Electrical Engineering

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Tetrathienoanthracene (TTA), a new discotic core fragment, is explored that shows a remarkably high hole mobility (μh) of 4.22 cm2 V-1 s-1 at room temperature when used in space-charge limited current (SCLC) devices. Strong co-facial interactions (π-π, S⋯S) among TTA cores along with a high tendency of the derivative to align homeotropically in the columnar mesophase over a large area in SCLC cells contributed to the top-class charge carrier mobility. This journal is © The Royal Society of Chemistry.

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(SoE) School of Engineering

Chemical Communications

High hole mobility in room temperature discotic liquid crystalline tetrathienoanthracenes

In this work, we investigate the effect of back-gate voltage on sensing metric of Dielectric Modulated (DM) Tunnel Field Effect Transistor (TFET) biosensor. Under this work we have investigated transfer characteristics, the variation of energy band, and hole concentration with back gate voltage and calculated the drain current sensitivity and selectivity for three different value of back-gate voltage. In this work, we have shown that with positive back gate voltage, drain current sensitivity is improved by nearly one order of magnitude and selectivity value is also enhanced by more than 2 times. © 2020 IEEE.

2020 International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2020

Impact of back-gate voltage on sensing metric of dielectric modulated Tunnel FET biosensor

This manuscript presents a compact triple frequency band antenna for wireless applications such as WiMAX and WLAN application. The planned antenna has a rectangular microstrip patch with rectangular shape ring slot and a small patch at the back-side of the upper conducting patch and a 9mm x 25mm ground plane. The dimension of the ground plane patch is optimized to obtain 2.4 Giga-Hertz, 3.6 Giga-Hertz and 5 Giga-Hertz frequencies with a sharp cut off at 2.4 GHz and 905 MHz bandwidth at 3.6 GHz and 481MHz bandwidth at 5 GHz resonant frequencies. Simple structure, wider bandwidth, compact size and flat gain make this antenna useful for wireless applications. © 2020 IEEE.

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

Novel triple-band antenna for wireless communication

This paper presents a novel approach to transform the implicit current-voltage (I-V) expression of a double-diode model (DDM) of a photovoltaic (PV) module into an explicit expression. This is achieved by using the Taylor Series expansion of the exponential term in the I-V expression of a DDM PV module. The derived explicit expression is validated against the implicit expression for three different PV modules under different environmental conditions (DEC). The maximum power point (MPP) is estimated using the simple and computationally efficient Bisection method, using the derived explicit expression. The proposed method is then compared with other MPP estimation methods under DEC. The results obtained from the comparison indicate better accuracy of the proposed method. To further validate the results, the proposed method is compared with experimental data collected from KC200GT PV module. © 2020 IEEE.

2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, PESGRE 2020

MPP Estimation of a DDM PV Module Using Explicit I-V Expression

Distributed generator (DG) size and site are an important aspect while deciding DG capacity introduction in distribution system. The paper proposes the methodology of successive optimization for placement of multiple DG units in primary distribution system. The placement is optimized for system loss minimization. The paper investigates the effect of multiple DG units placement of same type as well as the effect of a mix of DG types is also investigated. © Springer Nature Singapore Pte Ltd 2020.

Lecture Notes in Electrical Engineering

Successive optimization using analytical method for multiple dg placement in primary distribution system

Extreme learning machine (ELM) is a single layer feedforward neural network algorithm used for classification problems due to its accuracy and speed. It provides a robust learning algorithm, free of local minima, suitable for high-speed computation along with fast learning speed. In this paper, ELM algorithm implementation on hardware and software is discussed. A low-cost hardware implementation of 16-bit H-matrix generation on FPGA is discussed in the paper. Hardware implementation is carried out on Nexys-4 board using MATLAB and hardware description language (HDL). Generation of H-matrix is carried out using two activation functions, piecewise log-sigmoid and piecewise tan-sigmoid. This paper aims at optimizing the hardware implementation of ELM algorithm by minimizing the utilized resources of the FPGA. Finally, the ELM algorithm accuracy and hardware utilization for both activation functions are compared. © Springer Nature Singapore Pte Ltd 2020.

Fpga implementation of random feature mapping in elm algorithm for binary classification

Continuous scaling of CMOS technology has led to reliability issues and process variability that affect the circuit performance of the SRAM cell. The dynamic behavior of SRAM cells are characterized by critical read-stability (Tread) and critical write-ability (Twrite) while the Static Noise Margins (SNMs) are deduced by the static metrics that are the key performance metrics. The work in this paper demonstrates the cumulative impact of process variability and Negative Bias Temperature Instability (NBTI) degradation on the dynamic metrics of the SRAM cell under varied temperature conditions. Degradation due to NBTI is incorporated by considering different activity factors (α) for the dynamic metrics. Time-zero or process variability is performed for fresh-case, symmetric and asymmetric degradation by Monte Carlo run simulations using foundry models in addition to examining the effect of correlation with their corresponding static metrics. © Springer Nature Singapore Pte Ltd 2020.

Analyzing the impact of nbti and process variability on dynamic sram metrics under temperature variations

This paper presents an intelligent wind speed sensor less maximum power point tracking (MPPT) method for a variable speed wind energy conversion system (VS-WECS) based on a Q-Learning algorithm. The Q-Learning algorithm consists of Q-values for each state action pair which is updated using reward and learning rate. Inputs to define these states are electrical power received by grid and rotational speed of the generator. In this paper, Q-Learning is equipped with peak detection technique, which drives the system towards peak power even if learning is incomplete which makes the real time tracking faster. To make the learning uniform, each state has its separate learning parameter instead of common learning parameter for all states as is the case in conventional Q-Learning. Therefore, if half learned system is running at peak point, it does not affect the learning of unvisited states. Also, wind speed change detection is combined with proposed algorithm which makes it eligible to work for varying wind speed conditions. In addition, the information of wind turbine characteristics and wind speed measurement is not needed. The algorithm is verified through simulations and experimentation and also compared with perturbation and observation (P&O) algorithm. © 1986-2012 IEEE.

IEEE Transactions on Energy Conversion

Q-Learning based Maximum Power Extraction for Wind Energy Conversion System with Variable Wind Speed

In this work, we investigated the performance of single input single output (SISO) downlink channel considering 5G new radio (NR). A number of parameters such as different modulation schemes, channel coding with varying code rates, scalable numerology μ and 3GPP channel models have been considered for evaluation. In addition, the minimum mean square error-interference rejection combining (MMSE-IRC) technique for interference mitigation and bit error rate (BER) performance is analyzed and presented. We also compared the sum-rate performance of LTE and 5G NR. It is observed that MMSE-IRC receiver successfully mitigates the interferences compared to only MMSE based receiver. Simulation results also show performance improvement over various parameters like sum-rate, interference mitigation and BER compared to prior technologies i.e. 4G-LTE, WiMAX, etc. © 2020 IEEE.

2020 IEEE 3rd 5G World Forum, 5GWF 2020 - Conference Proceedings

Performance of Downlink SISO NR System using MMSE-IRC Receiver

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal. © 2020 Institution of Engineering and Technology. All rights reserved.