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Power Electronics and Control (SEPOC), Seminar on

作者： Moazzam Shehzad Nasir Jamal Hafiz Abdul Muqeet Noman Sohaib Qureshi Dept. of Electrical Engineering Lincoln University College Petaling Jaya Malaysia Dept. of Electrical Engineering Punjab Tianjin University of Technology Lahore Pakistan Dept. of Computer Science University of Engineering and Technology (RCET Campus) Lahore Pakistan

ISBN: (数字)9798350379419

ISBN: (纸本)9798350379426

With fuel prices rising and environmental concerns intensifying, the financial burden on consumers and businesses has shown an upward trajectory, prompting a search for sustainable alternatives sources. This study investigates the financial feasibility of installing a grid-tied solar photovoltaic (PV) system in residential buildings in suburban areas of Punjab, Pakistan. The research uses a complete intelligent financial model to assess the feasibility of a grid-connected solar PV system, incorporating parameters such as solar irradiance levels, system size, capital costs, operating expenses, energy rates, its incentives, and payback periods in a systematic computational optimization methodology. The shift to solar photovoltaic systems provides an affordable way to lower energy bills that can also lessen the dependency on fossil fuels. Additionally, solar PV systems help significantly to reduce greenhouse gas emissions and environmental damages by utilizing the clean energy of the sun. This is in line with worldwide efforts to battle climate change and protect the planet from environmental concerns. This research uses RET Screen software to evaluate the financial feasibility with GHG reduction analysis as well. The study’s findings are useful for homeowners, policymakers, and stakeholders interested in encouraging renewable energy adoption and sustainable development in Punjab, Pakistan’s suburban region.

关键词： Climate change Meters Computational modeling Optimization methods Fuels Pricing Power grids Photovoltaic systems Financial management Energy management Buildings Solar power generation Economics

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On inside-out Dissections of Polygons and Polyhedra

arXiv

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arXiv 2024年

作者： Akpanya, Reymond Rivkin, Adi Stock, Frederick RWTH Aachen University Aachen Germany Dept. of Computer Science Technion-Israel Institute of Technology Haifa Israel Miner School of Computer & Information Sciences LowellMA United States

In this work we study inside-out dissections of polygons and polyhedra. We first show that an arbitrary polygon can be inside-out dissected with 2n+1 pieces, thereby improving the best previous upper bound of 4(n - 2) pieces. Additionally, we establish that a regular polygon can be inside-out dissected with at most 6 pieces. Lastly, we prove that any polyhedron that can be decomposed into finitely many regular tetrahedra and octahedra can be inside-out *** Codes 68U05 Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Effect of Ultra Violet Light on Planar Field Emitters

Effect of Ultra Violet Light on Planar Field Emitters

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International Conference on Vacuum Electronics

作者： Ranajoy Bhattacharya Marco Turchetti P. Donald Keithley Karl K. Berggren Jim Browning Dept. of Electrical & Computer Engineering Boise State University Boise ID USA Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA Dept. Of Electrical Engineering and Computer Science and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA

Two different types of planar nano-scale (4–5 nm) field emitters, bowtie and diode devices, were characterized for use as electron sources for vacuum nano-transistors. I-V measurements were performed before and after Ultra-violet (UV) light exposure. Experiments showed that the field emission current was $\approx 2.7 \text{nA}$ before the UV exposure and was $\approx 1.8\text{nA}$ after the exposure for the bow-die devices. Experiments also showed that the field emission current was $\approx 5.6\text{nA}$ before the UV exposure and was $\approx 3.6 \text{nA}$ after the exposure for the diode devices. This reduction in current could be attributed to the removal of water vapor by UV exposure resulting in the reduction in the surface leakage current. A long-term stability tests conducted after the UV exposure showed both the bow-tie and diode devices emitted a stable current (<5% drop) for > 1100 hrs. of continuous operation at a DC bias of 5 V.

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Smart Locker 2.0: Leveraging IoT and Machine Learning for Secure, User-Friendly Public Storage

Smart Locker 2.0: Leveraging IoT and Machine Learning for Se...

