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

This paper proposes a new method of generating higher number of levels in the voltage waveform by stacking multilevel converters with lower voltage space vector structures. An important feature of this stacked structure is the use of low voltage devices while attaining higher number of levels. This will find extensive applications in electric vehicles since direct battery drive is possible. The voltages of all the capacitors in the structure can be controlled within a switching cycle using the switching state redundancies (pole voltage redundancies). This helps in reducing the capacitor size. Also, the capacitor voltages can be balanced irrespective of modulation index and load power factor. To verify the concept experimentally, a nine-level inverter is developed by stacking two five-level inverters and an induction motor is run using V/f control scheme. Both steady state and transient results are presented.

World University root node

Department of Electrical Engineering

IEEE Transactions on Power Electronics

Generation of Higher Number of Voltage Levels by Stacking Inverters of Lower Multilevel Structures with Low Voltage Devices for Drives

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

The purpose of this article is to illustrate the role of connections and symmetries in the Wheeled Inverted Pendulum (WIP) mechanism—an underactuated system with rolling constraints—popularized commercially as the Segway, and thereby arrive at a set of simpler dynamical equations that could serve as the starting point for more complex feedback control designs. The first part of the article views the nonholonomic constraints enforced by the rolling assumption as defining an Ehresmann connection on a fiber bundle. The resulting equations are the reduced Euler Lagrange equations, which are identical to the Lagrange–d’Alembert equations of motion. In the second part, we explore conserved quantities, in particular, nonholonomic momenta. To do so, we first introduce the notion of a symmetry group, whose action leaves both the Lagrangian and distribution invariant. We examine two symmetry groups—SE(2) and SE(2) × S1. The first group leads to the purely kinematic case while the second gives rise to nonholonomic momentum equations. © 2017, Springer Science+Business Media B.V.

Fulltext

Nonlinear Dynamics

Symmetries in the wheeled inverted pendulum mechanism

This work presents wide range of compact and reliable single-pole-multi-throw (SPMT) radio frequency (RF) microelectromechanical system (MEMS) switches where the M (output) varies from 3 to 14 throws. The single dc-contact switch dimensions are 0.144 mm × 0.29 mm which are fabricated on 635 m alumina substrate using a surface micromachining process. SPMT switching networks demonstrate a measured return loss of more than 14 dB, a worst case insertion loss of ∼1.76 dB and isolation of ∼14.5 dB up to 12 GHz. The maximum area of the fabricated SPMT switch is ∼1.2 mm2. The SPMT switches are capable of handling 1 W of RF power up to &gt;1 billion cycles at 25 °C, and sustained even up to &gt;80 million cycles with 0.5 W at 85 °C. To the best of our knowledge, this is the first reported wide range of MEMS SPMT switches and their respective performance evaluations in the literature that has undergone extensive measurement stages. © 2016 IOP Publishing Ltd.

Journal of Micromechanics and Microengineering

Extensive performance evaluations of RF MEMS single-pole-multi-throw (SP3T to SP14T) switches up to X-band frequency

Heart sound segmentation is the process of identifying the various events like S1, S2, S3, S4, and murmurs present in the phonocardiogram (PCG) signal. In this paper, a heart sound segmentation algorithm for segmenting S1 and S2 heart sound which corresponds to the systolic and diastolic activity of the heart is presented. The proposed method is based on variational mode decomposition (VMD) algorithm. The recorded PCG is decomposed into various modes using VMD. A novel criterion is proposed to select an appropriate mode which is based on center frequency and relative energy. Then the Shannon entropy envelope of the selected mode is computed. S1 and S2 heart sound is segmented by applying dynamic threshold on the Shannon entropy envelope. The segmentation algorithm is tested on both available standard database (Physionet, Pascal, Michigan, and eGeneralMedical) and recorded signal. The simulation results demonstrate that the proposed method achieved the sensitivity, positive predictivity, and the accuracy of above 95% with detection error rate of below 6%. © 2017 IEEE.

IEEE Region 10 Annual International Conference, Proceedings/TENCON

S1 and S2 heart sound segmentation using variational mode decomposition

In this paper, we present a new method based on sparse signal decomposition for effectively removing baseline wander (BW) noise from the ECG signal. Our proposed method not only eliminates BW noise from ECG signal but also preserves the morphological shape of local waves of the signal. Our proposed method is tested and validated on real ECG signals taken from MIT-BIH arrhythmia database. Comparative results depict the superior performance of the proposed method over the state of art methods. © 2016 IEEE.

