Department of Electrical Engineering

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

Accurate determination of pulse onset and peak is important in many photoplethysmogram (PPG) signal analysis applications. This paper presents a new automatic robust pulse onset and peak detection method which consists of the stationary wavelet transform; the multiscale sum and product; the adaptive amplitude thresholding; the smooth Shannon entropy envelope extraction; the Gaussian derivative filter-based peak finding; and the peak and onset determination and correction. The proposed method achieves an average sensitivity (Se) of 99.66\%, positive predictivity (Pp) of 99.90\%, and overall accuracy (OA) of 99.55\% on a total number of 1 16 255 beats taken from the Massachusetts Institute of Technology-Beth Israel Hospital polysomnographic sleep and complex system laboratory databases and finger pulse database. We further studied the robustness of three peak detection methods such as bandpass filter and Hilbert transform (BPF + HT) {[}12], pulse waveform delineator (PUD) {[}20], and the proposed method using the noisy PPG signals with a signal-to-noise ratio (SNR) ranging from 30 to 10 dB. The method achieved an average Se of 99.51\%, Pp of 99.92\%, and OA of 99.43\% for the PPG signals with SNR of 10 dB whereas the BPF + HT and PUD methods achieved an average Se = 99.66\%, Pp = 87.71\%, and OA = 87.45\%, and Se = 68.04\%, Pp = 70.38\%, and OA = 52.89\%, respectively. The proposed method significantly outperforms the other methods in terms of detection accuracy and robustness under noise-free and noisy PPG recordings. Unlike the existing methods, the proposed method does not use search-back algorithms to reject or include the noise peaks or missed peaks.

World University root node

IEEE Transactions on Instrumentation and Measurement

A robust pulse onset and peak detection method for automated PPG signal analysis system

This paper proposes a new automated generalized method for detecting an epileptic seizure event from the EEG signals. It consists of preprocessing, variational mode decomposition (VMD), mode sample spectral entropy extraction and thresholding rule. The preprocessing first performs the mean removal and then performs principal component analysis (PCA) based channel selection if required under multi-channel EEG recordings. The VMD is used to decompose the EEG signal into a series of bandlimited modes with specific central frequencies. The mode spectral entropies are extracted from the selected modes by discarding the baseline and artifacts based on the mode central frequencies. The extracted mode spectral entropy features are compared for detecting the presence of the epileptic seizures. The method is evaluated using the standard EEG databases such as Physionet CHB-MIT EEG database and Bonn database. The proposed method achieves an overall accuracy (ACC) of 98-100% with sensitivity (SE) of 100% and specificity (SP) of 97.5-100% for seven EEG groups of the Bonn database. Evaluation results demonstrate that the mode spectral entropy based generic method achieves an average SE = 88.55 %, SP =94.86 % and ACC = 72.92% on the CHB-MIT EEG dataset. © 2018 IEEE.

2018 IEEE 13th International Conference on Industrial and Information …

Detection of epileptic seizure event in EEG signals using variational mode decomposition and mode spectral entropy

A capacitance-to-digital converter (CDC) designed to possess negligible sensitivity to non-ideal circuit parameters is presented in this paper. The scheme is based on switched capacitor integrating technique and the conversion process is such that the final output is fundamentally insensitive to leakage resistance and stray capacitance of the sensor, offset voltage and bias current of the opamp, delay, offset voltage, and bias current of the comparator, leakage current, delay, and charge injection of the switches employed, and so on. Such a characteristic is highly useful for precision measurement. The proposed CDC achieves it without any special compensating circuits, unlike some of the existing CDCs that partially meet the above feature. The performance of the CDC has been experimentally evaluated. Results show high insensitivity to the non-ideal circuit parameters. In addition, the worst case non-linearity error of the prototype CDC was found to be less than 0.1%, when tested for a range of 10 to 400 pF. © 2001-2012 IEEE.

IEEE Sensors Journal

A robust switched-capacitor CDC

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies to meet the diverse and stringent requirements of the 5G use cases. In a C-RAN system with massive MIMO, fronthaul is often the bottleneck due to its finite capacity and transmit precoding is moved to the remote radio head to reduce the capacity requirements on fronthaul. For such a system, we optimize the power allocated to the users to maximize first the weighted sum rate and then the energy efficiency (EE) while explicitly incorporating the capacity constraints on fronthaul. We consider two different fronthaul constraints, which model capacity constraints on different parts of the fronthaul network. We develop successive convex approximation algorithms that achieve a stationary point of these non-convex problems. To this end, we first present novel, locally tight bounds for the user rate expression. They are used to obtain convex approximations of the original non-convex problems, which are then solved by solving their dual problems. In EE maximization, we also employ the Dinkelbach algorithm to handle the fractional form of the objective function. Numerical results show that the proposed algorithms significantly improve the network performance compared to a case with no power control and achieves a better performance than an existing algorithm.

arXiv preprint arXiv:1810.

