183
Acoustic Signature Recognition of Moving Vehicles Using Elman Neural Network
1School of Mechatronic Engineering, Universiti
Malaysia Perlis, Perlis, Malaysia.
2Faculty of Engineering, Karpagam University,
Coimbatore, India
Acoustic Signature Recognition of Moving Vehicles Using ElmanNeural Network
Paulraj M P1, Abdul Hamid Adom1, Hema C R2, Sathishkumar Sundararaj1
ABSTRACT
Hearing impaired people cannot distinguish the sound
of moving vehicles approaching them from behind. Since,
it is difficult for hearing impaired to hear and judge sound
information of vehicles, they often encounter risky situ-
ations while they are outdoors. In this paper, a simple
algorithm is proposed to classify the type and distance
of the moving vehicles based on the sound signature. A
simple experimental protocol is designed to record the
vehicle sound under different environment conditions
and also for different speed of the vehicles. The noise
emanated from the moving vehicles along the roadside
is recorded along with the type and distance of the ve-
hicle. Autoregressive modeling algorithm is used to ex-
tract features from the recorded sound signal. Elman
neural network models are developed and trained using
backpropagation algorithm to classify the vehicle type
and its distance. The effectiveness of the network is vali-
dated through simulation.
Keywords: Hearing impaired, Sound signature,
Autoregressive model, Elman neural network.
I. INTRODUCTION
People with hearing impairment are exposed to many
environmental hazards, one such danger is when they
walk on roads and are unaware of the vehicles
approaching them from behind. Acoustic sensors could
provide rehabilitation to such individuals to tackle such
situation. Statistics compiled by the Social Welfare
Department of Malaysia (SWDM), shows that the
number of registered deaf people in Malaysia as of the
year 2006 is 29,522 the actual numbers can be possibly
higher [1]. According to the 2005 estimates of World
Health Organization (WHO), 278 million people have
disability of hearing in both the ears [2].
Several research studies have developed devices to
provide acoustic information through senses of touch
or vision for hearing impaired. In 1973, Frank Saunders,
proposed an electro tactile sound detector using two
microphones and converted the sound into electrical
pulses. The source of sound is localized based on the
difference in the intensity of the pulses [3]. Saunders
et.al developed a tactile aid for the profoundly hearing
impaired children to help understand speech [4]. In 1986,
A. Boothroyd et.al, have developed a wearable tactile
sensory aid which presents a vibratory signal
representative of voice pitch and intonation patterns to
the skin [5].
Acoustic noise signature emanated from moving vehicle
is mainly influenced by the engine vibration and the
friction between the tires and the road. Vehicles of similar
types are known to generate similar noise signature [6].
Nooralahiyan et.al, proposed a vehicle classification
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
method based on acoustic signature analysis using linear
predictive coding [7]. Eigen method to recognize the
vehicle sound signature based on the frequency vector
principal component analysis has been proposed by Wu
et.al [8]. A review on the state of the art vehicle
classification analysis was done by Xiao et al [9]. Okada,
et.al have computed the direction of the vehicle by
comparing the amplitude of the sound signals captured
by two microphones [10].
In recent years wavelets have been used to identifying
and classifying the vehicle type. Lopez et al proposed
a wavelet-based feature extraction for target
identification [11]. Averbuch et.al, have also used
wavelet packet algorithm for classification of vehicles
and detection of moving vehicles [12, 13]. Using noise
signature to identify moving vehicles has been
investigated by Maciejewski et. al.[14].
Literature reveals that most of the studies on vehicle
noise signature have been restricted to identification of
vehicles only. However the distance of the vehicle also
plays an important role in designing devices for the
hearing impaired. In this study the type of vehicle as
well as the distance of the vehicle is taken into
consideration.
II METHODS
Data Collection
According to the Doppler principle the pitch and the
original frequency of sound changes when a vehicle
passes from the source to the observer. The pitch and
the frequency increases when the sound emitting source
approaches the observer and decreases when it moves
away from the observer [15]. Based on this an experiment
is developed to capture the sound emanated from the
moving vehicle. For this work, a particular section of
the road is considered and the various sound affecting
parameters such as road condition, speed limit,
background noise, and weather condition and wind
direction are also studied. The road selected is a two way
lane with a speed limit of 60 km per hour. The sound
signal from the vehicles moving from X to Y is measured
separately. Two locations A and B with a distance of
separation 100 meters are chosen along the road as shown
in Figure 1. A sound recorder ICD-SX700 is placed at
the location B. which is used to record the noise from
the vehicles. The sound is recorded between A and B.
The corresponding time taken by the vehicle to travel
from A to B is also observed. The number of samples
collected along with the type of vehicle is shown in
Table 1.
Fig. 1 Experimental Recording Setup
TABLE. I Type and number of vehicles
Vehicle type Number of Vehicles
Car 35
Bike 35
Lorry 35
Truck 35
Total 140
Preprocessing and Feature Extraction
The sound signal is recorded at a sampling frequency of
44100 Hz. The normal human auditory system responds
to the frequency ranges from 20 Hz to 20 kHz [16]. The
signal is down sampled to 22050 Hz for further
processing. The time taken by the vehicle to travel the
185
Acoustic Signature Recognition of Moving Vehicles Using Elman Neural Network
distance of 100 meters is also calculated. The distance
between A and B is divided into four equal distance zones
namely zone 1, zone 2, zone 3 and zone 4 such that each
zone distance is fixed as 25 meters. The four zone
representation is shown in Figure 2. The signal
corresponding to each zone is further segmented into
frames of size 1024. The number of frames
corresponding to a particular zone varies as the speed
of the vehicles is not same. The number of frames in
each zone ranges between 10 to 50.
Fig. 2 Typical representation of zone segmentationof a signal
Autoregressive model is used to extract the data from
the sound signal. A pthorder AR model shown in equation
(1) is used to derive the AR coeffecients [17].
∑=
− +=p
ininin xax
1
ε (1)
where the nx is the thn value which can be predicted
by its previous p successive values:
1−nx , 2−nx , …, pxn − .
ia
(i = 1,2,3, …, p. nε is the ûtting error for nx )
The goal of an AR model is to estimate the AR
coefficients that can ût the original data as much as
possible through an optimization process.
In order to determine the optimal number of consecutive
frames that will produce high classification accuracy, a
simple analysis proposed using the AR features extracted
from the consecutive frames. In order to determine the
type and the position of vehicle, AR coefficients are
extracted as features from the segmented frame signals.
The AR features extracted from the frames are then
associated to the vehicle type and zone position and a
database is created. Further, the features from two
consecutive two frames are combined together and
associated to the vehicle type and associated to the zone
position and the second database is created. In a similar
manner the AR features obtained from 3, 4, 5, 6 and 7
consecutive frames are combined together and five more
databases are created. Thus seven different feature
databases are created by combining consecutive framefeatures.
Classification Results
A dynamic Elman neural network is trained using a back
propagation (BP) training algorithm [18]. The BP training
algorithm involves three stages, the feed forward of the
input training pattern, the calculation and back
propagation of the associated weight error and the weight
adjustments. Using the seven data bases, seven network
models are developed to classify the vehicle type. The
features data are normalized using binary normalization
method so as to rescale the values into a definite range
(0.1 – 0.9). The normalized data are further randomized
and for each data set three different sets of training
samples namely 60 %, 70 % and 80 % of the total samples
are selected. Using the guideline proposed in Master [19],
the numbers of hidden neurons are chosen, for all the
network models, the number of output neurons is fixed
as three. The maximum epoch is fixed as 5000. The
training tolerance was set to 0.05. Hyperbolic tangent
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
sigmoidal activation function was used as an activation
function for the hidden layer activation and logistic
sigmoidal activation function was used for the output
layer neurons.
The network is tested using the testing data samples and
its performance is validated by measuring the
classification accuracy. The network model is trained for
25 times and considered as one trail. Five such trails are
carried out and the average classification accuracy, for
all the network models is shown in Table. II. From Table.
II it is observed that the features obtained from the four
consecutive frames has the highest mean classification
accuracies of 89.48%, 92.01% and 93.52% respectively
for the 60%, 70% and 80% training data sets.
Seven Elman neural network models are developed to
classify the vehicle position. The network models are
trained using gradient descent backpropagation
algorithm. For all the network models, the numbers of
output neurons are fixed as three. The maximum iteration
number was fixed as 5000. The training tolerance was
set to 0.05. Hyperbolic tangent sigmoidal activation
function was used as an activation function for the
hidden layer activation and logistic sigmoidal activation
function was used for the output layer neurons. The
network was tested using the testing data samples and
its performance is validated by measuring the
classification accuracy. The network model is trained
for 25 times and considered as one trail. Five such trails
are carried out and the average classification accuracy,
for all the network models is shown in Table. III, from
Table. III it can be observed that the features obtained
four frames has the highest mean classification accuracy
of 87.34%, 89.955% and 92.871% respectively for the
60%, 70% and 80% training data sets.
TABLE. II Neural Network training results for theclassification of the type of vehicle
Classific ation accuracy
Fra
me
No
. Elman Network
60% 70% 80%
Min Max Mean Min
Max Mean Min
Max Mean
1 83.4 87.6 86.5 86.4 87.8 87.1 86.2 89. 1 87.6
2 83.9 87.9 86.9 85 89.1 88.5 87.8 89. 9 88.8
3 84.3 90.4 87.9 87.9 90.1 90.9 89.4 93. 9 91.7
4 87.8 91.2 89.4 89.9 93.8 92 91.7 95. 4 93.5
5 83.3 89.3 88.3 87.2 90.5 90.9 89.3 92. 7 91.5
6 85.7 89.4 88.9 86.1 91.1 90.6 90.6 92. 9 92.2
7 87.0 90.7 89 86.7 90.9 91.3 90.3 93. 5 92.4
TABLE. III Neural Network training results for theclassification of vehicle position
Classification accuracy
Fra
me
No
Elman Network
60% 70% 80%
Min Max Me an Min Max Me a
n Min Max Mean
1 81 83.8 82.4 83 87.4 85.2 85.7 88.9 87.2
2 81.7 83.6 82.7 83.2 88.6 85.9 86.1 89.9 88
3 84.8 86.4 85.3 86.9 89.9 88.4 89.8 92.5 91.2
4 86 88.6 87.3 89.9 92.9 89.9 91.9 94.8 92.8
5 84.5 86.6 85 86.8 90.6 88.2 90.6 92.5 91.6
6 83.1 85.3 84.2 85.5 89.2 87.4 90.4 93.7 91
7 83.4 85.6 84.5 86.4 89.9 88.1 91.8 93.9 92.4
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Acoustic Signature Recognition of Moving Vehicles Using Elman Neural Network
III. CONCLUSION
This paper presents an experimental procedure to capture
the sound signals emanated from the moving vehicle.
Autoregressive model features are extracted from the
recorded signal. Simple neural network models are
developed and trained using backpropagation algorithm
to classify the type of vehicle and its distance from the
subject. The training results show that the use of
autoregressive modeling features differs from the frames
contributes towards a better classification of the vehicle
type and its distance from the subject.
ACKNOWLEDGEMENT
The authors would like to acknowledge the support and
encouragement by the Vice Chancellor of Universiti
Malaysia Perlis, Y.Bhg. Kol.Prof Dato’ Dr. Khamarudin
b. Hussin. This work is financially assisted by the
Fundamental Research Grant Scheme (FRGS) (9003-
00186): by the Ministry of Higher Education, Malaysia
REFERENCES
[1]. O.S.Hock, Salimah Mokhtar, and Roziati
Zainuddin, (2007) “A Review on the Teaching and
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[5]. Arthur Boothroyd, “Wearable Tactile Sensory Aid
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[6]. R. B. Randall, “Frequency Analysis,” Brüel &
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[10]. Kazuhide Okada, Gwan Kim and Pyong Sik Pak,
“Sound Information Notification System by Two-
Channel Electrotactile Stimulation for Hearing
Impaired Persons”, Proceedings of the 29th Annual
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Internationale Lyon, France.
[11]. Joes E.Lopez, Hung Han Chen and Jennifer
Saulnier, “Target Identitfication Using Wavelet-
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Classifiers” Cytel Systems,inc. Hudson.
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[12]. Averbuch, E. Hulata, V. Zheludev and I. Kozlov,
“A Wavelet Packet Algorithm for Classification
and Detection of Moving Vehicles”,
Multidimensional Systems and Signal Processing,
vol. 12, pp. 9-31, 2001
[13]. Averbuch, V. A. Zheludev, N. Rabin, and A.
Schclar, “Wavelet-based acoustic detection of
moving vehicles”, Multidimensional Systems
and Signal Processing, vol. 20, pp. 55-80, 2009.
[14]. H. Maciejewski, J. Mazurkiewicz, K. Skowron,
and T. Walkowiak, “Neural Networks for Vehicle
Recognition,” in Proceeding of the 6th International
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Networks, Evolutionary and Fuzzy Systems, 1997,
p. 5.
[15]. Dev Maulik, “Doppler Ultrasound in obstetrics and
Gynecology”, pp 13, ISBN 3-540-23088-2,
Springer Verlag Berlin Heidelberg Newyork.
[16]. Weixiang zhao, Cristina E.Davis, “Autoregressive
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75397-1, Springer 2008.
[18]. S.N Sivanandam and Paulraj M, “An Introduction
to Artificial Neural Networks”, Vikhas Publication,
India, 2003.
[19]. T. Masters, “Practical Neural Network Recipes in
C++”, Academic Press, New York 1993.
Author’s Biography
Paulraj MP received his BE in Electrical
and Electronics Engineering from
Madras University (1983), Master of
Engineering in Computer Science and
Engineering (1991) as well as Ph.D. in Computer
Science from Bharathiyar University (2001), India. He
is currently working as an Associate Professor in the
School of Mechatronic Engineering, University Malaysia
Perlis, and Malaysia. His research interests include
Principle, Analysis and Design of Intelligent Learning
Algorithms, Brain Machine Interfacing, Dynamic Human
Movement Analysis, Fuzzy Systems, and Acoustic
Applications. He has co-authored a book on neural
networks and 290 contributions in international journals
and conference papers. He is a member of IEEE, member
of the Institute of Engineers (India), member of Computer
Society of India and a life member in the System Society
of India.
Abdul Hamid Bin Adom is currently
the Dean of School of Mechatronic
Engineering at University Malaysia
Perlis, Malaysia. He received his B.E,
MSc and PhD from LJMU, UK. His
research interests include Neural Networks, System
Modeling and Control, System Identification, Electronic
Nose/ Tongue, Mobile Robots. He holds various research
grants and published several research papers. Currently
his research interests have ventured into Mobile Robot
development and applications, as well as Human
Mimicking Electronic Sensor Systems for agricultural
and environmental applications.