A robust sparse signal decomposition framework for baseline wander removal from ECG signal

Objective: Automatic detection and classification of noises can play a vital role in the development of robust unsupervised electrocardiogram (ECG) analysis systems. This paper proposes a novel unified framework for automatic detection, localization, and classification of single and combined ECG noises. Methods : The proposed framework consists of the modified ensemble empirical mode decomposition (CEEMD), the short-term temporal feature extraction, and the decision-rule-based noise detection and classification. In the proposed framework, ECG signals are first decomposed using the modified CEEMD algorithm for discriminating the ECG components from the noises and artifacts. Then, the short-term temporal features such as maximum absolute amplitude, number of zerocrossings, and local maximum peak amplitude of the autocorelation function are computed from the extracted high-frequency and low-frequency signals. Finally, a decision rule-based algorithm is presented for detecting the presence of noises and classifying the processed ECG signals into six signal groups: noise-free ECG, ECG+BW, ECG+MA, ECG+PLI, ECG+BW+PLI, and ECG+BW+MA. Results: The proposed framework is rigorously evaluated on five benchmark ECG databases and the real-time ECG signals. The proposed framework achieves an average sensitivity of 99.12%, specificity of 98.56%, and overall accuracy of 98.90% in detecting the presence of noises. Classification results show that the framework achieves an average sensitivity, positive predictivity, and classification accuracy of 98.93%, 98.39%, and 97.38%, respectively. Conclusion: The proposed framework not only achieves better noise detection and classification rates than the current state-of-the-art methods but also accurately localizes short bursts of noises with low endpoint delineation errors. Significance: Extensive studies on benchmark databases demonstrate that the proposed framework is more suitable for reducing false alarm rates and selecting appropriate noise-specific denoising techniques in automated ECG analysis applications. © 2013 IEEE.

IEEE Journal of biomedical and health informatics

Automated ECG noise detection and classification system for unsupervised healthcare monitoring

PCG is one of the most widely used physiological signals for cardiac monitoring due to its simplicity. PCG segmentation involves identifying or classifying S1 and S2 sounds that correspond to systolic and diastolic periods respectively. There are many PCG segmentation algorithms that can identify the start and end time instants of S1 and S2 sounds. However, most of the algorithms distinguish S1 and S2 sounds based on the time duration threshold, which may fail for abnormal patients. In order to solve this ambiguity, multi-channel approaches are used. In this work, a hardware for simultaneous acquisition of PCG and PPG (from the fingertip of the subject) is developed. Then by using the proposed low complex PCG and PPG processing algorithms, the correlation of occurrence of S2 event of PCG with the systolic peak point and foot point of PPG is analyzed. The correlation parameters of PCG and PPG are reported and validated with the existing multi-channel approaches. The simulation results performed on real-time database demonstrates that the proposed S2 sound detection method achieved the sensitivity of 98.23%, positive predictivity of 99.64%, and the accuracy of 94.50%. The detection error rate of proposed S2 sound detection algorithm is 7.07%. © 2017 IEEE.

TENCON 2017-2017 IEEE Region 10 Conference

Real-time detection of S2 sound using simultaneous recording of PCG and PPG

This book provides comprehensive knowledge on diseases in livestock that are caused by viruses, parasites and bacteria. Emerging and re-emerging pathogens are presented in detail for various animal groups and in-depth insights into pathogenesis and epidemiology will be provided for each of them. In addition, state-of-the-art treatment possibilities, control measures as well as vaccination strategies are discussed. The recent years have witnessed a sharp increase in the number of emerging and re-emerging infectious diseases of livestock and many of these, including Influenza, Corona and Hanta are of public health importance. The reasons for this development are manifold:changes in the climate, life cycle of vectors and increased global travel. Also, due to extensive deforestation, livestock are increasingly coming in direct contact with wild animals that are reservoirs of many emerging pathogens. Recent progress in diagnosis and management of emerging infectious diseases are also topic of this book. © Springer International Publishing AG 2017.