Majlesi Journal of Electrical Engineering
Volume:5 Issue: 1, Mar 2011

The number of attacks in computer networks has grown extensively, and many new intrusive methods have appeared. Intrusion detection is known as an effective method to secure the information and communication systems. In this paper, the performance of Elman and partial-connected dynamic neural network (PCDNN) architectures are investigated for misuse detection in computer networks. To select the most significant features, logistic regression is also used to rank the input features of mentioned neural networks (NNs) based on the Chi-square values for different selected subsets in this work. In addition, genetic algorithm (GA) is used as an optimization search scheme to determine the sub-optimal architecture of investigated NNs with selected input features. International knowledge discovery and data mining group (KDD) dataset is used for training and test of the mentioned models in this study. The features of KDD data are categorized as basic, content, time-based traffic, and host-based traffic features. Empirical results show that PCDNN with selected input features and categorized input connections offers better detection rate (DR) among the investigated models. The mentioned NN also performs better in terms of cost per example (CPE) when compared to other proposed models in this study. False alarm rate (FAR) of the PCDNN with selected input features and categorized input connections is better than other proposed models, as well.

The main objective of this work is to analyze the computing capabilities of human neuroblastoma cultured cells and to define stimulation patterns able to modulate the neural activity in response to an image for controlling an autonomous robot. Multielectrode Arrays Setups have been designed for direct culturing neural cells over silicon or glass substrates, providing the capability to stimulate and record simultaneously populations of neural cells. If we are able to modify the selective responses of some cells with a external pattern stimuli over different time scales, the neuroblastoma-cultured structure could be trained to process image sequences.

Wireless baseband processing, which is characterized by high computational complexity and high data throughput, is regarded as the most challenging issue for software radio (SR) systems, especially for the General Purpose Processor (GPP)-based SR systems. To overcome this implementation difficulty in SR systems, the multicore architecture has been proposed as the GPP-based SR platform, for example, multicore Central Processing Unit (CPU), Graphic Processing Unit (GPU) and Cell processors. In this paper, the Cell processor is considered as the core component in the GPP-based SR platform, and the channel decoding modules for convolutional, Turbo and Low-density parity-check (LDPC) codes of WiMAX systems are investigated and efficiently implemented on Cell processor. With a single Synergistic Processor Element (SPE) running at 3.2GHz, the implemented channel decoders can throughput up to 30Mbps, 1.36Mbps and 1.71Mbps for the above three codes, respectively. Moreover, the decoding modules can be easily integrated to the SR system and can provide a highly integrated SR solution.

Cognitive radios utilize spectrum sensors to provide information about the surrounding radio environment. This enables cognitive radios to communicate at the same frequency bands with existing (primary) radio systems, and thereby improve the utilization of spectral resources. Furthermore, the spectrum sensor must be able to guarantee that the cognitive radio devices do not interfere with the primary system transmissions. This paper describes a hardware implementation of a spectrum sensor based on cyclostationary feature detector, which has an improved detection performance achieved by decimation of the cyclic spectrum. Decimation also provides a simple way to control detection time and, therefore, allows trading the detection time to better probability of detection and vice versa. Implementation complexity in terms of power consumption and silicon area for a 65 nm CMOS process is evaluated. Measured detection performance is presented and detection of a 802.11g WLAN signal through air interface is demonstrated.

There are several approaches for Radio Resource Management (RRM) in multicarrier cellular systems. This work analyzes and compares two of them: rate-adaptive resource allocation (sub-carriers and power) based on instantaneous data rates, and utility-based packet scheduling based on average data rates. A fundamental RRM problem in wireless cellular networks was chosen as a background to evaluate the aforementioned approaches: the trade-off between system spectral efficiency and fairness among the users when opportunistic allocation is used. Extensive system-level simulations were performed and important network metrics such as total cell throughput, mean user throughput, system fairness index and user satisfaction were assessed. It was concluded from the simulation results that it is possible to achieve an efficient trade-off between resource efficiency and fairness using any of the two RRM approaches. However, utility-based packet scheduling algorithms based on average data rates have the advantage of presenting higher user satisfaction with less computational complexity.

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of ANFIS-based fuzzy models based on SSA and information granulation (IG). In comparison with “conventional” evolutionary algorithms (such as PSO), SSA leads not only to better search performance to find global optimization but is also more computationally effective. In the hybrid optimization of ANFIS-based fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of ANFIS-based fuzzy models comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using three representative numerical examples such as Non-linear function, gas furnace, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some “conventional” fuzzy models already encountered in the literature.

Efficient evaluation of decision fusion algorithms becomes particularly important when different fusion schemes have to be compared with respect to an underlying performance metric or when a large number of evaluations are required for optimization purposes. In this paper, we present explicit expressions for the global error probabilities of decision fusion for distributed detection with side information. In the considered distributed detection problem, the sensors compress their observations independently and transmit local decisions to a fusion center that combines the received decisions with respect to available side information and computes the final detection result. In the special case of identical sensors, computationally efficient expressions are obtained by using the multinomial distribution. Numerical results obtained by considering the Gaussian detection problem reveal the influence of different qualities of side information on the overall detection performance

Cognitive radio is a promising solution to the problem of spectrum scarcity by means of allowing secondary radio networks access the spectrum opportunistically. One of the most important issues in cognitive radio is how to detect existing over-the-air signals reliably. Not a few literatures have reported that signals could be detected via their inherent or embedded properties. However, this approach may not be reliable and flexible enough for all kinds of signals with different modulation types. In this paper, we propose a type of multitone beacon signal carrying cyclostationary signatures, which is able to enhance the reliability and efficiency of signal detection at low cost of spectrum overhead. This beacon not only can indicate the presence or absence of user signal but also can reveal some other information helpful to opportunistic spectrum access through the information bits carried on its cyclostationary signatures. It could be applied to device/network identification, indication of spectrum allocation and spectrum rendezvous, both for primary and secondary users. Based on our previous work reported in [1], the generation and detection algorithm of the beacon signal are extended with improved spectral efficiency. Performance is discussed with both computer simulation and testbed validation.

This paper presents a computational study on the dynamic of nitric oxide (NO) in both the biological and artificial environments, by means the analysis of important nitric oxide diffusion attributes, which are defined in this work. We apply the compartmental model of NO diffusion as a formal tool, using a computational neuroscience point of view. The main objective is the analyses of the emergence and dynamic of complex structures, essentially diffusion neighbourhood (DNB), in environments with volume transmission (VT). The study is performed by the observation of the NO diffusion attributes, the NO directionality (NOD), the average influence (AI) and the center of DNB (CDNB). We present a study of the influences and dependences with respect to associated features to the NO synthesis-diffusion process, and to the different environments where it spreads (non-isotropy and non-homogeneity). The paper is structured into three sets of experiences which cover the aforementioned aspects: influence of the NO synthesis process, isolated and multiple processes, influence of distance to the element where NO is synthesized, and influence of features of the diffusion environment. The developments have been performed in mono bi-and three-dimensional environments, with endothelial cell features. The study contributes the needed formalism to management the dynamic of NO in artificial an biological environments also to quantify the information representation capacity that a type of NO diffusion-based signaling presents and their implications in many other underlying neural mechanisms, such as neural recruitment, synchronization of computations between neurons and in the brain activity in general.