Electrical Engineering

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An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process. Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures. However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity. Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis. Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band. These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients. To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures. Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysis. © 1994-2012 IEEE.

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(SoE) School of Engineering

IEEE Signal Processing Letters

Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality with Reference to Noise-Free EEG Signal

This paper proposes, an annular slot antenna with inner feeding network to generate Orbital Angular Momentum (OAM) waves at 5.4 GHz frequency, for WLAN application. The proposed OAM antenna system is smaller in size because open-ended microstrip lines are used as radiator along with annular slotted ground plane. Parametric variation and far field radiation patterns are presented and discussed. 3D phase patterns proves the generation of OAM waves of different OAM modes using proposed OAM antenna. © 2020 IEEE.

2020 7th International Conference on Signal Processing and Integrated Networks, SPIN 2020

OAM wave generation using inner feed annular slot microstrip radiator

This letter describes the use of slot array equivalence of single square-shaped patch antenna for the generation of orbital angular momentum (OAM) wave. Single patch antenna with phase-shifted power divider network behaves as 4-element slot array and can generate +1 or -1 OAM mode. Design, analysis, and measurement of the proposed novel OAM antenna that resonates at frequency 5.5 GHz are presented. © 2002-2011 IEEE.

IEEE Antennas and Wireless Propagation Letters

OAM Wave Generation Using Square-Shaped Patch Antenna as Slot Array Equivalence

This paper proposes a hexagon patch antenna with six elliptical slots for V2V communication range as stated in IEEE 802.11p standard. The proposed geometry (Design I) is fabricated on FR-4 substrate and fed with the coaxial probe. The elliptical shape cuts are used to improve the directivity and gain. The measurement results are in good agreement with the simulation results. In Design II, the same circuit with defective ground structure has also been simulated, which further enhances the performance. The simulated results of design II show the gain of 6.021 dB and directivity of 9.443 dB in the band from 5.8 GHz to 6 GHz. © 2020 IEEE.

High gain hexagonal patch antenna for V2V communication

Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing technique called Graph Discrete Fourier Transform (GDFT) for the detection of epilepsy. EEG data points are projected on the Eigen space of Laplacian matrix of graph to produce GDFT coefficients. The Laplacian matrix is generated from weighted visibility graph constructed from EEG signals. It proposes Gaussian kernel based edge weights between the nodes. The proposed GDFT based feature vectors are then used to detect the seizure class from the given EEG signal using a crisp rule based classification. Simulation results show that the proposed GDFT based features from Gaussian Weighted Visibility Graph (VG) can detect epileptic seizure with 100 \% accuracy. © 2020 IEEE.

Graph signal processing of EEG signals for detection of epilepsy

In this work, we examined the impact of gate work-function and back-gate bias to enhance sensing metrics of a Dielectric Modulated (DM) p-type Tunnel Field Effect Transistor (p-TFET) based biosensor. The sensing metrics, namely Sensitivity (S) and Selectivity (ΔS) are considerably improved by using a lower value of gate work-function and positive back gate voltages. It is shown that by appropriate selection of gate work-function and back gate bias, Band-to-Band Tunneling (BTBT) of carriers is reduced and a significant change in electrical characteristics is observed in a device with an empty cavity. Therefore, the relative change in the drain current due to the presence of biomolecules in the nanogap cavity is maximized. Results indicate a Sensitivity of ∼109 for 3-aminopropyltriethoxysilane (APTES) biomolecule, and Selectivity of ∼102 for APTES concerning Biotin biomolecule in an optimally designed p-TFET biosensor. The impact of location, as well as the charge of biomolecules, are also analyzed in this work. Results showcase the additional degree of freedom through device optimization which facilitates the tunability of sensing metrics for improved biosensor performance. © 2020 IOP Publishing Ltd.

