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Modeling of Electrostatics and Currents in a Forward-Biased p-n Junction

IEEE Transactions on Electron Devices 2024年第8期71卷 4489-4497页

作者： Lin, Kuan-Wun Taur, Yuan National Taiwan University Graduate Institute of Electronics Engineering Taipei10617 Taiwan University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92093 United States

A general model is developed for forward-biased p-n junctions with arbitrary doping concentrations biased at any injection level. It goes beyond the commonly practiced depletion approximation, which is valid only under the low-level injection condition. It is shown that when the applied junction voltage exceeds the built-in potential, the profile of net charge density shrinks to two antisymmetric spikes, one going positive and one going negative, with the same magnitude given by the average of the n-type and p-type doping concentrations. It is also shown that under high-level injection condition, the drift current components become dominant, in some cases yielding an exp(qV/kT) dependence of the total current instead of the commonly perceived exp(qV/2kT) dependence (here, V is the intrinsic junction voltage). By equating the sum of drift and diffusion current densities on both sides of the junction, analytical expressions are derived for the electric fields and, therefore, the total currents. Finally, the decay lengths of excess carriers in the quasi-neutral regions are solved numerically from the current continuity equations coupled to Poisson's equation with no assumptions of charge neutrality. It is shown that the decay lengths in the high-level injection regime are doping-dependent and are longer than the well-known minority-carrier diffusion lengths in the low-level injection regime. © 1963-2012 IEEE.

关键词： Electric fields

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Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

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Multimedia Tools and Applications 2025年第16期84卷 16909-16943页

作者： El-Shafai, Walid El-Mesady, A. Kamal, F.M. Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Physics and Engineering Mathematics Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University PO 35516 Mansoura Egypt

The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t

关键词： Cryptography

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IEC61850 GOOSE message application for a power transformer voltage control 33

IEC61850 GOOSE message application for a power transformer v...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Wellen, Joniff Krishnamurthy, Senthil Ratshitanga, Mukovhe Department of Electrical Electronics and Computer Engineering PO BOX 1906 Bellville7535 South Africa

ISBN: (纸本)9798331535162

Voltage regulation of power transformers is critical to controlling and maintaining system voltage levels in the electrical transmission and distribution network system. It is vital to keep the transmission and distribution network's voltage levels within a specified range and to avoid voltage levels from fluctuating outside of the predetermined range of allowed tolerance levels. Two SEL-2414 IEDs were employed in this study to perform the voltage control function of the parallel power transformers using a master-follower control scheme. The automatic tap changers that execute this voltage regulating function incorporate the IEC61850 GOOSE message application as the communication protocol. The paper demonstrates the control function, a hardware-in-the-loop simulation in a closed-loop simulation using Real Time Digital Simulator (RTDS), and the two transformer tap changer IEDs SEL-2414 were utilized for lab-scale implementation and validating the simulation results. © 2025 IEEE.

关键词： Hardware-in-the-loop simulation

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Distance Protection Application for 33kV Traction Network 33

Distance Protection Application for 33kV Traction Network

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nkalitshana, Luthando Krishnamurthy, Senthil Mataifa, Haltor Department of Electrical Electronics and Computer Engineering PO BOX 1906 Bellville7535 South Africa

ISBN: (纸本)9798331535162

The research investigates the laboratory-scale setup and functional testing of distance protection techniques for the Transnet traction system. The Transnet network, which powers South Africa's railway system, requires a strong protection mechanism to assure the safety and dependability of the electrical systems that power locomotives. Distance protection, essential in securing high-voltage transmission networks, is critical to detecting faults, isolating affected portions, and minimizing system interruptions. In this study, a simulation environment was created to simulate the features of the Transnet traction system, including traction substations, overhead power lines, and associated equipment. A lab-scale model was developed utilizing protection functional simulation to simulate real-time fault situations and evaluate the distance protection relay's response under different fault conditions. The performance and efficacy of the protection mechanism were assessed by varying key factors such as fault impedance, fault location, and fault types. © 2025 IEEE.

关键词： Distribution protection Mho characteristics Traction network and Locomotives

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A Data-Driven Approach to Dataflow-Aware Online Scheduling for Graph Neural Network Inference 25

A Data-Driven Approach to Dataflow-Aware Online Scheduling f...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Puigdemont, Pol Russo, Enrico Wassington, Axel Das, Abhijit Abadal, Sergi Palesi, Maurizio Barcelona Spain Department of Electrical Electronics and Computer Engineering Univ. of Catania Catania Italy

ISBN: (纸本)9798400706356

Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for efficient computation, leading to the development of specialized GNN accelerator architectures that surpass traditional CPU and GPU performance. Despite this, the structural diversity of input graphs results in varying performance across different GNN accelerators, depending on their dataflows. This variability in performance due to differing dataflows and graph properties remains largely unexplored, limiting the adaptability of GNN accelerators. To address this, we propose a data-driven framework for dataflow-aware latency prediction in GNN inference. Our approach involves training regressors to predict the latency of executing specific graphs on particular dataflows, using simulations on synthetic graphs. Experimental results indicate that our regressors can predict the optimal dataflow for a given graph with up to 91.28% accuracy and a Mean Absolute Percentage Error (MAPE) of 3.78%. Additionally, we introduce an online scheduling algorithm that uses these regressors to enhance scheduling decisions. Our experiments demonstrate that this algorithm achieves up to 3.17× speedup in mean completion time and 6.26× speedup in mean execution time compared to the best feasible baseline across all datasets. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Data flow graphs