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Acoustic Signature Recognition of Moving Vehicles Using Elman Neural Network
Hema C R obtained her BE and MS in
EEE from Madurai Kamaraj
University, India and University
Malaysia Sabah, Malaysia in 1989 and
2005 respectively. She obtained her
PhD in Mechatronic Engineering at University Malaysia
Perlis, Malaysia in 2010. She is currently the Dean
Engineering Research at Karpagam University, India. Her
research interests include EEG signal processing, Neural
Networks and Machine Vision. She holds many research
grants and has published 8 books and 5 book chapters
and around 108 papers in referred Journals and
International Conferences.. She has received gold and
Bronze medals in National and International exhibitions
for her research products on vision and brain machine
interfaces .She is cited in WHO IS WHO in the world
2009 to 2011. She is a member the IEEE, IEEE EMB
Society and IEEE WIE Society.
Sathishkumar Sundararaj obtained his
B.Tech in Information Technology from
Anna University (2008). He is currently
pursuing Masters Degree in Mechatronic
engineering at University Malaysia Perlis. His research
interest includes signal processing and Neural Networks.
He published some papers in conferences
190
Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Fuzzy MPPT Based Voltage Regulation on Photovoltaic PowerSupply System For Continuously Varying Illumination Condition
A. Durgadevi1, S. Arulselvi2
ABSTRACT
Due to scarcity of fossil fuel and increasing demand of
power supply we are forced to utilize the renewable
energy resources with the shortage of energy and ever
increasing of the oil price. Researches on the renewable
and green energy sources, especially solar arrays and
fuel cells become more and more important. Considering
easy availability and vast potential, world has turned to
solar photovoltaic energy to meet its ever increasing
energy demand. Due to high initial investment on PV
systems and non linearity of PV cell output
characteristics counteract its wide commercialization.
The PV array has an optimum operating point to generate
maximum power at some particular point called
maximum power point (MPP). To track this maximum
power point and to draw maximum power from PV
arrays, MPPT controller is required in a stand-alone PV
system. Due to the nonlinearity in the output
characteristics of PV array, it is very much essential to
track the MPPT of the PV array for varying maximum
power point due to the insolation variation. In order to
track the MPPT intelligent controller like fuzzy logic
controller is proposed and simulated. The output of the
controller, pulse generated from PWM can switch
MOSFET to change the duty cycle of buck DC-DC
converter. The result reveals that the maximum power
point is tracked satisfactorily for varying insolation
condition.
Key words:
Photovoltaic, Pulse Width Modulation, Fuzzy Logic
Controller.
I. INTRODUCTION
Today photovoltaic (PV) systems are becoming more
and more popular with increase of energy demand and
there is also a great environmental pollution around the
world due to fossils and oxides. Solar energy which is
free and abundant in most parts of world has proven to
be economical source of energy in many applications
[1]. The energy that the earth receives from the sun is so
enormous and so lasting that the total energy consumed
annually by the entire world is supplied in as short a
time as half an hour. The sun is a clean and renewable
energy source, which produces neither green house effect
gas nor toxic waste through its utilization. It can
withstand severe weather conditions, including cloudy
weather. The watt peak price is decreased since the
seventies, this leads to large scale promising areas. It
does not have any moving parts and no materials
consumed or emitted. Unfortunately, this system has two
major disadvantages, which the low conversion
efficiency of electric power generation (9 to 16%),
especially under low irradiation conditions and the
amount of electric power generated by solar array
changes continuously with the weather conditions like
irradiation and temperature. To overcome this problem,
maximum power point tracking (MPPT) technique will
be used.
Department of Electronics and Instrumentation
Engineering Annamalai University, Chidambaram,
Tamil Nadu, India.
Email: [email protected] and
191
Fuzzy MPPT Based Voltage Regulation on Photovoltaic
Power Supply System For Continuously Varying Illumination Condition
In order to regulate the converter due to non linearity
artificial intelligent controller like fuzzy logic controller
is proposed and simulated. The tracking algorithm
integrated with a solar PV system has been simulated
with buck DC-DC converter for the application of
battery charging in stand - alone PV system. The proposed
MPPT system with buck DC-DC converter is shown in
Fig.1.
Fig.1. Photovoltaic module with DC-DC buck converter.
II. PHOTOVOLTAIC CELL MODELING
The proposed MPPT is based on the behaviour of the
photovoltaic array by means of temperature and
irradiation variation [2]. This the mathematical model of
PV array is implemented in the form of current source
controlled by voltage, sensible to two impact parameters,
that is, temperature (°C) and solar irradiation power (w/
m2).
An equivalent simplified electric circuit of a photovoltaic
cell presented in Fig.2.
Fig.2. Equivalent circuit of photovoltaic cell.
The expressions obtained from fig.2. are given below.
The load current IL is obtained is given in equation (1)
as,
(1)
IL is the photo electric current related to the given
irradiation condition given by equation (2),
(2)
The diode saturation current (Io) is given by the equation
(3),
(3)
where - ID is the diode current; I-
L- is the photoelectric
current related to a given condition of radiation and
temperature; V is the output voltage [V]; Io is the
saturation diode current [A]; is the form factor which
represents an index of the cell failing; Rs is the series
resistance of the cell [Ù]; q is the electric charge
(1.602*10-19C); k is the Boltzmann’s constant
(1.381*10-23K); Tc is the module temperature [K]. E
g is
the energy gap of the material with which the cell is made
(for the silicon it is 1.12 eV); G is the radiation [W/m2];
GREF is the radiation under standard conditions [W/m2]
IL,REF
is the photoelectric current under standard
conditions [A]; TC,REF
is the module temperature under
standard conditions [K]; is the temperature
coefficient of the short circuit current [A/K], given by
the manufacturer according to CEI EN 60891 standard
[3-4].
Fig. 3. shows the simulated P-V characteristics for
varying irradiation and temperature in MATLAB/
SIMULINK environment.
(a) (b)
Fig.3. Simulated waveforms showing the effect of (a) radiation and
(b) temperature on P-V characteristics.
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
It can be observed from simulated results as shown in
Fig. 3(a), the photo current is directly proportional to
irradiation. It is noted from Fig. 3(b) that the terminal
voltage increases with decreasing temperature.
The manufacturers data at standard conditions are
given as Pmax
= 80W, Imax
= 4.515 A and Vmax
= 21.6V.
The simulation results obtained were: Pmax
= 78.51W,
Imax
= 4.515 A and Vmax
= 21.65V. It is seen that the
simulation model showed excellent correspondence to
manufacturer’s data and therefore this model was
considered sufficient for the purpose of further study
[4-8].
Simulated I-V, P-V characteristics for the maximum
power point tracking (MPPT) is shown in figure.4.
At this Maximum Power Point (MPP), the solar array is
matched to its load and when operated at this point the
array will yield the maximum power output. From Fig. 4
(a) & (b), it is observed that the power output has an
almost linear relationship with array voltage unit, hence
the MPP is attained. Any further increase in voltage
results in power reduction [5].
(a) (b)
Fig.4. PV array simulated curves (a) I-V curve (25°C) and (b) P-Vcurve (1000w/m2).
III. DESIGNING OF BUCK CONVERTER
A. Circuit diagram of buck converter.
Fig. (5) shows the schematic diagram of buck
converter with varying irradiation, which consists of DC
supply voltage Vs, as PV generator controlled switch S,
diode D, buck inductor L, filter capacitor C and load
resistance R. The current and voltage waveforms of the
converter in CCM are presented in fig.6.
Fig.5. Circuit diagram of buck converter with PV
module.
It can be seen from the circuit that when the switch S is
commanded to the on state, the Diode D is reverse biased.
When the switch S is off, the diode conducts to support
an uninterrupted current in the inductor through the
output RC circuit using faradays law for the buck
inductor as given in (4)
Fig.6. Theoretical voltage and current waveforms of buck converter.
(4)
The DC voltage transfer function turns out to be,
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Fuzzy MPPT Based Voltage Regulation on Photovoltaic
Power Supply System For Continuously Varying Illumination Condition
(5)
The buck converter operates in the CCM for L>Lb. The
calculated value of inductance L=15µH. To limit the
ripples in the output side, larger filter capacitor is
required. The filter capacitor must provide the output dc
current to the load when diode D is off. The minimum
value of filter capacitance calculated that results in the
voltage ripple Vr is given by C
min =12.675uF.
Thus the buck converter is designed in the open loop for
the supply voltage of 21.7V DC, which is generated by
the Photovoltaic panel for 1000w/m2 and 25°C. Fig.7
shows the simulated voltage and current waveforms of
buck converter. It is seen that these waveforms are agreed
closely with theoretical waveforms as shown in fig.6.
Fig.7. Simulated waveforms showing the voltage and current ofbuck converter.
IV. MPPT WITH INTELLIGENT FUZZY METHOD
The block diagram of closed loop simulation with fuzzy
logic controller is shown in fig.8. To regulate the output
voltage Vo, the switching frequency of the PWM pulses
are varied depends on error.
Fig. 8. Block diagram of the proposed fuzzy based MPPT scheme.
A. MPPT with the Intelligent Fuzzy Method
To track and extract maximum power from the PV
arrays for a varying insolation level and at a given cell
temperature, a novel fuzzy logic controller is proposed
based on the intelligent fuzzy logic method.
It consists of three parts: fuzzification, inference
engine and defuzzification [9-11].
An error function (E) and change of error (ÄE) are
calculated from (6) and (7) and created during
fuzzificaion. These variables are then compared to a set
of pre-designed values during inference method in order
to determine the appropriate response.
Defuzzification is for converting the fuzzy subset of
control from inference back to crisp values [12-15].
(6)
(7)
The E and ÄE function is compared to the graph of Fig.
9. to obtain a variable NB or ZE, then this parameter
will be used to locate the respective output function (u)
from the fuzzy rule table I.
The inference mechanism is implemented with mamdani
algorithm and the centre of gravity method is used for
defuzzification.
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Fig No. 9, Fuzzy Logic membership function
TABLE IFUZZY RULE TABLE
E
NB NM NS Z PS PM PB
NB Z NM NS NB NS PB PB
NM NM Z Z NB PM Z NB NS PM NB Z NS Z PM NS
Z NB NB Z Z Z Z PS PS NM PM NS Z Z NB Z
PM PM NS NS NB Z Z NB PB NB PB PM Z NS NS Z
The insolation variation from 100 w/m2 to 1000 w/m2,input voltage (V-
in) and output voltage (V
o) are shown
in Fig.10.
Fig.10. Simulation results depicting the change of insolation from100 to 1000w/m2, input voltage (V
in) and output voltage (V
o).
V. CONCLUSION
An intelligent fuzzy method for MPPT of Photovoltaic
array is presented in this paper on the base of fuzzy logic
control algorithm. The simulation results show that the
fuzzy controller has the merits such as simplicity fast
response, low over-tuning, high control, precision and
easy implementation.
REFERENCES
[1] Bimal K. Bose, “Global Warming Energy:
Environmental Pollution and the Impact of Power
Electronics”, IEEE Industrial Electronics
Magazine, pp.1-17, March 2010.
[2] Adel Mellit, “Artificial Intelligence technique for
modelling and forecasting of solar radiation data:
a review,” Int. J. Artificial Intelligence and Soft
Computing Vol. 1. No. 1, 2008.
[3] Akihiro Oi, “Design and Simulation of Photovoltaic
Water Pumping System”, Master of Science Thesis,
California Polytechnic State University, 2005.
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Fuzzy MPPT Based Voltage Regulation on Photovoltaic
Power Supply System For Continuously Varying Illumination Condition
[4] Adedamola Omole, “Analysis, Modelling and
Simulation of Optimal Power Tracking of Multiple-
Modules of Paralleled Solar Cell Systems”, Master
of Science Thesis, The Florida State University
College of Engineering, 2006.
[5] V. Di Dio, D. La Cascia, R. Miceli, “A Mathematical
Model to Determine the Electrical Energy
Production in Photovoltaic Fields under Mismatch
Effect”, Proceedings of the 978(1) IEEE, pp.46-51,
August 2009.
[6] Renji V Chacko, Z.V. Lakaprrampil, K.A. Fathima,
“Solar Inverter with MPP Tracking for Pumps and
UPS Application”, ERDC, pp.1-12, 2000.
[7] Christopher A. Otieno, George N. Nyakoe, Cyrus
W. Wekesa, “A Neural Fuzzy Based Maximum
Power Point Tracker for a Photovoltaic System”,
IEEE AFRICON, September 23-25, pp.1-6, 2009.
[8] A. Mellit, “An ANFIS-Based Prediction for Monthly
Clearness Index and Daily Solar Radiation:
Application for sizing of a Stand-Alone Photovoltaic
System”, Journal of Physical Science, Vol. 18(2),
pp.15–35, 2007.
[9] N. Patcharaprakiti and S. Premrudeepreechacharn,
“Maximum PowerPoint Tracking Using Adaptive
Fuzzy Logic Control for Gridconnected
Photovoltaic System”, PESW2002, volume 1,
PP:372-377, 002.
[10]Marcelo Gradella Villalva, Jonas Rafael Gazoli, and
Ernesto Ruppert Filho , “Comprehensive Approach
to Modeling and Simulation of Photovoltaic Arrays”,
IEEE Transactions on Power Electronics, vol 24,
no. 5, 2009, pp 1198-1208.
[11] A. Durgadevi, “Design of intelligent controller for
quasi resonant converters”, M.E. dissertation,
Departmne of Electronics and Instrumentation
Engineering, Annamalai Univ., Chidambaram, 2008.
[12]C. Y. Won, D. H. Kim, et al, “A New Maximum
Power Point Tracker of Photovoltaic Arrays Using
Fuzzy Controller”, PESC’1994 Record, PP:396-403,
1994.
[13]H. Yamashita and K. Tamahashi, et al, “A Novel
Simulation Technique of the PV Generation System
Using Real Weather Conditions,’’ in Proceedings of
the PCC,2002, volume2, PP:839-844, 2002
[14]Cung-Yuen Won, Duk-Heon Kim, Sei-chan Kim,
Won-Sam Kim, hack_Sung Kim, “A New Maximum
Power Point Tracker of Photovoltaic Arrays Using
Fuzzy Controller”, PESC’94.
[15]Guohui Zeng, Qizhong Liu, “An Intelligent Fuzzy
Method for MPPT of Photovoltaic arrays”, 2009
Second International Symposium on Computational
Intelligence and Design, pp. 356-359.