Engineering Research Express

Investigation the impact of the gate work-function and biases on the sensing metrics of TFET based biosensors

Background: In modern electrical power distribution systems, Power Quality has become an important concern due to the escalating use of automatic, microprocessor and microcontroller based end user applications. Methods: In this paper, power quality improvement has done using Photovoltaic based Distribution Static Compensator (PV-DSTATCOM). Complete simulation modelling and control of Photovoltaic based Distribution Static Compensator have been provided in the presented paper. In this configuration, DSTATCOM is fed by solar photovoltaic array and PV module is also helpful to maintain the DC link voltage. The switching of PV-STATCOM is controlled by Unit template based control theory. Results: The performance of PV-DSTATCOM has been evaluated for Unity Power Factor (UPF) and AC Voltage Control (ACVC) modes. Here, for studying the power quality issues three-phase distribution system is considered and results have been verified through simulation based on MATLAB software. Conclusion: Different power quality issues and their improvement are studied and presented here for harmonic reduction, DC voltage regulation and power factor correction. © 2020 Bentham Science Publishers.

Recent Advances in Electrical and Electronic Engineering

Unit template based control of PV-DSTATCOM

Neuromorphic circuits are becoming quite popular due to their ability to mimic the structure and behavior of human brain. Current research focuses on approximating spiking biological neuron behavior. Various neuron models have been proposed in the past that aid in investigating the behavior of neuronal systems mathematically. Mihalas–Niebur (MN) neuron model is one among them. In this paper log-domain based MN neuron model is implemented at 45 nm technology node. The paper studies the effects of process-temperature variations and also investigates the impact of Hot Carrier Injection (HCI), Bias Temperature Instability (BTI) on the performance of MN circuit. Average power consumption and spiking frequency are chosen as key performance measures to analyze the circuit performance before and after degradation. © Springer Nature Singapore Pte Ltd 2020.

Lecture Notes in Electrical Engineering

Investigating the impact of bti and hci on log-domain based mihalas–niebur neuron circuit

Traffic congestion has been an emerging issue when it comes to problems faced by commuters on road on a daily basis. It leads to loss of time, money, and fuel when one is stuck in a traffic jam. This has led to the need of more path-breaking technologies in the field of intelligent transport systems (ITS). Today, a lot of data are available which can be used to extract important information and perform the desired analysis. With CCTV surveillance cameras at almost every traffic pole, information like count of vehicles can be used to analyze the traffic patterns at a particular location. In this paper, different methods have been used to get the accurate count of vehicles and their performances have been analyzed. Popular image processing method background subtraction and deep learning algorithms: R-CNN, Fast R-CNN and Faster R-CNN have been implemented. © 2020, Springer Nature Singapore Pte Ltd.

Traffic Control Enhancement with Video Camera Images Using AI

In this article, we introduce two types of real-valued sums known as Complex Conjugate Pair Sums (CCPSs) denoted as CCPS^{(1)} and CCPS^{(2)}, and discuss a few of their properties. Using each type of CCPSs and their circular shifts, we construct two non-orthogonal Nested Periodic Matrices (NPMs). As NPMs are non-singular, this introduces two non-orthogonal transforms known as Complex Conjugate Periodic Transforms (CCPTs) denoted as CCPT^{(1)} and CCPT^{(2)}. We propose another NPM, which uses both types of CCPSs such that its columns are mutually orthogonal, this transform is known as Orthogonal CCPT (OCCPT). After a brief study of a few OCCPT properties like periodicity, circular shift, etc., we present two different interpretations of it. Further, we propose a Decimation-In-Time (DIT) based fast computation algorithm for OCCPT (termed as FOCCPT), whenever the length of the signal is equal to 2^v, v in mathbb {N}. The proposed sums and transforms are inspired by Ramanujan sums and Ramanujan Period Transform (RPT). Finally, we show that the period (both divisor and non-divisor) and frequency information of a signal can be estimated using the proposed transforms with a significant reduction in the computational complexity over Discrete Fourier Transform (DFT). © 1991-2012 IEEE.