Electrical Engineering

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This paper presents the generation of various OAM modes using single octagonal shape patch antenna. The proposed OAM antenna is compact in size and can generate multiple OAM modes using proper feeding techniques. The proposed OAM antenna is designed and simulated for resonating frequency of 5.2 GHz, WLAN band and it can generate OAM beam with topology charge l = 0, ±1, ±2 and ±3. Beam of topology charge ±3 is also generated but with little distortion.

World University root node

(SoE) School of Engineering

An Octagon Patch Antenna for OAM Mode Generation

5G is expected to support enhanced Mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communication (uRLLC). 5G vision relies on a successful rollout of mmWave frequency technology. The millimeter (mm) Wave frequency band 6–100 GHz range provides more BW to achieve higher data rate but it supports 50–200 m short range only. 5G NR leverages the latest development in multi-input multi-output antennas (MIMO) and beam formation to minimize spectral efficiency and provide better quality of service for large number of users. Beam forming technology has been introduced in mmWave frequency band to compensate high path losses due to mmWave frequencies. Beam formation aims to generate different beams for different user equipment (UE) using an array of antennas elements at the base station (BS) to communicate between different users. The effective beams undergo minimum power loss and interferences and are aimed to direct towards the user equipment. In this paper, we have simulated different types of arrays and analyzed different beam contours that are generated from the arrays of antennas to transmit to the user equipment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

Impact of Beam Formation on 5G Mobile Communication Network in mmWave Frequency Band

This work proposes a design of triangular metamaterial loaded antenna with low RCS, which can be employed for space research and stealth applications. Circular and upturned C-shaped resonating slots are introduced in the rectangular patch to aid in miniaturization and impedance matching respectively. The reduced RCS, triangular SRR and CSRR are entrenched on the top and ground plane respectively; these have the added advantage of a compact unit cell. The two radiating edges of the antenna are loaded with dual-section stepped impedance resonators to curb the reflection of the impinging signal. The proposed antenna resonates at 2.25 GHz with a reflection coefficient of -20.83dB. The gain of the antenna is 4.11dBi, which is typical of a low RCS radiator. The impedance bandwidth is 46.5MHz and the antenna has a low monostatic RCS of -23.796dBsm at the fundamental resonant frequency. Since the antenna has a low RCS at the resonance frequency, it can be utilized for defense and stealth applications. © 2022 IEEE.

2022 International Conference for Advancement in Technology, ICONAT 2022

Triangular Metamaterial Loaded Antenna for Low RCS Stealth Application

Reliability aspects such as bias temperature instability (BTI) and hot carrier injection (HCI) affecting devices in advanced CMOS-based technology have been the subject of active research in recent decades. Due to these reliability issues, various digital and analog circuits were investigated for degradation. However, circuit blocks like the neuron circuits of neuromorphic systems are not fully explored. This work is inclined toward examining the collective degradation impact of BTI and HCI due to aging in an adaptive exponential “integrate and fire” (I&F) model-based, neuromorphic neuron circuit. Detailed degradation analysis of the stimulated neuron circuit aided in identifying possible mismatches/faults associated with neuron spikes. These factors could reduce the efficiency of the neuronal circuit by potentially affecting the transmission of information in a neuromorphic system. Various performance parameters were then derived to quantify the extent of circuit deterioration. The proposed reliability-aware design aims to improve the circuit degradation through its effectiveness in alleviating the overall reliability impact. It demonstrates enhanced circuit operation in spike generation even after aging. The circuit performance is validated through simulations at “Time0” (pre-degradation) and “Aged” (post-degradation) neuron netlists, which is then compared with the proposed reliability-aware circuit. © 2022 John Wiley & Sons Ltd.

International Journal of Circuit Theory and Applications

Enabling efficient rate and temporal coding using reliability-aware design of a neuromorphic circuit

In this manuscript a compact wide band antenna for sub-THz application is proposed. The antenna is designed on a polyimide substrate using graphene as conducting patch and ground. The antenna has an extremely small overall dimension of 1000μm×1000μm. The proposed H-shaped slot antenna has center frequency 0.57THz and other resonances at 0.465THz, 0.58THz &amp; 0.654THz. The proposed design exhibits good impedance matching as well as large bandwidth of 230GHz (40.35%). The simulation result shows that the proposed antenna has maximum gain and efficiency of 8.58dBi and 95.5% at 0.465THz. This antenna is suitable for applications like 6G technology.