Author’s Biography
A. Durgadevi received her B.E. in Elec-
trical and Electronics Engineering from
I.FE.T engineering college, villupuram
Madras University in 1999 and M.E. in
Process Control and Instrumentation En-
gineering from Annamalai University in 2008. Currently
she is a Doctorate student at department of Electronics
and Instrumentation Engineering in Annamalai Univer-
sity. Her research interests are in Solar Energy Systems
and Power Electronics.
196
Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Design and Development of a Gripper System for an IndoorService Robot
Hema C. R.1 Vivian Tang Sui Lot2 Paulraj M.P.2
ABSTRACT
Service robot are designed to provide assistance in home
environments, floor cleaning, material transporting and
security purpose. This paper focuses on the design and
development of a gripper system for a housekeeping
robot ‘ROOMBOT’ to perform pick and place operations.
A gripper with three degree of freedom is designed and
developed and tested for pick and place of several objects.
Keywords : Roliot Geipprens, Robot Coutiol, Dinner
Degree of Freedom.
I. INTRODUCTION
Robotic grippers find wide applications in industrial,
medical and domestic use. Research in this area has been
continuously evolving to provide more dexterous
grippers which can mimic the human arm. Despite a
number of prototypes of robotic gripper that have been
developed in more than 20 years of research mainly in
academic institutions among many others [1], effort is
being devoted to seek and evaluate alternative solutions
with sufficient dexterity to perform in any case non trivial
operations on wide range of objects.
A gripper is a device which enables the holding of an
object to be manipulated. The easier way to describe a
gripper is to think of the human hand. Just like a hand, a
1Faculty of Engineering, Karpagam University,Coimbatore, INDIA2School of Mechatronic Engineering, UniversityMalaysia Perlis, Perlis, MALAYSIAhemacr@ karpagam.ac.in
gripper enables holding, tightening, handling and
releasing of an object. Gripper is just one component of
an automated system. A gripper can be attached to a robot
or it can be part of a fixed automation system.
The basic operating principal of the gripper consists of
three primary motions which is parallel, angular and
toggle [2]. These operating principals refer to the motion
of the gripper jaws in relation to the gripper body (refer
Table 1). Rotary actuator is a device use to alternate the
rotated position of an object. Just like the human wrist
the actuator enables the rotation of an object, except that
rotary actuators are available in a wide variety of models
with different sizes, torques, and rotation angles.
The robot gripper can be divided into two sections, each
with different function:
1. Arm and body – the arm and body of a robot are
used to move and position parts or tools within a
work envelope.
2. Wrist or end effectors – these parts are used to orient
the parts or tools at the work location.
II. DESIGN OF THE GRIPPER
The ROOMBOT robot is a housekeeping robot which
was required to collect crushed paper in a controlled
environment.
Identification of the object to be picked is accomplished
by a hybrid vision system. Co-ordinates of the object to
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Design and Development of a Gripper System for an Indoor Service Robot
be picked will be provided by this system to the gripper.
The mechanical design of the gripper is required to fulfill
the interaction between the robot and the object in order
to pick and place i.e. the object from the ground to the
dustbin at the back of the robot. The initial target of object
is identified as crushed paper. The movement mechanism
of the gripper is simple and straightforward. It will react
once the input is received from the vision system from
the main controller.
Table 1 – Gripper Operating Principal
Operating Principal
Illustration
Parallel
Angular
Toggle
Design Specification
The mechanical design of the gripper is inspired by the
following issues [3-5, 7-13]:
a. The gripper should be able to manipulate object in
round shape. The primary object to be grasped is a
crash paper. The surface of a crash paper is rough
and its shape being almost like round shape. The
size targeted for the crash paper is 3cm to 8cm in
diameter.
b. Grasping area is 25cm in front of the robot. The
gripper should be able to extend to 25cm distance
from the robot front body.
c. Grasping precision and position repeatability of the
gripper is a requirement as memory of location is
preset in controller.
d. Power consumption by the actuator should be small
considering larger supply in need will increase the
burden of the robot weight by installing larger battery
cell. The strength of grasping should be enough to
lift up a crash paper and hold it when transferring.
Mechanical Design
From the required design specification, the gripper is
constructed first by using the Solid Works software to
draft out the architecture of the whole gripper system.
There are three parts in construction which is the base,
the arm and the end effectors.
The base of the gripper system is constructed using L-
shape aluminum bar with dimension of 9x8cm. One
motor is use as the base driving actuator. The height of
the gripper base is extended 3cm from ground by using
cylinder screw. Figure 1 shows a drawing illustration of
the base by using the Solid Works software.
Figure 1 - Gripper base design drawing inSolid Works
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
The arm have two joints linking the arm and the wrist to
the end effectors, labeled A and B in figure 2. Two motor
is use to drive the first arm joint A, while the wrist only
uses one of the standard servo motor for its drive. Two
motor is use in for supporting and driving the first arm
linked to the base.
Figure 2 - Gripper arm and wrist design drawing inSolid Works
The design of the end effectors is using the pivoting or
rotating jaws mechanism. This design is simple and only
requires one actuator to drive the mechanism (refer figure
3). This design of mechanism requires sufficient accuracy
in center of grasping of the object. Otherwise, the object
may slip off when the gripper is closing the end effectors
or the object may drop off from the end effectors in the
middle of transferring the object to the dustbin.
Figure 3 - End effectors design drawing in SolidWorks
Gripper Degrees of Freedom
To locate an object in space, one need to specify the
location of the selected object, thus it requires three
pieces of information to be located as desired. These three
pieces of information are the X, Y, and Z coordinates
value [4]. With the three pieces of the information, the
object location is specified. Hence, there is need for a
total of six pieces of information to fully specify the
location and orientation of the gripper.
The designed gripper only consists of 3 degrees of
freedom, where it can only move along the X, and Y
axes (refer figure 4). In this case, no orientation can be
specified. All the robot can do is to pick up the object
and move it to the dustbin. The orientation is always
remains the same.
The end effectors are not considered as one of the degrees
of freedom. All the gripper has this additional capability,
which may appear to be similar to a degree of freedom.
However, none of the movements in the end effectors
are counted towards the gripper’s degrees of freedom.
The weight limitation was important in construction of
the gripper. The material used should have low density
and good yield in strength properties and are easy to
machine. Aluminum was choose as it criteria fits in.
Aluminum is soft, durable, lightweight. It is ductile, and
easily machined, cast and extruded. Hence, aluminum
provides easy machinery and shaping with the tools, and
allowing a high accuracy in part dimensions to be
obtained. The aluminum sheet use has thickness of 2mm.
This concludes that the whole gripper weight after the
construction is 0.5kg.
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Design and Development of a Gripper System for an Indoor Service Robot
Gripper Actuation
Parallax standard servo motor is selected as the gripper
actuator because of its precision in positioning, and its
weight is 45grams. The servo motor can produce torque
up to 3.4kg-cm and it have small size casing with low
price compare other similar servo motor that offer
equivalent strength. One standard motor is use to drive
the rotation of the base. Standard servo motor has
limitation in rotation up to 180 degrees [6]. In that case,
the base of the gripper only can turn in one side of area
at 0 to 180 degrees. This rotation is sufficient to cover
the area of object that could be detected in front of the
robot.
Two motors are used to drive the first arm joint with the
base. The weight induced over the end effectors are
insufficient to be lifted up by only one motor torque. To
avoid over current draw by the motor in producing
sufficient torque, two motors are applied. The two motor
sequences should be synchronized.
Gripper end effectors are driven by only one standard
motor (figure 3). Two gears is used in series, and one of
the gears is attached to the motor. When the motor rotate,
the gear attach to the motor will rotate and drive the
second gear in series with it to rotate in opposite direction.
This effect will operate the end effectors to open wide
up reaching to 180 degrees.
Figure 4 - Gripper design drawing – Isometric view
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Drive and Control System
The gripper control program is written in assembly
language, and running in Basic Stamp controller. The
gripper program is executed together with mobile
platform and ultrasonic sensor program in one processor.
Another controller used was the servo controller. Servo
controller can control up to 16 motor at one time. Servo
controller communicates with Basic Stamp controller
through serial cable. Signal is sent from Basic Stamp
controller to the servo controller in pulse form.
The main features of the flow for the gripper program
are shown in Figure 5. When the robot is powered up,
the gripper will move to its standby position which is
heading toward the back of the robot. When the main
program is executed, the robot will move around with
ultrasonic sensor as primary sensor to detect any object
appears. When an object is been encounter, the vision
system will activate to capture and recognize the
encountered object. If an object is identified, the vision
system will send input through the serial communication
between the mobile platform main controllers and initiate
the gripper system.
When the gripper is initiated, the base motor will rotate
the body arm of the gripper towards the front side of the
robot. Followed, the controller will instruct the first arm
joint two motor to lift down the end effectors to a set
height. At the set height the end effectors will open to
180 degrees wide. Again the gripper wrist will lower
down bringing the end effectors closer to the object and
perform the picking operation.
When the object is secure in the end effectors, the gripper
is instructed by the controller to returns and place the
object into the bin at the back of the robot before it
reposition back to its standby position. After the operation
of pick and place is completed, the robot will continue
to move around in the environment.
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Figure 5 - Program Flow Chart
III. EXPERIMENTS AND RESULTS
There were two types of experiments is done towards
the designed gripper system to test it reliability and
efficiency, the offline and online experiments. Offline
testing is conducted on the gripper simulation without
installation it onto the robot mobile platform. This is to
test on the gripper functionality and define the coverage
area of the gripper in picking object. A model of mobile
platform is build to install the gripper system to perform
the testing.
Online testing is conducted when the gripper system
is assembled onto the mobile platform together with
ultrasonic system and vision system. This testing
conducted to test on the serial communication between
the controller of PC and Basic Stamp which sending
signal to trigger the gripper system when an object is
been identified. Another testing is tested on the relocation
of the Y-position when there is an input of Y-value from
the vision system.
Discussion
From the testing, the result shows the gripper system is
fully operate able. The average time for one cycle of pick
and place operation is 12 seconds (Table 2). Once the
gripper is triggered by the main controller, the gripper
can operate to complete the task in that particular short
of time period. This result shows the efficiency of the
gripper in decreasing the operating time as less delay is
created due to the pick and place action.
The gripper system can pick able to vary object. 5 type
of objects is giving a testing which are the plastic bag,
crash paper, empty bottle (500ml), carbonate drink can
(325ml) and a small boxes. 4 of the item show pick able
by the gripper. Empty bottle is unable to be picked
because of its plastic body surfaces that give frictionless
to the grasping force from the end effectors.
The gripper has a limitation in picking object with weight
greater than 85grams. Object that weight over 85grams
make the gripper to draw more current to produce more
torque in lifting up the object and transferring it to the
dustbin.
The communication between the Basic Stamp and the
servo controller is via RS 232 serial protocol. The
firmware number which replies by the servo controller
when communication is established shows the version
of the servo controller. After the confirmation of the
firmware version, the baud rate is elevated to 38K4 baud.
Now the servo motor can be control by entering the
position command. Each position command is comprised
of a heater, three parameters: C, R, PW, and a command
terminator. The C parameter is a binary number 0-31
corresponding to the servo channel number. The R
parameter is a binary number 0-63 that controls the ramp
function for each channel. The PW parameter is the 16
bit Word that corresponds to the desired servo position.
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Design and Development of a Gripper System for an Indoor Service Robot
When the robot detects an object, the distance for the
robot to stop is 25cm away from the object regardless of
which location of the object maybe laying in the Y-axis.
The possible area of object fall in is coverage by the
wide open angle of the gripper end effectors.
Coordination of Y-position happen when the object fall
in the camera coverage area where servoing is unable to
perform to adjust the robot position to change the location
of the object to the center in front of the robot.
Referring to Figure 6, the location Y1, Y2, and Y3 is
pre-taught to the gripper. These three locations are set
on the gripper base servo motor. Once there is an input
from the vision system signaling the location of the object
through the serial com, the gripper is activated and based
on the data received from the vision system, the program
command line will call to the desired preset location of
the Y1, Y2, or Y3 and turn the gripper towards the
location and perform pick and place.
IV. CONCLUSION
The gripper is designed with 3 degrees of freedom, build
using light weight aluminum with total weight of 0.5kg,
and drive by 5 standard servo motor. The gripper
operation time is 12 seconds with pick able of vary object
of plastic bag, small boxes, carbonate drink can and
crashed paper.
The gripper also can manipulate in Y-position with the
input from the vision system.
In conclusion to this project, the primary objective is
achieved successfully. The crash paper selected as initial
target of object is successfully picked able by the gripper
in the range of 25cm distance apart from the front of the
robot.
There still have a lot of improvement area in the gripper
system that can be done to make it more reliable and
effective. Any further improvement and upgrade on the
version can still be carry on to perfected the system and
commercialize for public usage.
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Figure 6 - Y-positioning of the gripper relative to thelocation of the objects.
Table 2 – Time taken for one completed cycle of pickand place
Trials Time Taken (s)
1 12.64
2 12.76
3 12.60
Average 12.66
There are many design improvements that can be done
in the proposed gripper system. The standard motor can
be replaced by more better and powerful actuator to
increase the torque and more degrees of freedom. A
stepper motor can provide higher torque and 360 degrees
of angle rotation compare with servo motor. So as, the
area coverage by the gripper will extended more than
just 25cm in radius.
The end effectors function can be improved by adding a
degree of subsystem flexibility like variable jaw geometry
202
Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
featuring wide range gripper end effectors encompassing
a board spectrum of object dimensions. Additional force
sensor can be implemented on the end effectors to control
the grasping force applied. It also can prevent the two
finger of the gripper to over strain the object picked.
REFERENCES
[1] L. Biagiotti, C. Melchiorri, G. Vassura 2003. A
Dexterous Robotic Gripper for Autonomous
Grasping. Research Paper Volume 30, University
of Bologna, Bologna, Italy.
[2] Applied Robotics: Solutions in reach, April 1,
2008. Tutorials on Grippers and Rotary Actuators
http://www.arobotics.com/technical/tutorials.aspx.
[3] Zaytran, Inc. 1996. Robotic Gripper Sizing: The
Science, Technology and Lore. Elyria, Ohio USA.
http://www.grippers.com/size.htm
[4] Saeed B. Niku, 2001. Introduction to Robotics
Analysis, Systems, Applications. 1st Edition,
Prentice Hall, Upper Saddle River, New Jersey.
[5] B. S. Baker, S. Fortune and E. Groose, 1995. Stable
Prehension with a Multi-Fingered Hand. IEEE
International Conference on Robotics and
Automation, pp. 570-575.
