Signal Processing: An International Journal (SPIJ) Volume 4, Issue 4

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Signal Processing: An
International Journal (SPIJ)
Volume 4, Issue 4, 2010

Edited By
Computer Science Journals
www.cscjournals.org

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Editor in Chief Dr. Saif alZahir
Signal Processing: An International Journal
(SPIJ)
Book: 2010 Volume 4 Issue 4
Publishing Date: 30-10-2010
Proceedings
ISSN (Online): 1985-2339
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Editorial Preface
This is fourth issue of volume four of the Signal Processing: An International Journal
(SPIJ). SPIJ is an International refereed journal for publication of current research in
signal processing technologies. SPIJ publishes research papers dealing primarily with
the technological aspects of signal processing (analogue and digital) in new and
emerging technologies. Publications of SPIJ are beneficial for researchers,
academics, scholars, advanced students, practitioners, and those seeking an update
on current experience, state of the art research theories and future prospects in
relation to computer science in general but specific to computer security studies.
Some important topics covers by SPIJ are Signal Filtering, Signal Processing
Systems, Signal Processing Technology and Signal Theory etc.
This journal publishes new dissertations and state of the art research to target its
readership that not only includes researchers, industrialists and scientist but also
advanced students and practitioners. The aim of SPIJ is to publish research which is
not only technically proficient, but contains innovation or information for our
international readers. In order to position SPIJ as one of the top International journal
in signal processing, a group of highly valuable and senior International scholars are
serving its Editorial Board who ensures that each issue must publish qualitative
research articles from International research communities relevant to signal
processing fields.

SPIJ editors understand that how much it is important for authors and researchers to
have their work published with a minimum delay after submission of their papers.
They also strongly believe that the direct communication between the editors and
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readers. Therefore, all activities from paper submission to paper publication are
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panel and review system that ensures rapid decision with least delays in the
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To build its international reputation, we are disseminating the publication information
through Google Books, Google Scholar, Directory of Open Access Journals (DOAJ),
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are working on establishing ISI listing and a good impact factor for SPIJ. We would
like to remind you that the success of our journal depends directly on the number of
quality articles submitted for review. Accordingly, we would like to request your
participation by submitting quality manuscripts for review and encouraging your
colleagues to submit quality manuscripts for review. One of the great benefits we can
provide to our prospective authors is the mentoring nature of our review process.
SPIJ provides authors with high quality, helpful reviews that are shaped to assist
authors in improving their manuscripts.
Editorial Board Members
Signal Processing: An International Journal (SPIJ)

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Editorial Board
Editor-in-Chief (EiC)
Dr. Saif alZahir
University of N. British Columbia (Canada)
Associate Editors (AEiCs)
Professor. Raj Senani
Netaji Subhas Institute of Technology (India)
Professor. Wilmar Hernandez
Universidad Politecnica de Madrid (Spain)
Dr. Tao Wang
Universite Catholique de Louvain (Belgium)
Dr. Francis F. Li
The University of Salford (United Kingdom)
Editorial Board Members (EBMs)
Dr. Thomas Yang
Embry-Riddle Aeronautical University (United States of America)
Dr. Jan Jurjens
University Dortmund (Germany)
Dr. Jyoti Singhai
Maulana Azad National Institute of Technology (India)

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Table of Content
Volume 4, Issue 4, October 2010
Pages
175 - 200
Multi-Target Classification Using Acoustic Signatures in
Wireless Sensor Networks: A survey

Ahmad Aljaafreh, Ala Al-Fuqaha
201 - 212 Effective Preprocessing in Modeling Head-Related Impulse
Responses Based on Principal Components Analysis

Hugeng, Wahidin Wahab, Dadang Gunawan
213 - 218 Performance Analysis of Convolution Coded WLAN Physical
Layer under Different Modulation Techniques
Ginni Sharma, Sanjeev Kumar, Anita Suman, Praveen Kumar
219 - 227 Spectral Analysis of Sample Rate Converter
Manish Sabraj, Vipan Kakkar
228 - 238 A Gaussian Clustering Based Voice Activity Detector for Noisy
Environments Using Spectro-Temporal Domain
Sara Valipour, Farbod Razzazi, Azim Fard & Nafiseh
Esfandian
239 -246 On Channel Estimation of OFDM-BPSK and -QPSK over
Nakagami-m Fading Channels
Neetu Sood, Ajay K Sharma & Moin Uddin

