Analysis of Asynchronous Band-Limited DS-CDMA with MMSE Multiuser Detector over Generalized-<Emphasis Type="Italic">k</Emphasis> Fading Channels

In this paper, we investigate bit-error-rate (BER) performance of a minimum mean-squared error (MMSE) multiuser receiver for asynchronous band-limited direct- sequence code-division multiple-access (DS-CDMA) systems. We focus on the BER performance in the presence of multitone jamming (MTJ) over frequency-selective multipath fading channels. We consider the generalized-K fading model in our analysis, as it can model a large spectrum of fading-channel characteristics. We also analyze the effects of band- limited pulse shape on the BER performance of the system. Multipath diversity based on the maximal-ratio combining (MRC) scheme is employed to combat fading effects. Our analytical expressions are valid for arbitrary diversity levels and fading parameters. Spectrum raised cosine (SRC) and Beaulieu–Tan–Damen (BTD) pulse shapes are employed for numerical analysis. Numerical results show that in the presence of MTJ and under various channel conditions, the MMSE based receiver gives better BER performance than the one without it. Moreover, the system with BTD pulses outperforms the one with SRC pulses.     

Soft error modeling and remediation techniques in ASIC designs

Soft errors due to cosmic radiations are the main reliability threat during lifetime operation of digital systems. Fast and accurate estimation of soft error rate (SER) is essential in obtaining the reliability parameters of a digital system in order to balance reliability, performance, and cost of the system. Previous techniques for SER estimation are mainly based on fault injection and random simulations. In this paper, we present an analytical SER modeling technique for ASIC designs that can significantly reduce SER estimation time while achieving very high accuracy. This technique can be used for both combinational and sequential circuits. We also present an approach to obtain uncertainty bounds on estimated error propagation probability (EPP) values used in our SER modeling framework. Comparison of this method with the Monte-Carlo fault injection and simulation approach confirms the accuracy and speed-up of the presented technique for both the computed EPP values and uncertainty bounds.Based on our SER estimation framework, we also present efficient soft error hardening techniques based on selective gate resizing to maximize soft error suppression for the entire logic-level design while minimizing area and delay penalties. Experimental results confirm that these techniques are able to significantly reduce soft error rate with modest area and delay overhead.     

Learning Automata Based Face-Aware Mobicast

Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protocol, called “mobicast”. In this protocol, it is assumed that the area around the moving target, called the delivery zone, is known at any given time during the operation of the network. The aim of the protocol is to awake sensor nodes, which will be within the delivery zone in the near future, to be prepared for tracking the approaching moving target. In this paper, we propose a novel mobicast algorithm, aiming at reducing the number of awakened sensor nodes. To this end, we equipped every sensor node with a learning automaton, which helps the node in determining the sensor nodes it must awaken. To evaluate the performance of the proposed algorithm, several experiments have been conducted. The results have shown that the proposed algorithm can significantly outperform other existing algorithms such as forward-zone constrained and FAR in terms of energy consumption, number of active nodes, number of exchanged packets and slack time.     

All two-input logic gates by a single-stage single electron box

In this paper, the design of all two-input logic gates is presented by only a single-stage single electron box (SEB) for the first time. All gates are constructed based on a same circuit. We have used unique periodic characteristics of SEB to design these gates and present all two-input logic gates (monotonic/non-monotonic, symmetric/non-symmetric) by a single-stage design. In conventional monotonic devices, such as MOSFETs, implementing non-monotonic logic gates such as XOR and XNOR is impossible by only a single-stage design, and a multistage design is required which leads to more complexity, higher power consumption and less speed of the gates. We present qualitative design at first and then detailed designs are investigated and optimised by using our previous works. All designs are verified by a single electron simulator which shows correct operation of the gates.     