[6] Seattle Robotics Society, March 16, 2009. Whats
a Servo: A Quick Tutorial, available online at http:/
/www.seattlerobotics.org/guide/servos.html.
[7] Gareth J. Monkman, Stefan Hesse, Ralf
Steinmann, Henrik Schumk, 2007. Robot
Grippers. 1st Edition, WILEY-VCH Verlag GmbH
& Co,KGaA, Weinheim.
[8] Thomas R. Kurfess, 2005. Robotic and
Automation Handbook. 1st Edition, CRC PRESS,
USA.
[9] Seguna, C.M., Saliba, M.A. The Mechanical and
Control System Design of a Dexterous Robotic
Gripper. IEEE, pp. 1195-1201.
[10] Andres Herrera, Andres Bernal, David Isaza,
Malek Adjouadi. Design of an Electrical Prosthetic
Gripper using EMG and Linear Motion Approach.
Center for Advance Technology and Education,
Department of Electrical and Computer
Engineering Florida International University,
Florida.
http://fcrar.ucf.edu/papers/tp2_ahabdima_fiu.pdf
[11] Chen Guoliang, Huang Xinhan, 2004. Research
on Vacuum Micro-Gripper of Intelligent
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2004 IEEE International Conference on Robotics
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[12] Dominik Henrich and Heinz Wörn. 2000. Robot
Manupulation of Deformable Objects. 1st Edition,
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[13] K. Venugopal Varma and U. Tasch, 1991.
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1446.
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Design and Development of a Gripper System for an Indoor Service Robot
Author’s Biography
Hema C R obtained her BE and MS
in EEE from Madurai Kamaraj Uni-
versity, India and University Malay-
sia Sabah, Malaysia in 1989 and 2005
respectively. She obtained her PhD
in Mechatronic Engineering at Uni-
versity Malaysia Perlis, Malaysia in
2010. She is currently the Dean Engineering Research
at Karpagam University, India. Her research interests
include EEG signal processing, Neural Networks and
Machine Vision. She holds many research grants and has
published 8 books and 5 book chapters and around 108
papers in referred Journals and International Confer-
ences.. She has received gold and Bronze medals in
National and International exhibitions for her research
products on vision and brain machine interfaces .She is
cited in WHO IS WHO in the world 2009 to 2011. She
is a member the IEEE, IEEE EMB Society and IEEE
WIE Society.
Vivian Tang Sui Lot is an
undergraduate student of the School
of Mechatronics Engineering,
University Malaysia Perlis, Malaysia.
Paulraj MP received his BE in
Electrical and Electronics
Engineering from Madras University (1983), Master of
Engineering in Computer Science and Engineering
(1991) as well as Ph.D. in Computer Science from
Bharathiyar University (2001), India. He is currently
working as an Associate Professor in the School of
Mechatronic Engineering, University Malaysia Perlis,
and Malaysia. His research interests include Principle,
Analysis and Design of Intelligent Learning Algorithms,
Brain Machine Interfacing, Dynamic Human Movement
Analysis, Fuzzy Systems, and Acoustic Applications. He
has co-authored a book on neural networks and 290
contributions in international journals and conference
papers. He is a member of IEEE, member of the Institute
of Engineers (India), member of Computer Society of
India and a life member in the System Society of India.
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
ABSTRACT
One of the most important and challenging issues in
real-time applications of resource-constrained wireless
sensor networks (WSNs) is providing end-to-end delay
requirement. Many wireless sensor network (WSN)
applications require real-time communication. In order
to address this challenge, we propose the Power Aware
Routing Protocol, which attains application-specified
communication delays at low energy cost by dynamically
adapting transmission power and routing decisions.
Extensive simulation results prove that the proposed
Protocol attains better QoS and reduced power
consumption.
Keywords: WSN, Robust nodes, link Quality
I. INTRODUCTION
Smart environments represent the next evolutionary
development step in building, utilities, industrial, home,
shipboard, and transportation systems automation.
Like any sentient organism, the smart environment relies
first and foremost on sensory data from the real world.
Sensory data comes from multiple sensors of different
modalities in distributed locations [1,2,3] . The smart
environment needs information about its surroundings
as well as about its internal workings; this is captured
in biological systems by the distinction between
exteroceptors and proprioceptors.
Power Adaption Routing Protocol For Realtime ApplicationsIn Wireless Sensor Networks
R.Prema1, R.Rangarajan2
1. Research Scholar Bharathiar University Tamilnadu,India E-mail ID:[email protected]
2. VSB Engineering College, Tamilnadu, India. E-mailID:[email protected]
Wireless sensor networks (WSN) represent a new
generation of embedded systems for routing sensory
data from the originator sensor node to the control station
[4]. Recent technological advances have enabled the
development of tiny battery-operated sensors [5,6,7].
Although energy efficiency is usually the primary concern
in WSNs, the requirement of low latency
communication is getting more and more important in
new applications. For example, a surveillance system
needs to alert authorities of an intruder within a few
seconds of detection.
Supporting real-time communication in WSNs is very
challenging. First, WSNs have lossy links that are greatly
affected by environmental factors [8][9]. As a result,
communication delays are highly unpredictable. Second,
many WSN applications (e.g., border surveillance) must
operate for months without wired power supplies.
Therefore, WSNs must meet the delay requirements at
minimum energy cost. Third, different packets may have
different delay requirements.
Literature Review
Power-aware algorithms for routing in WSNs have
received considerable attention over the past few years.
A distributed position-based algorithm to form topologies
containing a minimum total energy route between any
pair of connected nodes is proposed in [10]. Based on
this initial work, a computationally simpler protocol with
better performance is described in [11,12]. Similar
topology control algorithms based on discretization of
the coverage region of a node into cones are proposed in
[13,14]. The idea is to select appropriate transmitter
205
Power Adaption Routing Protocol For Realtime Applications In Wireless Sensor Networks
power levels to guarantee network connectivity while at
the same time transmission energy is saved.
Putting a node into sleep mode whenever its active
collaboration in the current network task is not required
is another way to save energy. The geographical adaptive
fidelity (GAF) algorithm [15] conserves energy by
turning off nodes that are equivalent from a routing
perspective, thereby keeping a constant level of routing
fidelity. An improvement of GAF based on a relationship
between optimal transmission range and traffic is
described [16]. In Span [17], the decision whether a node
should be awake or sleep is made depending on how
many of its neighbors will get benefit and how much
remaining energy it has. The sparse topology and energy
management (STEM) protocol [18] puts nodes
aggressively into sleep mode and only wakes them up
when they are needed to forward data. Data fusion is a
technique that can be used to reduce the amount of
redundant information prevalent in dense sensor
networks. By combining data with equal semantics,
unnecessary power consumption due to transmission and
processing of duplicate data is prevented. Two prominent
routing protocols that use upper layer information for
data fusion as well as making routing decisions are
Directed Diffusion [19] and SPIN [20]. Application-
specific fusion enables even more sophisticated data and
node management functionalities insideWSNs [21,22].
Both sleep scheduling and data fusion are desirable
functionalities which may complement energy-efficient
MAC androuting protocols.
Proposed Work
Among all the sensor nodes in the network, there are
some robust nodes. These robust nodes serve as the
backbone for the routing in wireless sensor networks.
The remaining sensor nodes are common sensor nodes.
Each robust node maintains a table of sensor node power
at other robust nodes. So in the route, each robust node
will compute the end-to-end power from itself to any
other robust nodes. The sensor node power is estimated
and updated periodically by each robust node. The robust
node which is nearest to the source node finds the robust
nodes which are along the route towards destination
sensor node. Then packets will be forwarded through
these robust nodes to the destination node. Since robust
nodes have better communication capability than
common nodes, most of the time the power is less than
the maximum power. This protocol is compared with
AODV protocol. This protocol shows better power
adaption than AODV protocol.
Estimation of Link Quality
The communication in mobile ad-hoc network
is based on electronic signals. In mobile ad-hoc networks
it is possible that a communication path (route) will break.
This will happen primarily because of the nodes present
in the network are moving around the region. The fig.1,
depicts the scenario when the link is active. In the fig.1,
three nodes are present namely a, b and c. The node-b is
within the range of the node-a and node-c. But, the node-
a is not within the range of node-c and node-c is not
within the range of node-a. Hence for transmission of
data from node-a to node-c, the node-b acts as an
intermediate node. After certain duration, due to the
mobility of sensor nodes, the link gets break and the data
communication between the nodes becomes unreliable.
Fig.1 Before the link breaks
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Fig.2 After the link breaks
to the mobility of nodes present in wireless sensor
network it becomes mandatory to consider the quality of
the link.
To be able to see that when a node in the wireless
sensor network is moving and hence a route is about to
break as shown in Fig.2. So that factor, it is probable to
measure the quality of the signal and based upon that
presumption, when the link is going to break. This
information which is identified by the physical layer is
send to the upper layer when packets are received from
a node, and then indicate that node is in pre-emptive zone.
Pre-emptive zone is the region where the signal strength
is weaker which leads to the link failure. Pre-emptive
zone uses the pre-emptive threshold value to fix the pr-
emptive zone’s location. Thus, using the received signal
strength from physical layer, the quality of the link is
predicted and then the links which are having low signal
strength will be discarded from the route selection.
When a sending node broadcasts RTS packet,
it piggybacks its transmission power. While receiving
the RTS packet, the projected node quantifies the strength
of the signal received.
Lq = P
R
Where,
PR refers Power of the Receiving node,
PT stands for Power of the Transmitting node,
ë stands for wavelength carrier,
d is the distance between the sending and the
receiving node,
UGR stands for unity gain of receiving omni-
directional antenna
UGT stands for unity gain of transmitting omni-
directional antenna.
T POW
= max (Lq &
RPOW
)
Where,
CV = Cost Value,
Lq = Link quality
R POW
= Residual Power of the sensor node
Election of robust node
At the start, one robust node is set in each grid.
We need an election mechanism to produce new Robust
nodes because robust nodes also move around. When a
Robust node leaves its current grid or due to any other
reason there is no robust node in the grid. Suppose, there
are more Robust nodes in the current grid of the network,
then, the next node with maximum weighted value from
the sorted list will be chosen as the new Robust node for
the grid. In the proposed routing algorithm, we need to
compute the minimum delay between two robust nodes,
and find the path with the minimum delay.
For each valid path Pi,
For every node nk in Pi
t_power = t_power + power (nL, nk) + power (nk)
If t_power >= max_power, delete this path, break.
If t_power >= min_power, delete this path, break.
If nk is the destination D, and t_power < min_power,
min_power = t_power;
best_path = Pi + {nk};
Else add node nk to the end of the path,
End For
End For
Pseudo code for Robust Sensor node election
Simulation Settings & Graphs
207
Power Adaption Routing Protocol For Realtime Applications In Wireless Sensor Networks
No. of Nodes 50, 75, 100, 125 and 150
Area Size 1000 X 1000
Mac 802.11
Radio Range 250m
Simulation Time
50 sec
Traffic Source CBR
Packet Size 512 KB
Mobility Model
Random Way Point
Speed 5 m/s
Pause time 100 Seconds
�
02468
101214
50 75 100 125 150
Po
wer
(W
atts
)
No. of Nodes
TOTAL POW ER CONSUMPTION OF SENSOR NODES (AODV Vs PARP)
AODV
PARP
Table 1. Simulation Settings
Fig.3 Pause time Vs Paper Consumption
It is proved that from Fig. 3 the Power adaption routing
protocol produces better power consumption than AODV
protocol.
II. Conclusion
This paper addresses the issue of Power adaption and
QoS effective routing by design and development of
Power Adaption Routing Protocol (PARP). Also,
scalability issue is kept in mind when the number of
nodes in the network is increased from the range of 50
to 150 nodes. The total power consumption, delivery ratio
and delay are taken as the performance metrics and the
simulation results proved PARP is better than AODV
protocol.
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209
Power Adaption Routing Protocol For Realtime Applications In Wireless Sensor Networks
Author’s Biography
Mrs. R. Prema working as Assistant
Professor in the Department of
Electroincs in Karpagam University.
She is pursuing her PhD in Bharathiar
University. She has presented about 7
papers in the National Conference and 1 in International
Conference. She has published a paper in an International
Journal of Wireless Sensor Networks. Her research area
is Wireless Sensor Networks.
Dr. R. Rangarajan is the Director in
V.S.B Engineering College, Karur. He
has a distinguished career in teaching
and research for more than 35 years.
Dr.Rangarajan has to his credit 20 technical papers in
National and International Journals. He has presented
about 35 papers in national and international
Conferences.
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Path Planning in AI (Artificial Intelligence)
T.C. Manjunath 1 G.V. Jayaramaiah 2
ABSTRACT
This paper presents a technique of designing a wide path
motion heuristic path in robots using artificial intelli-
gence in 2D scenario, i.e., using simulations. The simu-
lation results done in C++ show the effectiveness of the
method used.
Keywords:
Motion heuristics, Wide path motion heuristics, Path
Planning, Artificial Intelligence.
1.INTRODUCTION
In the recent days, path planning using Artificial
Intelligence has gained a lot of importance, similar to
those searching the path in the real world by humans.
These concepts are being used widely in the field of
autonomous agents such as the robots. Wong in [1]
presented a heuristic-based motion estimation technique
which reduced both the number of search locations (via
sub-sampling) as well as the number of operations to
perform at each search location (via simplified signature),
thus achieving a high speedup factor [1]. Test results
showed that quality of the motion vectors found by this
heuristic-based technique was close to that of full-search
motion vectors [1].
1. Principal, HKBK College of Engineering, S.No. 22/1,Nagawara, Arabic College Post, Bangalore-45, Karnataka.Email : [email protected]
2. Professor & HOD, Dept. of ECE, Dr. Ambedkar Inst. ofTech., Bangalore, Karnataka.Email : [email protected];[email protected] ;gv_ [email protected]
The issue of motion planning for closed-loop
mechanisms, such as parallel manipulators or robots, is
still an open question. Houssem Abdellatif & Bodo
Heimann proposed a novel approach for motion planning
of spatial parallel robots in [2]. In this paper, the
framework for the geometric modeling was based on the
visibility graph methodology. It was opted for a multiple-
heuristics approach, where different influences were
integrated in a multiplicative way within the heuristic
cost function.
