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 simple broadband polarization-insensitive circuit analog absorber (CAA) with wide angular stability. It comprises of dual-cut square loop resonator loaded with eight lumped resistors as top patch and an inductive grid. The normal incidence reveals a 90% absorption bandwidth of 20.51 GHz (110% fractional bandwidth) covering the entire X to K-band with a minimum flat absorption rate of 92.82%. Equivalent circuit analysis is carried out and responses are validated with full-wave simulations. The proposed prototype is insensitive to different polarization angles under normal incidence and stable with 45° in transverse electric (TE) and transverse magnetic (TM) incidences. The design's novelty lies in using two resistors on each side of the square ring, which improves the absorptivity performance. The inductive grid enhances TM mode's angular stability. The proposed absorber is fabricated and the measured results are in good agreement. In-band bistatic radar cross section reduction of 12.97 dBsm is obtained with the absorber using an X-band antenna. The temperature profile of the prototype is illustrated using near-in-lock infrared thermography and 2.1°F of temperature rise is noticed in the absorption band. The proposed structure possesses a low profile, broad bandwidth, polarization insensitivity and wide angular stability to the best of the author's knowledge. © 2022 Wiley Periodicals LLC.

World University root node

International Journal of RF and Microwave Computer‐Aided Engineering, e23085,

Broadband polarization insensitive wide angular stable dual-split square ring circuit analog absorber for radar cross section and electromagnetic interference shielding applications

In this Chapter an overview on different classes of passive components that can be realized using radio frequency microelectromechanical systems (RF MEMS) technology is provided. Focus is oriented toward the performance description of RF passive components in micromachining technology, compared to their standard semiconductor counterparts. It includes, micromachined coplanar waveguide transmission line, varactors, inductors, power divider, coupler, and impedance matching tuners. Conductor Backed Coplanar Waveguide (CBCPW) lines on Si micromachined substrate are in use for more than a decade and it is given prime emphasis in this Chapter. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Micromachined Circuits and Devices

Micromachined Microwave Passive Circuits

In this Chapter new techniques that enable broader bandwidths and operation at higher frequencies in conventional micromachined switches are presented. Focus is on the use of defective ground structure (DGS) and use of secondary switches to improve MEMS switch performance. Detailed performance analysis of metamaterial inspired MEMS switches are then discussed for application in millimetre wave frequency bands up to about 170 GHz. In addition, new design techniques to improve reliability by taking advantage of Casimir force to reduce stiction is reported. Layout and results of metamaterial inspired capacitive as well as resistive contact micro machined MEMS switches for application in modern electronic circuits and 5G/6G communication systems are also presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Micromachined Metamaterial Inspired Switches

In the recent past, employing wideband signals for underwater 3D acoustic imaging has been gaining momentum, which makes application of the existing frequency domain beamforming algorithms infeasible and complex. One potential solution is to employ the time domain delay and sum beamforming which is a popular technique used in medical ultrasound imaging, but rarely used for underwater acoustic 3D imaging applications due to the high computational complexity. In this paper, a fast and an efficient beamforming algorithm in the time domain is attempted for the reconstruction of a 3D image. The proposed method makes use of parallel 2D time domain delay and sum beamforming for the reconstruction of a 3D underwater object, followed by averaging and interpolation to reconstruct the 3D volume. Since the method adopts dynamic focusing, it is supposed to give better resolution and contrast 2D slices and hence the higher resolution 3D volume. The computational complexity and memory requirements have been reduced significantly as compared with that of conventional time-domain 3D beamforming. The reduction factor depends upon the total number of sensors used in the uniform 2D receiving array. © 2022 IEEE.

Oceans Conference Record (IEEE)

Exploring time domain beamforming algorithm for underwater 3D acoustic imaging

In the recent past, there have been many efforts to accelerate adaptive beamforming for ultrasound (US) imaging using neural networks (NNs). However, most of these efforts are based on static models, i.e., they are trained to learn a single adaptive beamforming approach (e g , minimum variance distortionless response (MVDR)) assuming that they result in the best image quality. Moreover, the training of such NNs is initiated only after acquiring a large set of data that consumes several gigabytes (GBs) of storage space. In this study, an active learning framework for beamforming is described for the first time in the context of NNs. The best quality image chosen by the user serves as the ground truth for the proposed technique, which trains the NN concurrently with data acqusition. On average, the active learning approach takes 0.5 s to complete a single iteration of training.

Learning While Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging

This article proposes a dual-layer modulation technique that combines two- and three-level modulation techniques to control the average neutral-point current (ANC) in neutral-point-clamped (NPC) converters. In the proposed technique, two modulation layers are employed to independently synthesize the desired ac-side voltage reference and ANC reference. The first (outer) modulating layer operates at a lower frequency and synthesizes the ANC reference from the three-phase currents. It determines the duration of applying each modulation technique, based on the ANC reference and the maximum ANC injection limits of the converter. The second (inner) modulating layer operates at the switching frequency and synthesizes the three-phase voltages of the NPC converter from the common dc voltages by comparing the calculated duty cycles and a carrier waveform. The proposed dual-layer modulation technique has advantages of faster dynamics and reduced mathematical computations compared to the conventional approach. The feasibility of the proposed modulation technique is demonstrated experimentally on a laboratory prototype. A detailed performance comparison among different modulation techniques is also presented. © 1986-2012 IEEE.