Power Allocation for Massive MIMO-based, Fronthaul-constrained Cloud RAN Systems

Certain power system situations force protection schemes to mal-operate. Such mal-operations associated with backup protection (BP) schemes might worsen the situation by initiating the cascading effect, damaging critical elements such as generation units, transformers etc. of the power system, or leading to complete collapse of the power system. To adapt to such a network structure, the relays which are going to participate in BP of transmission line have to evolve. Integration of protection systems, communication network, and computer technology has to be the approach for faster information sharing and decision making. Analysis of recent blackouts clearly shows the importance of advancement of wide-area measurement systems and adaptive nature of relaying. Synchro-phasors assisted BP scheme is proposed in this work, which helps the relays adapt according to the prevailing system situations and decisive action is taken considering security and dependability aspects of the relays. © The Institution of Engineering and Technology 2018.

Fulltext

IET Generation, Transmission and Distribution

Synchro-phasors assisted back-up protection of transmission line

The effect of inverter dead time on the inverter output voltage is well studied in the literature. This paper studies the effect of the dead time on the inverter input current. An expression for the switching-cycle-averaged inverter input current including the dead time effect is derived, which shows the dead time effect to primarily result in a reduction in the average value of the inverter dc input current. The effect of dead time on the input current is incorporated into the dc-link dynamic model of a diode-bridge-rectifier (DBR) fed voltage source inverter (VSI). A switching-cycle-average model of a DBR-VSI-fed induction motor is proposed, which considers the effect of dead time on both the ac output voltage and dc input current of the VSI. The proposed average model is used to study the light-load instability phenomenon in a 100-kW open-loop motor drive. The proposed model is shown to predict the region of oscillatory behavior (i.e., region exhibiting light-load instability) with better accuracy than existing average models. The proposed model also accurately predicts the dc-link voltage swing at such oscillatory operating points. While being comparable in accuracy in predicting the system dynamics, the proposed model is much more computationally efficient than the state-of-the-art switching model. The validity of the proposed average model is also confirmed by experimental measurements on a 100-kW induction motor drive. © 2017 IEEE.

IEEE Transactions on Industry Applications

Impact of Dead Time on Inverter Input Current, DC-Link Dynamics, and Light-Load Instability in Rectifier-Inverter-Fed Induction Motor Drives

An event triggered fault detection problem for linear systems is addressed using generalized likelihood ratio test. Using measurements obtained from any general event sampling mechanism and assuming known fault magnitude, fault occurrence time is estimated. While estimating the fault occurrence time, state estimates are required in computing likelihood functions. For measurements satisfying the event triggering mechanism, states are estimated with the obtained measurements, while for the other measurements, event triggering information is used to obtain the state estimates. The effectiveness of the proposed approach is demonstrated using an illustrative example. © 2018

IFAC-PapersOnLine

Event triggered fault detection in linear systems using Generalized likelihood ratio test

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.

Logarithmic cost based constrained adaptive filtering algorithms for sensor array beamforming

A mechanically coupled array technique to enable a fully differential operation of single-crystal silicon thermal-piezoresistive resonators (TPRs) has been demonstrated to alleviate resistive feedthrough issues often seen in TPRs, therefore, featuring clear resonance behavior with decent signal-to-feedthrough ratio. The proposed thermal-piezoresistive dual-II-BARs exhibits feedthrough reduction of more than 67 dB, and no spurious mode, is found within a wide frequency span. The resonator array together with board-level sustaining circuitry also works as a thermal-piezoresistive oscillator (TPO) and demonstrated its performances for real-time mass sensing. Finally, the proposed TPO mass sensor possesses several key features, including real-time monitoring, fast response time, and most importantly, excellent mass resolution of only 83 fg extracted from the measured TPO's Allan deviation. [2017-0085]. © 1992-2012 IEEE.

Journal of Microelectromechanical Systems

Thermal-Piezoresistive SOI-MEMS Oscillators Based on a Fully Differential Mechanically Coupled Resonator Array for Mass Sensing Applications

Active noise control in a non-linear spatial region is a challenging problem, especially for low-frequency noise control applications. The investigation of the existing literature reveals that this problem is tackled by systems with multiple sensors and loudspeakers with a centralised processor. However, the use of centralised techniques is bulky, computationally expensive and lacks scalability. Therefore, the authors propose a distributed learning approach for noise cancellation using a diffusion collaborative strategy. The proposed Legendre-functional link network diffusion filtered s least mean square (FsLMS) algorithm is compared with the standard multi-channel FsLMS algorithm. For different non-linear scenarios, the performance of the proposed method is evaluated in terms of noise reduction performance and computational complexity. It is demonstrated that the proposed method offers significant improvement in noise mitigation performance and computational load as compared with its centralised multi-channel counterpart.