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SHINSAI: A 586mm2Reusable Active TSV Interposer with Programmable Interconnect Fabric and 512Mb 3D Underdeck Memory 72

SHINSAI: A 586mm2Reusable Active TSV Interposer with Program...

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72nd IEEE International Solid-State Circuits conference, ISSCC 2025

作者： Jiao, Bo Zhu, Haozhe Zeng, Yuman Li, Yongjiang Liao, Jie Jia, Siyao Tian, Mochen Chen, Zexing Zhu, Jundong Wen, Dexin Wang, Yan Wang, Yu Xu, Jian Wang, Feng Tao, Jun Chen, Chixiao Liu, Qi Liu, Ming Fudan University Shanghai China Kiwimoore Semiconductors Shanghai China

ISBN: (纸本)9798331541019

As high-density 3D integration technology progresses, active TSV interposers are emerging as a critical component in More-than-Moore strategies [1],[3], enabling the transformation of traditional multicore monolithic systems into compact two-layer stacking architectures. These active interposers, often referred to as base dies, typically occupy several hundred square millimeters and provide advantages, facilitating integrated power management, mature-technology-friendly system 10s, and efficient inter-chiplet communication through active buffers. Developing large-area silicon with TSV/bumping technology for dedicated designs is both costly and inefficient, analogous to reinventing the wheel. Reusable 2.5D/3D components have emerged as a promising solution [4], [6] to reduce the NRE costs and simplify the design complexity of 3D integrated systems. For example, a single active interposer can be configured in two different schemes [3]: one supports three CPU top dies, while the other operates with two domain-specific accelerators. However, the interconnect and interfaces are customized for specific chiplets in different configurations, leading to challenges in reusing signal links on the interposer. Moreover, the expansive active area of the interposer allows for the integration of considerable on-interposer memory, known as 3D underdeck memory. However, the orchestration between the top dies and the underdeck memory poses challenges due to the diverse requirements across different tasks. To address these issues, this paper presents a reusable active TSV-interposer, known as SHINSAI, in 28nm-CMOS/TSV-middle/micro-bump (μbump) technology. The key contributions are 1) heterogeneous dual-layer network-on-active-interposer (NoAI) with 512Mb underdeck SRAM;2) programmable horizontal (2.5D) die-to die link (H-Link) fabric, achieving independent μbump-to-μbump routing for various interconnect topologies;3) a reconfigurable vertical link (V-Link) bridging 3D NoC among the top

关键词： electronics packaging

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Electric Vehicle Charging in Smart Grids with Renewable Energy Sources Using the Secretary Bird Optimization Algorithm 2

Electric Vehicle Charging in Smart Grids with Renewable Ener...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ramkumar, M. Siva Sivaramkrishnan, M. Karthikeyan, G. Kanan, Mohammad Chandrasekaran, Gokul Giri, Jayant SNS College of Technology Dept of ECE India Karpagam College of Engnieering Dept of EEE India Sona College of Technology Department of Electrical and Electronics Engineering Salem India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641105 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

Sustainable energy management (EM) advances through integrating electric vehicles (EVs) with smart grids and renewable energy sources (RESs). Traditional optimization strategies face hurdles because EV charging is dynamic while preventing grid congestion remains essential. This paper proposes the Secretary Bird Optimization Algorithm (SBOA), a novel approach for optimizing EV charging in SGs integrated with RESs. The primary goals of the proposed method are to minimize energy costs, balance grid load, and accommodate user preferences. SBOA is applied to optimize the EV charging schedules within SGs, with the method implemented and tested on the MATLAB platform. Performance comparisons are made against established optimization techniques, including the Modified Snake Optimization Algorithm (MSOA), Efficient Black Widow Optimization Algorithm (EBWOA), Deep Neural Networks (DNN), and Multi-Objective Robust Optimization (MRO). The results indicate that SBOA significantly outperforms the other methods in terms of energy efficiency and grid stability, achieving a cost of energy (COE) of $136/kWh. These findings underscore the potential of SBOA as an effective tool for optimizing EV charging in SGs with renewable energy (RE) integration. © 2025 IEEE.

关键词： Multiobjective optimization

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Blockchain for Patient-Centric Solutions Using Distributed Ledger technology

Blockchain for Patient-Centric Solutions Using Distributed L...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Sandhya, G. Vaishnavi, T. Subashree, M. Naveen Kumar, A. Sri Krishna College of Technology Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Blockchain technology, with its decentralized and tamper-resistant nature, has the potential to revolutionize healthcare by improving data security, reliability, and interoperability. By leveraging encryption methods such as Attribute-Based Encryption (ABE), patient records can be securely stored and protected from unauthorized modifications or breaches. Additionally, the integration of smart contracts and standardized data formats enables seamless communication across diverse healthcare systems, fostering a more connected and efficient ecosystem. This patient-focused model enhances data privacy while granting individuals greater control over their medical information. However, the adoption of blockchain in healthcare encounters several challenges, including regulatory hurdles, scalability issues, implementation costs, and acceptance by users. Moreover, integrating blockchain with existing infrastructures and ensuring cooperation among stakeholders adds further complexity. Despite these obstacles, research underscores blockchain's potential to redefine healthcare data management. Addressing these challenges can pave the way for a more secure, interoperable, and patientdriven healthcare system in the future. © 2025 IEEE.

