signal processing for speech
enhancement. This change in emphasis comes at a fortuitous time in that recent advances in
digital technology provide the means for implementing substantially more advanced forms of
signal processing in modern hearing aids.
THE SOLUTION:
Developing an efficient and reliable single channel speech enhancement algorithm(with out
reference input) for modern digital hearing aids using Advanced Signal Processing Toolkit, DSP
Module and Adaptive Filter Toolkit with LABVIEW 8.6 real time software.
INTRODUCTION:
Hearing-aid users have great difficulty of understanding speech in a noisy and/or
reverberant acoustic environment. Speech enhancement means the improvement in intelligibility
and/or quality of degraded speech signal to reduce the listener’s fatigue by using signal
processing tools. Speech enhancement is a very difficult problem for two reasons. First, the
nature and characteristics of the noise signal can change dramatically in time and application to
application. It is therefore laborious to find versatile algorithms that really work in different
practical environments. Second, the performance measure can also be defined differently for
each application. The speech signal is a highly robust redundant signal so that if the loss of more
cues due to noise or other distortions will reduce speech intelligibility. As a consequence, people
with hearing loss are particularly susceptible to the damaging effects of background noise on
speech intelligibility.
COMMON SOURCES OF NOISE:
There are many factors that affect the speech signal during transmission at various
stages. In Fig given below the effect of various noise sources is shown. In the transmission side,
the effect of background noise is added with the desired signal and the signal from other speakers
are treated as noise for the desired speaker. The signal with background noise is transmitted
through the channel where the transmission noise is added with the desired signal.
PROBLEMS IN THE EXISTING TECHNIQUES:
The noise spectrum is estimated during pauses in the speech and then subtracted from the
speech-plus-noise spectrum when speech is present. Although this technique is effective in
Desired
Speaker
Commn.
Channel
Enhancement
Process
+ Listener
Background
Noise
Other
Speakers
Transmission Noise
reducing the background noise level, speech intelligibility remains essentially unchanged, or
reduced to some extent as a result of audible signal-processing distortions. Even with a small
decrement in intelligibility, listeners with hearing loss who are especially sensitive to
background noise have indicated a preference for the processed, slightly distorted signals over
the noisy unprocessed signals.
PROPOSED METHOD:
In this proposed system speech signal enhancement is done using adaptive LMS filter without
noise reference. It further reduces the residual effect usually exist in the most commonly used
algorithms like spectral subtraction, sub space method etc.,
SYSTEM IMPLEMENTATION:
The system basically divided in to three modules LMS filter,Delay loop,SNR calculation.
LMS FILTER:
LMS filter has been given with two inputs.One is the noisy signal input which is taken from the
CSLU (Center for Spoken Language Understanding) database.Another input is from the delay
loop output.The filter length has been a fixed one.The step size for the LMS filter will be varied
automatically by calculating the step size for the given input.
DELAY LOOP:
The second input for the LMS filter is given from this module.Thus the input for this module is
noisy speech , the sample delay and the maximum delay is fixed for all the samples of speeches.
SIGNAL TO NOISE RATIO (SNR):
The global SNR values are determined by the following equation,
( )
( ) ( ) [ ]
−
=
n
n
dB
s n s n
s n
SNR
2
2
10
ˆ
10 log --- (4.1)
s(n) = clean speech.
Sˆ(n) = Enhanced speech.
If the summation is performed over the whole signal length, the operation is called as
global SNR. As SNR decreases, the observation signal becomes noisier.
POWER SPECTRAL DENSITY (PSD) AND SPECTROGRAM PLOT:
In addition to the above validation parameter frequency domain analysis has to be done as far as
speech is concerned. In this work the PSD and Spectrogram (time -frequency) plot of the
enhanced speech and clean speech are more comparable.
MEAN OPINION SCORE (MOS):
The mean opinion score (MOS) provides a numerical measure of the quality of human speech
and it is necessary to make it distinguishable to the listener. The scheme uses subjective tests
(opinionated scores) that are mathematically averaged to obtain a quantitative indicator of the
system performance. To determine MOS, a number of listeners rate the quality of test sentences
by hearing test. Based on the perceived speech more comparable with clean speech. Quality of
enhanced speech listener gives a rating for each sentence as follows: (1) Bad (2) Poor (3) Fair
(4) Good (5) Excellent. The MOS is the arithmetic mean of all the individual scores, and can
range from 1 (worst) to 5 (best). In this work opinion score was collected from 10 listeners. The
average MOS obtained for this proposed method is between 3.5 to 4.
0
0.5
1
1.5
2
2.5
3
3.5
4
EMF Wiener
Filtering
BWT WPT SS Proposed
Method
Avg MOS
VARIOUS NOISY SAMPLES TAKEN FOR COMPARISON:
The clean input for reference & noisy test samples have been taken from SPEAR (Speech
Enhancement Assessment Resource) database of CSLU (with various input SNR) dB values.
Table-1 shows the SNR improvement obtained in the proposed is compared with the reported
results of other techniques.
TABLE- 1: SNR VALUES OF VARIOUS METHODS COMPARED WITH PROPOSED
METHOD
Noisy input
speech
PSS
(dB)
Wiener
filtering
(dB)
EMF
(dB)
WPT
(dB)
BWT
(dB)
DEK
F
(dB)
NRA
F
(dB)
Proposed
method(d
B)
Pink noisy
(0dB)
0.5
2.5
7
3.2
7.3
5.5
-
9.87
Pink noisy
(6dB)
-
-
-
-
-
3.99
-
10.5722
Cell noisy
(0dB)
-
-
8.95
-
-
-
- 11.05
White
stationary
noisy (0dB)
1.5
2.5
4.99
6.5
8
7.60
-
11.55
White
stationary
noisy (7dB)
3.7
3.8
13
10
12.5
4.76
-
16.57
White
Bursting
noisy (0dB)
-
-
-
-
-
9.95
-
11.9
White
Bursting
noisy (3dB)
-
-
-
-
-
-
-
15.16
Figure 1&3. shows the PSD , Time domin and Spectrogram plot of clean reference for
database,noisy speech and enhanced speech obtained using the proposed method. It is understood
from that the results of proposed method is more comparable with the clean reference signal.
Figure 2 shows the results of the same approach in matlab environment and in time domain itself
lot of speech activity portion were lost.
Figure 1: PSD, Time domain and spectrogram plot of the enhanced speech from white stationary
noise (0dB)
Figure 2: Enhanced speech obtained in Matlab environment
Figure 3: PSD, Time domain and spectrogram plot of the enhanced speech from white
bursting noise (3dB)
MATLAB VS LABVIEW:
In Matlab environment the execution time for the proposed method takes about two minutes. But
in labview environment the execution time is only in the range of milliseconds.
Social Impact:
This method have fast processing speed , low computational time and high efficiency which
results in efficient hearing even in noisy environment and it reduces the listener’s fatigue.
CONCLUSION:
The system developed is highly reliable and efficient for the above-mentioned problems in
digital hearing aids. We were able to develop the algorithm for speech enhancement and
evaluated its performance by calculating SNR improvement and studying its power spectral
density and spectrogram plot of the enhanced speech.
Nanorobots in anasthesiology
ABSTRACT::
NANOTECH CONTROLLER
Nanotechnology will let us build fleets of computer controlled molecular tools and by
using this nano materials are created which these materials have very different properties than
materials at nano scale. They can be stronger, lighter, more electrically conductive, more
porus and less corressive than bulk materials. The nanomaterials have the potential to solve
unique biological challenges not currently possible.
The common examples of nanomaterials are found in scientific literature flurescence,
nanotubes, buckballs, quantum dots and nanoshells.
This was coined in1974 by Norio Taniguchi at the University of Tokyo.
Nanotechnology is actually a multitude of rapidly emerging technologies, most promising
potential of nanotechnology exists due to the laws of quantum physics.
Quantum physics laws take over at this scale enabling novel applications in optics,
electronics, magnetic storage, computing, catalysts and other areas.
It uses a basic unit of measure called a“nanometer” ( abbreviated nm) derived from
greek word for midget ,”nano” is a metric prefix and indicates a billionth part (10-9).There
are one million nm’s to ammeter
Each nm is only three to five atoms wide. They’re small. Really ~40,000 times smaller than
the width of an average human hair.
The invention of super small computers bacteria sized with todays MIPS capacity
processing power of billion laptops.
A nanometer is one thousandth of a micron and a thousandth of a millionth of meter
(a billionth of a meter). Imagine one billion nanometer in a meter perspective a nanometer. Is
about the width of six bounded carbon atoms, and approximately 40,000 are needed to equal
the width of an average human hair.
Another way to initialize nanometer 1 inch =25,400,000 nanometres.
A red blood cell is ~ 7,000 nm in diameter. And ~2,000 nm in height.
A virus is ~ 100 nm.
A buck ball is 1 nm.
A hydrogen atom is 1 nm.
For our purposes nanometers pertains to science, technology, manufacturing,
chemistry, health science, space programs, medical and engineering.
In engineering , it is used to reduce the size and increase the efficiency . But , in
medical it is used to overcome operations and severe risk taking disease. Think of your brain,
Now performing vastly superior levels Nano dots will become an as-needed addition to your
existing neurons extending your mental capabilities further than imagine .
The tablets, injections are used in nano technology for curing disease for living
organisms.
“But,in this tablets and injections are used for giving
instructions in brain ,and also we can be safe from any disease and we can cure any heart or
kidney problems without any surgery . During this treatment the human can continue his/her
regular work as usual. This can be taken as tablet, injection, food, cool drinks, oil applied to
hair etc”. for this construction a computer and the nano medicine is used.
Introduction
Background
The medical area of nano science application is one of the most potentially valuable, with
many projected benefits to humanity. Cells themselves are very complex and efficient nanomachines,
and chemists and biochemists have been working at the nano scale for some time
without using the nano label. Some areas of nano science aim to learn from biological nano
systems, while others are focusing on the integration of the organic and inorganic at the nano
scale. Many possible applications arising from this science are being researched.