Compact Wideband H-Shaped Slot Antenna for Sub-THz Applications

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multi-channel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs. © 2001-2012 IEEE.

IEEE Sensors Journal

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Mental task categorization using single/limited channel(s) electroencephalogram (EEG) signals is crucial for designing portable brain–computer interface and neurofeedback systems. However, EEG signals are corrupted with ocular artifacts and muscle artifacts, which degrade the robustness of existing features resulting inaccurate categorization of mental tasks. Therefore, in this paper, we propose a real-time mental task categorization method using stationary wavelet transform (SWT)-based novel discriminant features and different classifiers. It consists of four major stages: EEG signal decomposition and reconstruction using SWT into subbands; computation of proposed relative subband energy features (i.e., total energy of all subbands(Et), relative energy of each subband k (Er(k)) and sum of relative energy pair ratio and difference); significant feature selection; classification using seven classifiers. The robustness of the proposed method is evaluated using two publicly available datasets and in-house dataset recorded using single-channel EEG headset. Performance evaluation results show that the proposed method with support vector machine classifier achieves the highest average subject-dependent accuracy of 98% for classification of mental/mental and baseline/mental tasks. Extensive comparative performance analysis depicts the superiority of the proposed method as compared to existing techniques in terms of accurate categorization of mental tasks, computational complexity, EEG processing length and robustness under artifact-contaminated EEG signals. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Circuits, Systems, and Signal Processing

Discriminatory Features Based on Wavelet Energy for Effective Analysis of Electroencephalogram During Mental Tasks

In today’s world, where we as humans are constantly aiming towards improving machines and giving them a brain of their own, it is about time that our traffic trends and parking systems become as efficient and automated too. The aim of this paper is to acknowledge the increasing traffic, unorganized parking, and the time wasted while searching for a parking spot in an urban high vehicle density area and make an efficient parking system to avoid the same. Our approach includes the live navigation through our android app, which solves the problem of searching for parking lots in the area. Secondly, an android application is used to show the slots available/occupied in a parking lot. Thirdly, nodes at the parking slot help in making the parking organized. Lastly, an OTP-driven convenient payment method is made for fast functioning of parking exit systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Smart Parking Management System in Smart City

Waste management has become one of the most important issues in our country. From densely populated cities to smaller rural communities, waste management systems keep our homes and communities free from unwanted clutter. The key to efficient waste management is to ensure useful segregation and disposal. The main problem in our country is the delay in the collection and disposal of waste. This is mainly due to the human intervention involved in the process of waste management. Smart Waste Management System deals with everyday problems of waste disposal by reducing this human intervention. In this paper we have implemented an efficient waste management system for the segregation and timely disposal of waste. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lecture Notes in Networks and Systems

Smart Waste Management System in Smart City

In recent development, the High Electron Mobility Transistor (HEMT) technology was used for biosensor applications. In this contribution, an analytical model of MgZnO/ZnO MOSHEMT (Metal Oxide Semiconductor HEMT) was reported. For the first time, a nanogap cavity-based biosensor aimed at detecting various biomolecules, such as uricase, streptavidin, ChOx, and proteins, was designed. The developed analytical model was used to calculate the two-dimensional electron gas (2DEG) density, device capacitance, drain current (ID), output conductance (gd), and transconductance (gm) by using the dielectric modulation approach. MOSHEMTs used dielectric modulation, with the nanogap cavity considered under the gate region. The effective capacitance in the cavity region was obtained by using dielectric modulation. Hence, the nanogap cavity acts as a sensing area for the immobilisation of target biomolecules. The sensitivity of the device was analysed by observing the strong changes in drain current with the addition of different biomolecules in the nanogap cavity region. The proposed MgZnO/ZnO MOSHEMT biosensor exhibits a quick response to biomolecule changes since its drain-on-sensitivity (SIon) is comparatively higher than that of other semiconductor-based biosensors. The obtained analytical model results were verified by a comparison with other works available in the literature, and they showed good agreement with our results. © 2021 Elsevier Ltd