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IEEE Conference on Vehicular Technology (VTC)

作者： Kiana Ghassabi Peyman Pahlevani Daniel E. Lucani Department of Computer Science Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran Department of Electronic Systems Aalborg University Copenhagen Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

With the increasing amount of digital data, data deduplication has become an increasingly popular method for reducing data in large-scale storage systems. Generalized deduplication is an alternative technique for reducing the cost of data storage by identifying similar data chunks. This paper proposes TL-GD, a method for improving cloud storage efficiency using generalized deduplication focusing on textual datasets. The core concept of this study is to develop an efficient deduplication system that combines an alternative technique for splitting data into smaller pieces and a new approach for transforming data pieces into bases and deviations. The performance of the system has been validated using two real-world datasets. We also compare the results to state-of-the-art deduplication methods. Our evaluation results show that TL-GD achieves nearly 67% lossless compression for textual navigation instructions datasets, which is a 25% improvement on average compared to existing deduplication techniques.

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Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrates

Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrate...

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2024 IEEE Silicon Photonics Conference, SiPhotonics 2024

作者： Wan, Zeyu Yang, Yun-Cheng Chen, Wei-Hsin Chiu, Chih-Chuan Zhao, Yunlong Feifel, Markus Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui The University of British Columbia Department Of Materials Engineering Vancouver Canada Fraunhofer Institute For Solar Energy Systems Heidenhofstrasse Freiburg Germany The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada National Taiwan University Graduate Institute Of Electronics Engineering Taipei Taiwan National Taiwan University Graduate Institute Of Photonics And Optoelectronics Taipei Taiwan National Taiwan University Graduate School Of Advanced Technology Taipei Taiwan

ISBN: (纸本)9798350394047

This work successfully developed an independent Ge-VCSEL epitaxy and fabrication technology route. This is the second successful Ge-VCSEL technology reported worldwide, and the first Ge-VCSEL technology with key details disclosed, including Ge substrate specification, transition layer structure and composition, and fabrication process etc. Compared with the GaAs counterparts, after epitaxy optimization, the Ge-VCSEL wafer has a 40% lower surface root-mean square roughness and 72% lower average bow-warp. Additionally, the Ge-VCSEL has a 10% lower threshold current density and a 19% higher maximum slope efficiency than the GaAs-VCSEL. © 2024 IEEE.

关键词： Surface emitting lasers

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Early Retinal Degeneration Detection with Deep Faster CNNs 2

Early Retinal Degeneration Detection with Deep Faster CNNs

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Priyadharshini, K. Balraj, Sindhusaranya Kumar, J.V. Rama Vidhate, Deepak A. Thilakavathy, P. Vanathi, P. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India SRKR Engineering College Andhra Pradesh Bhimavaram India Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Maharashtra Ahmednagar India Vels Institute of Science Technology and Advanced Studies Chennai India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (纸本)9798350383867

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included . This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration;the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large. ©2024 IEEE.

关键词： Ophthalmology

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Enhanced ferromagnetism in an artificially stretched lattice in quasi-two-dimensional Cr2Ge2Te6

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Physical Review B 2025年第2期111卷 L020402-L020402页

作者： Hiroshi Idzuchi Andres E. Llacsahuanga Allcca Anh Khoa Augustin Lu Mitsuhiro Saito Subhadip Das John F. Ribeiro Michel Houssa Ruishen Meng Kazutoshi Inoue WPI Advanced Institute for Materials Research (AIMR) Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University Sendai 980-8577 Japan Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Mathematics for Advanced Materials Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology Sendai 980-8577 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Institute of Engineering Innovation The University of Tokyo Tokyo 113-8656 Japan Institute of Physics and Astronomy and Villum Centers for Dirac Materials and for Hybrid Quantum Materials and Devices Aarhus University 8000 Aarhus-C Denmark School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Katholieke Universiteit Leuven 3001 Leuven Belgium

Crystal lattice is key in understanding magnetic interactions between atoms in solids. As the effective control of the lattice via tensile stress is limited, there are only a few demonstrations of controlling magnetic properties by expanding the lattice. This study reveals a clear correlation between enhanced magnetism, where Curie temperature (TC) is doubled from ∼60 to ∼120 K, and lattice expansion in prototypical van der Waals magnet Cr2Ge2Te6 with sputtered dielectric overlayers NiO and MgO. We ascribe the mechanism of this TC increase to a change in exchange interactions induced by strain, studied by several experimental probes and computational approaches.

