In this paper, a split-gate junctionless transistor (SG-JLT) is modeled, and the simulation is carried out. The I–V characteristic of the SG-JLT was compared and analyzed with the symmetric double gate junctionless transistor (SDG JLT) under the same parameters using Cogenda Visual 3D TCAD. The result shows that SG-JLT has a high ON-state current. The device characteristics of SG-JLT, such as drain current variation with drain voltage under different oxide thicknesses and surface potential variations, were studied.
In this research article, a Vertical Junctionless Field Effect Transistor (V-JLFET) is designed for Hydrogen (H2) gas sensing. This device utilizes a palladium (Pd) electrode which is a catalytic metal for sensing of hydrogen gas. When the H2 gas meets catalytic Pd electrode, it creates pressure, causing a shift in the Pd gate workfunction. This change in workfunction is employed to detect the occurrence of H2 gas. Several electrical characteristics such as current through drain (IDS), transconductance (gm), drain conductance (gd), surface potential (Φs), and electron concentration were studied to sense the existence of H2 gas. Additionally, a sensitivity analysis is carried out to assess the effectiveness of the device in terms of threshold voltage (Vth) with varying pressure.
The primary focus in the era of aiming for a clean and green ecosystem is to limit the emissions released when the energy generation takes place through conventional method. One way to achieve this target is to develop a feasible alternative to fossil or gas-based conventional power plants by combining several distributed resources (DRs) or distributed energy resources (DERs) to form a network of virtual power plant (VPP). This paper explores the potential and avenues of a VPP, one of the latest technological advancements the energy industry has witnessed in the last few years. A VPP identifies the small-scale DERs and combines them, aggregated afterwards to have an energy hub that can cater to the need of power requirement. They expand the economic profits for the members, including micro DER owners, prosumers and system operators and also reduce emissions as the majority of sources compiled in the VPP network are renewables. To extract the maximum potential of VPP, optimal scheduling of the resources is essential to leverage the developed network’s efficacy. This can be achieved by employing highly advanced metaheuristic techniques to manage these flexible resources given their intermittencies and uncertain nature. The selected algorithm must be capable of handling a complex problem by reducing the computational burden and achieving the best optimum solution in line with the target objective function. The multiple benefits obtained from employing the VPP to perform effective energy management are highlighted in this work by involving various scheduling strategies and a possible direction of leveraging the potential of small-scale DERs that can contribute to grid-related services.
This work is intended to present a nested Photonic Crystal Fiber (PCF) for OAM mode propagation. The proposed PCF comprises of a dense flint SF6 and a very light flint LLF1. The finite-element method (FEM) is used for the modal analysis to obtain propagating HE/EH modes. The proposed fiber has shown flat dispersion during the propagation of multiple orbital angular momentum (OAM) modes. Furthermore, the designed PCF’s nonlinearity (gamma) and confinement loss (CL) are calculated to prove the practical feasibility of the design.
Various real-world applications, including as text categorization, categorization of gender in facial recognition for medical evaluation, fraud detection, and satellites analysis of images for oil-spill monitoring, are frequently plagued by imbalanced data. The majority class is commonly the primary focus of machine learning algorithms, with the minority samples being ignored or classified in a secondary manner. Nevertheless, despite their rarity, these minority samples are very important. When it comes to classification tasks, the issue of class imbalance—where one class is underrepresented relative to another—presents a significant barrier. Specialized approaches including SMOTE, ADASYN, and cost-sensitive voting classifiers have been developed to address this problem. The minority class is oversampled in these methods, synthetic samples are created adaptively, and different prices are placed on misclassification mistakes in order to solve the issue of class imbalance. As a result, rigorous assessment utilizing pertinent metrics and cost considerations are required. The efficacy of these strategies, however, depends on dataset features and problem-specific factors. Class imbalance is still a hot topic for study, and there has been constant innovation in novel methods that are adapted to certain dataset characteristics and application fields.