Drug Delivery Using Nanoparticles and Molecular Carriers
Finally, drug delivery is likely to benefit from the development of nanotechnology.
With nanoparticles it is possible that drugs may be given better solubility, leading to better
absorption. Also, drugs may be contained within a molecular carrier, either to protect them
from stomach acids or to control the release of the drug to a specific targeted area, reducing
the likelihood of side effects. Such drugs are already beginning pre-clinical or clinical trials,
adhering to the strict regulatory requirements for new pharmaceuticals. Due to this,
development costs are often high and outcomes of research sometimes limited.
Lab on a Chip and Advanced Drug Delivery Systems
The ultimate combination of the laboratory-on-a-chip and advanced drug delivery
technologies would be a device that was implantable in the body, which would continuously
monitor the level of various biochemicals in the bloodstream and in response would release
appropriate drugs. For example, an insulin-dependent diabetic could use such a device to
continuously monitor and adjust insulin levels autonomously. There is no doubt that this is
the direction that current advances in which micro fluidics and drug delivery are heading.
Anesthesia
Anesthesia, or anaesthesia has traditionally meant the condition of having sensation
(including the feeling of pain) blocked. This allows patients to undergo surgery and other
procedures without the distress and pain they would otherwise experience. The word was
coined by Oliver Wendell Holmes, Sr. in 1846. Another definition is a "reversible lack of
awareness", whether this is a total lack of awareness (e.g. a general anaesthestic) or a lack of
awareness of a part of a the body such as a spinal anaesthetic or another nerve block would
cause. Anesthesia differs from analgesia in blocking all sensation, not only pain.
Today, the term general anesthesia in its most general form can include:
• Analgesia: blocking the conscious sensation of pain;
• Hypnosis: produces unconsciousness without analgesia;
• Amnesia: preventing memory formation;
• Relaxation: preventing unwanted movement or muscle tone;
• Obtundation of reflexes, preventing exaggerated autonomic reflexes.
Patients undergoing surgery usually undergo preoperative evaluation. It includes gathering
history of previous anesthetics, and any other medical problems, physical examination,
ordering required blood work and consultations prior to surgery.
There are several forms of anesthesia. The following forms refer to states achieved by
anesthetics working on the brain:
• General anesthesia: "Drug-induced loss of consciousness during which patients are
not arousable, even by painful stimulation." Patients undergoing general anesthesia
can often neither maintain their own airway nor breathe on their own. While usually
administered with inhalational agents, general anesthesia can be achieved with
intravenous agents, such as propofol.
• Deep sedation/analgesia: "Drug-induced depression of consciousness during which
patients cannot be easily aroused but respond purposefully following repeated or
painful stimulation." Patients may sometimes be unable to maintain their airway and
breathe on their own.
• Moderate sedation/analgesia or conscious sedation: "Drug-induced depression of
consciousness during which patients respond purposefully to verbal commands, either
alone or accompanied by light tactile stimulation." In this state, patients can breathe
on their own and need no help maintaining an airway.
• Minimal sedation or anxiolysis: "Drug-induced state during which patients respond
normally to verbal commands." Though concentration, memory, and coordination
may be impaired, patients need no help breathing or maintaining an airway.
The level of anesthesia achieved ranges on a continuum of depth of consciousness from
minimal sedation to general anesthesia. The depth of consciousness of a patient may change
from one minute to the next.
The following refer to the states achieved by anesthetics working outside of the brain:
• Regional anesthesia: Loss of pain sensation, with varying degrees of muscle
relaxation, in certain regions of the body. Administered with local anesthesia to
peripheral nerve bundles, such as the brachial plexus in the neck. Examples include
the interscalene block for shoulder surgery, axillary block for wrist surgery, and
femoral nerve block for leg surgery. While traditionally administered as a single
injection, newer techniques involve placement of indwelling catheters for continuous
or intermittent administration of local anesthetics.
o Spinal anesthesia: also known as subarachnoid block. Refers to a Regional
block resulting from a small volume of local anesthetics being injected into the
spinal canal. The spinal canal is covered by the dura mater, through which the
spinal needle enters. The spinal canal contains cerebrospinal fluid and the
spinal cord. The sub arachnoid block is usually injected between the 4th and
5th lumbar vertebrae, because the spinal cord usually stops at the 1st lumbar
vertebra, while the canal continues to the sacral vertebrae. It results in a loss of
pain sensation and muscle strength, usually up to the level of the chest (nipple
line or 4th thoracic dermatome).
o Epidural anesthesia: Regional block resulting from an injection of a large
volume of local anesthetic into the epidural space. The epidural space is a
potential space that lies underneath the ligamenta flava, and outside the dura
mater (outside layer of the spinal canal). This is basically an injection around
the spinal canal.
• Local anesthesia is similar to regional anesthesia, but exerts its effect on a smaller
area of the body.
History
Herbal derivatives
The first anesthesia (a herbal remedy) was administered in prehistory. Opium poppy capsules
were collected in 4200 BC, and opium poppies were farmed in Sumeria and succeeding
empires. The use of opium-like preparations in anaesthesia is recorded in the Ebers Papyrus
of 1500 BC
Non-pharmacological methods
Hypnotism and acupuncture have a long history of use as anesthetic techniques. In China,
Taoist medical practitioners developed anesthesia by means of acupuncture. Chilling tissue
(e.g. with ice) can temporarily cause nerve fibers (axons) to stop conducting sensation, while
hyperventilation can cause brief alteration in conscious perception of stimuli including pain
(see Lamaze).
Anesthetic agents
Local anesthetics
inhaled general anesthetic agents
intravenous anesthetic agents (non-opioid)
Current intravenous opioid analgesic agents
Current muscle relaxants
intravenous reversal agents
Anesthetic equipment
In modern anesthesia, a wide variety of medical equipment is desirable depending on the
necessity for portable field use, surgical operations or intensive care support. Anesthesia
practitioners must possess a comprehensive and intricate knowledge of the production and
use of various medical gases, anaesthetic agents and vapours, medical breathing circuits
and the variety of anaesthetic machines (including vaporizers, ventilators and pressure
gauges) and their corresponding safety features, hazards and limitations of each piece of
equipment, for the safe, clinical competence and practical application for day to day practice.
Anesthetic monitoring
Patients being treated under general anesthetics must be monitored continuously to ensure the
patient's safety.
Anesthesia record
The anesthesia record is the medical and legal documentation of events during an anesthetic.
It reflects a detailed and continuous account of drugs,
NANOTECHNOLOGY::
Nano is one billionth of one. Now we have the so-called microprocessors
and microarray technology that would reach the nano level within a few decades, we suppose.
Some call this technology to be nanotechnology and some others name it the molecular
nanotechnology, to be specific.
REASONS FOR APPLYIING NANOTECH TO BIIOLOGIICAL SYSTEMS::
Most animal cells are 10,000 to 20,000 nanometers in diameter. This means that
nanoscale devices (having at least one dimension less than 100 nanometers) can enter cells
and the organelles inside them to interact with DNA and proteins. Tools developed through
nanotechnology may be able to detect disease in a very small amount of cells or tissue. They
may also be able to enter and monitor cells within a living body. Miniaturization will allow
the tools for many different tests to be situated together on the same small device. This
means that nanotechnology could make it possible to run many diagnostic tests
simultaneously as well as with more sensitivity. In general, nanotechnology may offer a
faster and more efficient means for us to do much of what we do now.
NANOMEDICINE::
The emerging field of nanorobotics is aimed at overcoming the
shortcomings present in the traditional way of treatment of patients. Our bodies are filled
with intricate, active molecular structures. When those structures are damaged, health
suffers. Modern medicine can affect the work of the body in many ways, but from a
molecular viewpoint it remains crude. Molecular manufacturing can construct a range of
medical instruments and devices with greater abilities. The human body can be seen as a
workyard, construction site, and battleground form molecular machines. It works
remarkably well; using systems so complex that medical science still doesn’t understand
many of them.
BIOMEDICAL APPILICATIONS OF NANOROBOTS::
The enormous potential in the biomedical capabilities of nanorobots and
the imprecision and side effects of medical treatments today make nanorobots very
desirable. But today, in this revolutionary era we propose for nanomedical robots, since
they will have no difficulty in identifying the target site cells even at the very early stages
which cannot be done in the traditional treatment and will ultimately be able to track them
down and destroy them wherever they may be growing. By having these Robots, we can
refine the treatment of diseases by using biomedical, nanotechnological engineering.
Nanorobot designed to perform cell surgery
WHAT IS A MEDICINAL NANOROBOT ?
Nanorobots are theoretical microscopic devices measured on the scale of nanometers (1
nm equals one millionth of a millimeter). When fully realized from the hypothetical stage, they
would work at the atomic, molecular and cellular level to perform tasks in both the medical and
industrial fields that have heretofore been the stuff of science fiction Nanomedicine’s nanorobots
are so tiny that they can easily traverse the human body. Scientists report the exterior of a
nanorobot will likely be constructed of carbon atoms in a diamondoid structure because of its
inert properties and strength. Super-smooth surfaces will lessen the likelihood of triggering the
body’s immune system, allowing the nanorobots to go about their business unimpeded. Glucose
or natural body sugars and oxygen might be a source for propulsion, and the nanorobot will have
other biochemical or molecular parts depending on its task.
Nanorobot in Nanoscale
According to current theories, nanorobots will possess at least rudimentary two-way
communication; will respond to acoustic signals; and will be able to receive power or even reprogramming
instructions from an external source via sound waves. A network of special
stationary nanorobots might be strategically positioned throughout the body, logging each active
nanorobot as it passes, then reporting those results, allowing an interface to keep track of all of
the devices in the body. A doctor could not only monitor a patient’s progress but change the
instructions of the nanorobots in vivo to progress to another stage of healing. When the task is
completed, the nanorobots would be flushed from the body.