Automatic facial expression recognition: feature extraction and selection

In this paper, we investigate feature extraction and feature selection methods as well as classification methods for automatic facial expression recognition (FER) system. The FER system is fully automatic and consists of the following modules: face detection, facial detection, feature extraction, selection of optimal features, and classification. Face detection is based on AdaBoost algorithm and is followed by the extraction of frame with the maximum intensity of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features using different methods including: Gabor filters, log Gabor filter, local binary pattern (LBP) operator, higher-order local autocorrelation (HLAC) and a recent proposed method called HLAC-like features (HLACLF). The most informative features are selected based on both wrapper and filter feature selection methods. Experiments on several facial expression databases show comparisons of different methods.     

Engineering ATM networks for congestion avoidance

With the combination of telecommunication, entertainment and computer industries, computer networking is adopting a new method called Asynchronous Transfer Mode (ATM) networking. Congestion control plays an important role in the effective and stable operation of ATM networks. Traffic management concerns with the design of a set of mechanisms which ensure that the network bandwidth, buffer and computational resources are efficiently utilized while meeting the various Quality of Service (QoS) guarantees given to sources as part of a traffic contract. In this paper, the most widely recognized congestion control schemes for ABR service are investigated. Some of these schemes show either lack of scalability or fairness while other well‐behaved schemes may require a highly complex switch algorithm that is unsuitable for implementation in cell‐switching high‐speed ATM networks. A new and improved congestion control scheme is proposed to support the best‐effort ABR traffic. This algorithm provides the congestion avoidance ability with high throughput and low delay, in addition to achieving the max–min fairness allocation. This revised version was published online in June 2006 with corrections to the Cover Date.     

A self‐adaptive algorithm for super‐peer selection considering the mobility of peers in cognitive mobile peer‐to‐peer networks

Cognitive peer‐to‐peer networks are obtained from a combination of cognitive networking concepts and peer‐to‐peer networks. These networks are able to improve their performance while operating under dynamic and unknown environments. A cognitive peer‐to‐peer network tries to learn an appropriate configuration for itself considering the unknown physical properties of peers. Cognitive mobile peer‐to‐peer networks refer to cognitive peer‐to‐peer networks which are built over mobile ad hoc networks. In these networks, heterogeneity of the mobility of peers and resource limitation in wireless networks create challenges for network management algorithms. Because of the dynamicity of these networks, the management algorithms should be designated in self‐adaptive manner. In one type of these networks, some peers, called super‐peers, undertake to perform network managerial tasks. The mobility of peers leads to connection failure among peers and reselection of new super‐peers. Therefore, the selection of super‐peers, due to their influential role, requires an algorithm that considers the peers' mobility. Up to now, no self‐adaptive algorithm has been designated for super‐peer selection considering the mobility of peers in a self‐adaptive manner. This paper proposes M‐SSBLA, a self‐adaptive algorithm for super‐peer selection considering the mobility of peers based on learning automata. The proposed algorithm is obtained from cooperation between a learning automata‐based cognitive engine and MIS. MIS is a well‐known super‐peer selection algorithm in mobile peer‐to‐peer networks. We compared the proposed algorithm with recently reported algorithms, especially for a network with high mobility. Simulation results show that the proposed algorithm can cover maximum ordinary‐peer with a few super‐peer and improve robustness against super‐peer failures while decreasing maintenance overhead.     

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

     

An Improvement Energy Consumption Policy Using Communication Reduction in Wireless Body Sensor Network

Dedicated short-range communications (DSRC) is an important wireless technology for current and future automotive safety and mitigation of traffic jams. In this work, we have designed a Coplanar waveguide microstrip patch antenna with linear, upper and bottom and side slots for application in DSRC. The patch antenna was designed using glass epoxy substrate (FR4). Various parametric analyses such as the current distribution, reflection coefficient, radiation pattern on E- and H-plane as well as the realized gain (dB) were performed. The results were obtained by simulation using high-frequency structure simulator tool. The proposed antenna covers a frequency band of 5.8–5.9 GHz which is highly dedicated to the DSRC wireless communication technology for enhancement of safety of the automotive transport system. The designed antenna shows a good return loss of ??19 dB at 5.9 GHz.The designed antenna shows a promising gain, return loss and radiation pattern for use in DSRC for automotive transport systems.