IEEE Transactions on Power Electronics

Dual-Layer Pulsewidth Modulation Technique for Average Neutral Point Current Control in Neutral-Point-Clamped Converters

Grid connected transformer-less Photovoltaic inverter known as highly efficient and reliable inverter concept (HERIC) is derived from the industrial-standard full-bridge topology with two additional switches on ac side. This paper presents a detection and localization method for open-circuit (OC) failures in the semiconductor switches of HERIC inverter. Fault detection based on sliding mode observer (SMO) is proposed for the ac bypass switches and an average current estimator is used to detect failures in the main switches of full bridge inverter. Two parallel SMOs are developed based on the state space model of the converter under different OC fault conditions in ac bypass leg. Observed grid current and measured grid current are used to obtain the residual which determines the location of OC fault in the inverter. The key advantage of the proposed method is that it does not require additional sensors for fault diagnosis which will improve the system robustness. Efficacy and reliability of the proposed method is validated using simulation studies. © 2022 IEEE.

10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022

Sliding Mode Observer Based Open-Circuit Fault Detection in the HERIC PV Inverter

Recent research reveals the merits of single-stage half-bridge matrix-based (MB) dual-active-bridge (DAB) converter for modular medium-voltage ac (MVAC) to low-voltage dc (LVDC) conversion. The complete set of modulation modes for this ac-dc converter is yet to be analyzed in the literature. Such holistic investigation is necessary before carrying out a design optimization of the ac-dc converter for given voltage and power specifications. The objective of this article is to present a comprehensive analysis of the half-bridge MB-DAB converter's modulation possibilities and optimal design. The exhaustive sets of zero-current-switching (ZCS) and zero-voltage-switching (ZVS) constrained modulation modes for the half-bridge MB-DAB converter are systematically investigated and the analysis reveals the boundaries of operation and feasible modulation schemes. Suitable simulation results illustrating modulation limits of the ac-dc converter reinforce the theoretical analysis. Additionally, an efficiency (η)-power density (ρ) optimal converter design algorithm is presented, which reveals that an all-SiC half-bridge MB-DAB design with ZVS constrained modulation offers the best η - ρ design. The hardware prototype's experimental results validate the converter's operation. © 2021 IEEE.

IEEE Journal of Emerging and Selected Topics in Power Electronics

Analysis of Modulation and Optimal Design Methodology for Half-Bridge Matrix-Based Dual-Active-Bridge (MB-DAB) AC-DC Converter

Automatic electrocardiogram (ECG) signal quality assessment is most essential for reducing false alarm rates and energy consumption of health monitoring devices. In this paper, we propose an automatic ECG signal quality assessment method by using derivative ECG (dECG) signal and convolutional neural network (CNN). The proposed dECG-CNN method is tested by using the noise-free ECG and noisy-ECG signals collected from standard databases. On the benchmark performance metrics, the proposed method had an accuracy of 100%. Evaluation results further showed that the proposed method can achieve false alarm reduction rate of 100% for a total number of 30850 noisy segments. It is observed that the proposed method outperforms the recent ECG quality assessment methods such as pretrained CNN models, deep belief network. The method has great potential in improving accuracy and reliability of cardiovascular diseases (CVD) diagnostic systems and improving energy efficiency of battery powered wearable and portable medical devices. The optimal CNN model for ECG signal quality assessment is implemented on Raspberry pi-4 as real time computing platform. © 2022 IEEE.

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

Convolutional Neural Network Based ECG Quality Assessment Using Derivative Signal

With the recent outbreak of COVID-19, ultrasound is fast becoming an inevitable diagnostic tool for regular and continuous monitoring of the lung. However, lung ultrasound (LUS) is unique in the perspective that, the artefacts created by acoustic wave propagation is aiding clinicians in diagnosis. In this work, a novel approach is presented to extract acoustic wave propagation driven features such as acoustic shadows, local phase-based feature symmetry, and integrated backscattering to automatically detect the pleura and to aid a pretrained neural network to classify the severity of lung infection based on the region below pleura. A detailed analysis of the proposed approach on LUS images over the infection to full recovery period of ten confirmed COVID-19 subjects across 400 videos shows an average five-fold cross-validation accuracy, sensitivity, and specificity of 97%, 92%, and 98% respectively over randomly selected 5000 frames. The results and analysis show that, when the input dataset is limited and diverse as in the case of COVID-19 pandemic, an aided effort of combining acoustic propagation-based features along with the gray scale images, as proposed in this work, improves the performance of the neural network significantly even when tested against a completely new data acquisition.