Nanorobot performing operations on blood cells
IMPLEMENTATION::
Anestthesiia USING NANOTECHNOLOGY::
Automated anesthesia delivery.
Automated delivery of inhalational anesthetics.
Automated I.V. anesthesia delivery system.
Nano assisted titration of I.V. agents with target controlled infusion.
CREATIION OF NANO DEVIICES::
The creation of the nano devices can be done using any of the two techniques that
are available. They are
• Top-down approach
• Bottom-up approach
CHALLENGES FACED BY NANOROBOTS::
While designing nonorobots in nanoscale dimensions there should be a better
understanding of how matter behaves on this small scale. Matter behaves differently on the
nanoscale than it does at larger levels. So the behaviour of the nanorobots must be taken
care so that the do not affect us both inside and outside the body.
Other challenges apply specifically to the use of nanostructures within biological
systems. Nanostructures can be so small that the body may clear them too rapidly for them
to be effective in detection or imaging. Larger nanoparticles may accumulate in vital
organs, creating a toxicity problem. So we need to consider these factors as they anticipate
how nanostructures will behave in the human body and attempt to create devices the body
will accept.
DESIIGN OF NANOROBOTS::
The nanorobots that we describe here will be floating freely inside the body
exploring and detect the various receptors eg GABA receptors in the brain, opioid
receptors, neuromuscular junction receptors. So, while designing such a nanorobot for
anesthesia, the main factors that are to be considered are given below.
TECHNIIQUE USED::
We use the bottom-up approach, which involves assembling structures atom-byatom
or molecule-by-molecule which will be useful in manufacturing devices used in
medicine.
SIIZE::
Nanorobots will typically be .5 to 3 microns large with 1-100 nm parts. Three microns
is the upper limit of any nanorobot because nanorobots of larger size will block capillary
flow.
STRUCTURE::
The nanorobot’s structure will have two spaces that are
Interior:
It will be a closed, vacuum environment into which liquids from the outside cannot
normally enter unless it is needed for chemical analysis.
Exterior: It will be subjected to various chemical liquids in our bodies.
CHEMIICAL ELEMENTS::
Carbon will likely be the principal element comprising the bulk of a
medical nanorobot, probably in the form of diamond or diamondoid/fullerene
nanocomposites largely because of the tremendous strength and chemicalinertness of
diamond. Many other light elements such as hydrogen, sulfur, oxygen,nitrogen, fluorine,
silicon, etc. may also be used
ABIILIITY TO DEFEND FROM IIMMUNE SYSTEM::
Immune system response is primarily a reaction to a "foreign" surface..
Passive diamond exteriors may turn out to be ideal. Several experimental studies hint that
the smoother and more flawless the diamond surface, the less leukocyte activity and the less
fibrinogen adsorption we will get. So it seems reasonable to hope that when diamond
coatings can be laid down with almost flawless atomic precision, making nanorobot exterior
surfaces with near-nanometer smoothness that these surfaces may have very low
bioactivity. Due to the extremely high surface energy of the passivated diamond surface and
the strong hydrophobicity of the diamond surface, the diamond exterior is almost
completely chemically inert and so opsonization should be minimized. If flawless diamond
surfaces alone do not prove fully bioinactive as hoped, active surface management of the
nanorobot exterior can be used to ensure complete nanodevice biocompatibility. Allergic
and shock reactions are similarly easily avoided.
ACQUIIRING POWER::
It could metabolize local glucose and oxygen for energy. Another possibility is
externally supplied acoustic power, which is probably most appropriate in a clinical setting.
There are literally dozens of useful power sources that are potentially available in the
human body.
COMMUNIICATON::
Having nanorobots inside the body it is very essential to know the actions done by it.
There are many different ways to do this. One of the simplest ways to send broadcast-type
messages into the body, to be received by nanorobots, is acoustic messaging. A device
similar to an ultrasound probe would encode messages on acoustic carrier waves at
frequencies between 1-10 MHz.
TRACKIING::
A navigational network may be installed in the body, with stationkeeping
navigational elements providing high positional accuracy to all passing nanorobots
that interrogate them, wanting to know their location. Physical positions can be
reported continuously using an in vivo communications network.
STRUCTURE OF NANOROBOT::
The nanorobot consists of three main parts like the receptor sensor, CPU, effector and the
power system. The purpose of receptor sensor is to identify the different anesthesia receptors on
the cell. The effector is used to produce the post receptor event.The CPU controls all the activities
.The power system provides the necessary energy for the working of the nanorobot.
GP120
layer GABA
layer
REQIREMENTS OF THE NANOROBOT:
1. It should e very small so that the blood capillary flow is not affected.
2. It should not be affected by the WBC.
3. It should be capable of attaching to anesthesia receptors only.
4. It should make its operations in the brain with GABA receptors, in the muscles with
neuromuscular junction and in the spinal cord with the opioid receptors .
5. It should be made of cheaper rates, so that the patient can afford it easily.
OPERATION::
The designed anesthesia nanorobots are injected into the blood stream. These nanorobots attaches
to the various receptor in the different parts of the body and produces the effects.
GABA receptors produces the loss of consciousness and amesia
Neuromuscular junction produces the full muscle relaxation
These two gives the good intubation conditions for securing the airway.
Opioid receptors produce the good analgesia.
In Spinal cord attaches to the sodium channel receptor and produces the spinal anesthesia
Arrangement
to produce
the effect
Recept
or
Power System
Central
Processin
g Unit
effector
Site for
receptor
attachment
ADVANTAGES::
1. More than million people in this world are undergoing surgery where anesthesia is essential.
Currently an anesthesiologist is required to give the anesthesia and carefull titration of the
drugs is essential to prevent the side effects like hypotension, desaturation, preventing the
intubation response.
2. As the nanorobot do not generate any harmful activities there is no side effect. It operates at
specific site only.
3. The initial cost of development is only high but the manufacturing by batch processing
reduces the cost.
4. Can be used in both general as well as spinal anesthesia.
5. Reduces the mortality and morbidity associated with anesthesia.
6. Patient satisfaction.
7. Less drug consumption and hence less side effects.
8. No peaks and downs in pain relief.
9. Labour analgesia; can provide pain relief for the mother at the time of delivery.
DISADVANTAGES::
1.The nanorobot should be very accurate, otherwise harmful effects may occur.
2.The initial design cost is very high.
3.The design of this nanorobot is a very complicated one
CONCLUSION::
The paper is just a recent advancement in the field of nanotechnology gives the hope of
the effective use of this technology in medical field. This paper gives an idea of giving exhalent
pain relief to millions of patients who undergo various types of surgery and also pain relief to
terminally ill cancer patients. Using this technology we can conduct safe and painless delivery
and provide exhalent care to the mother and baby.
NANOTECH CONTROLLER
Nanotechnology will let us build fleets of computer controlled molecular tools and by
using this nano materials are created which these materials have very different properties than
materials at nano scale. They can be stronger, lighter, more electrically conductive, more
porus and less corressive than bulk materials. The nanomaterials have the potential to solve
unique biological challenges not currently possible.
The common examples of nanomaterials are found in scientific literature flurescence,
nanotubes, buckballs, quantum dots and nanoshells.
This was coined in1974 by Norio Taniguchi at the University of Tokyo.
Nanotechnology is actually a multitude of rapidly emerging technologies, most promising
potential of nanotechnology exists due to the laws of quantum physics.
Quantum physics laws take over at this scale enabling novel applications in optics,
electronics, magnetic storage, computing, catalysts and other areas.
It uses a basic unit of measure called a“nanometer” ( abbreviated nm) derived from
greek word for midget ,”nano” is a metric prefix and indicates a billionth part (10-9).There
are one million nm’s to ammeter
Each nm is only three to five atoms wide. They’re small. Really ~40,000 times smaller than
the width of an average human hair.
The invention of super small computers bacteria sized with todays MIPS capacity
processing power of billion laptops.
A nanometer is one thousandth of a micron and a thousandth of a millionth of meter
(a billionth of a meter). Imagine one billion nanometer in a meter perspective a nanometer. Is
about the width of six bounded carbon atoms, and approximately 40,000 are needed to equal
the width of an average human hair.
Another way to initialize nanometer 1 inch =25,400,000 nanometres.
A red blood cell is ~ 7,000 nm in diameter. And ~2,000 nm in height.
A virus is ~ 100 nm.
A buck ball is 1 nm.
A hydrogen atom is 1 nm.
For our purposes nanometers pertains to science, technology, manufacturing,
chemistry, health science, space programs, medical and engineering.
In engineering , it is used to reduce the size and increase the efficiency . But , in
medical it is used to overcome operations and severe risk taking disease. Think of your brain,
Now performing vastly superior levels Nano dots will become an as-needed addition to your
existing neurons extending your mental capabilities further than imagine .
The tablets, injections are used in nano technology for curing disease for living
organisms.
“But,in this tablets and injections are used for giving
instructions in brain ,and also we can be safe from any disease and we can cure any heart or
kidney problems without any surgery . During this treatment the human can continue his/her
regular work as usual. This can be taken as tablet, injection, food, cool drinks, oil applied to
hair etc”. for this construction a computer and the nano medicine is used.
Introduction
Background
The medical area of nano science application is one of the most potentially valuable, with
many projected benefits to humanity. Cells themselves are very complex and efficient nanomachines,
and chemists and biochemists have been working at the nano scale for some time
without using the nano label. Some areas of nano science aim to learn from biological nano
systems, while others are focusing on the integration of the organic and inorganic at the nano
scale. Many possible applications arising from this science are being researched.
Drug Delivery Using Nanoparticles and Molecular Carriers
Finally, drug delivery is likely to benefit from the development of nanotechnology.