引用

Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Chandrappa S Prithviraj Aditya Pai H Rajesh I S Manjunath Sargur Krishnamurthy R Sneha Shetty R Dept. of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India Dept. of Computer Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte India Department of CSE MIT Art Design and Technology University Pune India Dept. of AI & ML BMS Institute of Technology and Management Bengaluru India JP Morgan & Chase Co Houston USA Dept. of Artificial Intelligence and Machine Learning Nitte (Deemed to be University) NMAM Institute of Technology Nitte India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Smart locker systems are a revolutionary technology, combining advanced automation and connectivity to address increasingly demanding requirements for secure, efficient, and convenient storage solutions in numerous scenarios. These intelligent lockers use the latest technologies, such as the Internet of Things (IoT) and mobile applications, to revolutionize the way people store their belongings. Existing Smart Locker System lacks measurable results. To sustain its claims, we include key findings. For security, the system utilizes RFID authentication and biometric security, which reduces unauthorized access by 98% and blocks spoofing attacks. For convenience, a study with 50 users found 92% satisfaction, with 87% preferring it over traditional lockers due to faster access, averaging 3 seconds. For environmental benefits, the system cut power use by using sleep mechanism and saved paper usage by switching to cloud logs. Real-World tests achieved 98% user satisfaction with the university system proving its efficiency, security, and sustainability. This system expounds on multifaceted benefits and functionalities in smart lockers. First, it covers how these lockers provide better security compared to old lock-and-key systems. Using access controls reduces the risk of theft and illegal access to one's valuables and sensitive belongings by a great degree. It has an easy reservation, access, and management of personal lockers via a remote means; this proves to be seamless without needing any physical keys and contactless interactions, ideal for modern spaces considering hygiene and efficiency is top priority. Moreover, it goes on to analyze the flexibility of smart locker systems. From corporate offices to co-working spaces, universities, shopping malls, and public transportation hubs, these lockers cater to diverse user needs, creating flexible and scalable solutions adaptable to a variety of environments.

关键词： Biometrics Cloud computing Switches Machine learning Mobile applications Internet of Things Sustainable development Secure storage Public transportation Radiofrequency identification

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A Fair Federated Learning Framework for Collaborative Network Traffic Prediction and Resource Allocation

arXiv

引用

arXiv 2025年

作者： Panda, Saroj Kumar Panayiotou, Tania Ellinas, Georgios Behera, Sadananda Dept. of Electronics and Communication Eng. National Institute of Technology Rourkela Odisha India KIOS CoE Dept. of Electrical and Computer Eng. University of Cyprus Nicosia Cyprus

In the beyond 5G era, AI/ML empowered real-world digital twins (DTs) will enable diverse network operators to collaboratively optimize their networks, ultimately improving end-user experience. Although centralized AI-based learning techniques have been shown to achieve significant network traffic accuracy, resulting in efficient network operations, they require sharing of sensitive data among operators, leading to privacy and security concerns. Distributed learning, and specifically federated learning (FL), that keeps data isolated at local clients, has emerged as an effective and promising solution for mitigating such concerns. Federated learning poses, however, new challenges in ensuring fairness both in terms of collaborative training contributions from heterogeneous data and in mitigating bias in model predictions with respect to sensitive attributes. To address these challenges, a fair FL framework is proposed for collaborative network traffic prediction and resource allocation. To demonstrate the effectiveness of the proposed approach, non-iid and imbalanced federated datasets based on real-word traffic traces are utilized for an elastic optical network. The assumption is that different optical nodes may be managed by different operators. Fairness is evaluated according to the coefficient of variations measure in terms of accuracy across the operators and in terms of quality-of-service across the connections (i.e., reflecting end-user experience). It is shown that fair traffic prediction across the operators result in fairer resource allocations across the connections. © 2025, CC BY.

关键词： Federated learning

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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Samruddhi Maheshkumar Kolekar Agnesh Chandra Yadav Suchita Yadav Daba Prasad Dash Dept. Electrical Engineering Thaper Institue of Engineering and Technology Patiala India Dept. Electrical Engineering India Instite of Technology Patna Patna India Dept. of Computer Science and Engineering Bennett University Gr. Noida India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes.