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A practical evaluation of an IEC 61499 standard-based Low-Cost Gateway for photovoltaic power plants 33

A practical evaluation of an IEC 61499 standard-based Low-Co...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Luwaca, Emmanuel Krishnamurthy, Senthil Department of Electrical Electronics and Computer Engineering PO BOX 1906 Bellville7535 South Africa

ISBN: (纸本)9798331535162

Communication plays a significant role in an electrical grid that includes Distributed Energy Resources (DERs). Modern control and automation systems used by these systems are predominantly distributed using domain standards such as IEC 61131-3. However, the IEC 61131-3 standard does not address communication. The IEC 61499 standard, in contrast, introduces the use of the Service Interface Function Block (SIFB) concept and high-level communication patterns to achieve hardware-independent access to communication services. This paper recommends the application of the IEC 61499 standard for DER control and automation and the application of SIFB to facilitate the ease of system integration in a DER plant. A low-cost gateway application using open-source IEC61499 tools is implemented, and its simulation results are presented. © 2025 IEEE.

关键词： Costs

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Edge detection using the Prewitt operator with fractional order telegraph partial differential equations (PreFOTPDE)

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Multimedia Tools and Applications 2025年第13期84卷 12329-12345页

作者： Tenekeci, Mehmet Emin Abdulazeez, Sadeq Taha Karadağ, Kerim Modanli, Mahmut Department of Computer Engineering Faculty of Engineering Harran University Şanlıurfa Turkey Department of Mathematics College of Basic Education University of Duhok Duhok Iraq Department of Electrical and Electronics Engineering Faculty of Engineering Harran University Şanlıurfa Turkey Depertmants of Mathematics Faculty of Arts and Sciences Harran University Sanliurfa Turkey

Detecting edges in image processing is an important process in image analysis or enhancement. Many methods detected edge information based on the differences in brightness values. Prewitt, the most widely used edge detection method, performs the extraction of edges by partial differential calculation. This study performed fractional order partial differential calculation with the Telegraph equation in the Prewitt method calculation. Filter coefficients are determined by using Grumwald-Letnikov series expansion for fractional partial differential equation calculation. To determine the most appropriate fraction value, five different coefficients were tried. The proposed method is compared with the conventional edge detection methods Sobel, Canny and standard Prewitt. The obtained result showed that the proposed method detects the edges more accurately. In addition, it has been compared with Prewitt fractional order edge detection (FOED) and edge detection method based on ant colony optimization (ACO) methods developed in recent years. Pratt's figure of merit (FOM) metric was used to compare these studies. It was realized that the FOM values calculated with the proposed method were more accurate than the others methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Edge detection

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Smart Aeroponic Farms with IoT-Enabled Efficient Automation and Monitoring 2

Smart Aeroponic Farms with IoT-Enabled Efficient Automation ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Jeyabharath, R. Tamilvani, P. Karthikeyan, G. Vijayakumar, P. Rohini, J. Mohammadha Hussaini, M. Kavin Kumar, K. K.S.R. College of Engineering Department of Electrical and Electronics Engineering Tiruchengode India M.P.Nachimuthu M.Jaganathan Engineering College Department OfElectrical and Electronics Engineering Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Kongu Engineering College Master of Computer Applications Erode Perundurai India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India Government College of Engineering Department of Electrical and Electronics Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The Internet of Things (IoT) has enabled the adoption of automated aeroponics equipment, which has transformed our understanding of horticulture. Aeroponics is a method of vegetative production that does not use soil. Plant cultivation takes place in an airborne or vapour environment. The use of Internet of Things (IoT)-enabled aeroponics devices allows farms to autonomously monitor and manage variables such as temperature, lighting, humidity, and fertiliser concentrations. This allows for real-time adjustments to be made to optimize plant growth and yield. The IoT-enabled automated aeroponics system utilizes sensors and actuators to collect data on environmental conditions and automate the delivery of nutrients and water to the plants. Additionally, the system can be programmed to send alerts and notifications to farmers in case of any irregularities or issues that require attention. This level of automation not only increases efficiency and productivity but also reduces the need for manual labor and minimizes resource wastage. The IoT-enabled automated aeroponics systems have the potential to revolutionize farming by providing a more sustainable and efficient method of crop production. The computer system uses environmental elements to evaluate the results of aeroponics studies. The decision was made to use an automated aeroponics methodology to develop maize plants. According to the findings of this study, applying this method could potentially reduce labour expenses while increasing the value of the product. © 2024 IEEE.

关键词： Internet of things

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of electronics and Information Engineers.

关键词： Demand response

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