With nanoparticles it is possible that drugs may be given better solubility, leading to better
absorption. Also, drugs may be contained within a molecular carrier, either to protect them
from stomach acids or to control the release of the drug to a specific targeted area, reducing
the likelihood of side effects. Such drugs are already beginning pre-clinical or clinical trials,
adhering to the strict regulatory requirements for new pharmaceuticals. Due to this,
development costs are often high and outcomes of research sometimes limited.
Lab on a Chip and Advanced Drug Delivery Systems
The ultimate combination of the laboratory-on-a-chip and advanced drug delivery
technologies would be a device that was implantable in the body, which would continuously
monitor the level of various biochemicals in the bloodstream and in response would release
appropriate drugs. For example, an insulin-dependent diabetic could use such a device to
continuously monitor and adjust insulin levels autonomously. There is no doubt that this is
the direction that current advances in which micro fluidics and drug delivery are heading.
Anesthesia
Anesthesia, or anaesthesia has traditionally meant the condition of having sensation
(including the feeling of pain) blocked. This allows patients to undergo surgery and other
procedures without the distress and pain they would otherwise experience. The word was
coined by Oliver Wendell Holmes, Sr. in 1846. Another definition is a "reversible lack of
awareness", whether this is a total lack of awareness (e.g. a general anaesthestic) or a lack of
awareness of a part of a the body such as a spinal anaesthetic or another nerve block would
cause. Anesthesia differs from analgesia in blocking all sensation, not only pain.
Today, the term general anesthesia in its most general form can include:
• Analgesia: blocking the conscious sensation of pain;
• Hypnosis: produces unconsciousness without analgesia;
• Amnesia: preventing memory formation;
• Relaxation: preventing unwanted movement or muscle tone;
• Obtundation of reflexes, preventing exaggerated autonomic reflexes.
Patients undergoing surgery usually undergo preoperative evaluation. It includes gathering
history of previous anesthetics, and any other medical problems, physical examination,
ordering required blood work and consultations prior to surgery.
There are several forms of anesthesia. The following forms refer to states achieved by
anesthetics working on the brain:
• General anesthesia: "Drug-induced loss of consciousness during which patients are
not arousable, even by painful stimulation." Patients undergoing general anesthesia
can often neither maintain their own airway nor breathe on their own. While usually
administered with inhalational agents, general anesthesia can be achieved with
intravenous agents, such as propofol.
• Deep sedation/analgesia: "Drug-induced depression of consciousness during which
patients cannot be easily aroused but respond purposefully following repeated or
painful stimulation." Patients may sometimes be unable to maintain their airway and
breathe on their own.
• Moderate sedation/analgesia or conscious sedation: "Drug-induced depression of
consciousness during which patients respond purposefully to verbal commands, either
alone or accompanied by light tactile stimulation." In this state, patients can breathe
on their own and need no help maintaining an airway.
• Minimal sedation or anxiolysis: "Drug-induced state during which patients respond
normally to verbal commands." Though concentration, memory, and coordination
may be impaired, patients need no help breathing or maintaining an airway.
The level of anesthesia achieved ranges on a continuum of depth of consciousness from
minimal sedation to general anesthesia. The depth of consciousness of a patient may change
from one minute to the next.
The following refer to the states achieved by anesthetics working outside of the brain:
• Regional anesthesia: Loss of pain sensation, with varying degrees of muscle
relaxation, in certain regions of the body. Administered with local anesthesia to
peripheral nerve bundles, such as the brachial plexus in the neck. Examples include
the interscalene block for shoulder surgery, axillary block for wrist surgery, and
femoral nerve block for leg surgery. While traditionally administered as a single
injection, newer techniques involve placement of indwelling catheters for continuous
or intermittent administration of local anesthetics.
o Spinal anesthesia: also known as subarachnoid block. Refers to a Regional
block resulting from a small volume of local anesthetics being injected into the
spinal canal. The spinal canal is covered by the dura mater, through which the
spinal needle enters. The spinal canal contains cerebrospinal fluid and the
spinal cord. The sub arachnoid block is usually injected between the 4th and
5th lumbar vertebrae, because the spinal cord usually stops at the 1st lumbar
vertebra, while the canal continues to the sacral vertebrae. It results in a loss of
pain sensation and muscle strength, usually up to the level of the chest (nipple
line or 4th thoracic dermatome).
o Epidural anesthesia: Regional block resulting from an injection of a large
volume of local anesthetic into the epidural space. The epidural space is a
potential space that lies underneath the ligamenta flava, and outside the dura
mater (outside layer of the spinal canal). This is basically an injection around
the spinal canal.
• Local anesthesia is similar to regional anesthesia, but exerts its effect on a smaller
area of the body.
History
Herbal derivatives
The first anesthesia (a herbal remedy) was administered in prehistory. Opium poppy capsules
were collected in 4200 BC, and opium poppies were farmed in Sumeria and succeeding
empires. The use of opium-like preparations in anaesthesia is recorded in the Ebers Papyrus
of 1500 BC
Non-pharmacological methods
Hypnotism and acupuncture have a long history of use as anesthetic techniques. In China,
Taoist medical practitioners developed anesthesia by means of acupuncture. Chilling tissue
(e.g. with ice) can temporarily cause nerve fibers (axons) to stop conducting sensation, while
hyperventilation can cause brief alteration in conscious perception of stimuli including pain
(see Lamaze).
Anesthetic agents
Local anesthetics
inhaled general anesthetic agents
intravenous anesthetic agents (non-opioid)
Current intravenous opioid analgesic agents
Current muscle relaxants
intravenous reversal agents
Anesthetic equipment
In modern anesthesia, a wide variety of medical equipment is desirable depending on the
necessity for portable field use, surgical operations or intensive care support. Anesthesia
practitioners must possess a comprehensive and intricate knowledge of the production and
use of various medical gases, anaesthetic agents and vapours, medical breathing circuits
and the variety of anaesthetic machines (including vaporizers, ventilators and pressure
gauges) and their corresponding safety features, hazards and limitations of each piece of
equipment, for the safe, clinical competence and practical application for day to day practice.
Anesthetic monitoring
Patients being treated under general anesthetics must be monitored continuously to ensure the
patient's safety.
Anesthesia record
The anesthesia record is the medical and legal documentation of events during an anesthetic.
It reflects a detailed and continuous account of drugs,
NANOTECHNOLOGY::
Nano is one billionth of one. Now we have the so-called microprocessors
and microarray technology that would reach the nano level within a few decades, we suppose.
Some call this technology to be nanotechnology and some others name it the molecular
nanotechnology, to be specific.
REASONS FOR APPLYIING NANOTECH TO BIIOLOGIICAL SYSTEMS::
Most animal cells are 10,000 to 20,000 nanometers in diameter. This means that
nanoscale devices (having at least one dimension less than 100 nanometers) can enter cells
and the organelles inside them to interact with DNA and proteins. Tools developed through
nanotechnology may be able to detect disease in a very small amount of cells or tissue. They
may also be able to enter and monitor cells within a living body. Miniaturization will allow
the tools for many different tests to be situated together on the same small device. This
means that nanotechnology could make it possible to run many diagnostic tests
simultaneously as well as with more sensitivity. In general, nanotechnology may offer a
faster and more efficient means for us to do much of what we do now.
NANOMEDICINE::
The emerging field of nanorobotics is aimed at overcoming the
shortcomings present in the traditional way of treatment of patients. Our bodies are filled
with intricate, active molecular structures. When those structures are damaged, health
suffers. Modern medicine can affect the work of the body in many ways, but from a
molecular viewpoint it remains crude. Molecular manufacturing can construct a range of
medical instruments and devices with greater abilities. The human body can be seen as a
workyard, construction site, and battleground form molecular machines. It works
remarkably well; using systems so complex that medical science still doesn’t understand
many of them.
BIOMEDICAL APPILICATIONS OF NANOROBOTS::
The enormous potential in the biomedical capabilities of nanorobots and
the imprecision and side effects of medical treatments today make nanorobots very
desirable. But today, in this revolutionary era we propose for nanomedical robots, since
they will have no difficulty in identifying the target site cells even at the very early stages
which cannot be done in the traditional treatment and will ultimately be able to track them
down and destroy them wherever they may be growing. By having these Robots, we can
refine the treatment of diseases by using biomedical, nanotechnological engineering.
Nanorobot designed to perform cell surgery
WHAT IS A MEDICINAL NANOROBOT ?
Nanorobots are theoretical microscopic devices measured on the scale of nanometers (1
nm equals one millionth of a millimeter). When fully realized from the hypothetical stage, they
would work at the atomic, molecular and cellular level to perform tasks in both the medical and
industrial fields that have heretofore been the stuff of science fiction Nanomedicine’s nanorobots
are so tiny that they can easily traverse the human body. Scientists report the exterior of a
nanorobot will likely be constructed of carbon atoms in a diamondoid structure because of its
inert properties and strength. Super-smooth surfaces will lessen the likelihood of triggering the
body’s immune system, allowing the nanorobots to go about their business unimpeded. Glucose
or natural body sugars and oxygen might be a source for propulsion, and the nanorobot will have
other biochemical or molecular parts depending on its task.
Nanorobot in Nanoscale
According to current theories, nanorobots will possess at least rudimentary two-way
communication; will respond to acoustic signals; and will be able to receive power or even reprogramming
instructions from an external source via sound waves. A network of special
stationary nanorobots might be strategically positioned throughout the body, logging each active
nanorobot as it passes, then reporting those results, allowing an interface to keep track of all of
the devices in the body. A doctor could not only monitor a patient’s progress but change the
instructions of the nanorobots in vivo to progress to another stage of healing. When the task is
completed, the nanorobots would be flushed from the body.
Nanorobot performing operations on blood cells
IMPLEMENTATION::
Anestthesiia USING NANOTECHNOLOGY::
Automated anesthesia delivery.
