Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

Conventional adaptive filters, which assume Gaussian distribution for signal and noise, exhibit significant performance degradation when operating in non-Gaussian environments. Recently proposed fractional-order adaptive filters (FoAFs) address this concern by assuming that the signal and noise are symmetric α-stable random processes. However, the literature does not include any VLSI architectures for these algorithms. Toward that end, this article develops hardware-efficient architecture for fractional-order correntropy adaptive filter (FoCAF). We first reformulate the FoCAF for its efficient real-time VLSI implementation and then demonstrate that these reformulations cause negligible performance degradation under the 16-bit fixed-point implementation.

World University root node

Department of Electrical Engineering

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Novel VLSI Architecture for Fractional-Order Correntropy Adaptive Filtering Algorithm

Detection of a weak signal in additive Generalized Cauchy (GC) noise is important in many applications. The locally optimum detector (LOD) for a weak signal in GC noise is nonlinear in nature. When noise variance is unknown, the maximum likelihood estimator (MLE) is nonlinear and the resultant detector is complicated. Since VLSI implementation of complex nonlinearities is a challenging task, we develop an order statistics framework for detection in GC noise. We propose linear and ratio detectors, for weak signals in GC noise with known and unknown but deterministic variance, respectively. We provide extensive simulation results to show that performance loss of proposed linear and ratio detectors, is very small compared to LOD and nonlinear detector using MLE, respectively. We propose SORT - N (for running ordering of samples) and its VLSI architecture, using which we develop two-stage VLSI architectures for proposed linear and ratio detectors. Synthesis results for arbitrary waveform case indicate, for same throughput and latency, linear detector consumes lesser area to LOD, upto a sample size of N=64. For same throughput, ratio detector renders substantial area savings (≈50%) over nonlinear detector using MLE, for any N. Finally, we propose a reconfigurable architecture for efficient realization of all the detectors. © 2004-2012 IEEE.

IEEE Transactions on Circuits and Systems I: Regular Papers

Area Efficient VLSI Architectures for Weak Signal Detection in Additive Generalized Cauchy Noise

This brief proposes robust adaptive filtering algorithms and their VLSI architectures for sparse system identification under impulsive noise. Several robust algorithms are derived by combining error nonlinear adaptive filtering algorithms with proportionate adaptation. We make a comparative study of the derived algorithms and their VLSI architectures in terms of convergence rate and hardware complexity to show that the hardware overhead is negligible for the achieved improvement in robustness. © 1993-2012 IEEE.

Robust proportionate adaptive filter architectures under impulsive noise

This paper introduces a novel family of constrained adaptive filtering algorithms for sensor array beamforming. These algorithms, namely, constrained least mean logarithmic square (CLMLS), ℓ1-norm CLMLS (ℓ1-CLMLS) and its weighted version ℓ1-WCLMLS, are developed based on a relative logarithmic cost function. The proposed algorithms gracefully adjust cost function depending on the amount of the error thereby achieving better performance compared to constrained least mean square (CLMS) family of algorithms. The transient and steady-state performance analysis of the proposed CLMLS algorithm is presented and these analytical results are validated through extensive simulations. Proposed CLMLS algorithm is then extended to sparse system identification problem by incorporating the ℓ1-norm penalty into CLMLS cost function. We show that the resultant ℓ1-CLMLS and ℓ1-WCLMLS algorithms outperform their CLMS counterparts in sparse system identification. When applied to sparse sensor array synthesis, these algorithms achieve desired beampattern with lesser number of sensor elements compared to state-of-the-art algorithms. © 2001-2012 IEEE.

IEEE Sensors Journal

Logarithmic cost based constrained adaptive filtering algorithms for sensor array beamforming

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. © 2018 Elsevier Inc.

Digital Signal Processing: A Review Journal

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

Proportionate-type normalized LMS (Pt-NLMS) family of adaptive filtering algorithms for sparse system identification pose significant implementation challenges due to their high computational complexity especially for real-time applications like network echo cancelation. In this paper, we make the first attempt to implement Pt-NLMS algorithms in hardware. Several reformulations are proposed to simplify the original Pt-NLMS algorithms, thereby making them amenable to real-time VLSI implementations and the reformulated algorithms referred as delayed μ-law proportionate LMS (DMPLMS) algorithm for white input and delayed wavelet MPLMS (DWMPLMS) for colored input are then implemented in hardware. Simulation studies demonstrate that the performance loss is very small for the proposed reformulations. We implemented the proposed designs considering 16-bit fixed point representation in hardware, and synthesis results show that the DMPLMS architecture with 30% increase in hardware over the state-of-the-art conventional delayed LMS architecture achieves 3 × improvement in convergence rate for white input and the DWMPLMS architecture with 70\% increase in hardware achieves 10 × improvement in convergence rate for correlated input conditions. © 1993-2012 IEEE.

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

arXiv e-prints, arXiv: 1711.

A Family of Constrained Adaptive filtering Algorithms Based on Logarithmic Cost

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. © 2017 Elsevier B.V.

Signal Processing: Image Communication

A novel framework for compressed sensing based scalable video coding

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-α 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-α 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. © 2017 IEEE.

Algorithm and Architecture Design of Adaptive Filters with Error Nonlinearities

The detection of a weak signal in additive Cauchy noise is of great importance in many applications. A locally optimum detector (LOD) exists for such a scenario; however, it is non-linear in nature. In general, implementation of non-linear detectors is difficult in practice, and linear detectors with good properties, such as high asymptotic relative efficiency (ARE) with respect to the LOD, are often desirable. In this paper, we propose a linear detector for a weak signal in additive Cauchy noise. The proposed test statistic is a linear combination of order statistics. For the special case of a constant signal in additive Cauchy noise, we prove the asymptotic normality of the trimmed linear detector, and show that the ARE of the trimmed linear detector with respect to the LOD is unity. Extensive simulation results are provided to demonstrate that the loss in the performance of the linear detector is very small compared with the non-linear LOD. We also discuss the hardware complexities of the LOD and the linear detector, and demonstrate the advantages of the linear detector over the LOD, in terms of hardware implementation. © 1972-2012 IEEE.

IEEE Transactions on Communications

Linear detection of a weak signal in additive cauchy noise

arXiv e-prints, arXiv: 1707.

Proportionate Adaptive Filtering under Correntropy Criterion in Impulsive Noise Environments

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.