Since the issue of singularities was a fundamental
one for parallel robots, it was emphasized on the
avoidance of such configurations. To include singularity-
free planning within the heuristic approach, two heuristic
functions were proposed by them, the inverse local
dexterity as well as a novel defined “next-singularity”
function, in such a way, well conditioned motions can
be provided by a single planning procedure [2]. Paolo
Fiorini & Zvi SIIillCr in [3]
Since the issue of singularities was a fundamental
one for parallel robots, it was emphasized on the
avoidance of such configurations. To include singularity-
free planning within the heuristic approach, two heuristic
functions were proposed by them, the inverse local
dexterity as well as a novel defined “next-singularity”
function, in such a way, well conditioned motions can
be provided by a single planning procedure [2]. Paolo
Fiorini & Zvi SIIillCr in [3] presented heuristic methods
for motion planning in dynamic environments, based on
the concept of Velocity Obstacle (VO). Two heuristic
strategies, viz., selecting the maximum velocity along
Path Planning in AI (Artificial Intelligence)
211
the line to the target, and selecting the maximum feasible
velocity within a specified angle from the straight line to
the target were presented [3].
In this paper, we present a method of designing a
wide path motion based on motion heuristics techniques.
The path uses the concept of graph theory & going in
mid-way between the obstacles so that the obstacles are
avoided. The paper is organized in the following
sequence. Firstly, a brief introduction to the related work
was presented in the previous paragraphs. Section 2 gives
a in-depth concept on the motion heuristics approach
used in the AI techniques. In section 3, the mathematical
modeling of the wide path motion heuristic approach is
dealt with. Section 4 shows the simulation results. The
paper concludes with the conclusions followed by the
references.
I.CONCEPT OF MOTION HEURISTICS
Motion Heuristics is defined as the method of
searching an obstacle collision free path in the free work
space of the robot from the source to the destination by
making use of search techniques such as the graph theory
( AND / OR graphs ), chain coding techniques and the
state space search techniques (best first search, breadth
first search) used in Artificial Intelligence [4]. The search
techniques used in AI to find the path from the source S
to the goal G are called as motion heuristics or the robot
problem solving techniques. The word ‘heuristic’ means
to search, what to search ? an obstacle collision free path
to search [5].
Holding the object by the gripper ⊥r to its width and
searching for an obstacle collision free path in the work
space of the robot from S to G is known as wide path
motion heuristics [6]. In this type of motion heuristics
method of searching a path, the mobile object / part which
is to be held by the gripper is rectangular in shape with a
length of L units and width of W units [7]. The object is
held by the gripper ⊥r to its width and the point p of the
gripper should match with the center of gravity of the
object, like how we hold a duster while rubbing the board.
II.MATHEMATICAL CONCEPTS
Consider the moving / mobile part ABCD to be rectangular
in shape as shown in Fig. 1. Let W and L be the width and
length of the rectangular mobile part or object and let W ≤
L. Let P1 and P
2 be the two reference points marked on the
major axis of the mobile rectangle at a distance of 4
L units
from each ends and at a distance of 2
W units from the parallel
side as shown in the Fig. 1 [8].
These two points will always be moving along the
GVD path. G is the centre of gravity of the rectangular
mobile object. Two reference points are used in order to
simplify the wide path motion heuristics. The two
reference points are like the front and back wheels of a
two wheeler automobile. The front point P2 is advanced
incrementally along the GVD path. The back point P1 is
found by drawing a GVD circle of radius 2
L centered at
P2. This circle cuts the major axis of the mobile rectangle
ABCD at the point P1. Thus, both the points on the GVD
path are obtained [9].
�
Figure 1. Wide path motion heuristics
A mobile part ( Rectangle – ABCD ) ;L → Length ; W → Width ;P
1, P
2 → Reference points ; G → Centroid ;
R → Radius of GVD circle
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Let us determine how the wide path motion
heuristics is applicable. Consider two circles ; one with
P1 as center
222
42
+
== LWRRadius and another circle
with P2 as center [10].
The entire mobile rectangular part is contained
within the two overlapping circles. The wide path motion
heuristic is therefore attempted when the minimum radius
of the chosen GVD path is greater than the radius of
either of the circles. i.e., when the radius of the path in
between the obstacles is greater than the radius R of the
mobile part (of either of the circles on the mobile part),
then, there will be sufficient space for the mobile part to
move in between the obstacles and as a result of which
there will be no collision of the part with the edges of
the obstacle [11].
The minimum radii constraint of the chosen path is
given by the formula which is derived as follows [13].
Consider any of the circle with either P1 or P
2 as center.
Consider the triangle DP2E [12].
DE = EC = W2 ;
P2E =
14 L
=
L4
∴, minimum radius [14],
P2 D = P
2 C = P
1A = P
1B >
�
22
42
+
LW
R > �
22
42
+
LW
i.e., R > � 2
122
42
+
LW (1)
As long as the space between the obstacles is greater
than this minimum radii constraint R as given by the
equation (1), then the part will not collide with the ob-
stacle, since it is at [16] a safer distance from the ob-
stacle and clearance exists between the walls of the ob-
stacles and the part [15].
III. WIDE PATH MOTION HEURISTICS
In this section, we present an example of a motion
planned with wide path motion heuristics. An example
of a motion planned with wide path motion heuristics is
shown in the Fig. 2 [17]. The workspace consists of three
obstacles ; viz., a rhombus, a triangle, a rectangle inside
the robot workspace boundary which is a rectangle. The
mobile part is considered as a small rectangle. The mobile
part is at source position, S. It has to reach the point G
[18].
A shortest path is found out using the GVD
techniques and the motion heuristics. The mobile part
starts at S, moves along the [19] shortest GVD path as
shown in the Fig. 2 in which the center point of the mobile
part lies always on the GVD path. Note that translations
of the mobile part is performed along the freeways and
rotations of the part has to performed [20] at the junction
the freeways in order to obtain a smooth motion.
Sophisticated Wide path motion heuristics
Many a times, ordinary wide path motion heuristics is
not sufficient to determine an obstacle collision free path
from the source to the destination [21]. In this case,
sophisticated wide path motion heuristics comes into the
picture as the work space is fully cluttered with obstacles
[22]. When the work space is cluttered with obstacles,
then sophisticated wide path motion heuristics has to be
used. More sophisticated wide path motion heuristic
techniques can be applied to the original path if the only
available path satisfies R > 2
W; but, fails to satisfy the
[31] minimum radius constraint as given by Eqn (1) ;
i.e., the space in between the obstacles is greater than
only half the width of the mobile part [33]. If this is the
condition [30]; then, a well designed path has to be
Path Planning in AI (Artificial Intelligence)
213
planned ; since, there is only minimum space left between
the mobile part and the obstacle [23].
�
S
B
A
C
G
Source
Goal
GVD path
Figure 2. : Wide path motion heuristicsA, B, C - Obstacles ; S - Source ; G – Destination /
Goal
An example of a path planned in a cluttered
workspace using more sophisticated motion heuristics
is shown in the Fig. 2. In a cluttered / densely packed
obstacle zone, the GVD graph will be a complicated
graph and the shortest path consists of a huge number of
nodes, arcs and segments [24].
IV. SIMULATION EXAMPLE
The best example of an sophisticated wide path motion
heuristics can be a real time application, i.e., when a
human being is driving his vehicle in a densely packed
traffic jam, then he searches a path [32] in the 2D space
( i.e., on the road ) by moving in between the obstacles,
avoiding all the collisions between his vehicular system
and the other vehicles which are acting as the obstacles
[25].
�
S
G
A
D
C
B
E
Figure 3.: Sophisticated wide path motion heuristicsA, B, C, D, E - Obstacles ;
S, G - Source and goal scene of the mobile part
The human brain which is acting as the computer tries
to search for a obstacle collision free GVD path on the
road from the source to the destination [28]. Normally,
when we are driving our vehicle from the source to the
destination, we make use of the GVD technique to move
along the desired path [26].
A GUI was developed in C/C++ to simulate the same
for an obstacle collision free path in the 2D space with
the workspace is cluttered with simple 2D obstacles, say
a square [29]. The C/C++ code was run the & the
following simulation results were obtained as shown in
the Figs. 4 to 6 respectively [27].
Simulation Results
Figure 4. Inputting the parameters
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Figure 5. Robot navigation
Figure 6. Source
Figure 7. Destination by avoiding the obstacles bymoving around them
V. CONCLUSIONS
A brief conceptual design of the wide path
motion heuristics was presented in this paper
along with the mathematical modeling concepts
followed by the simulations. The paper can be
further extended to 3 D objects & obstacles in
the work space. In this paper, the authors have
tried to explore the existing literature available
into programming form so that any generalized
path planning algorithm can be implemented
using the software.
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Applications, IFS Publishers, Springer, UK.
[11]. Crane, Joseph Duffy, Kinematic Analysis of Robotic
Manipulators, Cambridge Press, UK.
[12]. Manjunath, T.C., (2005), Fundamentals of
Robotics, Fourth edn., Nandu Publishers, Mumbai.
Path Planning in AI (Artificial Intelligence)
215
[13]. Manjunath, T.C., (2005), Fast Track to Robotics,
Second edn., Nandu Publishers, Mumbai.
[14]. William Burns and Janet Evans, (2000), Practical
Robotics - Systems, Interfacing, Applications,
Reston Publishing Co.
[15]. Fundamentals of Robotics : Analysis and Control
– Robert J Schilling ; PHI, New Delhi.
[16]. Robotics Series ( Volume I to VIII ) – Phillip
Coiffette ; Kogan Page, London.
[17]. Robotic Engineering – Klafter, Thomas, Negin ;
PHI, New Delhi.
[18]. A Robotic Engineering Text Book – Mohsen
Shahinpoor ; Harper & Row Publishers.
[19]. Robotics and Image Processing – Janakiraman ;
Tata McGraw Hill.
[20]. Robotic Manipulators – Richard A Paul ; MIT
press, Cambridge.
[21]. Computer Vision for Robotic Systems – Fairhunt
; New Delhi.
[22]. Robotics for Engineers – Yoram Koren ; McGraw
Hill.
[23]. Industrial Robotics – Groover, Weiss, Nagel, Odrey;
McGraw Hill.
[24]. Robotics : Control, Sensing, Vision & Intelligence
– Fu, Gonzalez and Lee ; McGraw Hill.
[25]. Industrial Robotics – Bernard Hodges ; Jaico
Publishing House.
[26]. Foundations of Robotics : Analysis and Control –
Tsuneo Yoshikawa ; PHI.
[27]. Robotics : Principles & Practice – Dr. Jain and Dr.
Aggarwal ; Khanna Publishers, Delhi.
[28]. Modeling and Control of Robotic Manipulators –
Lorenzo and Siciliano, McGraw Hill.
[29]. Mechanotronics of Robotics Systems – Dr.
Amitabha Bhattacharya.
[30]. Industrial Robotics - S.R. Deb, Tata MacGraw Hill.
[31]. Robot Modelling, control and applications – P G
Ranky and C Y Ho,IFS publishers, Springler,UK.
[32]. Industrial Robots and Robotics – Edward Kafrissen and Mark
Stephans, Prentice Hall Inc, Virginia.
[33]. Fundamentals of Industrial Robots and Robotics – Rex Miller,
PWS Kent Pub Co., Boston.
Author’s Biography
Dr. T.C. Manjunath was born
in Bangalore, Karnataka, India
on Feb. 6, 1966 & received the
B.E. Degree (Bachelor of Engg.)
in Electrical Engg. from R.V.
College of Engg. (Bangalore
University) in the year 1989,
M.Tech. degree in Electrical Engg. with specialization
in Automation, Control & Robotics from the Govt.’s L.D.
College of Engg. (Gujarat University) in the year 1995
and Ph.D. in Systems & Control Engg. from the
prestigious Indian Institute of Technology Bombay (IIT
Bombay) in the year 2007 respectively. Engineering (IIT
Bombay, India) and worked on control of space launch
vehicles using FOS feedback technique in IITB. He has
published a number of publications in the various
National, International journals and Conferences and
published 3 textbooks on Robotics,He has also published
a research monograph in the International level from the
Springer-Verlag publishers based on my Ph.D. thesis
topic titled, “Modeling, Control and Implementation of
Smart Structures”, Vol. 350, LNCIS, costing 79.95 Euros.
He is a member of IEEE for the past 12 years, He was
awarded with the “Best research scholar award in
engineering discipline” for the academic year 2006-07
from the Research Scholars Forum (RSF) from Indian
Institute of Technology Bombay (IITB). His current areas
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
of interest are Control System Engineering, Robotics,
Signals & systems (continuous time and discrete time),
Digital Signal Processing, Digital Image Processing,
Electrical Circuits & Networks - Analysis & Synthesis,
Elements of Electrical Engineering, Mechatronics,
Artificial Intelligence, Matlab Programming.
Dr. Jayaramaiah GV was born in
Tumkur district, Karnataka, India &
received the B.E. Degree (Bachelor
of Engg.) from Siddaganga Institute
of Tech., Tumkur (Bangalore
University) in the year 1990, M.Tech.
degree with specialization in Power Electronics from the
BMS College of Engg., Bangalore (Bangalore
University) in the year 1992 & Ph.D. in Electrical Engg.
from the prestigious Indian Institute of Technology
Bombay (IIT Bombay) in the year 2008 respectively.
He is currently working as Professor & Head of the
Department of Electronics & Communications in Dr. BR
Ambedkar Institute of Technology (Dr. AIT), Bangalore,
Karnataka, India. He has published more than 1 dozen
publications in the various National, International
journals and Conferences.
Efficient Object Detection and Classification using Hybrid ELM
with Analytic Hierarchy Process and Bayesian Network
217
Efficient Object Detection and Classification using Hybrid ELM
with Analytic Hierarchy Process and Bayesian NetworkN.V. Balaji1, M. Punithavalli2,
ABSTRACT
Detecting object is one of the typical difficulties in computer
technology which has its usage to surveillance, robotics, mul-
timedia processing, and HCI. The multi-resolution framework
is utilized by the proposed technique for object detection. In
this efficient object detection, the lower resolution features
are first used to discard the majority of negative windows at
comparatively small cost, leaving a relatively small amount of
windows to be processed in higher resolutions and this helps
to attain better detection accuracy. Then the frameworks on
Histograms of Oriented Gradient (HOG) features are used to
detect the objects. For training and detection, the classifier
used previously is Support Vector Machine (SVM) and Ex-
treme Learning Machine (ELM). Hybrid ELM is used in the
proposed technique to reduce the time for detection and im-
prove the accuracy of classification. The input weights and
hidden biases are created with the help of integrated Analytic
Hierarchy Process (AHP) and Bayesian Network (BN) model.
The experimental result shows that the proposed technique
achieves better detection rate when compared to the existing
techniques.