Automated delivery of inhalational anesthetics.
Automated I.V. anesthesia delivery system.
Nano assisted titration of I.V. agents with target controlled infusion.
CREATIION OF NANO DEVIICES::
The creation of the nano devices can be done using any of the two techniques that
are available. They are
• Top-down approach
• Bottom-up approach
CHALLENGES FACED BY NANOROBOTS::
While designing nonorobots in nanoscale dimensions there should be a better
understanding of how matter behaves on this small scale. Matter behaves differently on the
nanoscale than it does at larger levels. So the behaviour of the nanorobots must be taken
care so that the do not affect us both inside and outside the body.
Other challenges apply specifically to the use of nanostructures within biological
systems. Nanostructures can be so small that the body may clear them too rapidly for them
to be effective in detection or imaging. Larger nanoparticles may accumulate in vital
organs, creating a toxicity problem. So we need to consider these factors as they anticipate
how nanostructures will behave in the human body and attempt to create devices the body
will accept.
DESIIGN OF NANOROBOTS::
The nanorobots that we describe here will be floating freely inside the body
exploring and detect the various receptors eg GABA receptors in the brain, opioid
receptors, neuromuscular junction receptors. So, while designing such a nanorobot for
anesthesia, the main factors that are to be considered are given below.
TECHNIIQUE USED::
We use the bottom-up approach, which involves assembling structures atom-byatom
or molecule-by-molecule which will be useful in manufacturing devices used in
medicine.
SIIZE::
Nanorobots will typically be .5 to 3 microns large with 1-100 nm parts. Three microns
is the upper limit of any nanorobot because nanorobots of larger size will block capillary
flow.
STRUCTURE::
The nanorobot’s structure will have two spaces that are
Interior:
It will be a closed, vacuum environment into which liquids from the outside cannot
normally enter unless it is needed for chemical analysis.
Exterior: It will be subjected to various chemical liquids in our bodies.
CHEMIICAL ELEMENTS::
Carbon will likely be the principal element comprising the bulk of a
medical nanorobot, probably in the form of diamond or diamondoid/fullerene
nanocomposites largely because of the tremendous strength and chemicalinertness of
diamond. Many other light elements such as hydrogen, sulfur, oxygen,nitrogen, fluorine,
silicon, etc. may also be used
ABIILIITY TO DEFEND FROM IIMMUNE SYSTEM::
Immune system response is primarily a reaction to a "foreign" surface..
Passive diamond exteriors may turn out to be ideal. Several experimental studies hint that
the smoother and more flawless the diamond surface, the less leukocyte activity and the less
fibrinogen adsorption we will get. So it seems reasonable to hope that when diamond
coatings can be laid down with almost flawless atomic precision, making nanorobot exterior
surfaces with near-nanometer smoothness that these surfaces may have very low
bioactivity. Due to the extremely high surface energy of the passivated diamond surface and
the strong hydrophobicity of the diamond surface, the diamond exterior is almost
completely chemically inert and so opsonization should be minimized. If flawless diamond
surfaces alone do not prove fully bioinactive as hoped, active surface management of the
nanorobot exterior can be used to ensure complete nanodevice biocompatibility. Allergic
and shock reactions are similarly easily avoided.
ACQUIIRING POWER::
It could metabolize local glucose and oxygen for energy. Another possibility is
externally supplied acoustic power, which is probably most appropriate in a clinical setting.
There are literally dozens of useful power sources that are potentially available in the
human body.
COMMUNIICATON::
Having nanorobots inside the body it is very essential to know the actions done by it.
There are many different ways to do this. One of the simplest ways to send broadcast-type
messages into the body, to be received by nanorobots, is acoustic messaging. A device
similar to an ultrasound probe would encode messages on acoustic carrier waves at
frequencies between 1-10 MHz.
TRACKIING::
A navigational network may be installed in the body, with stationkeeping
navigational elements providing high positional accuracy to all passing nanorobots
that interrogate them, wanting to know their location. Physical positions can be
reported continuously using an in vivo communications network.
STRUCTURE OF NANOROBOT::
The nanorobot consists of three main parts like the receptor sensor, CPU, effector and the
power system. The purpose of receptor sensor is to identify the different anesthesia receptors on
the cell. The effector is used to produce the post receptor event.The CPU controls all the activities
.The power system provides the necessary energy for the working of the nanorobot.
GP120
layer GABA
layer
REQIREMENTS OF THE NANOROBOT:
1. It should e very small so that the blood capillary flow is not affected.
2. It should not be affected by the WBC.
3. It should be capable of attaching to anesthesia receptors only.
4. It should make its operations in the brain with GABA receptors, in the muscles with
neuromuscular junction and in the spinal cord with the opioid receptors .
5. It should be made of cheaper rates, so that the patient can afford it easily.
OPERATION::
The designed anesthesia nanorobots are injected into the blood stream. These nanorobots attaches
to the various receptor in the different parts of the body and produces the effects.
GABA receptors produces the loss of consciousness and amesia
Neuromuscular junction produces the full muscle relaxation
These two gives the good intubation conditions for securing the airway.
Opioid receptors produce the good analgesia.
In Spinal cord attaches to the sodium channel receptor and produces the spinal anesthesia
Arrangement
to produce
the effect
Recept
or
Power System
Central
Processin
g Unit
effector
Site for
receptor
attachment
ADVANTAGES::
1. More than million people in this world are undergoing surgery where anesthesia is essential.
Currently an anesthesiologist is required to give the anesthesia and carefull titration of the
drugs is essential to prevent the side effects like hypotension, desaturation, preventing the
intubation response.
2. As the nanorobot do not generate any harmful activities there is no side effect. It operates at
specific site only.
3. The initial cost of development is only high but the manufacturing by batch processing
reduces the cost.
4. Can be used in both general as well as spinal anesthesia.
5. Reduces the mortality and morbidity associated with anesthesia.
6. Patient satisfaction.
7. Less drug consumption and hence less side effects.
8. No peaks and downs in pain relief.
9. Labour analgesia; can provide pain relief for the mother at the time of delivery.
DISADVANTAGES::
1.The nanorobot should be very accurate, otherwise harmful effects may occur.
2.The initial design cost is very high.
3.The design of this nanorobot is a very complicated one
CONCLUSION::
The paper is just a recent advancement in the field of nanotechnology gives the hope of
the effective use of this technology in medical field. This paper gives an idea of giving exhalent
pain relief to millions of patients who undergo various types of surgery and also pain relief to
terminally ill cancer patients. Using this technology we can conduct safe and painless delivery
and provide exhalent care to the mother and baby.
4G communications
ABSTRACT
The approaching 4G (fourth generation) mobile communication systems are projected to
solve still remaining problems of 3G (third generation) systems and to provide a wide variety of
new services, from high-quality voice to high definition video to high-data-rate wireless
channels. The term 4G is used broadly to include several types of broadband wireless access
communication systems, not only cellular telephone systems. One of the terms used to describe
4G is MAGIC Mobile multimedia, anytime any-where, Global mobility support, integrated
wireless solution, and customized personal service. As a promise for the future, 4G systems, that
is, cellular broadband wireless access systems have been attracting much interest in the mobile
communication arena. The 4G systems not only will support the next generation of mobile
service, but also will support the fixed wireless networks.
This paper presents an overall vision of the 4G features, framework, and integration of
mobile communication. The features of 4G systems might be summarized with one word
integration. The 4G systems are about seamlessly integrating terminals, networks, and
applications to satisfy increasing user demands.
The continuous expansion of mobile communication and wireless networks shows
evidence of exceptional growth in the areas of mobile subscriber, wireless network access,
mobile services, and applications. An estimate of 1 billion users by the end of 2003 justifies the
study and research for 4G systems.
History
The history and evolution of mobile service from the 1G (first generation) to fourth
generation are discussed in this section. Table 1 presents a short history of mobile telephone
technologies.
This process began with the designs in the 1970s that have become known as 1G. The
earliest systems were implemented based on analog technology and the basic cellular structure of
mobile communication. Many fundamental problems were solved by these early systems.
Numerous incompatible analog systems were placed in service around the world during the
1980s.
The 2G (second generation) systems designed in the 1980s were still used mainly for
voice applications but were based on digital technology, including digital signal processing
techniques. These 2G systems provided circuit switched data communication services at a low
speed. The competitive rush to design and implement digital systems led again to a variety of
different and incompatible standards such as GSM (global system Mobile), mainly in Europe;
TDMA (time division multiple access) (IS-54/IS-136) in the U.S.; PDC (personal digital
cellular) in Japan; and CDMA (code division multiple access) (IS-95), another U.S. system.
These systems operate nationwide or internationally and are today's mainstream systems,
although the data rate for users in these system is very limited.
During the 1990s, two organizations worked to define the next, or 3G, mobile system,
which would eliminate previous incompatibilities and become a truly global system. The 3G
systems would have higher quality voice channels, as well as broadband data capabilities, up to
2Mbps. unfortunately, the two groups could not reconcile their differences, and this decade will
see the introduction of two mobile standards for 3G. In addition, China is on the verge of
implementing a third 3G systems.
An interim step is being taken between 2G and 3G, the 2.5G. It is basically an
enhancement of the two major 2G technologies to provide increased capacity on the 2G RF
(radio frequency) channels and to introduce higher throughput for data service, up to 384 kbps.
A very important aspect of 2.5G is that the data channels are optimized for packet data, which
introduces access to the Internet from mobile devices, whether telephone, PDA (personal digital
assistant) or laptop.
Although the new, third generation (3G) wireless technology has not yet been
implemented, leading companies in the industry are already laying the groundwork for what
some are calling fourth generation (4G) technology. Researchers are continuing their ideas in the
development of an undefined wireless world, which could become operational by 2010. The first
generation (1G) and second generation (2G) of mobile telephony were intended primarily for
voice transmission. The third generation of mobile telephony (3G) will serve both voice and data
applications.
