iosr-JCE   Volume-5 ~ Issue-1

Abstract: In several applications, such as wideband spectrum sensing for cognitive radio, only the power spectrum (a.k.a. the power spectral density) is of interest and there is no need to recover the original signal itself. In addition, high-rate analog-to-digital converters (ADCs) are too power hungry for direct wideband spectrum sensing. These two facts have motivated us to investigate compressive wideband power spectrum sensing, which consists of a compressive sampling procedure and a reconstruction method that is able to recover the unknown power spectrum of a wide-sense stationary signal from the obtained sub-Nyquist rate samples. The task oriented brain activity analysis and classification is a prime issue in EEG signal processing . The similar attempt has been done here to estimate the brain activity on the basis of power spectrum analysis. For this, the modified approach involving both Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methodologies has been used in this paper to investigate the behavior of brain's electrical activity for a simple case of visual attention.The input EEG signals are analyzed with the aid of Fast Independent Component Analysis (FastICA), a Statistical Signal Processing Technique, to obtain the components related to the detection of epileptic seizures. The BackPropagation Neural Network is trained with the obtained components for effective detection of epileptic seizures. Index Terms : EEG signal, ICA and PCA, BPNN, ANN.

[1] Dyonisius Dony Ariananda, Student Member, IEEE, and Geert Leus, Fellow,IEEE (september 2012),IEEE transactions on signal
processing, "Compressive Wideband Power Spectrum Estimation" Vol. 60, No.9,
[2] Mitul Kumar Ahirwal and Narendra D londhe, (March 2012) "Power Spectrum Analysis of EEG Signals for Estimating Visual
Attention." Assistant Professors National Institute of Technology Raipur, Raipur-492010, International Journal of Computer
Applications (0975 – 8887) Volume 42– No.15,
[3] Digital Analysis Of EEG Brain Signal Mr. Rash Dubey,(2011) Asst. Prof., E & IE, APJ College of Engg., Sohna, Gurgaon, 121003
– India
[4] M. Mishali and Y. Eldar,( Apr. 2010) "From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals," IEEE J.
Sel. Top. Signal Process., Vol. 4, No. 2.
[5] Sivasankari. N and Dr. K. Thanushkodi Int. J. (2009) Automated Epileptic Seizure Detection in EEG Signals Using FastICA and
Neural Network Advance. Soft Comput Appl., Vol. 1, No. 2,
[6] Lias, S., Sulaiman, N., Murat, Z.M., and Taib, M.N. 2010 IQ Index using Alpha-Beta Correlation of EEG Power Spectrum Density
(PSD), Paper Presented at the IEEE Symposium on Industrial Electronics and Applications (ISIEA) Penang, Malaysia.
[7] EEG/ERP data vailable for free public download. Obtained through the Internet: http: //sccn.ucsd.edu
/~arno/fam2data/publicly_available_EEG_data.html, [accessed 15/01/2011].
[8] EEGLAB a Matlab tool box for EEG analysis Obtained through the Internet:http://sccn.ucsd.edu [accessed 12/11/2010].
[9] Hamid N.H.A., Sulaiman, N., Aris, S.M.A., Murat Z.H., and Taib, M.N., 2010 Evaluation of Human Stress Using EEG Power
Spectrum, Paper Presented at the International Colloquium on Signal Processing & Its Applications (CSPA).
[10] M. Kim and J. Takada, (Sep. 2009) "Efficient multichannel wideband spectrum sensing technique using filter bank," in Proc. IEEE
Int. Symp. Pers. IndoorMobileRadio Commun. (PIMRC 2009), Tokyo, Japan,

Abstract: Use of Internet is increasing day by day. Wi-Fi provides wireless access to the internet but range is limited to certain meters only. So concept of WiMAX is introduced to increase the range. WiMAX[1] means worldwide interoperability for microwave access. WiMAX is also known as wireless broadband. The WiMAX based on IEEE 802.16, has been one of the most important technologies in communication networks providing voice, data and video services with different type of QoS (Quality of Service) during last few years. The main purpose of this paper is to see and evaluate the performance of a WiMAX physical layer scenario for various modulation schemes, in the presence and absence of jammer. The simulator encompasses the blocks that build the physical layer of IEEE 802.16e. BER results are presented with the presence of jamming under different digital modulations schemes as well as different antennas. Simulation approach is main concern here. OPNET MODELER is the software used for the simulation purpose.

Keywords– WiMAX, Physical Layer Jamming, Scrambling, OPNET MODELER, Modulation Schemes.

1] Jeffrey G. Andrews, Arunabha Ghosh, Rias Muhamed, Fundamentals of WiMAX Understanding Broadband Wireless
Networking: Prentice-Hall.
[2] Simon Haykin, Michael Moher, Modern Wireless communication, Prentice-Hall.
[3] Syed Ahson, Mohmmad Ilyas, Syed Ahson, Mohammad Ilyas, WiMAX Standards and Security. Boca Raton: CRC Press, 2008.
[4] Taeshik Shon and Wook Choi (2007), "An Analysis of Mobile WiMAX security: Vulnerabilities and Solutions"
[5] LUO Cuilan (2009), "A Simple Encryption Scheme Based on WiMAX", Department of Electronics Jiangxi University of Finance
and Economics Nanchang, China .
[6] Boris Makarevitch, "Jamming Resistant Architecture for WiMAX Mesh Network", communications Laboratory Helsinki University of Technology.
[7] Juan Li, Sven-Gustav Häggman (2006), "Performance of IEEE 802.16 Based System in Jamming Enviornment and Its
Improvement With Link Adaption", The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio
Communications (PIMRC'06).
[8] Mahmoud Nasreldin, Heba Aslan, Magdy El-Henna wy, Adel El-Hennawy (2008), "WiMAX Security", 22nd International
Conference on Advanced Information Networking and applications.
[9] White paper by Motorola (2007), "WiMAX Security for Real- World Network Service Provider Deployments".
[10] Rakesh Kumar Jha and Dr Upena Dalal "Security Comparison of Wired and Wireless Network with Firewall and Virtual
Private Network (VPN)" International Conference on Recent Trends in Information, Telecommunication and Comput ing, IEEE
Xplore,kerela,March 2010.

Abstract: Sensor nodes are usually deployed in an open environment therefore they are subjected to various kinds of attacks like Worm Hole attack, Black Hole attack, False Data Injection attacks. Since the attackers can cause disruption and failure to the network, it's very important to detect these compromised nodes and revoke them before any major disruption occurs. Therefore, it's very important to safe guard the network from further disruption. For this purpose a method called Biased SPRT (Sequential Probability Ratio Test) is used by setting up some Threshold Value and Trust Aggregator in the network scenario for which the network is divided into number of Zones.

Key terms – Compromised Node, SPRT, B-SPRT, Trust Aggregator, Zones, False Positives, False Negatives.

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[3] J. Ho, M. Wright, and S.K. Das. Fast Detection of Replica Node Attacks in Sensor Networks Using Sequential Analysis. In IEEE
INFOCOM,April 2009.
[4] X. Hu, T. Park, and K. G. Shin. Attack-tolerant time-synchronization in wireless sensor networks. In IEEE INFOCOM, April 2008.
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[10] Y. Yang, X. Wang, S. Zhu, and G. Cao. Distributed software-basedattestation for node compromise detection in sensor networks. In IEEE SRDS, October 2007.onal Conference on Recent Trends in Information, Telecommunication and Comput ing, IEEE