Keywords:
Histograms of Oriented Gradient (HOG), Modified Extreme
Learning Machine, Multi-Resolution Framework, Analytic
Hierarchy Process (AHP), Bayesian Network (BN).
I.INTRODUCTION
Object detection and recognition in noisy and cluttered
images is challenging problem in computer vision. The
goal of this research is to identify objects in an image
accurately. Today, there is an increased need for object
detection. There are several problems in detecting and
recognizing the objects in an image. It is an important
part in many applications such as image search, image
auto-annotation and scene understanding; however it is
still an open difficulty due to the complexity of object
classes and images. [3] The robot usage will reduce the
work load for the housekeepers. Some of the works
performed by home robots are cleaning, tidying, fetching
objects, etc. These tasks need some degree of semantic
knowledge about human surroundings so that they can
find the way, search for objects and communicate with
humans successfully. For example, for a robot to execute
the command “bring me an apple”, a robot must recognize
the term “apple” and to know which places in the
surroundings are expected to contain it (e.g., the kitchen).
This paper provides the better object detection technique.
Initially, multi-resolution framework is used to eliminate
the unnecessary features from the image which will help
in speedy processing.[4] The learning machine used in
this work is Hybrid ELM. Finally, Histograms of Oriented
Gradient features are applied for accurate detection of
object in the image.
II. RELATED WORKS
Broussard et al., [5] put forth the physiologically
motivated image fusion for object detection using a pulse
1 Assistant Professor Department of Computer Science,Karpagam University, Coimbatore, India.2 Director Department of Computer Applications, Sri RamakrishnaEngineering College,Coimbatore, India.
218
Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
coupled neural network. PCNN are implemented to
combine the results of several object detection techniques
to improve object detection accuracy. The object
detection assets of the obtained image fusion network
are demonstrated on mammograms and forward-looking
infrared radar (FLIR) images. This technique exceeded
the accuracy obtained by any individual filtering methods
or by logical ANDing the individual objects detection
technique results.
Kerekes et al., [6] had a look on spectral imaging
technique analytical model for sub-pixel object detection
[16]. To learn the system parameters in the context of
land cover classification, an end-to-end remote sensing
system modeling approach was previously developed.
In this work, the author extends this technique to sub-
pixel object detection applications by including a linear
mixing model for an unresolved object in a background
and using object detection algorithms and probability of
detection (PD) versus false alarm (P
FA) curves to
characterize performance.
Kubo et al., [7] developed an image processing system
for direction detection of an object using neural network.
The proposed direction detection system was considered
to consist of two modules of neural networks; one is an
edge detection module and the other is a direction
detection module. The edge detection technique could
detect edges when differences of gray level between the
object and the background were larger than thirteen at
any gray level of the object.
Wen-jie Wang et al., [8] illustrate the Object-oriented
multi-feature fusion change detection method for high
resolution remote sensing image. The key steps of this
technique are segment the image, choose optimized
features from spectral features, texture features, shape
features of the segment objects, use optimized features
to do change detection, make fusion of the preliminary
change detection results of the different optimized
features to get the final result.
III. METHODOLOGY
The initial process for the proposed object detection
technique is the multi-resolution framework which is
applied in different resolution space. The next process
is the application of training and detection technique with
the help of Hybrid ELM. Finally, Histogram of Oriented
Gradients (HOG) features are applied for detection of
objects.
Multiple Resolution Framework
The common approach of object detection using multi-
resolution is shown in Fig. 1. The framework encodes
resolution together with scale spaces in 2D coordinate
scheme.
Along the vertical axis in scale space, the image gets
down sampled in order to use a fixed size detection
window to locate objects of larger scales. Along the
horizontal axis in resolution space, any detection window
is classified hierarchically from its lowest resolution to
full resolution. A window rejected in lower resolution
will not be passed to any higher resolutions.
Figure 2 shows why multi-resolution is useful for object
detection. A Canny edge detector was applied to images
from the VOC 2006 at different resolutions. Images in
lower resolutions were created by downsampling and
then upsampling to the original size for easier
comparison. For the motorbikes and cows categories, two
samples are picked from each and computed the shape
context matching distances shown in the figure.
Efficient Object Detection and Classification using Hybrid ELM
with Analytic Hierarchy Process and Bayesian Network
219
Figure 1. Multiple resolution framework for object detection.
Figure 2. Sample images from VOC 2006 database, with edge detected images at different resolutions
Training/detection algorithms
For a typical object detection system, the training set is
composed of normalized image patches. Negative patches
were scanned from images that do not contain any target
object instances. Suppose R resolutions are used, where
r=1,...,R from the lowest resolution to the full resolution.
Assume the step of downsampling ratio is á. For each
resolution r, the training set is defined as Tr = {(Ir(i),
lr(i)), i=1, ...,Nr}, where Ir(i) is the image patch, lr(i) “
{1,”1} is the associated label and Nr is the number of
patches. The training
starts from the lowest resolution, and then loops between
bootstrap and training towards higher resolutions until
the full resolution. The output of the training is a
hierarchical classifier with components from each
resolution. Suppose that the image is downsampled with
a ratio of â for each scale up, and that it have S scales
s=1,...,S, for larger to smaller objects. Suppose the
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window size at each resolution is (wr,hr)=(w,h)/áR”r,
and that the size is fixed across all scales. For each
resolution r and scale s, a list of status of detection
windows ãr, s(k)”{1,”1} is kept. The status is initialized
to be 1, and set to “1 if the window is rejected by a
classifier.
The training and detection is further improved by Hybrid
Extreme Learning Machine (ELM). Previous approaches
have used SVM and ELM for the purpose of training
and detection.
SVM Algorithm
SVM is used to find an optimal separating hyper plane
(OSH) which generates a maximum margin between the
categories of data. To build an SVM classifier, a kernel
function and its parameters need to be chosen. So far, no
analytical or empirical studies have established the
superiority of one kernel over another conclusively. The
following three kernel functions have been applied to
build SVM classifiers:
1) Linear kernel function, K(x, z) =⟨x, z⟩ ;
2) Polynomial kernel function K(x, z) =(⟨x, z ⟩+1) d is the
degree of polynomial;
3) Radial basis function K(x,z)=exp is the
width of the function.
The usage of SVM will reduce the time requirement for
classification and also the accuracy of classification but
it can be improved using ELM.
Extreme Learning Machine
Extreme Learning Machine (ELM) meant for Single
Hidden Layer Feed-Forward Neural Networks (SLFNs)
that will randomly select the input weights and
analytically determines the output weights of SLFNs. The
ELM has several interesting and significant features
different from traditional popular learning algorithms for
feed forward neural networks.
Extreme Learning Machine Training Algorithm
If there are N samples (xi, t
i), where x
i = [x
i1, x
i2… x
in] T
∈Rn and ti = [t
i1, t
i2, … , t
im]T∈Rn, then the standard SLFN
with N hidden neurons and activation function g(x) is
defined as:
where wi = [w
i1, w
i2, … , w
in]T represents the weight vec-
tor that links the ith hidden neuron and the input neu-
rons, ßi = [ß
i1, ßi2, … , ßim]T represents weight vector that
links the ith neuron and the output neurons, and bi repre-
sents the threshold of the ith hidden neuron. The “.” in
wi . x
j indicates the inner product of w
i and x
j. The SLFN
try to reduce the difference between oj and t
j.
More in a matrix format as H ß = T, where
�
and
Efficient Object Detection and Classification using Hybrid ELM
with Analytic Hierarchy Process and Bayesian Network
221
The result reduces the norm of this least squares equation
is
where H† is known as Moore-Penrose generalized
inverse.
The usage of Hybrid ELM will reduce the time
requirement for classification and also the accuracy of
classification can be improved over the previous
techniques.
Modified Extreme Learning Machine
A Hybrid ELM technique which uses ELM and LM
technique can be described as below:
Initially, the input weights and hidden biases are created
with the help of integrated Analytic Hierarchy Process
(AHP) and Bayesian Network (BN) model.
By integrating AHP and Bayesian Network in such a way,
that the weights, the output of AHP will be given as input
to BN. Bayesian Network can obtain inputs from several
points, but in the combination BN adopts the weights, as
inputs in leafs of the graph; and these weights are
structured in vector format. The serial of comparative
weights of computed by AHP method can be written in a
vector format, this vector will be given as input to BN.
Next, the corresponding output weights are analytically
determined with the help of ELM algorithm during the
first iteration and randomly generate the output hidden
biases. Then, the parameters (all weights and biases) are
restructured with the help of LM algorithm.
The process for the Hybrid Extreme Learning Machine
is described below:
Provided a training set
activation functions , and hidden nodes
namely of first and second hidden layer.
Step 1: Randomly choose the starting values of input
weight vectors and bias vector with the help of
AHP-BN technique and bias vector without using
the AHP-BN technique.
Step2: Determine the first hidden layer output matrix
. With the help of ELM algorithm, determine the
output weight
Step3: Determine the second hidden layer output
matrix , errors
Determine the elements of the Jacobian matrix with the
equations
and
Application to HOG
In Histogram of oriented Gradients (HOG) feature for
pedestrian detection, pixels are first grouped into smaller
spatial units called cells. For each cell, a histogram
feature on gradients orientations is extracted. The
magnitude of the gradient is applied as the weight for
voting into the histogram. The descriptor of each block
is the concatenation of all cell features. Inside each
detection window, densely sampled and overlapping
blocks produce redundant descriptors, which is important
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
for better performance. Finally, a linear Support Vector
Machine (SVM) is used to classify individual detection
windows. In the proposed multi-resolution approach
based on the above method, the author used blocks of
varying sizes to capture features in different spatial
frequencies. An important consequence of the feature
hierarchy is that, both more-global (low resolution) and
more-local (high resolution) features are encoded.
IV. EXPERIMENTAL RESULTS
The proposed technique for object detection is
experimented on PASCAL Visual Object Classes
challenge 2006 database (VOC 2006). There are 10
groups of objects are presented in the database such as
bicycles, buses, cats, cars, cows, dogs, horses,
motorbikes, people and sheep. The multi-resolution
approach is applied for the sheep and car categories. The
proposed technique obtained the best results among all
participants on these two categories.
Figure 3. Comparison between Proposed and Existingmethods using Detection Error Trade-off curves on sheep
category in VOC2006 database
Figure 4. Comparison between Proposed andExisting methods using Detection Error Trade-off
curves on car category in VOC2006 database.
Figure 3 shows the comparison of proposed method with
existing object detection method using DET curves in
case of sheep category. From the graph it can be observed
that the resulted miss rate for the proposed system with
Hybrid ELM is lesser when compared to the existing
system with SVM and ELM. This shows that the Hybrid
ELM gives better classification result for the proposed
technique. Figure 4 shows the comparison of proposed
method with existing object detection method using DET
curves in case of car category. From the figure, it can be
clearly seen that the miss rate is lesser for the proposed
technique for all levels of false positive per window.
V. CONCLUSION
Object detection from the image is an important tech-
nique applied in many fields such as image browsing,
robotics, etc. The existing techniques lack accuracy and
take more time detection. To overcome these drawbacks,
the multi-resolution based detection system is proposed
in this paper. The first step is to discard the negative
windows using multi-resolution framework, the next step
is learning and detection in which the training samples
Efficient Object Detection and Classification using Hybrid ELM
with Analytic Hierarchy Process and Bayesian Network
223
are supplied for training the system and the detection is
performed accordingly. To improve the accuracy of clas-
sification, Hybrid ELM is applied. The input weights
and hidden biases are created with the help of integrated
Analytic Hierarchy Process (AHP) and Bayesian Net-
work (BN) model. Finally, with the help of Histogram
of Oriented Gradients (HOG) features, the object is de-
tected from the image. The VOC 2006 database is used
for the purpose of experimentation and result shows that
this technique yields better accuracy for detection and
also miss rate for the proposed technique is lesser when
compared to the conventional techniques.
REFERENCES
[1] Jiqiang Song, Min Cai and M. S Lyu, “Edge color
distribution transform: an efficient tool for object
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Recognition, Proceedings. 16th International
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[2] Saad Ali and Mubarak Shah, “A supervised learning
framework for generic object detection in images”,
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Vision, ICCV 2005, Pp: 1347 – 1354, Vol. 2, 2005.
[3] S. Sheraizin and S. Itzikowitz , “Unmanned object
detection for image surveillance systems”, IEEE
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[4] A. Steel and D. Brunner, “Detection and
Characterization of Urban Objects from VHR
Optical Image Data”, IEEE International conference
on Geoscience and Remote Sensing Symposium,
IGARSS 2008. Pp: III - 1256 - III – 1259, Vol.3,
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[5] R.P. Broussard, R.P, S.K. Rogers, M.E. Oxley and
G.L. Tarr, “Physiologically motivated image fusion
for object detection using a pulse coupled neural
network”, IEEE Transactions on Neural Networks,
Pp: 554 – 563, Vol.10, 1999.
[6] J.P Kerekes and J.E Baum, “Spectral imaging system
analytical model for subpixel object detection”,
IEEE Transactions on Geoscience and Remote
Sensing, Page(s): 1088 – 1101, vol.40, 2002.
[7] T. Kubo, M. Obuchi, G. Ohashi and Y. Shimodaira,
“Image processing system for direction detection of
an object using neural network”, The 1998 IEEE
Asia-Pacific Conference on Circuits and Systems,
IEEE APCCAS 1998. Pp: 571 - 574, 1998.
[8] Wen-jie Wang, Zhong-ming Zhao and Hai-qing Zhu,
“ Object-oriented multi-feature fusion change
detection method for high resolution remote sensing
image”, 17th International Conference on
Geoinformatics, Page(s): 1 – 6, 2009.
[9] S. Belongie, J. Malik, and J. Puzicha. Shape
matching and object recognition using shape
contexts. PAMI, 24(4):509-522, 2002.
[10]S. C. Brubaker, M. D. Mullin, and J. M. Rehg.
Towards optimal training of cascaded detectors. In
ECCV06, pages I: 325-337.
[11] J. Fasola and M. Veloso. Real-time object detection
using segmented and grayscale images. In ICRA06,
pages 4088-4093.
[12]B. Leibe, E. Seemann, and B. Schiele. Pedestrian
detection in crowded scenes. In CVPR05, pages I:
878-885.
[13]S. Mitri, A. Nuchter, K. Pervolz, and H. Surmann.
Robust object detection in regions of interest with
an application in ball recognition. In ICRA05, pages
125-130.
[14]P. Viola, M. J. Jones, and D. Snow. Detecting
pedestrians using patterns of motion and appearance.