There really is no clear definition of what 4G will be. It is generally accepted that 4G will
be a super-enhanced version of 3G i.e., an entirely packet switched network with all digital
network elements and extremely high available bandwidth. For the most part, it is believed that
4G will bring true multimedia capabilities such as high-speed data access and video
conferencing to the handset. It is also envisioned that 4G systems will be deployed with
software-defined radios, allowing the equipment to be upgraded to new protocols and services
via software upgrades. 4G also holds the promise of worldwide roaming using a single handheld
device.
As with all technology progressions, the “next” upgrades must be in planning and
development phases while its predecessors are being deployed. This statement holds true with all
mobile telecommunications to date. It seems that it will also hold true for the next generations of
wireless networks. The original analog cellular systems are considered the first generation of
mobile telephony (1G). In the early 1980s, 1G system was deployed. At the same time, the
cellular industry began developing the second generation of mobile telephony (2G). The
difference between 1G and 2G is in the signaling techniques used: 1G used analog signaling, 2G
used digital signaling. As experience shows, the lead-time for mobile phone systems
development is about 10 years. It was not until the early to mid 1990s that 2G was deployed.
Primary thinking and concept development on 3G generally began around 1991 as 2G systems
just started to roll out. Since the general model of 10 years to develop a new mobile system is
being followed, that timeline would suggest 4G should be operational some time around 2011.
4G would build on the second phase of 3G, when all networks are expected to embrace Internet
protocol (IP) technology. During the last year, companies such as Ericsson, Motorola, Lucent,
Nortel and Qualcomm came up with "3G-plus" concepts that would push performance of
approved, though still emerging, standards beyond current ones.
However, the demand for higher access speed multi-media communication in today's
society, which greatly depends on computer communication in digital format, seems unlimited.
According to the historical indication of a generation revolution occurring once a decade, the
present appears to be the right time to begin the research on a 4G mobile communication system.
b
Legend:
1xRTT = 2.5G CDMA data service up to 384 kbps
AMPS = Advanced Mobile Phone Service
CDMA = Code Division Multiple Access
EDGE = Enhanced Data for Global Evolution
FDMA = Frequency Division Multiple Access
GPRS = General Packet Radio Service
GSM = Global System for Mobile communication
NMT = Nordic Mobile Telephone
PDC = Personal Digital Cellular
PSTN = Pubic Switched Telephone Network
TACS = Total Access Communications System
TDMA = Time Division Multiple Access
WCDMA = Wideband CDMA
4G Communication
This new generation of wireless is intended to complement and replace the 3G systems,
perhaps in 5 to 10 years. Accessing information anywhere, anytime, with a seamless connection
to a wide range of information and services, and receiving a large volume of information, data,
pictures, video, and so on, are the keys of the 4G infrastructures. The future 4G infrastructures
will consist of a set of various networks using IP (Internet protocol) as a common protocol so
that users are in control because they will be able to choose every application and environment.
Based on the developing trends of mobile communication, 4G will have broader
bandwidth, higher data rate, and smoother and quicker handoff and will focus on ensuring
seamless service across a multitude of wireless systems and networks. The key concept is
integrating the 4G capabilities with all of the existing mobile technologies through advanced
technologies.
Application adaptability and being highly dynamic are the main features of 4G services
of interest to users. These features mean services can be delivered and be available to the
personal preference of different users and support the users' traffic, air interfaces, radio
environment, and quality of service. Connection with the network applications can be transferred
into various forms and levels correctly and efficiently. The dominant methods of access to this
pool of information will be the mobile telephone, PDA, and laptop to seamlessly access the
voice communication, high-speed information services, and entertainment broadcast services.
Figure 1 illustrates elements and techniques to support the adaptability of the 4G domains
The fourth generation will encompass all systems from various networks, public to
private; operator driven broadband networks to personal areas; and ad hoc net works. The 4G
systems will interoperate with 2G and 3G systems, as well as with digital (broadband)
broadcasting systems. In addition, 4G systems will be fully IP based wireless Internet.
This all encompassing integrated perspective shows the broad range of systems that the
fourth generation intends to integrate, from satellite broadband to high altitude platform to
cellular 3G and 3G systems to WLL (wireless local loop) and FWA (fixed wireless access) to
WLAN (wireless local area network) and PAN (personal area net work), all with IP as the
integrating mechanism.
With 4G, a range of new services and models will be available. These services and
models need to be further examined for their interface with the design of 4G systems. Figures 2
and 3 demonstrate the key elements and the seamless connectivity of the networks.
Table 2: Comparisons between the earlier technologies
Feature 2G 2G+ 3G
Handsets Voice only terminals
New type of terminal
Dual mode TDMA and
CDMA Voice and data
terminals WAP, no
multimedia support
New type of terminal
Multiple modes Voice,
data and video terminals
WAP, multimedia mgmt
Databases HLR, VLR, EIR, Auk HLR, VLR, EIR, Auk
Enhanced HLR, VLR,
EIR, AuC
Data Rates Up to 9.6 Kbps
Up to 57.6 Kbps (HSCSD)
Up to 115Kbps (GPRS)
Up to 384 Kbps (EDGE)
Up to 2Mbps
Applications
Advanced voice, Short
Message Service
(SMS)
SMS, Internet Internet, multimedia
Compatibility Not compatible to 3G Not compatible to 3G
Compatible to 2G, 2G+
and Blue tooth
Interoperability and the Evolution of Network Architectures
One of the most challenging issues facing deployment of 4G technologies is how to make
the network architectures compatible with each other. New signaling techniques are being
designed specifically to enhance today's second generation (2G) networks, deliver unprecedented
functionality for 3G, and successfully drive the Fourth Generation (4G) of wireless, thus
delivering immediate and long-term benefits to carriers. With the architecture of each generation
of wireless devices addressed in the development of advanced technologies, carriers can easily
evolve their systems without additional network modifications, significantly reducing costs and
implementation time. Currently, different wireless technologies (e.g., GSM, CDMA, and
TDMA) are used throughout the world for the 2G, 2.5G, and virtually 3G networks. There are
two approaches being used to develop 4G access techniques: 3xRTT (currently 1xRTT for 2.5
and 3G) and Wideband CDMA (W-CDMA). These disparate access techniques currently do not
interoperate. This issue may be solved with software-defined radios. Link Air Communications
is developing a new access technology called large-area-synchronized code-division multiple
access (LAS-CDMA). LAS-CDMA will be compatible with all current and future standards, and
there is a relatively easy transition from existing systems to LAS-CDMA (using software defined
radios). Link Air emphasizes that LAS-CDMA will accommodate all the advanced technologies
planned for 4G and that LAS-CDMA will further enhance either 3xRTT or W-CDMA system’s
performance or capacity.
Quality of Service Challenges
In wireless networks, Quality of Service (QOS) refers to the measure of the performance
for a system reflecting its transmission quality and service availability (e.g., 4G is expected to
have at least a reliability of 99.99%). Supporting QOS in 4G networks will be a major challenge.
When considering QOS, the major hurdles to overcome in 4G include:
Varying rate,
Channel characteristics,
Bandwidth allocations,
Fault tolerance levels, and Handoff support among heterogeneous wireless networks.
Fortunately, QOS support can occur at the packet, transaction, circuit, and network
levels. QOS will be able to be tweaked at these different operating levels, making the network
more flexible and possibly more tolerant to QOS issues. Varying rate channel characteristics
refers to the fact that 4G applications will
Have varying bandwidth and transition rate requirements. In order to provide solid network
access to support the anticipated 4G applications, the 4G networks must be designed with both
flexibility and scalability. Varying rate channel characteristics must be considered to effectively
meet user demand and ensure efficient network management. Spectrum is a finite resource. In
current wireless systems, frequency licensing and efficient spectrum management are key issues.
In 4G systems, bandwidth allocations may still be a concern. Another concern is
interoperability between the signaling techniques that are planned to be used in 4G (e.g., 3xRTT,
W-CDMA). In comparison with current 2G and 2.5G networks, 4G will have more fault
tolerance capabilities built-in to avoid unnecessary network failure, poor coverage, and dropped
calls. 4G technology promises to enhance QOS by the use of better diagnostic techniques and
alarms tools. 4G will have better support of roaming and handoffs across heterogeneous
networks.
Users, even in today’s wireless market, demand service transparency and roaming. 4G
may support interoperability between disparate network technologies by using techniques such
as LAS-CDMA signaling. Other solutions such as software-defined radios could also support
roaming across disparate network technologies in 4G systems. These major challenges to QOS
in 4G networks are currently being studied and solutions are being developed. Developers
believe that QOS in 4G will rival that of any current 2G or 2.5G network. It is anticipated that
the QOS in 4G networks will closely approximate the QOS requirements in the wire line
environment (99.999% reliability).
4G Applications and Their Benefits to
Public Safety
One of the most notable advanced applications for 4G systems is location-based services.
4G location applications would be based on visualized, virtual navigation schemes that would
support a remote database containing graphical representations of streets, buildings, and other
physical characteristics of a large metropolitan area. This database could be accessed by a
subscriber in a moving vehicle equipped with the appropriate wireless device, which would
provide the platform on which would appear a virtual representation of the environment ahead.
For example, one would be able to see the internal layout of a building during an emergency
rescue. This type of application is sometimes referred to as "Telegeoprocessing", which is a
combination of Geographical Information Systems (GIS) and Global Positioning Systems (GPS)
working in concert over a high-capacity wireless mobile system. Telegeoprocessing over 4G
networks will make it possible for the public safety community to have wireless operational
functionality and specialized applications for everyday operations, as well as for crisis
management. The emergence of next generation wireless technologies will enhance the
effectiveness of the existing methods used by public safety. 3G technologies and beyond could
possibly bring the following new features to public safety:
Virtual navigation: As described, a remote database contains the graphical representation of
streets, buildings, and physical characteristics of a large metropolis. Blocks of this database are
transmitted in rapid sequence to a vehicle, where a rendering program permits the occupants to
visualize the environment ahead. They may also "virtually" see the internal layout of buildings to
plan an emergency rescue, or to plan to engage hostile elements hidden in the building.
