Efficient Transmitter/Receiver Architectures for High Performance Wireless Applications

The thesis focuses on efficient design methods and reconfiguration architectures suitable for higher performance wireless communication .The work presented in this thesis describes the development of compact,inexpensive and low power communication devices that are robust,testable and capable of handling multiple communication standards.A new multistandard Decimation Filter Design Toolbox is developed in MATLAB GUIDE environment.RNS based dual-mode decimation filters reconfigurable for WCDMA/WiMAX and WCDMA/WLANa standards are designed and implemented.It offers high speed operation with lesser area requirement and lower dynamic power dissipation.A novel sigma-delta based direct analog-to-residue converter that reduces the complexity of RNS conversion circuitry is presented.The performance of an OFDM communication system with a new RRNS-convolutional concatenated coding is analysed and improved BER performance is obtained under different channel conditions. Easily testable MAC units for filters are presented using Reed-Muller logic for realization.

Complexity quantification of dense array EEG using sample entropy analysis

n this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.