The prevalence of IoT botnet attacks has steadily increased as a result of the widespread use of IoT devices. Hence, it is crucial to identify botnet assaults in IoT network at an early stage. However, a steady rise in data packets causes an overfitting issue, and employing current models with inconsistent and ambiguous information about traffic patterns lowers the detection rate. To overcome this issue, in this paper a robust Fuzzy Chaotic Cuckoo Search Relief Feature selection algorithm (FCCRF) is developed. FCCRF handles inconsistent data by adopting fuzzy triangular membership function, chaotic mapping for searching a diverse population, and cuckoo searching strategy to find best feature subset most relevant to detect botnet attacks. This work used UNSW-NB 15 dataset and for validating support vector machine, naïve Bayes classifier and Logistic regression are applied on both reduced feature subset and whole feature set. The results proved feature subset of FCCRF improves the performance of SVM, NB, and LR classifiers with the accuracy of 0.92, 0.84, and 0.78
Junctionless Tunnel Field Effect Transistor (JL-TFET) is widely studied as a replacement for standard Metal Oxide Semiconductor Field Effect Transistor (MOSFET) structure for low power applications. These JL-TFET structures are explored to provide a higher current ratio and Subthreshold Slope (SS) well below 60 mV/decade. This paper proposes a Ta2O5 oxide-based heterojunction JL-TFET together with GaAs layers sandwiching the drain region, to achieve the current ratio of the order of 109 and SS of 31.21 mV/decade. This paper further explores the effect of oxide thickness and the choice of dielectric material for gate oxide for the proposed design.
Wireless sensor networks are infrastructure-less networks that had emerged from the development in the domains of communication, system-on-chip, and fabrication technologies, which in turn had fueled the cost-effective deployment of wireless sensor networks for a plethora of application scenarios. But, with the increasing number of application scenarios, the focus is gradually shifting to a very important question and that is, how secure are the deployed wireless sensor networks? A malicious neighbour node may hinder communication between a benevolent source and destination nodes by dropping the relay packets or by relaying the packet to other malicious nodes. Therefore, securing communication between nodes is an imminent area of concern. Through intensive research, one potent solution came in the form of the use of trust as a parameter to secure data traversing through wireless sensor networks. A comparative analysis of two prominent trust-based security mechanisms, viz. PeerTrust and linguistic fuzzy trust model is presented in this article. The two trust-based mechanisms are simulated to compare their accuracy and energy consumption for randomly deployed sensor nodes. The simulations were carried out employing TRMSim-WSN v0.5, a Java-based simulator.
In photovoltaic applications, transformer-less inverters are used mostly because of their advantages like higher efficiency, reduced size, and lower cost. Unfortunately, in the absence of transformer, leakage current flows in the circuit. For safety requirements, in a transformer-less inverter, the leakage current should be eliminated. So, the leakage current should be eliminated with the lower number of components used in the circuit, thus reducing the cost and the conduction losses in the circuit. In this brief, discussion of different types of topologies used to reduce or eliminate the leakage current. Multilevel inverters are used in this because of their advantages like dv/dt, low THD output, reduced electromagnetic interference, low switching frequency, etc. Photovoltaic panel is low voltage; so, voltage balancing and voltage boosting circuit are used in a lot of topologies so that there will be no requirement of using extra circuit for boosting the voltage.
Aquaculture is trending toward intense controlled environment production, which increases productivity but increases the danger of catastrophic loss from equipment or management failures. To optimize potential, intense production facility managers require reliable, real-time system status and performance information. This study designed and implemented low-cost short-range wireless sensor network modules for Productive Aquaculture to monitor and maintain environmental factors such as the toxic or harmful level of water used for aquaculture by using different types of sensors and collecting information through a wireless mess network. The results demonstrate that the proposed method optimizes the aquaculture system’s capacity for monitoring, control, and recording. Aquaculture environments with a high risk of fish mortality may be made safer via constant monitoring of the most important parameters. The outcome is a reduction in labor expenses and energy consumption with an increase in consumer safety, consumer trust, and economic benefit from aquaculture.