Tele-medicine: A paramedic assisting a victim of a traffic accident in a remote location
could access medical records (e.g., x-rays) and establish a videoconference so that a remotely
based surgeon could provide “on-scene” assistance. In such a circumstance, the paramedic could
relay the victim's vital information (recorded locally) back to the hospital in real time, for review
by the surgeon.
Crisis-management applications: These arise, for example, as a result of natural disasters
where the entire communications infrastructure is in disarray. In such circumstances, restoring
communications quickly is essential. With wideband wireless mobile communications, both
limited and complete communications capabilities, including Internet and video services, could
be set up in a matter of hours. In comparison, it may take days or even weeks to re-establish
communications capabilities when a wire line network is rendered inoperable.
Limitations of 4G
Although the concept of 4G communications shows much promise, there are still
limitations that must be addressed. One major limitation is operating area. Although 2G
networks are becoming more ubiquitous, there are still many areas not served. Rural areas and
many buildings in metropolitan areas are not being served well by existing wireless networks.
This limitation of today’s networks will carry over into future generations of wireless
systems. The hype that is being created by 3G networks is giving the general public unrealistic
expectations of always on, always available, anywhere, anytime communications. The public
must realize that although high-speed data communications will be delivered, it will not be
equivalent to the wired Internet – at least not at first. If measures are not taken now to correct
perception issues, when 3G and later 4G services are deployed, there may be a great deal of
disappointment associated with the deployment of the technology, and perceptions could become
negative. If this were to happen, neither 3G nor 4G may realize its full potential. Another
limitation is cost. The equipment required to implement a next-generation network is still very
expensive. Carriers and providers have to plan carefully to make sure that expenses are kept
realistic. One technique currently being implemented in Asian networks is a Pay-Per-Use model
of services. This model will be difficult to implement in the United States, where the public is
used to a service-for-free model (e.g., the Internet).
Conclusion
As the history of mobile communications shows attempts have been made to reduce a
number of technologies to a single global standard. Projected 4G systems offer this promise of a
standard that can be embraced worldwide through its key concept of integration. Future wireless
networks will need to support diverse IP multimedia applications to allow sharing of resources
among multiple users. There must be a low complexity of implementation and an efficient means
of negotiation between the end users and the wireless infrastructure. The fourth generation
promises to fulfill the goal of PCC (personal computing and communication)—a vision that
affordably provides high data rates everywhere over a wireless network
4G networks may eventually deliver on all the promises. At times, it seems that
technological advances are being made on a daily basis. These advances will make high-speed
data/voice-over-Internet-protocol (Vo IP) networks a reality. In the meantime, it is important for
industry to develop a strong 3G offering that is palatable for the general public. Equally as
important, industry must ensure that expectations are realistic and that services meet and exceed
those expectations. If all goes according to what the industry envisions, it may be sooner, rather
than later that we will see wireless communications evolve. This evolution will give the general
public as well as the public safety community amazing functionality from the convenience of a
single handheld device.
The approaching 4G (fourth generation) mobile communication systems are projected to
solve still remaining problems of 3G (third generation) systems and to provide a wide variety of
new services, from high-quality voice to high definition video to high-data-rate wireless
channels. The term 4G is used broadly to include several types of broadband wireless access
communication systems, not only cellular telephone systems. One of the terms used to describe
4G is MAGIC Mobile multimedia, anytime any-where, Global mobility support, integrated
wireless solution, and customized personal service. As a promise for the future, 4G systems, that
is, cellular broadband wireless access systems have been attracting much interest in the mobile
communication arena. The 4G systems not only will support the next generation of mobile
service, but also will support the fixed wireless networks.
This paper presents an overall vision of the 4G features, framework, and integration of
mobile communication. The features of 4G systems might be summarized with one word
integration. The 4G systems are about seamlessly integrating terminals, networks, and
applications to satisfy increasing user demands.
The continuous expansion of mobile communication and wireless networks shows
evidence of exceptional growth in the areas of mobile subscriber, wireless network access,
mobile services, and applications. An estimate of 1 billion users by the end of 2003 justifies the
study and research for 4G systems.
History
The history and evolution of mobile service from the 1G (first generation) to fourth
generation are discussed in this section. Table 1 presents a short history of mobile telephone
technologies.
This process began with the designs in the 1970s that have become known as 1G. The
earliest systems were implemented based on analog technology and the basic cellular structure of
mobile communication. Many fundamental problems were solved by these early systems.
Numerous incompatible analog systems were placed in service around the world during the
1980s.
The 2G (second generation) systems designed in the 1980s were still used mainly for
voice applications but were based on digital technology, including digital signal processing
techniques. These 2G systems provided circuit switched data communication services at a low
speed. The competitive rush to design and implement digital systems led again to a variety of
different and incompatible standards such as GSM (global system Mobile), mainly in Europe;
TDMA (time division multiple access) (IS-54/IS-136) in the U.S.; PDC (personal digital
cellular) in Japan; and CDMA (code division multiple access) (IS-95), another U.S. system.
These systems operate nationwide or internationally and are today's mainstream systems,
although the data rate for users in these system is very limited.
During the 1990s, two organizations worked to define the next, or 3G, mobile system,
which would eliminate previous incompatibilities and become a truly global system. The 3G
systems would have higher quality voice channels, as well as broadband data capabilities, up to
2Mbps. unfortunately, the two groups could not reconcile their differences, and this decade will
see the introduction of two mobile standards for 3G. In addition, China is on the verge of
implementing a third 3G systems.
An interim step is being taken between 2G and 3G, the 2.5G. It is basically an
enhancement of the two major 2G technologies to provide increased capacity on the 2G RF
(radio frequency) channels and to introduce higher throughput for data service, up to 384 kbps.
A very important aspect of 2.5G is that the data channels are optimized for packet data, which
introduces access to the Internet from mobile devices, whether telephone, PDA (personal digital
assistant) or laptop.
Although the new, third generation (3G) wireless technology has not yet been
implemented, leading companies in the industry are already laying the groundwork for what
some are calling fourth generation (4G) technology. Researchers are continuing their ideas in the
development of an undefined wireless world, which could become operational by 2010. The first
generation (1G) and second generation (2G) of mobile telephony were intended primarily for
voice transmission. The third generation of mobile telephony (3G) will serve both voice and data
applications.
There really is no clear definition of what 4G will be. It is generally accepted that 4G will
be a super-enhanced version of 3G i.e., an entirely packet switched network with all digital
network elements and extremely high available bandwidth. For the most part, it is believed that
4G will bring true multimedia capabilities such as high-speed data access and video
conferencing to the handset. It is also envisioned that 4G systems will be deployed with
software-defined radios, allowing the equipment to be upgraded to new protocols and services
via software upgrades. 4G also holds the promise of worldwide roaming using a single handheld
device.
As with all technology progressions, the “next” upgrades must be in planning and
development phases while its predecessors are being deployed. This statement holds true with all
mobile telecommunications to date. It seems that it will also hold true for the next generations of
wireless networks. The original analog cellular systems are considered the first generation of
mobile telephony (1G). In the early 1980s, 1G system was deployed. At the same time, the
cellular industry began developing the second generation of mobile telephony (2G). The
difference between 1G and 2G is in the signaling techniques used: 1G used analog signaling, 2G
used digital signaling. As experience shows, the lead-time for mobile phone systems
development is about 10 years. It was not until the early to mid 1990s that 2G was deployed.
Primary thinking and concept development on 3G generally began around 1991 as 2G systems
just started to roll out. Since the general model of 10 years to develop a new mobile system is
being followed, that timeline would suggest 4G should be operational some time around 2011.
4G would build on the second phase of 3G, when all networks are expected to embrace Internet
protocol (IP) technology. During the last year, companies such as Ericsson, Motorola, Lucent,
Nortel and Qualcomm came up with "3G-plus" concepts that would push performance of
approved, though still emerging, standards beyond current ones.
However, the demand for higher access speed multi-media communication in today's
society, which greatly depends on computer communication in digital format, seems unlimited.
According to the historical indication of a generation revolution occurring once a decade, the
present appears to be the right time to begin the research on a 4G mobile communication system.
b
Legend:
1xRTT = 2.5G CDMA data service up to 384 kbps
AMPS = Advanced Mobile Phone Service
CDMA = Code Division Multiple Access
EDGE = Enhanced Data for Global Evolution
FDMA = Frequency Division Multiple Access
GPRS = General Packet Radio Service
GSM = Global System for Mobile communication
NMT = Nordic Mobile Telephone
PDC = Personal Digital Cellular
PSTN = Pubic Switched Telephone Network
TACS = Total Access Communications System
TDMA = Time Division Multiple Access
WCDMA = Wideband CDMA
4G Communication
This new generation of wireless is intended to complement and replace the 3G systems,
perhaps in 5 to 10 years. Accessing information anywhere, anytime, with a seamless connection
to a wide range of information and services, and receiving a large volume of information, data,
pictures, video, and so on, are the keys of the 4G infrastructures. The future 4G infrastructures
will consist of a set of various networks using IP (Internet protocol) as a common protocol so
that users are in control because they will be able to choose every application and environment.
Based on the developing trends of mobile communication, 4G will have broader
bandwidth, higher data rate, and smoother and quicker handoff and will focus on ensuring
seamless service across a multitude of wireless systems and networks. The key concept is
integrating the 4G capabilities with all of the existing mobile technologies through advanced
technologies.