关键词： Van der Waals forces

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Multi-GHz Zeptojoule Computing Using Emerging Adiabatic Superconductor Circuits

Multi-GHz Zeptojoule Computing Using Emerging Adiabatic Supe...

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IEEE computer Society Annual Symposium on VLSI

作者： Christopher L. Ayala Nobuyuki Yoshikawa Yu Hoshika Yuto Omori Institute of Advanced Sciences Yokohama National University Yokohama Japan Department of Electrical & Computer Engineering Yokohama National University Yokohama Japan

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

The adiabatic quantum-flux-parametron (AQFP) is the basis of an emerging computing technology that shows tremendous promise towards building extremely energy efficient computing systems with bit energies approaching sub-zeptojoule levels at clock frequencies as high as 10 GHz. We review some of the latest AQFP circuit demonstrations including a RISC-like microprocessor called MANA and a SHA-3 permutation unit breakout chip. We briefly explore other computing possibilities for AQFP and hybrid AQFP systems and present implementation challenges for AQFP technology such as limited integration den-sity and signal integrity to name a few. Finally, we present possible solutions to these challenges, and summarize the prospects of building practical, feature-rich AQFP chips that are comparable to the modern CMOS circuits used today.

关键词： Quantum computing Superconducting logic circuits Reviews Microprocessors Buildings Very large scale integration Logic

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Explanations of Large Language Models Explain Language Representations in the Brain

arXiv

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arXiv 2025年

作者： Rahimi, Maryam Yaghoobzadeh, Yadollah Daliri, Mohammad Reza Biomedical Engineering Department School of Electrical Engineering Iran University of Science and Technology Tehran Iran Electrical and Computer Engineering Department University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

Large language models (LLMs) not only exhibit human-like performance but also share computational principles with the brain’s language processing mechanisms. While prior research has focused on mapping LLMs’ internal representations to neural activity, we propose a novel approach using explainable AI (XAI) to strengthen this link. Applying attribution methods, we quantify the influence of preceding words on LLMs’ next-word predictions and use these explanations to predict fMRI data from participants listening to narratives. We find that attribution methods robustly predict brain activity across the language network, revealing a hierarchical pattern: explanations from early layers align with the brain’s initial language processing stages, while later layers correspond to more advanced stages. Additionally, layers with greater influence on next-word prediction—reflected in higher attribution scores—demonstrate stronger brain alignment. These results underscore XAI’s potential for exploring the neural basis of language and suggest brain alignment for assessing the biological plausibility of explanation methods. © 2025, CC BY-SA.

关键词： Modeling languages

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Securing Smart Grids: Deep Reinforcement Learning Approach for Detecting Cyber-Attacks

Securing Smart Grids: Deep Reinforcement Learning Approach f...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Ahmed T. El-Toukhy Islam Elgarhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Mohamed I. Ibrahem Fathi Amsaad Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Network and Computer Security: Cybersecurity SUNY Polytechnic Institute Utica NY USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Computer Science and Engineering Wright State University Dayton OH USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Smart meters (SMs) are deployed in smart power grids to monitor customer power consumption and facilitate energy management. However, fraudulent customers can compromise these SMs to manipulate power readings and engage in electricity theft cyber-attacks, resulting in reduced electricity bills. While various machine learning approaches have been employed for detecting such attacks, the potential of reinforcement learning (RL) remains unexplored. To bridge this gap, we propose a deep reinforcement learning (DRL) approach that leverages RL's adapt-ability to dynamic cyber-attacks and consumption patterns. This approach integrates exploration and exploitation mechanisms, enabling optimal decision-making. In this study, we present our approach in two scenarios. Firstly, we develop comprehensive detection models using deep Q networks (DQN) and double deep Q networks (DDQN) with various deep neural network architectures. Secondly, we address the challenges of defending against newly launched cyber-attacks. Extensive experimentation provides strong evidence of the effectiveness of our DRL approach in improving the detection of electricity theft cyber-attacks, as well as its capacity to efficiently adapt and defend against newly launched cyber-attacks.

