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

In the Cycle Packing problem, we are given an undirected graph G, a positive integer r, and the task is to check whether there exist r vertex-disjoint cycles. In this paper, we study Cycle Packing with respect to a structural parameter, namely, distance to proper interval graphs (indifference graphs). In particular, we show that Cycle Packing is fixed-parameter tractable (FPT) when parameterized by t, the size of a proper interval deletion set. For this purpose, we design an algorithm with O(2 O ( t log t )nO ( 1 )) running time. Bodlaender et al. (Theor Comput Sci 511:117–136, 2013) studied several structural parameterizations for Cycle Packing and our FPT algorithm fills a gap in their ecology of parameterizations. We combine color coding, greedy strategy and dynamic programming based on structural properties of proper interval graphs in a non-trivial fashion to obtain the FPT algorithm. Our belief is that this approach is quite general and can be useful in solving many other problems with the same parameterization. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Department of Computer Science and Engineering

World University root node

Algorithmica

The Parameterized Complexity of Cycle Packing: Indifference is Not an Issue

The control of microbiologically influenced corrosion (MIC) is of great significance in many industrial applications. Attention has been devoted to emerging nanomaterials as ‘green' biocides for their remarkable antimicrobial function and relatively low environmental risk. Understanding the antimicrobial inhibition mechanisms is the key to increase the efficiency of nanoparticles and enhance the feasibility of their application against various MIC microorganisms under different environmental conditions. This study gives an overview of the recent advancements of different nanomaterials and nanocomposites used as biocides for the inhibition of MIC. The potential and associated challenges in developing NPs as effective biocides are highlighted.

Biological Sciences and Engineering

Current Opinion in Chemical Engineering

Recent advancements of nanomaterials as coatings and biocides for the inhibition of sulfate reducing bacteria induced corrosion

Phased array weather radars (PAWRs) are increasingly becoming viable because of their operational advantage in the agile scanning of large precipitation volumes without any mechanical motion and an efficient beam scheduling-and-tracking. Conventional PAWR estimates precipitation profiles through Fourier beamforming. However, this method is not robust to interference, clutter and mismatch in steering vectors. In this work, we offset these disadvantages by using advanced robust and adaptive beamformers in PAWR. The volumetric nature of the precipitation target makes direct application of these techniques to PAWR non-trivial. We validate our methods through data collected from the solid-state low-power X-band PAWR developed at IIT Palakkad. © 2019 URSI. All rights reserved.

Department of Electrical Engineering

2019 URSI Asia-Pacific Radio Science Conference, AP-RASC 2019

Robust beamforming for X-band phased array weather radar

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.

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Robust proportionate adaptive filter architectures under impulsive noise

State-dependent jump linear systems (SJLSs) are a set of linear systems whose switching is determined by a finite state random process with state-dependent transition rates. In this study, a SJLS is considered with state multiplicative noise and stochastic perturbations. In particular, the jump process is regarded to have dissimilar transition rates among sets of state evolution space. The aim of this study is to consider stochastic ℋ∞ control of such systems using state-feedback control input. Sufficient conditions for stochastic ℋ∞ are obtained by solving linear matrix inequalities, which are validated by a simulation example. © The Institution of Engineering and Technology 2018.

Fulltext

IET Control Theory and Applications

Stochastic ℋ∞ control of state-dependent jump linear systems with state-dependent noise

For a linear system having event based measurements and correlated noises, a state estimation algorithm is proposed. A general event based sampling is employed to obtain the measurements, where in the case of unavailability of measurements, event based strategy itself is used to obtain approximate state and covariance estimates. To deal with correlated noises, a two-step ahead prediction approach is employed to obtain recursive equations for estimated state and covariance. The obtained results are illustrated using a simulation example. © 2019

Journal of the Franklin Institute

Event based estimation with correlated noises

Wideband Rate Adaptation and Scheduling in OFDM Cellular Systems: Modeling, Analysis and Base Station Side Estimation

Most on-device and cloud processing based automatic speech recognition (ASR) systems had poor recognition performance due to the noisy speech signals corrupted by various kinds of background noises such as vehicle, train, aircraft, fan, wind, rain, air-conditioner, and machinery noises which are unavoidable realistic scenarios. In this paper, we propose a novel speech signal quality assessment (SSQA) method for automatically assessing the quality of a recorded speech signal before processing on-device and sending the recorded data to the cloud server. The proposed method is based on the spectrogram feature and two-dimensional convolutional neural networks (2D-CNNs). The proposed SSQA method is evaluated using a large scale of noise-free speech and noisy speech signals which are corrupted with various kinds of noises with different noise levels. Results show that the 2D-CNN based method had an average Se=90.92%, Sp=98.44% and OA =96.44%. The method had better results in detecting the noisy speech segments. Results showed that there is confusion in performing the manual labelling of noise-free and noisy speech segments. Therefore, the noise-free and noisy speech signals are given to the publicly available ASR system to obtain the corresponding text. Then the word error rate (WER) and character error rate (CER) metrics were used to know the level of noise wherein the ASR system fails to correctly recognize its text. In this way, the noise level is determined for each of the noises to label the recorded speech signal into acceptable and unacceptable speech segments. The proposed quality-aware ASR system has great potential in improving the lifetime of the battery of the portable ASR devices and reducing the bandwidth and speech recognition software utilization costs in the case of cloud processing based ASR system. © 2019 IEEE.

Proceedings - 2019 7th International Conference on Digital Information Processing and Communications, ICDIPC 2019

SSQA: Speech Signal Quality Assessment Method using Spectrogram and 2-D Convolutional Neural Networks for Improving Efficiency of ASR Devices

In this paper, specific emitter identification(SEI) problem has been investigated over fading channel. Different emitters are distinguished by the non-linear nature of the power amplifiers used by the emitters. This non-linearity is the fingerprint for the specific emitters. Based on this non-linearity SEI problem is solved. In this paper, we have considered several features like energy entropy, first and second order moment and correlation coefficient, which are calculated from the intrinsic mode functions(IMFs) obtained by using Variational mode decomposition(VMD). The SEI performance using EMD-EM2 method is compared with VMD-EM2 over fading channel, where entropy and first, second order moments are calculated from the Hilbert spectrum. The performance in terms of probability of correct identification for the second method is better than the first method for both single-hop and relaying case. Also the performance of VMD-CB is superior to VMD-EM2. © 2019 IEEE.

1st International Conference on Range Technology, ICORT 2019

Specific Emitter Identification over Fading Channels

This brief presents a new real-time photoplethysmogram (PPG) signal quality assessment system for significantly reducing an overall energy consumption and reducing frequent false alarms. The proposed algorithm is based on the hierarchical decision rules by combining with simple features, such as absolute amplitude, threshold crossing rate, and autocorrelation function features. The algorithm is validated on both MATLAB software and Arduino Due with 32-bit ARM core microcontroller by using the standard PPG datasets and our real-time PPG datasets. The algorithm achieved a sensitivity of 99.29\%, specificity of 95.31\% and accuracy of 97.76\% with 754 false positive segments and 182 false negative segments of a total of 16 068 unacceptable and 25 772 acceptable segments, respectively. The algorithm provides a promising false alarm reduction rate of 95.31\% on different kinds of artifacts and noise. The proposed quality-aware monitoring system can save transmission energy consumption from 3.43\% to 95.1\% for transmission of noisy 5-60 s PPG signal.