Application adaptability and being highly dynamic are the main features of 4G services
of interest to users. These features mean services can be delivered and be available to the
personal preference of different users and support the users' traffic, air interfaces, radio
environment, and quality of service. Connection with the network applications can be transferred
into various forms and levels correctly and efficiently. The dominant methods of access to this
pool of information will be the mobile telephone, PDA, and laptop to seamlessly access the
voice communication, high-speed information services, and entertainment broadcast services.
Figure 1 illustrates elements and techniques to support the adaptability of the 4G domains
The fourth generation will encompass all systems from various networks, public to
private; operator driven broadband networks to personal areas; and ad hoc net works. The 4G
systems will interoperate with 2G and 3G systems, as well as with digital (broadband)
broadcasting systems. In addition, 4G systems will be fully IP based wireless Internet.
This all encompassing integrated perspective shows the broad range of systems that the
fourth generation intends to integrate, from satellite broadband to high altitude platform to
cellular 3G and 3G systems to WLL (wireless local loop) and FWA (fixed wireless access) to
WLAN (wireless local area network) and PAN (personal area net work), all with IP as the
integrating mechanism.
With 4G, a range of new services and models will be available. These services and
models need to be further examined for their interface with the design of 4G systems. Figures 2
and 3 demonstrate the key elements and the seamless connectivity of the networks.
Table 2: Comparisons between the earlier technologies
Feature 2G 2G+ 3G
Handsets Voice only terminals
New type of terminal
Dual mode TDMA and
CDMA Voice and data
terminals WAP, no
multimedia support
New type of terminal
Multiple modes Voice,
data and video terminals
WAP, multimedia mgmt
Databases HLR, VLR, EIR, Auk HLR, VLR, EIR, Auk
Enhanced HLR, VLR,
EIR, AuC
Data Rates Up to 9.6 Kbps
Up to 57.6 Kbps (HSCSD)
Up to 115Kbps (GPRS)
Up to 384 Kbps (EDGE)
Up to 2Mbps
Applications
Advanced voice, Short
Message Service
(SMS)
SMS, Internet Internet, multimedia
Compatibility Not compatible to 3G Not compatible to 3G
Compatible to 2G, 2G+
and Blue tooth
Interoperability and the Evolution of Network Architectures
One of the most challenging issues facing deployment of 4G technologies is how to make
the network architectures compatible with each other. New signaling techniques are being
designed specifically to enhance today's second generation (2G) networks, deliver unprecedented
functionality for 3G, and successfully drive the Fourth Generation (4G) of wireless, thus
delivering immediate and long-term benefits to carriers. With the architecture of each generation
of wireless devices addressed in the development of advanced technologies, carriers can easily
evolve their systems without additional network modifications, significantly reducing costs and
implementation time. Currently, different wireless technologies (e.g., GSM, CDMA, and
TDMA) are used throughout the world for the 2G, 2.5G, and virtually 3G networks. There are
two approaches being used to develop 4G access techniques: 3xRTT (currently 1xRTT for 2.5
and 3G) and Wideband CDMA (W-CDMA). These disparate access techniques currently do not
interoperate. This issue may be solved with software-defined radios. Link Air Communications
is developing a new access technology called large-area-synchronized code-division multiple
access (LAS-CDMA). LAS-CDMA will be compatible with all current and future standards, and
there is a relatively easy transition from existing systems to LAS-CDMA (using software defined
radios). Link Air emphasizes that LAS-CDMA will accommodate all the advanced technologies
planned for 4G and that LAS-CDMA will further enhance either 3xRTT or W-CDMA system’s
performance or capacity.
Quality of Service Challenges
In wireless networks, Quality of Service (QOS) refers to the measure of the performance
for a system reflecting its transmission quality and service availability (e.g., 4G is expected to
have at least a reliability of 99.99%). Supporting QOS in 4G networks will be a major challenge.
When considering QOS, the major hurdles to overcome in 4G include:
Varying rate,
Channel characteristics,
Bandwidth allocations,
Fault tolerance levels, and Handoff support among heterogeneous wireless networks.
Fortunately, QOS support can occur at the packet, transaction, circuit, and network
levels. QOS will be able to be tweaked at these different operating levels, making the network
more flexible and possibly more tolerant to QOS issues. Varying rate channel characteristics
refers to the fact that 4G applications will
Have varying bandwidth and transition rate requirements. In order to provide solid network
access to support the anticipated 4G applications, the 4G networks must be designed with both
flexibility and scalability. Varying rate channel characteristics must be considered to effectively
meet user demand and ensure efficient network management. Spectrum is a finite resource. In
current wireless systems, frequency licensing and efficient spectrum management are key issues.
In 4G systems, bandwidth allocations may still be a concern. Another concern is
interoperability between the signaling techniques that are planned to be used in 4G (e.g., 3xRTT,
W-CDMA). In comparison with current 2G and 2.5G networks, 4G will have more fault
tolerance capabilities built-in to avoid unnecessary network failure, poor coverage, and dropped
calls. 4G technology promises to enhance QOS by the use of better diagnostic techniques and
alarms tools. 4G will have better support of roaming and handoffs across heterogeneous
networks.
Users, even in today’s wireless market, demand service transparency and roaming. 4G
may support interoperability between disparate network technologies by using techniques such
as LAS-CDMA signaling. Other solutions such as software-defined radios could also support
roaming across disparate network technologies in 4G systems. These major challenges to QOS
in 4G networks are currently being studied and solutions are being developed. Developers
believe that QOS in 4G will rival that of any current 2G or 2.5G network. It is anticipated that
the QOS in 4G networks will closely approximate the QOS requirements in the wire line
environment (99.999% reliability).
4G Applications and Their Benefits to
Public Safety
One of the most notable advanced applications for 4G systems is location-based services.
4G location applications would be based on visualized, virtual navigation schemes that would
support a remote database containing graphical representations of streets, buildings, and other
physical characteristics of a large metropolitan area. This database could be accessed by a
subscriber in a moving vehicle equipped with the appropriate wireless device, which would
provide the platform on which would appear a virtual representation of the environment ahead.
For example, one would be able to see the internal layout of a building during an emergency
rescue. This type of application is sometimes referred to as "Telegeoprocessing", which is a
combination of Geographical Information Systems (GIS) and Global Positioning Systems (GPS)
working in concert over a high-capacity wireless mobile system. Telegeoprocessing over 4G
networks will make it possible for the public safety community to have wireless operational
functionality and specialized applications for everyday operations, as well as for crisis
management. The emergence of next generation wireless technologies will enhance the
effectiveness of the existing methods used by public safety. 3G technologies and beyond could
possibly bring the following new features to public safety:
Virtual navigation: As described, a remote database contains the graphical representation of
streets, buildings, and physical characteristics of a large metropolis. Blocks of this database are
transmitted in rapid sequence to a vehicle, where a rendering program permits the occupants to
visualize the environment ahead. They may also "virtually" see the internal layout of buildings to
plan an emergency rescue, or to plan to engage hostile elements hidden in the building.
Tele-medicine: A paramedic assisting a victim of a traffic accident in a remote location
could access medical records (e.g., x-rays) and establish a videoconference so that a remotely
based surgeon could provide “on-scene” assistance. In such a circumstance, the paramedic could
relay the victim's vital information (recorded locally) back to the hospital in real time, for review
by the surgeon.
Crisis-management applications: These arise, for example, as a result of natural disasters
where the entire communications infrastructure is in disarray. In such circumstances, restoring
communications quickly is essential. With wideband wireless mobile communications, both
limited and complete communications capabilities, including Internet and video services, could
be set up in a matter of hours. In comparison, it may take days or even weeks to re-establish
communications capabilities when a wire line network is rendered inoperable.
Limitations of 4G
Although the concept of 4G communications shows much promise, there are still
limitations that must be addressed. One major limitation is operating area. Although 2G
networks are becoming more ubiquitous, there are still many areas not served. Rural areas and
many buildings in metropolitan areas are not being served well by existing wireless networks.
This limitation of today’s networks will carry over into future generations of wireless
systems. The hype that is being created by 3G networks is giving the general public unrealistic
expectations of always on, always available, anywhere, anytime communications. The public
must realize that although high-speed data communications will be delivered, it will not be
equivalent to the wired Internet – at least not at first. If measures are not taken now to correct
perception issues, when 3G and later 4G services are deployed, there may be a great deal of
disappointment associated with the deployment of the technology, and perceptions could become
negative. If this were to happen, neither 3G nor 4G may realize its full potential. Another
limitation is cost. The equipment required to implement a next-generation network is still very
expensive. Carriers and providers have to plan carefully to make sure that expenses are kept
realistic. One technique currently being implemented in Asian networks is a Pay-Per-Use model
of services. This model will be difficult to implement in the United States, where the public is
used to a service-for-free model (e.g., the Internet).
Conclusion
As the history of mobile communications shows attempts have been made to reduce a
number of technologies to a single global standard. Projected 4G systems offer this promise of a
standard that can be embraced worldwide through its key concept of integration. Future wireless
networks will need to support diverse IP multimedia applications to allow sharing of resources
among multiple users. There must be a low complexity of implementation and an efficient means
of negotiation between the end users and the wireless infrastructure. The fourth generation
promises to fulfill the goal of PCC (personal computing and communication)—a vision that
affordably provides high data rates everywhere over a wireless network
4G networks may eventually deliver on all the promises. At times, it seems that
technological advances are being made on a daily basis. These advances will make high-speed
data/voice-over-Internet-protocol (Vo IP) networks a reality. In the meantime, it is important for
industry to develop a strong 3G offering that is palatable for the general public. Equally as
important, industry must ensure that expectations are realistic and that services meet and exceed
those expectations. If all goes according to what the industry envisions, it may be sooner, rather
than later that we will see wireless communications evolve. This evolution will give the general
public as well as the public safety community amazing functionality from the convenience of a
single handheld device.
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