关键词： Representation learning Image segmentation Accuracy Three-dimensional displays Magnetic resonance imaging Brain tumors Morphology Manuals Logic gates Brain modeling

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Covid-19 Vaccine Certificate Verification using Sha Algorithm and IPFS in Blockchain technology

Covid-19 Vaccine Certificate Verification using Sha Algorith...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Naitik Agrawal Yash Veer Singh Vishnu Sharma Vikas Kumar Bedi Sumit Chhonker Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Dept. of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

The COVID-19 pandemic has suddenly arisen as a global health emergency requiring concerted international action. Governments are working to stop the virus from spreading and health organizations rushed to create vaccines that would protect people. Reduce Covid-19 morbidity, reduce strain on healthcare systems, and let economies to expand. The Vaccine Certificate has been adopted as a way forward after the COVID-19 vaccination to assist authorities in formulating policies by restricting cross-border travel. In this work, we employ blockchain technology to construct a safe and verifiable vaccination certificate, which alleviates substantial privacy concerns and eliminates the need for third parties to maintain trust and control the user’s data. Different modern technologies, such as smart contracts, are used in our approach.

关键词： COVID-19 Data privacy Pandemics computer viruses Smart contracts Government Vaccines

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Steganographic Encryption and Decryption Using Duality Preserving GANs

Steganographic Encryption and Decryption Using Duality Prese...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Aaryak Shah Alok Kumar Kamal Neeraj Dept. of Information Technology ABV-IIITM Gwalior Gwalior India Dept. of Electrical/Electronics ABV-IIITM Gwalior Gwalior India Dept. of Computer Science and Engineering Mahindra University Hyderabad India

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Image steganography has been a significant research area in the security sphere. The core idea revolves around embedding secret messages in existing images (covers) such that the resultant image displays no noticeable detectable artifacts of the secret while also being statistically similar to the original cover. Various steganographic techniques have been developed and tested. However, these traditional techniques often exploit some specific property of the image and are often easy to detect with the right tools. In recent times, generative adversarial networks, or GANs for short, have shown massive promise in their capability to modify and synthesize data. They are now ubiquitous in image synthesis and image translation tasks. With various recent advancements in this technology allowing these models to generate, in many cases, human-imperceptible data, GAN-based steganographic systems have become a significant focus of security research. This report analyzes and compares some existing models and attempts to provide a new solution that performs better on multiple well-defined metrics. A DualGAN-based architecture is proposed that trains both the encoder and decoder processes simultaneously while maintaining the underlying dual relationship of these tasks. The proposed scheme reports better results on the selected metrics (SBR, DER, PSNR) at three values of embedding capacity set at 0.3, 0.6, and 0.9.

关键词： Measurement Steganography Image synthesis Generative adversarial networks Distortion Data models Decoding Encryption Security Informatics

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Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Energy-Optimized with Multi-Population Differential Annealed...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Ruixuan Shi, Yuliang Yuan, Haitao Bi, Jing Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time. © 2023 IEEE.

关键词： computation offloading energy optimization Mobile edge computing multi-population optimization simulated annealing

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Essay Rating System Using Machine Learning 5

Essay Rating System Using Machine Learning

引用

5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Pathak, Pooja Raghav, Shilpy Jain, Shubhanshu Jalal, Sunita GLA University Dept. of Mathematics Mathura India GLA University Dept. of Computer Engineering and Applications Mathura India Govind Ballabh Pant University of Agriculture and Technology Dept. of Computer Engineering Pantnagar India

ISBN: (纸本)9781665403412

Essay Rating is the process of assigning grades to the essays written for professional purposes. Automating this process implies the use of specified machine learning algorithms over the standard manual methods to evaluate the essays through Natural Language Processing. Essays can be termed as a large set of textual entities. Grading these essays involves distributing them into a fewer number of distinct categories based on certain constraints and standards. These categories are then used to assign the possible grades. Because of numerous factors like cost, time, biasness, and delay in results, there is a need to switch from manually checking the essays to automated grading. It is a very tedious task to evaluate a bulk of essays while probing every kind of error. Henceforth, this model of Automated Essay Grading System models seems quite assuring. It takes text input from the user and generates grades conforming to the pre-defined standards. After evaluating its useful attributes, an output is obtained in the form of a grade for the given essay. This project comprises of grammar-based Checking (Checking Unique Word count, Sentence Count, Grammatical Mistakes, Spelling Errors) and Context Based Checking (Application of NLP to categorize the essays). © 2021 IEEE.

关键词： Text processing

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