Signal Processing: An International Journal (SPIJ) Volume (4): Issue (4)

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Ahmad Aljaafreh & Ala Al-Fuqaha
Multi-Target Classification Using Acoustic Signatures in
Wireless Sensor Networks: A survey


Ahmad Aljaafreh



aljaafreh@ieee.org
Electrical Engineering Department
Tafila Technical University
Tafila, 66110, P.O.Box 179, Jordan

Ala Al-Fuqaha





ala.al-fuqaha@wmich.edu
Computer Science Department
Western Michigan University
Kalamazoo, MI 49008, USA

Abstract

Classification of ground vehicles based on acoustic signals using wireless sensor
networks is a crucial task in many applications such as battlefield surveillance,
border monitoring, and traffic control. Different signal processing algorithms and
techniques that are used in classification of ground moving vehicles in wireless
sensor networks are surveyed in this paper. Feature extraction techniques and
classifiers are discussed for single and multiple vehicles based on acoustic
signals. This paper divides the corresponding literature into three main areas:
feature extraction, classification techniques, and collaboration and information
fusion techniques. The open research issues in these areas are also pointed out
in this paper. This paper evaluates five different classifiers using two different
feature extraction methods. The first one is based on the spectrum analysis and
the other one is based on wavelet packet transform.

Keywords: Signal classification, feature extraction, distributed sensors, sensor fusion.



1. INTRODUCTION
Wireless sensor network (WSN) is a network of spatially distributed, densely deployed, and self
organized sensor nodes, where a sensor node is a platform with sensing, computation and
communication capabilities. WSN is an emerging technology because of the advances in
technologies of: Micro-Electro-Mechanical Systems (MEMS), Microprocessors, wireless
communication and power supply. New technologies provide cheap small accurate: sensors,
processors, wireless transceivers, and long-life batteries. Sensor node is the integration of all of
these technologies in a small board, like the ones in Fig. 3 part (b), it is called mote. Fig. 3 part (a)
shows the basic architecture of the mote. All of the above motivate researchers and practitioners
to design, deploy and implement networks of these sensor nodes in many applications. WSN has
the following characteristics: concern is about the data but not about the sensor node itself, low
cost, constrained power supply, static network, topology may change because of sensor node or
link failure, sensor nodes are prone to destruction and failure, dense deployment, self-
organization, and spatial distribution. WSN is used in many remote sensing and data aggregation
applications [1],[2]. Detection, classification, and tracking are the main signal processing functions
Signal Processing-An International Journal (SPIJ), Volume (4): Issue (4)
175

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Ahmad Aljaafreh & Ala Al-Fuqaha
of the wireless sensor networks [3]. WSNs increase the covered area, redundancy of the sensors,
and decision makers, which improves the performance and reliability of the decision making. To
understand the work, design and operation of the WSNs see Refs. [4],[5]. Refs. [4],[6] categorizes
the applications and describes the implementation of the WSNs. A survey of the architecture and
sensor nodes deployment in WSNs is presented in Ref. [7]. WSN is a cost efficient technology.
However, it has some constraints. Limited energy, limited bandwidth, and limited computational
power are the main constraints of WSNs [8]. Therefore, to implement any digital signal
processing algorithm it needs to be an intelligent signal processing and decision making algorithm
with the following requirements: power efficiency, robustness, and scalability. In WSNs, observed
data could be processed at the sensor node itself, distributed over the network, or at the gateway
node. WSNs can be utilized for distributed digital signal processing [9]-[11]. Research in
classification in wireless sensor networks can be divided into two areas: hardware area
(platforms, sensors), and software area (signal processing algorithms, collaboration, and
networking techniques) [12]. The signal processing techniques and collaboration schemes that
are used in ground vehicle classification in WSN based on acoustic signals are surveyed, as in
Fig 2, in this paper. Target classification in WSN is to label or categorize a target that passing
through the area that is monitored by the WSN to one of a predefined classes based on an
extracted feature vector. Classification in WSNs can be considered as a process as in Fig. 4,
where a feature vector is extracted from the input signal, then classified, then the information is
fused to come up with the final decision. Most of the researcher are interested in improving the
performance of this process through selection and design an efficient tool, as in Table 1, for one
of the followings tasks :
• Feature Extraction
• Classification Techniques
• Information Fusion
The remainder of the paper is organized as follows. Section 2 presents the recent methods that
are used to extract features from the vehicle acoustic signals for single and multiple targets.
Section 3 discusses the classification techniques. Section 4 presents the information fusion
techniques. Section 5 outlines the the open research. And finally, conclusions are discussed in
section 6.