Perovskite materials have garnered attention in photovoltaic technology due to their impressive power conversion efficiency and narrow band gap. Despite achieving an overall efficiency of 25
Electric vehicles (EVs) are replacing conventional gasoline vehicles in the global transportation sector. The rise in penetration of EV load affects the reliability of the distribution systems (DSs). Reliability assessment is vital for designing DSs that operate economically and lessen customer load disruptions. It is important to achieve acceptable reliability regardless of the source, whether it is a distributed system, an electric utility system, or any other type. The integration of EVs and renewable resources (RRs) such as solar energy are significant feature of modern power system networks. The integration of solar energy acts as a backup option by supporting the power supply to the existing DS and improving the system’s reliability. Various reliability indices (RIs) can measure the duration and frequency of customer disruptions over an exact time. The main aim is to achieve consumer satisfaction, which needs proper planning. This paper confers a comprehensive study of different RIs and their importance in the power system. The reliability analysis is accomplished on an IEEE-33 bus radial DS after integrating EV load and solar energy source by applying teaching–learning-based optimization.
A multi-carrier energy system connects numerous energy carriers and incorporates a variety of energy sources to increase the system’s adaptability. This study proposes a coordinated multi-carrier energy system in which a network of multiple energy systems share their reserves to lower their costs. Each energy hub includes a number of sources, which are electrical chiller, absorption chiller, CHP, boiler, and renewable sources. Additionally, in order to boost the adaptability of energy hubs, energy storage for electrical, cooling, and heating systems has been taken into account. The used approach discovers the best solution, in contrast to using Nash-equilibrium point techniques, which locate the equilibrium point but provide no assurance to the optimal solution. In order to distribute the coalition’s overall gain depending on the participation and effectiveness of the energy hubs, the Shapley value is used. Also, the formulated problem is regarded as a mixed integer linear programming, and the reduction in the cost of interconnected energy system in the cooperative operation shows that the suggested strategy is effective. The results reveal that implementing the fair revenue method improved hub1, hub2, and hub3 by 19.61
Sharenting happens when parents or relatives post photos of their children, usually minors, on social media. However, this has privacy-compromising and child-risking consequences, which may lead to cyber fraud. The paper aims to validate a digital parental sharing scale (DPSS) in the Indian Context. A proper process of designing and validating the scale was carried out on a sample of 250 Indian parents. With the help of analytical tools, the validity and reliability of the analysis were validated. A fourteen-item scale was validated based on social acceptance, Privacy and control, Memories and milestones, and Cyber Security. With good psychometric properties, the scale is validated as a reliable instrument for recording the level of sharenting amongst Indian Parents. The research paper is limited to understanding the level of sharenting by Indian parents. It has not explored the effects of sharenting on Indian children.
This paper reports Ge source-based heterogate TFET on SELBOX substrate. The proposed TFET structure has been simulated based on analysis of DC parameters like transfer characteristic, sub-threshold swing (SS), ION/IOFF ratio, and capacitance over a temperature range from 300 to 500 K for Ge source heterogate TFET. The value of subthreshold swing and ION/IOFF ratio was found to be 30 mV/dec and 1.08 × 1012 that has shown a significant improvement as compared to recent reports. The effect of temperature on DC parameter has also been studied to ensure reliability of the proposed device.
Electrical energy demand has expanded exponentially due to population expansion and urbanization. Smart cities are using IoT and smart gadgets in homes to solve this problem. Demand response (DR) programs involve these devices in the power market. This study provides a cost-effective energy management system for IoT-based smart homes that reduces electricity prices, optimizes energy use, and manages peak to Average ratio (PAR). Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are optimized for price-based DR programs in this research. The report recommends utilizing GA and PSO to schedule smart home loads and regulate IoT device energy usage. The strategy reduces consumer energy expenditures and improves user comfort (UC) in smart homes, encouraging sustainability and cost-efficiency. This approach could improve global energy management by encouraging smart house residents to live sustainably.
