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

This work presents a flat-gain antipodal Vivaldi antenna (AVA) for ultra-wideband (UWB) microwave imaging applications. The proposed antenna demonstrates measured and simulated impedance bandwidth from 2.3 GHz to 20 GHz with a flat gain over the band, with minimum gain variation of 2.9 dBi. The antenna is tested initially for its practical utility by measuring its specific absorption rate (SAR) value. The SAR value is observed in the simulator by modeling a realistic homogeneous as well as a heterogeneous breast phantom. The SAR value obtained complies with both American and European standards when averaged over 1 g as well as 10 g of tissue. The designed antenna is further utilized to detect multiple tumors in a realistic homogeneous and heterogeneous breast phantoms developed in the laboratory environment. The widely popular delay and sum (DAS) algorithm is utilized to reconstruct the tumor images. The imaging is done outside the anechoic chamber with an in-house monostatic microwave imaging setup just to make it more convincing with real-world dynamic scenario. The homogeneous phantom with four embedded tumors each of radius 4 mm and the heterogeneous phantom with two tumors, one of radius 3.5 mm and another of radius 5 mm are imaged in this work. The imaging results demonstrate that tumors of different sizes can be detected accurately in the case of both homogeneous and heterogeneous breast phantoms. AVA with an oil paper layer for bandwidth enhancement as well for achieving flat-gain response is first of its kind to be reported in this work Also, the proposed work utilizes different phantoms for imaging several tumors of differing sizes, which has not before been described. © 2022 Wiley Periodicals LLC.

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International Journal of RF and Microwave Computer-Aided Engineering

Non-invasive breast tumor detection with antipodal Vivaldi antenna using monostatic approach

This research introduces a metamaterial-based compact radiator for breast tumor spotting. The structure is built on an FR4 material with a ϵr = 4.4 and thickness 1.6 mm. On the hind side of the developed structure, a Frequency selective surface (FSS) is attached for gain improvement. The suggested device has a broad bandwidth (S11 > 10 dB) in the sweep from 2.9 - 36 GHz and has maximum gain of 8.5 dBi at 14 GHz. To test the impact of radiation on the human breast, the developed radiator is subjected to a Specific Absorption Rate (SAR) study using a hemispherical breast phantom modelled in the CST Microwave studio EM Solver. © 2022 IEEE.

INDICON 2022 - 2022 IEEE 19th India Council International Conference

Metamerial-Based Monopole Antenna for Breast Cancer Detection

Grid impedance has a significant impact on the small-signal stability and control of grid-connected power converters used for connecting multiple distributed energy resources and in various motor drive applications. To apply advanced control schemes, such as adaptive control, real-time grid impedance is a necessary parameter that ensures stability margins of the system during operation. For real-time grid-impedance measurement purpose, broadband frequency-response measurement techniques based on the pseudorandom binary sequence can be used. In this article, a computationally effective perturbation signal quadratic residue binary (QRB) sequence is proposed for real-time grid impedance measurement. Unlike the conventional perturbation signals, the QRB sequence has low crest factor and less perturbation duration resulting in minimal disturbance during impedance measurement. To extract the grid impedance in real-time, the QRB sequence is added to the reference d-axis and q-axis currents of the battery energy storage system (BESS). Next, perturbed voltage and current signals are extracted in the abc-frame and converted to the d-q-frame, and grid impedance is assessed. Subsequently, the system stability is analyzed using the generalized Nyquist stability criterion. Later, an adaptive control algorithm is proposed and applied to the energy storage system (ESS) to enhance stability margins of the system based on the measured grid impedance. The effectiveness of the proposed perturbation sequence for impedance measurement and stability improvement algorithm are tested on a standard CIGRE microgrid system in the MATLAB/Simulink simulation environment. Experiments are conducted on a reduced scale 3 kVA laboratory testbed and results are presented to validate the efficacy of the proposed perturbation sequence and stability improvement algorithm. © 1972-2012 IEEE.

