Vinay Chakravarthi Gogineni

Performance analysis of proportionate-type LMS algorithms

For real-time sparse systems identification applications, Proportionate-type Least Mean Square (Pt-LMS) algorithms are often preferred to their normalized counterparts (Pt-NLMS) due to lower computational complexity of the former algorithms. In this paper, we present the convergence analysis of Pt-LMS algorithms. Without any assumptions on input, both first and second order convergence analysis are carried out and new convergence bounds are obtained. In particular, it establishes the universality of the steady-state mean square deviation. Detailed simulation results are presented to validate the analytical results.

An adaptive convex combination of APA and ZA-APA for identifying systems having variable sparsity and correlated input

Abstract In this paper, we present an efficient algorithm for identifying and tracking the impulse response of a sparse system that exhibits time varying sparseness and is driven by correlated input. The proposed method convexly combines the outputs of two filters, namely, the sparsity unaware affine projection algorithm (APA), and the sparsity aware zero attracting affine projection algorithm (ZA-APA), each trying to identify the same system using the same input. The combining parameter is adapted by following a steepest descent of the error variance at the convex combination output. A detailed performance analysis of the proposed combination is carried out, which reveals that while for highly non-sparse and highly sparse systems, the proposed combination converges respectively to the APA and ZA-APA (i.e., better of the two filters under the given levels of sparsity), for certain sparsity ranges, it leads to a combination filter that performs better than both the constituent filters. The claims made are validated by exhaustive simulation studies using white, colored as well as speech inputs.

Improved proportionate-type sparse adaptive filtering under maximum correntropy criterion in impulsive noise environments

Abstract An improved proportionate adaptive filter based on maximum correntropy criterion (IP-MCC) is proposed for identifying systems with variable sparsity in an impulsive noise environment. Utilization of the MCC mitigates the effect of the impulsive noise while improved proportionate concept exploits the underlying system sparsity to improve the convergence rate. Performance analysis of the proposed IP-MCC reveals that the steady-state excess mean square error (EMSE) of the proposed IP-MCC filter is similar to that of MCC filter. Extensive simulations demonstrate that the proposed IP-MCC outperforms state-of-the-art algorithms in terms of convergence rate and detailed complexity analysis reveals that IP-MCC requires much less computational effort.

A novel framework for compressed sensing based scalable video coding

Abstract Considering high throughput values as specified by modern video processing standards, Scalable Video Coding (SVC) systems intended for such standards are generally implemented by means of dedicated hardware. However, the high computational complexity associated with the current Compressed Sensing (CS) based video coding schemes makes their hardware realization considerably challenging. In this paper, we present a novel CS based SVC framework that is amenable to real-time VLSI implementation. At the encoder, after applying the Three-Dimensional Discrete Wavelet Transform (3-D DWT) on the input video frames, a novel Adaptive Measurement Scheme (AMS) in CS is introduced, which is applied on the high frequency sub-bands of the 3-D DWT frames. The proposed AMS along with 3-D DWT not only achieves scalability and better compression ratio, but also reduces the overall computational complexity of the system. We have also proposed an Enhanced Approximate Message Passing (EAMP) algorithm to reconstruct the high frequency sub-bands from the CS measurements at the decoder. The proposed EAMP procedure combines the benefits of Approximate Message Passing (AMP) and Iterative Hard Thresholding (IHT) algorithms thereby simultaneously achieving sparsity measurement trade-off and good reconstruction quality. We have carried out the detailed complexity analysis and simulations to demonstrate the superiority of the proposed framework over the existing schemes.

Algorithm and Architecture Design of Adaptive Filters With Error Nonlinearities

This paper presents a framework based on the logarithmic number system to implement adaptive filters with error nonlinearities in hardware. The framework is demonstrated through pipelined implementations of two recently proposed adaptive filtering algorithms based on logarithmic cost, namely, least mean logarithmic square (LMLS) and least logarithmic absolute difference (LLAD). To the best of our knowledge, the proposed architectures are the first attempts to implement both LMLS and LLAD algorithms in hardware. We derive error computing algorithms to realize the nonlinear error functions for LMLS and LLAD and map them onto hardware. We also propose a novel variable-

Proportionate-type hard thresholding adaptive filter for sparse system identification

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Distributed Multi-task APA over Adaptive Networks Based on Partial Diffusion.

scheme to enhance the original LMLS algorithm and prove its robustness and suitability for VLSI implementations in practical applications. Detailed bit width and error analysis are carried out for the proposed VLSI fixed point implementations. Postlayout implementation results show that with an additional multiplier over conventional least mean square (LMS), 7-dB improvement in steady-state mean square deviation performance can be achieved and with the proposed variable-

Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm.

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Algorithm and VLSI Architecture Design of Proportionate-Type LMS Adaptive Filters for Sparse System Identification

scheme, 12-dB improvement can be achieved without compromising the convergence. We will show that LMLS can potentially replace LMS in practical applications, by demonstrating a proof-of-concept by extending the framework to transform domain adaptive filters.

Logarithmic Cost Based Constrained Adaptive Filtering Algorithms for Sensor Array Beamforming

Recently proposed Hard Thresholding based Adaptive Filtering (HTAF) algorithm provides an on-line counterpart of a compressed sensing based greedy sparse recovery algorithm called iterative hard thresholding (IHT) by constructing a sliding-window based cost function. This leads to an adaptive algorithm with data reuse gradient term (i.e. with multi-regressors) followed by a fixed hard thresholding operator. The HTAF algorithm achieves both robustness against colored input (due to the data reuse in gradient update) and smaller steady state error (due to hard thresholding operator) while identifying a sparse system. In this paper, we propose a new sparse adaptive technique called Proportionate type Hard Thresholding Adaptive Filter (PtHTAF) using a proportionate-type gradient update followed by a variable hard thresholding operator. The proposed PtHTAF algorithm enjoys faster initial convergence rate (due to proportionate type gradient update) while maintaining low steady-state excess mean square error like the HTAF. Simulation results establish superiority of the proposed algorithm over existing sparse adaptive algorithms.