In this study, the performance analysis of an L-shaped Tunnel Field-Effect Transistor (TFET) is reported, and its electrical characteristics are examined using Silvaco TCAD device simulator. The L-shaped TFET is specifically designed to ensure that the tunneling current is dominant in the direction perpendicular to the gate. To address the issue of electric field crowding causing corner tunneling near the source region, the proposed L-shaped TFET incorporates drain doping (p +− doping for n-type operations). By strategically doping the drain region, the Ion/Ioff current ratio of the transistor is significantly enhanced by a factor of 109, with corner tunneling playing a major role in improving the switching characteristics. Drain doping demonstrates promising electrical characteristics highlighting its potential for enhancing performance in various applications. Optimization of the concentration of drain doping provides an effective approach to improve the transistor's switching behavior and achieve overall better performance.
This work reports the impact of hetero gate oxide and dielectric pocket on the performance of conventional TFET. The conventional TFET has been optimized by introducing hetero gate oxide of HfO2-SiO2 and dielectric air-pocket. Both these approaches improve the lateral electric field at the source-channel interface and weakens the lateral electric field at the drain-channel interface. Hence, the ON-current is enhanced and OFF-current is reduced. At last, the optimized value of work-function of the gate metal has been obtained at 4.3 eV. The TCAD simulation results show a significant increase in ON-current (ION = 10.24 × 10−4) and reduction in leakage current (IOFF = 9.659 × 10−17). The value of ION/IOFF ratio and subthreshold swing was found to be 1.06 × 1013 and 11.776 mV/decade respectively that confirms a considerable improvement in the performance of the proposed TFET structure. The transconductance for the proposed device structure was found to be 1.935mS. These improvements confirm that the proposed device DP-HGO-DG-TFET is suitable for high frequency and low power applications.
Neural Networks (NNs) are the most promising systems in this era of Artificial Intelligence (AI) and automation. Neural Networks are the replication of the human brain and nervous system as these imitate the processing and architecture of the human brain and natural intelligence. Machine Learning (ML) and Deep Learning (DL) are buzzwords for the current market trends and Neural Networks are the soul of these buzzwords. This article is dedicated to proposing a novel implementation of artificial neural networks using Vedic Mathematics for fast processing and better accuracy. The ML and DL models work well with enough large datasets. And, to process large datasets, these models take high computational time. Hence, the authors propose a Vedic Mathematics-Based Neural Network Design (i.e., VedNNet) improving the performance of ML/DL models and consuming less computational time. The proposed design is solely based on Vedic sutras and operations. The simulation results show that the proposed model named VedNNet is faster in comparison to the traditional neural network by 23.5
The progress in semiconductor technology has played a crucial role in enhancing human existence by introducing significant innovations. This has been achieved by moving away from traditional planar structures and the use of Silicon Dioxide (SiO2) as an oxide material. Instead, non-planar transistor structures like Fin Shaped Field Effect Transistor (FinFET) have been designed utilizing high-k (HK) dielectric constant oxide materials and metal gates (MG), leading to revolutionary advancements. Ashby’s approach to materials selection offers an organized and methodical technique for researchers to make informed choices based on performance objectives, material attributes, and environmental factors. The purpose of this article is to use Ashby’s technique in an effort to choose the prime oxide material from a range of HK materials. Gallium Arsenide (GaAs) is a versatile metal gate material with relative benefits for a range of applications. GaAs’s distinct properties, which include high electron mobility, a wide band gap, low resistivity, a high breakdown voltage, low noise performance, and compatibility with III–V compound semiconductors, make it an essential material for a wide range of applications in high-frequency electronics, optoelectronics, and power devices.