IEEE Transactions on Industry Applications

Small-Signal Stability Improvement of Microgrid with Battery Energy Storage System Based on Real-Time Grid Impedance Measurement

The generation and propagation of the common-mode (CM) noise depends on the pulsewidth modulation (PWM) technique, parasitic capacitances, and rate of change of voltages imposed on the motor. In the literature, the CM noise analysis of the brushless dc (BLdc) drive operated with 120^circ conduction mode is reported neglecting the impact of the floating phase, and the analysis is restricted to low-voltage silicon-based drives. In this article, an in-depth theoretical analysis of the CM noise in the BLdc drive is presented considering two commonly used schemes in 120^circ conduction mode: six-step commutation and bipolar PWM. It is proven through experiments that the impact of the floating phase cannot be neglected especially with the high-voltage slew rate imposed by silicon carbide (SiC)-based inverters on the drives. By considering the impact of the floating phase, the CM noise in the BLdc drive is modeled through mathematical equations, and suitable equivalent circuits are presented. Finally, a step-by-step design procedure of the CM filter for the BLdc drive is presented. Experimental results on a three-phase star-connected SiC-based BLdc drive are provided to substantiate the analysis and CM filter design. © 1986-2012 IEEE.

IEEE Transactions on Power Electronics

Analysis and Mitigation of the Common-Mode Noise in a Three-Phase SiC-Based Brushless DC Motor Drive With 120° Conduction Mode

In this paper, we present a straightforward automatic QRS complex detection method for electrocardiogram (ECG) signal analysis applications. The proposed method consists of stationary wavelet transform (SWT) for suppressing low-and high-frequency noises and extracting QRS complexes, amplitude thresholding to suppress the effect residual noise components, Shannon energy based peak amplitude normalization, negative zero-crossing for detecting peaks candidate smoothed QRS complex waveform and peak correction for determining true R peaks in the ECG signal. On the standard MIT-BIH database, our method had an accuracy of 99.50%, sensitivity of 99.69%, and a positive predictivity of 99.81 %. The proposed method outperforms other existing methods which included sets of amplitude-And duration-dependent thresholds to include or reject missed R peaks and noise peaks, respectively that may not work in practise for the case of QRS complex with irregular rates and long-pause between two consecutive QRS complexes. © 2022 IEEE.

2022 4th International Conference on Cognitive Computing and Information Processing, CCIP 2022

Automatic SWT Based QRS Detection Using Weighted Subbands and Shannon Energy Peak Amplification for ECG Signal Analysis Devices

In the modern-day era of technology, a paradigm shift has been witnessed in the areas involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Specifically, Deep Neural Networks (DNNs) have emerged as a popular field of interest in most AI applications such as computer vision, image and video processing, robotics, etc. In the context of developed digital technologies and the availability of authentic data and data handling infrastructure, DNNs have been a credible choice for solving more complex real-life problems. The performance and accuracy of a DNN is a way better than human intelligence in certain situations. However, it is noteworthy that the DNN is computationally too cumbersome in terms of the resources and time to handle these computations. Furthermore, general-purpose architectures like CPUs have issues in handling such computationally intensive algorithms. Therefore, a lot of interest and efforts have been invested by the research fraternity in specialized hardware architectures such as Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), and Coarse Grained Reconfigurable Array (CGRA) in the context of effective implementation of computationally intensive algorithms. This paper brings forward the various research works on the development and deployment of DNNs using the aforementioned specialized hardware architectures and embedded AI accelerators. The review discusses the detailed description of the specialized hardware-based accelerators used in the training and/or inference of DNN. A comparative study based on factors like power, area, and throughput, is also made on the various accelerators discussed. Finally, future research and development directions, such as future trends in DNN implementation on specialized hardware accelerators, are discussed. This review article is intended to guide hardware architects to accelerate and improve the effectiveness of deep learning research. © 2013 IEEE.

