Syed Ismail Shah

Techniques to obtain good resolution and concentrated time-frequency distributions: a review

We present a review of the diversity of concepts and motivations for improving the concentration and resolution of timefrequencydistributions (TFDs) along the individual components of the multi-component signals. The central idea has been to obtain a distribution that represents the signal’s energy concentration simultaneously in time and frequency without blur andcrosscomponents so that closely spaced components can be easily distinguished. The objective is the precise description of spectralcontent of a signal with respect to time, so that first, necessary mathematical and physical principles may be developed, andsecond, accurate understanding of a time-varying spectrum may become possible. The fundamentals in this area of research havebeen found developing steadily, with significant advances in the recent past.

An Application of GA for Symbol Detection in MIMO Communication Systems

Multi-input multi-output (MIMO) based communication system architecture promises increased capacity and high data rates. Increase in the number of transmit antennas and using higher order complex modulation schemes achieves even higher performance but with exponentially increasing complexity at the receiver end. This paper explores the application of genetic algorithm (GA) for reducing complexity in solving this NP hard problem. This approach is particularly attractive as GA is well suited for physically realizable, real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exhaustive search method is prohibitively complex, simulation results show that the GA optimized MIMO detection algorithm results in near optimal bit error rate (BER) performance, with significantly reduced complexity. Results also suggest that the GA based MIMO detection out-performs the Vertical Bell labs Layered Space Time (V-BLAST) detector in BER performance without severely increasing the systems complexity.

A particle swarm algorithm for symbols detection in wideband spatial multiplexing systems

This paper explores the application of the particle swarm algorithm for a NP-hard problem in the area of wireless communications. The specific problem is of detecting symbols in a Multi-Input Multi-Output (MIMO) communications system. This approach is particularly attractive as PSO is well suited for physically realizable, real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal Maximum Likelihood (ML) detection using an exhaustive search method is prohibitively complex, we show that the Swarm Intelligence (SI) optimized MIMO detection algorithm gives near-optimal Bit Error Rate (BER) performance in fewer iterations, thereby reducing the ML computational complexity significantly. The simulation results suggest that the proposed detector gives an acceptable performance complexity trade-off in comparison with ML and VBLAST detector.

High-resolution time-frequency methods performance analysis

This work evaluates the performance of high-resolution quadratic time-frequency distributions (TFDs) including the ones obtained by the reassignment method, the optimal radially Gaussian kernel method, the t-f autoregressive moving-average spectral estimation method and the neural network-based method. The approaches are rigorously compared to each other using several objective measures. Experimental results show that the neural network-based TFDs are better in concentration and resolution performance based on various examples.

PAPR reduction by using discrete wavelet transform

An alternative to the conventional orthogonal frequency division multiplexing (OFDM) scheme is to exploit the self and mutual orthogonality properties of wavelet packet basis function for multiplexing purposes. These systems are known as discrete wavelet transform based OFDM (DWT-OFDM) systems. In this paper a DWT-OFDM transceiver is implemented and the peak to average power ratio (PAPR) in DWT-OFDM signal is analysed. Simulations are carried out to select the best wavelet packet basis function to decrease the PAPR

Impact of Varying Neurons and Hidden Layers in Neural Network Architecture for a Time Frequency Application

In this paper, an experimental investigation is presented, to know the effect of varying the number of neurons and hidden layers in feed forward back propagation neural network architecture, for a time frequency application. Varying the number of neurons and hidden layers has been found to greatly affect the performance of neural network (NN), trained via various blurry spectrograms as input over highly concentrated time frequency distributions (TFDs) as targets, of the same signals. Number of neurons and hidden layers are varied during training and the impact is observed over test spectrograms of unknown multi component signals. Entropy and mean square error (MSE) is the decision criteria for the most optimum solution.

Minimum bit error rate multiuser detection for OFDM-SDMA using particle swarm optimization

The Minimum Bit Error Rate (MBER) detectors outperform the conventional Minimum Mean Squared Error (MMSE) detector by minimizing the Bit Error Rate (BER) directly. In this paper an MBER multiuser detector for Orthogonal Frequency Division Multiplexing-Space Division Multiple Access (OFDM-SDMA) system is proposed employing Particle Swarm Optimization (PSO) for finding the optimum weight vectors. Simulation results show that the proposed system achieves faster convergence with lower complexity as compared to Genetic Algorithms (GA) with same Bit Error Rate (BER) performance.

Diagnosis of liver disease induced by hepatitis virus using Artificial Neural Networks

This paper presents an artificial neural network based approach for the diagnosis of hepatitis virus. The dataset used for this purpose is taken from the UCI machine learning database. Both supervised and unsupervised neural network models have been analyzed with different architectures, learning and activation functions. It is concluded that the supervised model performed better than the unsupervised one. The paper also compares the results of the previous studies on the diagnosis of hepatitis which use the same dataset.

Validity-guided fuzzy clustering evaluation for neural network-based time-frequency reassignment

This paper describes the validity-guided fuzzy clustering evaluation for optimal training of localized neural networks (LNNs) used for reassigning time-frequency representations (TFRs). Our experiments show that the validity-guided fuzzy approach ameliorates the difficulty of choosing correct number of clusters and in conjunction with neural network-based processing technique utilizing a hybrid approach can effectively reduce the blur in the spectrograms. In the course of every partitioning problem the number of subsets must be given before the calculation, but it is rarely known apriori, in this case it must be searched also with using validity measures. Experimental results demonstrate the effectiveness of the approach.

Joint rate and cooperative MIMO scheme optimization for uniform energy distribution in Wireless Sensor Networks

An energy efficient adaptive rate cooperative MIMO selection scheme is proposed for uniform load distribution in the cluster based wireless sensor networks. The intrinsic data flow direction in multi-hop cluster based sensor networks causes uneven load distribution in the network. The transit clusters and the clusters near the base station carry more network traffic than the other clusters. Cooperative MIMO can artistically reduce the per bit energy consumption, Space-Time Block Codes are designed to achieve maximum diversity for a given number of transmit and receive antennas with very simple decoding algorithm. In radio fading channel, STBC require less transmission energy than SISO technique for the same Bit Error Rate and can be employed practically in Wireless Sensor Networks by using the cooperative MIMO scheme. Considering Alamouti and Tarokh Space-Time Block Codes, the number of antennas at both the transmission and the reception sides are selected with respect to the cluster load. The crude energy consumption per cluster then refined through adaptive rate transmission. It has been shown that the load based joint adaptive selection of rate and cooperative nodes in clusters renders uniform energy consumption in the network.