Reference
Feature
Classifier
Classes
Classification
Fusion
Extractor
Number
Rate
Method
[12]
TESPAR
ANN
2
up to 100%
-
[13] and [14]
DWT
MPP
2
98,25%
-
[15]
HLA, PSD
ANN
4
HLA: 92%,
-
PSD: 94%
[16]
HLA
ANN
18
88%
running sum
[17]
HLA
MAP
6
89%
-
[18]
MFCC
GMM, HMM
9
77%, 88%
-
and ML
[19]
FFT, DWT,
kNN, MPP
4
85%, 88%
MRI
STFT, PCA
[20]
STFT, PCA
ANN
3
-
-
[21]
FFT, PSD,
kNN, ML,
2
78% - 97%
-
AR
SVM
[22]
DWT
ANN
4
73%
-
[23]
CC
HMM
9
96%
-
[24]
WPT
LDA, CART
3
-
-
[25]
CWT
ANN
6
95%
-
[26]
TVAR, PCA
ANN
6
83%-95%
-
[27]
BHM
CART
9
90%
Decision
Fusion
[28]
STFT, RID
ANN, MVG
6
up to 87%
-
[29]
EE, PCA
ANN, Fuzzy
5
up to 97%
-
Signal Processing-An International Journal (SPIJ), Volume (4): Issue (4)
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Ahmad Aljaafreh & Ala Al-Fuqaha
Logic
[30]
AR
ANN
4
up to 84%
-
[31]
FFT, PSD
kNN
-
-%
-
[32]
FFT, WDT
kNN
2
62%
Dempsler-
Shafer, MV
[33]
FFT
Template
8
-%
template
Matching
storing
[34]
PSD
kNN, ML
2
77%, 89%
Distributed
Classification
[35]
-
kNN, ML,
2
69%, 68%,
MAP
SVM
69%
Bayesian,
Nearest
Neighbor,
Majority
Voting,
Distance-
based
[36]
Harmonic
SVM
5
85%
modified
and
Bayesian
Frequency
(decision
Components
level)
[37]
Harmonic
MVG
3-5
70-80%
-
set
[38]
STFT,PCA
C4.5, KNN,
4
60-93%
-
PNN, SVM
[39]
WPT
CART
-
-
-
[40]
MFCC
RNN
4
85%
-
[41]
PSD
KNN, ML,
2
up to 97%
-
SVM
[42]
PSD, PCA
SVM
3
up to 93%
-
[43]
MFCCs
GMM
2
up to 94.1%
CART
[44]
FFT, WT
KNN, MPP, K-
3
95.5%
-
Means
[45]
WPT
ML, ANN
3
up to 98%
-
[46]
PSD
ANN
4
up to 99%
-
[47]
WPT
cascaded
3
-
Dempster–
fuzzy
Shafer (DS)
classifier
(CFC)

Table 1: Recent feature extraction and classification techniques used for vehicle
classification based on acoustic signals.


2. FEATURE EXTRACTION OF ACOUSTIC SIGNATURE
Feature extraction is the most significant phase of the classification process. To classify an
object, a set of features of that object is extracted to label that object to one of a predefined
classes. This set of features is generated from a source signal as in Fig. 1. Feature extraction can
be considered as dimensionality reduction technique. In feature extraction certain transforms or
techniques are used to select and generate the features that represent the characteristic of the
source signal. This set of features is called a feature vector. Feature vectors could be generated
in time, frequency, or time \ frequency domain.

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Ahmad Aljaafreh & Ala Al-Fuqaha



Figure 1: Classification block diagram.









Figure 2: Taxonomy of the techniques that are used in target classification using acoustic
signature in wireless sensor networks


.
Figure 3: Wireless sensor node examples in part (b) and the common architecture of a
senor node in part (a).



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