JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)
Year:2012 | Volume:1 | Issue:3|Papers Count:6

In this paper, for secure transmission of speech signal in wireless sensor, a new method based on wavelet transform has been proposed. In this methodology, speech signal is compressed and encrypted simultaneously using characteristics of wavelet transform. The output speech which is in form of an obscure bit stream is required to be inserted in carrier signal. For insertion, five-stage wavelet transform is applied on speech signal and the optimum sub-bands are selected. The simulation results show that the proposed method provides higher security, lower complexity and lower consuming energy in comparison with popular methods in wireless sensor networks.

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique which is used for supporting high data rates. In this modulation scheme, Fourier transform is used to provide orthogonality between subcarriers. Since the channel is multi-path, a cyclic prefix having a length greater than channel delay spread is added to each OFDM symbol. The cyclic prefix removes the inter symbol interference, but, since it does not carry useful information, it decreases spectrum efficiency. In wavelet-based OFDM, there is no need for cyclic prefix. Wavelets have suitable localization, finite length and orthogonality both in time and frequency domains. Therefore, they could be used to generate suitable waveform for transmission through a fading channel. In this paper, a comparison has been made between performances of the Fourier transform and orthogonal wavelet families in OFDM-based systems in order to improve. Also, in order to select optimum filter order in these systems, the effect of increasing filter order is investigated. Simulation results show that wavelet transforms outperform the Fourier transform in terms of bit error rate (BER) and system efficiency and wavelet transforms with smaller filter orders should be used.

Nowadays, Dempster-Shafer theory of evidence is widely used in data fusion. This theory is a powerful tool for modelling, combination and inference of uncertain, imprecise and incomplete information. The fundamental object of D-S theory is a primitive function called Basic Probability Assignment (BPA), which should be determined based on characteristics of the process. How to determine BPA, is still a matter of discussion. In this paper, a novel method is proposed for basic probability assignment. In the proposed method, BPA is determined for each point according to its distances from the cluster centres, by means of fuzzy membership functions. The proposed method is used to determine the switch signal of a multiple MPC, and simulation results show that it considerably reduces the undesired switching. Moreover, the advantages of the proposed method when compared to one of the conventional methods would be discussed.

Studying the resting state functional magnetic resonance imaging (rs-fMRI) has been an important issue for diagnosing of the diseases related to brain function, nowadays. This paper proposes a novel method for diagnosis of the Attention-deficit/hyperactivity disorder (ADHD) diseases that includes three main modules. The first module is the feature extraction that extracts the correlation between the time series of the signals of the different brain regions. In the second module the SVM is used as the classifier. The third module is an optimizatrion algorithm, i.e. particle swarm optimization algorithm (PSO). PSO chooses the appropriate features by changing the length of its particles. The results show that the proposed method has high classification accuracy in comparison with others.

Security is one of the most important issues in modern computer systems. Among the major challenges in these systems is identifying normal and abnormal behaviors. But the boundary between these two is not well defined and it is a very complicated task to accomplish. Intrusion detection systems are one of the techniques that are used to maintain security in computer networks. Incorrect report of the intrusion alarm system is one of the major problems of security systems. Intrusion is defined as a set of activities and the purpose of these activities is jeopardizing the integrity, reliability and unauthorized system access to a particular resource. This paper proposes a mechanism based on the decision tree technique to detect intrusions in the network. Simulation results show that by the proposed method not only the duration of the training phase was reducedbut also the detection rate and falsealarm rate were improved.