IJCV, 63(2):153-161, 2005.
[15]V. Ferrari, T. Tuytelaars, and L. J. V. Gool. Object
detection by contour segment networks. In ECCV06,
pages: 14-28. http://pascallin.ecs.soton.ac.uk/
challenges/VOC/voc2006/index.html
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Author’s Biography
N.V. Balaji has obtained his
Bachelor of Science in Computer
Science from Sri Ramasamy Naidu
Memorial College, Sattur in 1997 and
Master of Science in Computer
Science from Dr. GRD College of
Science in 1997. Now he is doing
Ph.D., in Bharathiar University. He commences more
than nine years of experience in teaching field moreover
industrial experience in Cicada Solutions, Bangalore. At
present he is working as Asst. Professor & Training
Officer at Karpagam University. His research interests
are in the area of Image Processing and Networks. He
presented number of papers in reputed national and
international journals and conferences.
Dr. M. Punithavalli received the Ph.D
degree in Computer Science from
Alagappa University, Karaikudi in May
2007. She is currently serving as the
Adjunct Professor in Computer
Application Department, Sri Ramakrishna Engineering
College, Coimbatore. Her research interest lies in the
area of Data mining, Genetic Algorithms and Image
Processing. She has published more than 10 Technical
papers in International, National Journals and
conferences. She is Board of studies member various
universities and colleges. She is also reviewer in
International Journals. She has given many guest
lecturers and acted as chairperson in conference.
Currently 10 students are doing Ph.D., under her
supervision.
Empirical Analysis of Denoising Techniques in Video Processing
225
Empirical Analysis of Denoising Techniques in Video Processing
R.Revathi1, M. Hemalatha2
ABSTRACT
Video broadcast plays a very vital role in traffic applica-
tions. Existence of Noise in the video frames conceals
essential details. It negotiates with intensity of quality
of images in the video frame. So, it is crucial to elimi-
nate the noise from video frames. Removal of noise is
one of the pre-processing tasks in several video process-
ing techniques. Numerous researchers work on differ-
ent types of filters used to remove different types of
noises from video frames. There are some traditional fil-
ters, some filters derived from traditional filters and some
filters are new innovations. In this proposed method we
adapted the spatial video denoising methods, where noisy
image are applied to each frame individually. Since there
is a immense deal of eliminate noise from video con-
tent, this paper has been devoted to noise detection and
filtering technology with the aspire of eradicate unwanted
noise without affecting it negatively the clarity of scenes
that contain necessary detail and rapid motion. In this
proposed work we made a survey on different denoising
filters and concluded which works better among all..
Keywords:
Image Processing, Video Processing, Denoising and
Filters
I.INTRODUCTION
Image processing is a process to convert an image into
digital form and perform some operations on it, in order
to get an enhanced image or to extract some useful
information from it. Some type of signal allowance in
which input is image, like video frame or photograph
and output may be image or characteristics associated
with that image. Generally Image Processing system
includes treating images as two dimensional signals while
applying already set signal processing methods to them
[1].
Image Processing forms research area in engineering and
computer science disciplines too.
Image processing includes the following three steps.
Importing the image with the help of optical scanner or
by digital photography[1].
Data compression and image enhancement and spotting
patterns are analyzed and manipulating the image that
are not to human eyes like satellite photographs [1].
Output is the last stage in which result can be altered
image or report that is based on image analysis[1].
II. Purpose of Image Processing:
The purpose of image processing is divided into 5 groups.
They are:
Visualization - The objects are observed which are not
visible [1].
Image sharpening and restoration - To generate a
better image [1].1 Research Scholar, Karpagam University, Coimbatore,TamilNadu, India [email protected]. Head Dept of Software Systems, KarpagamUniversity, Coimbatore, TamilNadu, [email protected]
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Image retrieval - Seek for the image of interest [1].
Measurement of pattern – Measures various objects
in an image [1].
Image Recognition – Distinguish the objects in an
image.
Digital Processing techniques help in manipulation of
the digital images by using computers. As raw data from
imaging sensors from satellite platform contains
deficiencies. To get over such flaws and to get originality
of information, it has to undergo various phases of
processing. The three general phases that all types of
data have to undergo while using digital technique are
Pre- processing, enhancement and display, informationextraction [1].
III. APPLICATIONS
Intelligent Transportation Systems – This technique
can be used in Automatic number plate recognition and
Traffic sign recognition [1].
Remote Sensing – For this application, sensors capture
the pictures of the earth’s surface in remote sensing
satellites or multi – spectral scanner which is mounted
on an aircraft. These pictures are processed by
transmitting it to the Earth station. Techniques used to
interpret the objects and regions are used in flood control,
city planning, resource mobilization, agricultural
production monitoring, etc [1].
Moving object tracking – This application enables to
measure motion parameters and acquire visual record of
the moving object. The different types of approach to
track an object are:
Motion based tracking
Recognition based tracking
Defense surveillance – Aerial surveillance methods are
used to continuously keep an eye on the land and
oceans. This application is also used to locate the types
and formation of naval vessels of the ocean surface. The
important duty is to divide the various objects present in
the water body part of the image. The different parameters
such as length, breadth, area, perimeter, compactness are
set up to classify each of divided objects. It is important
to recognize the distribution of these objects in different
directions that are east, west, north, south, northeast,
northwest, southeast and south west to explain all
possible formations of the vessels. We can interpret the
entire oceanic scenario from the spatial distribution of
these objects [1].
Biomedical Imaging techniques – For medical
diagnosis, different types of imaging tools such as X-
ray, Ultrasound, computer aided tomography (CT) etc
are used. The diagrams of X- ray, MRI, and computer
aided tomography (CT) are given below.
Some of the applications of Biomedical imaging
applications are as follows:
Heart disease identification– The important diagnostic
features such as size of the heart and its shape are
required to know in order to classify the heart diseases. To
improve the diagnosis of heart diseases, image analysis
techniques are employed to radiographic images [1].
Lung disease identification – In X- rays, the regions
that appear dark contain air while region that appears
lighter are solid tissues. Bones are more radio opaque
than tissues. The ribs, the heart, thoracic spine, and the
Empirical Analysis of Denoising Techniques in Video Processing
227
diaphragm that separates the chest cavity from the
abdominal cavity are clearly seen on the X-ray film [1].
Digital mammograms – This is used to detect the breast
tumors. Mammograms can be analyzed using Image
processing techniques such as segmentation, shape
analysis, contrast enhancement, feature extraction, etc
[1].
Automatic Visual Inspection System – This application
improves the quality and productivity of the product in
the industries.
Automatic inspection of incandescent lamp filaments –
This involves examination of the bulb manufacturing
process. Due to no uniformity in the pitch of the wiring
in the lamp, the filament of the bulb gets fused within a
short duration. In this application, a binary image slice
of the filament is created from which the silhouette of
the filament is fabricated. Silhouettes are analyzed to
recognize the non uniformity in the pitch of the wiring
in the lamp. This system is being used by the General
Electric Corporation[1].
Automatic surface inspection systems – In metal
industries it is essential to detect the flaws on the surfaces.
For instance, it is essential to detect any kind of aberration
on the rolled metal surface in the hot or cold rolling mills
in a steel plant. Image processing techniques such as
texture identification, edge detection, fractal analysis etc
are used for the detection [1].
Faulty component identification – This application
identifies the faulty components in electronic or
electromechanical systems. Higher amount of thermal
energy is generated by these faulty components. The
Infra-red images are produced from the distribution of
thermal energies in the assembly. The faulty components
can be identified by analyzing the Infra-red images [1].
IV. VIDEO PROCESSING
Video processing uses hardware, software, and
combinations of the two for suppression the images and
sound recorded in video files. General algorithms in the
processing software and the peripheral equipment allow
the user to perform editing functions using various filters.
The most wanted things can be shaped by suppressed
frame by frame or in larger batches.
Video files are obtained from the recording device using
a universal standard bus (USB) cable or fire wire
attachment. The records are then full into a computer
software program or peripheral device. Most computers
used personal come with software that allows the user to
accumulate images and videos, change images, and create
videos on a incomplete level. Storyboards allows the
adding together of audio files and the alteration of chart
images, transitions, and audio files, which, together,
determine the overall length of the video. Videographers,
electrical engineers, and computer science professionals
use programs that are capable of a wider range of
functions. Signal processing frequently involves
applying a combination of pre filters, intra filters, and
post filters [2].
Video Processing used in Research Areas:
Now-a-days many researches focuses on applying image/
video processing techniques, like recognition of pattern
and various signal processing techniques, to work out
definite problems in real-world applications.
In an application is the computerized age-assessment of
skeletal babies development, children and young adults,
using X-rays of the human hand. A mismatch of the
skeletal age with the biological age indicates growth
abnormalities, and thus serves as good diagnostic tool
for a number of diseases. New methods for matching
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
the patterns were developed to create algorithms for the
evaluation of the skeletal age of the patient.
Though the image processing tribulations carry over to
video processing, but exaggerated by the larger data sizes
and the new dimension of time and motion. We have
developed video segmentation techniques will open the
door to some type of divide-&-conquer advance to many
of the video processing problems.
V. VIDEO DENOISING:
Video denoising is the process of extracting the noise
from a video signal. Video denoising methods can be
divided into spatial, temporal and spatio-temporal. Spatial
video denoising method is adopted in this paper. In this
method image noise reduction is applied to each frame
of a video individually. Images are frequently corrupted
by noises. Noises occur during image capture,
transmission, etc. [3] The most important process in video
processing is Noise removal. In this the results of the
noise removal have a strong control on the quality of the
video processing technique. Various techniques for noise
removal are well expanded in color image processing.
The nature of the noise removal problem depends on the
kind of the noise corrupting the image. In the field of
image noise reduction several linear and non linear
filtering methods have been projected. Linear filters are
not able to efficiently eradicate impulse noise as they
have a propensity to blur the edges of an image. On the
other hand non linear filters are suitable for dealing with
impulse noise. Several non linear filters based on
Classical and fuzzy techniques have emerged in the past
few years. For example most classical filters that
confiscate concurrently blur the edges, while fuzzy filters
have the ability to merge edge preservation and
smoothing. Evaluated to other non linear techniques,
fuzzy filters are able to symbolize acquaintance in an
understandable way. In this paper we present results for
different filtering techniques and we evaluated the results
for these technique
Image sequence restoration has been progressively
achieving importance with the increasing prevalence of
visual digital media. The demand for content increases
the pressure on archives to automate their restoration
activities for preservation of the cultural heritage that
they hold. The goal of this paper is to find a pre-
processing method based on noise removal.
Type of Denoising:
• Spatial video denoising methods, in this noise in
image is reducing by applying to each frame
individually.
• Temporal in this video denoising method the noise
between frames is reduced. [4]Motion compensation
may be used to avoid ghosting artifacts when
blending together pixels from several frames.
• Spatial-Temporal is another video denoising methods
which is a combination of spatial and temporal
denoising.
TYPES OF NOISE:
• Amplifier noise (Gaussian noise)
• Salt-and-pepper Noise
• Periodic Noise
Amplifier noise (Gaussian noise):The Amplifier noise
is also called as additive or Gaussian or independent at
Empirical Analysis of Denoising Techniques in Video Processing
229
each pixel and independent of the signal intensity, caused
primarily by Johnson–Nyquist noise (thermal noise),
including that which comes from the reset noise of
capacitors [3]. In color cameras more amplification is
used in the blue color channel other than in the green or
red channel, there can be more noise in the blue channel
[5].
Salt-and-pepper Noise: Salt-and-pepper noise or spike
noise is also called as Fat-tail distributed or “impulsive”
noise. The images of salt-and-pepper noise will have dark
pixels in bright regions and bright pixels in dark regions.
[4]This type of noise can be caused by the errors of analog-
to-digital converter, bit errors in transmission, etc. This
can be eliminated in large part by using dark frame
subtraction and by interpolating around dark/bright
pixels[6].
Periodic Noise: The signal-to-periodic noise ratio is the
ratio in decibels, of the nominal amplitude of the
luminance signal (100 IRE units) to the peak-to-peak
amplitude of the noise. Different performance objectives
are sometimes specified for periodic noise (single
frequency) between 1 kHz and the upper limit of the
video frequency band and the power supply hum,
including low-order harmonics.
Types of Filters
1. Average Filter
2. Median Filter
3. Wiener Filter
4. Rank Order Filter
5. Gaussian Filter
6. Non-Linear Filter
7. Outlier Filter
Average filter: Mean filter or average filter is a
windowed filter of linear class, which smoothens the
signal (image). The filter works as low-pass one. The
basic idea behind filter is for any element of the signal
(image) take an average across its neighbourhood.
Median filter: Median filtering is a nonlinear process is
used to reduce impulsive or salt-and-pepper noise. It is
also useful in preserving edges in an image while
reducing random noise. Impulsive or salt-and pepper
noise can occur due to a random bit error in a
communication channel. In a median filter, a window
slides along the image, and the median intensity value of
the pixels within the window becomes the output
intensity of the pixel being processed. For example,
suppose the pixel values within a window are 5, 6, 55,
10 and 15, and the pixel being processed has a value of
55. The output of the median filter and the current pixel
location is 10, which is the median of the five values [7].
Wiener Filter: A restoration technique for deconvolution
is inverse filtering, i.e., when the image is blurred by a
known low pass filter, it is possible to recover the image
by inverse filtering or generalized inverse filtering.
Additive noises are sensitive to inverse filtering. The
approaches for reducing degradation for restoring the
algorithms are developed for each type of degradation
and simply combine them. The Wiener filtering is
executed by an optimal tradeoff between inverse filtering
and noise smoothing. The mean square error is optimized
in wiener filtering. It minimizes the overall mean square
error in the process of inverse filtering and noise
smoothing [8]. The original images are linear estimated
to wiener filtering. [9]The orthogonally theory of implies
that the Wiener filter in Fourier domain can be expressed
as follows:
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
�
),(),(),(
),(),(),(
2121
2
21
2121*
21ffSffSffH
ffSffHffW
nnxx
xx
+=
Where ),(),,( 2121 ffSffS nnxx power spectra of the
original image and the additive are respectively noise,
and ),( 21 ffH is the blurring filter. The Wiener filter
is easy to separate two parts, an inverse filtering part
and a noise smoothing part. It not only performs the
disconsolation by inverse filtering (high pass filtering)
but also removes the noise with a compression operation
(low pass filtering).