IEEE Access

Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey

In the operation of battery energy storage systems (BESSs) based on the cascaded H-bridge (CHB) converters, it is desirable to balance the state of charge (SoC) among the submodules (SMs) within each phase arm. However, there are constraints on the active power distribution among the SMs that intend to balance the SoC. For instance, each SM in a practical system is designed with a specific power rating and can be damaged if it is exceeded. Therefore, a novel rule-based method is proposed in this article to achieve a fast SoC balancing, while respecting the power constraints of the SMs. The proposed method is general and readily applicable to hybrid BESSs, where the SMs are integrated with heterogeneous energy storage. Experimental results are obtained to verify the effectiveness of the proposed algorithm and to compare its performance with respect to existing methods. © 1986-2012 IEEE.

Fulltext

A Constrained Intersubmodule State-of-Charge Balancing Method for Battery Energy Storage Systems Based on the Cascaded H-Bridge Converter

In this article, a new model-based fault detection and localization scheme is proposed for cascaded H-bridge (CHB) multilevel converter to localize the open-circuit faults without needing any additional sensors. The grid current is estimated from the gird voltage, switching states, and dc bus voltages, and compared with the measured grid current. Any significant deviations in the estimated and actual grid currents of the CHB trigger an open-circuit (OC) fault. Once the fault is detected, modulation scheme is switched from phase-shifted unipolar SPWM to phase-shifted bipolar sine pulse width modulation (SPWM) to localize the OC faults. At selected time instants, the change in measured grid current for a specific period is calculated and compared with the estimated change in grid current from the system model. The abovementioned comparisons are used to localize the OC faults in the CHB. The proposed method is validated on a scale-down CHB prototype developed in the laboratory environment, and experimental results are presented to demonstrate the effectiveness of the proposed method. © 1972-2012 IEEE.

An Open-Circuit Fault Detection and Localization Scheme for Cascaded H-Bridge Multilevel Converter Without Additional Sensors

Power grids with increased penetration of photovoltaic (PV) systems face difficulties such as power quality issues, poor system inertia and voltage fluctuations. Hence, the development of flexible power point tracking (FPPT) algorithms, which controls the PV power output rather than the conventional method of extracting the maximum power, was motivated by the necessity to address these issues. Many FPPT algorithms have been presented in the literature for operation during uniform irradiance condition of the PV modules. Each algorithm has distinct benefits and drawbacks and under a particular circumstances, one algorithm may be preferable to another. This study examines the performance of various FPPT algorithms operating with normal and degraded PV modules. Simulation tests in MATLAB/Simulink and experimental results are taken to study their performances. A detailed comparison based on the algorithm's resilience, convergence rate, complexity, and the ease in obtaining the initial data is then presented.

Performance Comparison of Flexible Power Point Tracking Algorithms on Normal and Degraded Photovolatic Modules

Most wearable medical devices have stringent constraints of limited battery power, memory space and data rate, exploring lightweight efficient compression method is highly demanded to extend the battery life and seamlessly transmit electrocardiogram (ECG) signals to the edge computing and cloud computing server. In this paper, we present a design and analysis of digital compressed sensing (DCS)-based ECG encoder by exploring suitable sensing matrices such as deterministic binary block diagonal (DBBD) matrix, random Bernoulli sensing matrix (RBeSM), random sparse binary sensing matrix (RSBiSM) and random binary sensing matrix (RBiSM). The DCS-based ECG encoder was evaluated using a wide variety of ECG signals and benchmark performance metrics such as compression ratio (CR), signal-to-noise ratio (SNR), percentage root mean square difference (PRD) and reconstruction time. Effectiveness of each of the sensing matrices was studied with different sampling rates, measurement quantization bit and number of measurements. For all performance evaluation conditions, results showed that the DCS-based ECG encoder with DBBD sensing matrix can provide higher CRs with minimal reconstruction error (in terms of SNR and PRD) and reconstruction time (in ms). Our study further demonstrated a suitable selection of measurement quantization bit can significantly improve the compression efficiency of the DCS-based ECG encoder for a given PRD or SNR value. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.