关键词： Adaptation models Power demand Electricity Power system dynamics Detectors Deep reinforcement learning Smart meters

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A Bayesian Approach to Online Planning

arXiv

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arXiv 2024年

作者： Greshler, Nir Ben Eli, David Rabinovitz, Carmel Guetta, Gabi Gispan, Liran Zohar, Guy Tamar, Aviv General Motors Advanced Technical Center Israel Department of Electrical and Computer Engineering Tech-nion - Israel Institute of Technology Haifa Israel

The combination of Monte Carlo tree search and neural networks has revolutionized online planning. As neural network approximations are often imperfect, we ask whether uncertainty estimates about the network outputs could be used to improve planning. We develop a Bayesian planning approach that facilitates such uncertainty quantification, inspired by classical ideas from the meta-reasoning literature. We propose a Thompson sampling based algorithm for searching the tree of possible actions, for which we prove the first (to our knowledge) finite time Bayesian regret bound, and propose an efficient implementation for a restricted family of posterior distributions. In addition we propose a variant of the Bayes-UCB method applied to trees. Empirically, we demonstrate that on the ProcGen Maze and Leaper environments, when the uncertainty estimates are accurate but the neural network output is inaccurate, our Bayesian approach searches the tree much more effectively. In addition, we investigate whether popular uncertainty estimation methods are accurate enough to yield significant gains in planning. Our code is available at: https://***/nirgreshler/ bayesian-online-planning. © 2024, CC BY.

关键词： Uncertainty analysis

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Finding of Maturity Level of Crops for Harvesting using IOT and ML Based Structured Framework 4

Finding of Maturity Level of Crops for Harvesting using IOT ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Swaranambigai, R. Mythili, R. Aeron, Anurag Thilakavathy, P. Alassedi, Zainab Al-Azzawi, Waleed Jaishriram Engineering College Department of Ece Tiruppur India Srm Institute of Science and Technology Department of Information Technology Chennai Ramapuram India Miet Meerut Department of Cse Meerut India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

IoT designs make it possible to collect data for vast and remote agricultural regions, which may then be used to use machine learning algorithms to anticipate crops. In order to establish the suggested crop, recommendations are based on factors including temperature, humidity, rainfall, pH, N, P, and K. The dataset includes 2200 occurrences and 8 characteristics. For different combinations of these attributes, about 22 alternative crops are suggested. Using specific machine learning algorithms in WEKA, supervised learning techniques are used to create an ideal model. The rules-based classifier JRip, the decision table classifier, and the multilayer perceptron are the machine learning methods selected for classification. Establishing a model that can forecast high-yield crops and facilitate precision agriculture is the main goal of this case study. IoT technologies and crucial agricultural metrics are included in the suggested system modeling. A weighted average receiver operator characteristic of one and an accuracy of 98.2273% are shown by the performance evaluation of the chosen classifiers. 8.05 seconds is the maximum amount of time needed to construct the model. © 2024 IEEE.

关键词： Adversarial machine learning

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Lightweight, High-Entropy TRNG Using Quad Cross-Coupled Feedback Architecture

Lightweight, High-Entropy TRNG Using Quad Cross-Coupled Feed...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Williams, Shelby Kirtonia, Prosen Akter, Sonia Khalil, Kasem Bayoumi, Magdy Univ. of Mississippi Department of Electrical and Computer Engineering OxfordMS38677 United States Univ. of Louisiana at Lafayette Center for Advanced Computer Studies LafayetteLA70504 United States Univ. of Louisiana at Lafayette Dept. of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798331504847

This paper presents a lightweight, high-entropy true random number generator architecture featuring an innovative quad cross-coupled feedback mechanism to enhance randomness. The primary goal is to develop an efficient and secure true random number generator that addresses the growing demand for reliable random number generation in cryptographic and security-critical applications. The motivation stems from the need to improve entropy, reduce resource utilization, and ensure robustness across varying technologies. With the intention of achieving near-perfect randomness, the Quad-Input Oscillating Circuit module integrates self-coupled, jitter-inducing ring oscillators with cross-coupled feedback loops to induce metastability. Comprehensive evaluations confirm a Shannon entropy of 0.999818, a minimum entropy of 0.977257, and a collision entropy of 0.999636. The design was synthesized using Synopsys Design Compiler at 45 nm, 32 nm, and 14 nm, achieving a maximum frequency of 6.7 GHz, power consumption as low as 72 μW, and area utilization of 24 μ m2 at 14 nm. Rigorous validation through multiple statistical test suites, including the AIS-31, Autocorrelation, Deviation, Diehard, the National institute of Standards and Technologies SP800-22 and SP800-90B, and TestU01, confirms its efficiency and reliability. Real random bits were implemented as oscilloscope-viewable signals on the Cyclone V Field Programmable Gate Array developed by Altera, representing a significant advancement in secure random number generation technologies. © 2025 IEEE.

关键词： Field programmable gate arrays (FPGA)

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