Rank Order Filter: Rank order filters have a certain size,
but do not have any matrix values or a gain factor. A
rank order filter of size 3x3 for example, examines 9
pixel values of the input map at a time, sorts the values
from small to large, and selects for the output value that
value which is encountered at a certain rank order
number. So one value of the pixel values examined
becomes the output value, without any calculation
performed on the values itself. When a threshold is set,
the value of the centre pixel will only be replaced with
the new value if the difference between the original and
new value is smaller than or equal to the threshold.
Gaussian Filter: Gaussian filter is a filter which has the
impulse response is the Gaussian function. When
minimizing the rise and fall time these filters are
developed to overshoot. The minimum possible group
delay is connected closely with the Gaussian Filter. A
Gaussian filter is modified by the input signal of the
convolution with a Gaussian function mathematically;
this transformation is also known as the Weierstrass
transform.
The one-dimensional Gaussian filter has an impulse
response given by
�
2..)( xaea
xg −
Π=
or with the standard deviation as parameter
2
2
2...2
1)( σ
σ
x
exg−
Π=
In two dimensions, it is the product of two such
Gaussians, one per direction:
2
22
222
1),( σ
σ
yx
eyxg+−
Π=
Where x is the distance from the origin in the horizontal
axis, y is the distance from the origin in the vertical axis,
and ó is the standard deviation of the Gaussian
distribution[10].
Non-Linear Filters: A nonlinear filter is a signal-
processing device whose output is not a linear function
of its input. Terminology concerning the filtering problem
may refer to the time domain (state space) showing of
the signal or to the frequency domain representation of
the signal. When referring to filters with adjectives such
as “band pass, high pass, and low pass” one has in mind
the frequency domain. When resorting to terms like
“additive noise”, one has in mind the time domain, since
the noise that is to be added to the signal is added in the
state space representation of the signal. The state space
representation is more general and is used for the
advanced formulation of the filtering problem as a
mathematical problem in probability and statistics of
stochastic processes [11].
Outlier Filter: An adaptive filter is a filter that self-
adjusts its transfer function according to an optimization
Empirical Analysis of Denoising Techniques in Video Processing
231
algorithm driven by an error signal. Most adaptive filters
are digital filters has more complexity of algorithms in
optimizations. A non-adaptive filter has a static transfer
function. An error signal that has to be refined to its
transfer function to match the changing parameters is
used in adaptive filters. [12]The adaptive process is
involved the use of a cost function, which is also used as
a criterion for optimum performance of the filter, which
determines how to modify the filter algorithm to transfer
its function to minimize the cost on the next iteration.
Adaptive filters have become much more common and
are now often used in devices such as mobile phones
and other communication devices, camcorders and digital
cameras, and medical monitoring equipment.
VI. EVALUATION MEASURES FOR FILTERS
Mean Square Error : In the image coding and computer
vision literature, the most frequently used measures are
deviations between the original and coded images of
which the mean square error (MSE) or signal to noise
ratio (SNR) being the most common measures. The
reasons for these metrics widespread popularity are their
mathematical tractability and the fact that it is often
straightforward to design systems that minimize the MSE
but cannot capture the artifacts like blur or blocking
artifacts [13]. The effectiveness of the coder is optimized
by having the minimum MSE at a particular compression
[David Bethel (1997)] and MSE is computed using
Eq.(2),
MSE=2
1 1
' )),(),((1
jifjifMN
M
i
N
i∑∑
= =− (1)
Peak Signal – To – Noise-Ratio: Larger SNR and PSNR
indicate a smaller difference between the original
(without noise) and reconstructed image. This is the most
widely used objective image quality/ distortion measure.
The main advantage of this measure is ease of
computation but it does not reflect perceptual quality.
An important property of PSNR is that a slight spatial
shift of an image can cause a large numerical distortion
but no visual distortion and conversely a small average
distortion can result in a damaging visual artifact, if all
the error is concentrated in a small important region. This
metric neglects global and composite errors PSNR is
calculated using Eq. (2),
PSNR= dBRMSE
N
10log20 (2)
Average Difference: A lower value of Average Difference
(AD) gives a “cleaner” image as more noise is reduced
and it is computed using Eq. (3).
Average Difference (AD)
= [ ]∑∑= =
−M
i
N
j
jifjifMN 1 1
' ),().(1
(3)
Maximum Difference: Maximum difference (MD) is
calculated by using Eq. (4) and it has a good correlation
with MOS for all tested compression techniques so it is
preferred as a very simple measure by referring the
measure of the compressed picture quality in different
compression systems (Marta Mrak et al 2003).
Maximum Difference (MD) =
)),(),(( ' jifjifMax − (4)
Normalized Correlation: The proximity between two
digital images can also be quantified in terms of
correlation function. These measures measure the
similarity between two images, hence in this sense they
232
Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
are complementary to the difference based measures. All
the correlation based measures tend to 1, as the difference
between two images tend to zero. As difference measure
and correlation measures complement each other,
minimizing Distance measures are maximizing
correlation measure [Thomas Kratochevil et al (2005)]
and Normalised Correlation is given by Eq. (5)
Normalised Correlation (NK)
=
[ ]
∑∑
∑∑
= =
= =M
i
N
j
M
i
N
j
jif
jifjif
1 1
2
1 1
'
),(
),()..(
(5)
F. Mean Absolute Error:
Mean Absolute Error (MAE)
= )),(),((1
1 1
'∑∑
= =
−M
i
N
j
jifjifMN (6)
MAE is calculated used Eq. (6) and large value of MAE
means that the image is of poor quality [Ratchakit
Sakuldee et al (2007)].
Normalized Absolute Error
Normalised absolute error computed by Eq. (7) is a
measure of how far is the decompressed image from the
original image with the value of zero being the perfect
fit [Harker (1987)]. Large value of NAE indicates poor
quality of the image [Ratchakit Sakuldee et al (2007)].
Normalized Absolute Error (NAE)
=
[ ]
∑∑
∑∑
= =
= =M
i
N
M
i
N
j
jif
jifjif
1 1
1 1
'
),(
),().,(
(7)
Structural Correlation/Content:
A recognizable concept in image processing, that
estimates the likeness of the structure of two signals is
Correlated,. The measure are compared with the total
weight of an original signal to that of a coded or given.
This measure is also called as structural content. The
Structural content is given by Eq. (8) and if it is spread
at 1, then the decompressed image is of better quality
and large value of SC means that the image is of poor
quality [Ratchakit Sakuldee et al (2007)].
Structural Correlation/Content (SC)
=
[ ]
∑∑
∑∑
= =
= =M
i
N
j
M
i
N
j
jif
jif
1 1
2'
1 1
2
),(
),(
(8)
Empirical Analysis of Denoising Techniques in Video Processing
233
Table: 1 Evaluation Measures for Gaussian Noise
From the above Table: 1 for Gaussian Noise, the video for Wiener Filter out performs. The remaining
filters in the video are denoising
Original Data Noise Data Median Filter Winer Filter
Rank Order Average Data Gaussian Filter NL Filter
Outliner Filter
Fig: 1 Screen Shot for Gaussian Noise
Gaussian Noise
MSE PSNR MNCC AD SC MD NAE
Average Data 435.7165 21.9992 0.9616 1.8718 1.0534 161.5750 0.0962
Median Filter 256.0075 24.2621 0.9798 0.8614 1.0256 159.7333 0.0828
Wiener Filter 175.3028 25.9706 0.9798 0.8615 1.0308 85.9333 0.0678
Rank Order 202.2178 25.2960 0.9886 0.3427 1.0107 116.2333 0.0831
Gaussian Filter 226.5426 24.8181 0.9789 0.8713 1.0294 116.4417 0.0796
Non-Linear Filter 256.0075 24.2855 0.9798 0.8614 1.0256 159.7333 0.0828
Outlier Filter 345.1225 22.9543 0.9964 0.1580 0.9866 150.0750 0.1178
VII. RESULTS AND DISCUSSIONS :
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
Table: 2 Evaluation Measures for Salt and Pepper Noise
Salt & Pepper Noise
MSE PSNR MNCC AD SC MD NAE
Average Data 512.6008 21.2961 0.9582 104849 1.0558 162.2667 0.1181
Median Filter 255.9669 24.0606 0.9958 0.4679 0.9930 189.8667 0.0272
Wiener Filter 421.1141 22.1408 0.9673 1.0192 1.0417 208.1167 0.1010
Rank Order 187.3607 25.6188 0.9892 0.3037 1.0104 215.5667 0.0378
Gaussian Filter 435.6573 21.9709 0.9746 0.4647 1.0253 141.7417 0.1169
Non-Linear Filter 230.6177 24.7355 0.9802 0.8514 1.0265 198.0917 0.0501
Outlier Filter 257.5257 24.2373 0.9956 0.4818 0.9933 189.3417 0.0273
From the above Table: 2 for Salt and Pepper Noise, the video for Median Filter and Rank Order Filter performs
well. The remaining filters in the video are denoising.
Original Image Noise Data Median Filter Winer Rank Order
Average Data Gaussian NLFilter Outlier Filter
Fig: 2 Screen Shot for Salt & Pepper Noise
Empirical Analysis of Denoising Techniques in Video Processing
235
Periodic Noise
MSE PSNR MNCC AD SC MD NAE
Average Data 612.4158 16.7406 0.9381 0.6814 1.0452 171.7083 0.2343
2D Filter 150.0259 20.2936 0.9654 0.5665 1.0240 125.9833 0.1002
Wiener Filter 512.355 16.9289 0.9314 0.0260 1.0636 176.4833 0.2264
Rank Order 235.313 9.5164 0.9632 0.4357 0.7345 252.5833 0.5757
Gaussian Filter 356.218 17.1290 0.9486 1.6681 1.0302 172.0333 0.2260
Non-Linear Filter
235.51 14.4934 0.9622 2.5876 0.9885 249.2000 0.3010
Outlier Filter 178.86 15.6354 0.9526 0.7225 0.9917 225 0.2560
From the above Table: 3 for Periodic Noise, the video for 2D Filters performs well. The remaining filters in thevideo are denoising.
Original Data Noise Data Wiener Data Average Data Gaussian Data
NLFilter Median Filter 2D Filter Outlier Filter
Fig: 3 Screen Shot for Periodic Noise
Table: 3 Evaluation Measures for Periodic Noise
VIII. CONCLUSION
In this article, we conversed about different filtering
techniques for removing noises in video. Furthermore,
we presented and compared results for these filtering
techniques. From the results obtained we conclude that
with three different noises salt and pepper noise,
Gaussian noise and periodic noise applied for denoising
of the spatial video produces variant results over different
filtered techniques. From the results obtained using
various filtering techniques it is observed that for salt
and pepper noise median and rank order filter works
better than other techniques. In case of Gaussian noise
Weiner and rank order filter works fine. For Periodic
noise 2D filter works better than other filters.
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Karpagam Jcs Vol. 6 Issue 4 May. - June. 2012
REFERENCES
[1] http://www.engineersgarage.com/articles/image-
processing-tutorial-applications.
[2] http:/ /www.wisegeek.com/what-is-video-
processing.htm.
[3] C. Mythili,Dr. V. Kavitha, .”Efficient Technique
for Color Image Noise Reduction”, T h e R e s e a
r c h B u l l e t i n o f J o r d a n A C M, V o l. I I (I
I I ) P a g e | 4 1-44.
[4] http://en.wikipedia.org/wiki/Video_denoising
[5] http://en.wikipedia.org/wiki/Image_noise.
[6] Amit Yerpude, Dr. Sipi Dubey, Amit Yerpude, Dr.
Sipi Dubey, “Robust Method for Noisy Image
Segmentation”, IJCSET |February 2012| Vol 2,
Issue 2,891-895.
[7] http://nptel.iitm.ac.in/courses/Webcourse-
contents/IIT-KANPUR/Digi_Img_Pro/chapter_8/
8_16.html
[8] http://www.owlnet.rice.edu/~elec539/Projects 99/
BACH/proj2/wiener.html.
[9] Disha Sharma and Gagandeep Jindal, “Computer
Aided Diagnosis System for Detection of Lung
Cancer in CT Scan Images”, International Journal
of Computer and Electrical Engineering, Vol. 3,
No. 5, October 2011.
[10] http://en.wikipedia.org/wiki/Gaussian_filter.
[11] http://en.wikipedia.org/wiki/Nonlinear_filter
[12] http://en.wikipedia.org/wiki/Adaptive_filter.
[13] Sumathi Poobal, G.Ravindran,” The Performance
of Fractal Image Compression On Different
Imaging Modalities Using Objective Quality
Measures”, International Journal of Engineering
Science and Technology (IJEST), and Vol. 3 No.
1 Jan 2011, PP: 525-530.
Author’s Biography
M. Hemalatha completed MCA
MPhil., PhD in Computer
Science and currently working
as an Assistant Professor and
Head, Department of software
systems in Karpagam
University. More than ten years
of Experience in teaching and published more than fifty
papers in International Journals and also presented
seventy papers in various National conferences and one
International Conferences Area of research is Data
mining, Software Engineering, bioinformatics, Neural
Network. She is also reviewer in several National and
International journals.
R.Revathi was born and brought
up Trichy She received her
Bachelor of Computer Science
from Bharathidasan University
and Master degree in Information
Technology from Bharathidhasan
University. She completed her
M.Phil from Bharathidasan University. Currently
pursuing Ph.D., in computer science at Karpagam
University under the guidance of Dr.M.Hemalatha Head,
Dept of Software System, Karpagam University, and
Coimbatore. Area of Interest is Image Processing and
Networks. Area of Research is Image Processing.
Karpagam Journal of Computer Sciencewww.karpagameducation.com
Volume : 06 Issue : 04 May - June 2012
CONTENTS PAGE NO.
1. Acoustic Signature Recognition of Moving Vehicles Using Elman 183Neural NetworkPaulraj M P, Abdul Hamid Adom, Hema C R, Sathishkumar Sundararaj
2. Fuzzy MPPT Based Voltage Regulation on Photovoltaic Power Supply 190System For Continuously Varying Illumination ConditionA. Durgadevi, S. Arulselvi
3. Design and Development of a Gripper System for an Indoor 196Service RobotHema C. R., Vivian Tang Sui Lot, Paulraj M.P.
4. Power Adaption Routing Protocol For Realtime Applications 204in Wireless Sensor NetworksR.Prema, R.Rangarajan
5. Path Planning in AI (Artificial Intelligence) 210T.C. Manjunath, G.V. Jayaramaiah
6. Efficient Object Detection and Classification using Hybrid ELM 217
with Analytic Hierarchy Process and Bayesian Network
N.V. Balaji, M. Punithavalli
7. Empirical Analysis of Denoising Techniques in Video Processing 225
R.Revathi, M.Hemalatha