关键词： Information management

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Research on the Construction of Financial Intelligence systems of Colleges and Universities Based on the integration of Industry and Finance 24

Research on the Construction of Financial Intelligence Syste...

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Proceedings of the 2024 2nd International conference on Information Education and Artificial Intelligence

作者： Yue Zhuo Yan Xiong Xiaojuan Liu Yiguo Li Finance office Sichuan University Chengdu Sichuan China

ISBN: (纸本)9798400711732

Information technology in colleges and universities has emerged rapidly with the development of the information age and has become a development requirement for the modernization of college education. This article focuses on the integration of artificial intelligence technology and the construction of a university financial system, with A college for example, constructed based on the fusion of industry and finance financial intelligence management architecture of colleges and universities; the use of the actual business scenarios on the intelligent financial management module for the Application analysis of financial intelligent management module using real business scenarios; finally puts forward the application suggestions for the development of financial intelligence in colleges and universities.

关键词： Intelligent application systems intelligent financial systems

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SmartGrid EV: Efficient Charging and Energy Harvesting system

SmartGrid EV: Efficient Charging and Energy Harvesting Syste...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Baskar, P. Dharuna Gowsick Raja, M. Akesh, G. Harish, R. Sri Krishna College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331505745

The need for charging stations has significantly increased as a result of this project's advancement, making the creation of effective and sustainable charging solutions necessary. This project offers a cutting-edge charging station prototype made to support sustainable energy practices and satisfy the expanding demands of EV consumers. In addition to charging electric cars, the suggested system uses a dual power supply technique that transforms surplus energy into an AC supply for wider community usage. The charging station guarantees peak performance and energy efficiency by using a micro controller chip for accurate control and monitoring. The method supports grid stability and energy sustainability by allowing excess energy to be reinjected into the grid after EV batteries are completely charged. In order to promote a more environmentally friendly transportation future, this project intends to improve the usefulness of charging stations, encourage the incorporation of renewable energy sources, and offer a dependable infrastructure for electric cars. © 2025 IEEE.

关键词： Electric power transmission networks

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A Hybrid Learning-Based Q-Commerce Framework Evolving from E-Commerce 2

A Hybrid Learning-Based Q-Commerce Framework Evolving from E...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Presskila, X. Arogya Raj, J. Relin Francis Kumar, P. Selva Sankar, G. Ram Rajkumar, G. Vinoth Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Francis Xavier Engineering College Department of Management Studies Tirunelveli India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid evolution of E-Commerce has led to the emergence of Quick Commerce (Q-Commerce), a model that prioritizes ultra-fast deliveries and real-time decision-making. This paper proposes a Hybrid Learning-based framework that enhances Q-Commerce operations by integrating Self-Supervised, Supervised, Reinforcement, and Unsupervised Learning techniques. The model leverages BERT for self-supervised pretraining, enabling context-aware customer personalization and improved search relevance. LSTMs and CNNs are employed for demand forecasting and product classification, ensuring optimized inventory management. GNNs enhance personalized recommendations by analyzing user-product relationships. For logistics optimization, DQN and PPO are implemented to dynamically adjust last-mile delivery routes, reducing delivery times and operational costs. To ensure fraud prevention, AEs and GANs detect anomalous transaction behaviors, improving security while minimizing false positives. The proposed hybrid deployment strategy, combining cloud-based AI inference and Edge AI at micro-fulfillment centers (MFCs), enhances system responsiveness and scalability. The framework is evaluated using MAE, RMSE, HR, Fl-Score, and ROC curves, ensuring accurate demand prediction, optimized logistics, and effective fraud detection. Future advancements in Autonomous Deliveries, AI-Powered Warehousing, and Blockchain integration will further revolutionize Q-Commerce, making it an indispensable extension of E-Commerce. © 2025 IEEE.

关键词： Marketplaces

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GuavaCareNet: Empowering Guava Farms with Federated Learning for Real-Time Disease Diagnosis

GuavaCareNet: Empowering Guava Farms with Federated Learning...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Kirubha, D. Krishnan, R. Santhana Raj, J. Relin Francis Soundiraraj, N. Murali, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Rajarajeswari College of Engineering Department of Computer Science and Engineering Bangalore India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331505745

This research presents a Federated Learning-based approach for the detection of guava leaf diseases, aimed at enhancing agricultural practices while ensuring data privacy. Guava farms across various regions provide images of guava leaves, both healthy and diseased, to train a deep learning model for disease classification. The EfficientNet-B3 model, optimized with Neural Architecture Search (NAS) and Transfer Learning, is used for accurate disease detection, including conditions like Anthracnose and Powdery Mildew. To ensure privacy, Federated Learning (FL) is employed, where the model is trained locally on edge devices such as smartphones and Jetson Nano devices situated at guava farms. These devices process the data locally and send model updates, rather than raw data, to a central server. This method enables the aggregation of knowledge from multiple farms without sharing sensitive information, thereby protecting farmers' data privacy. The use of Federated Learning allows the model to adapt to specific conditions at each farm, such as different diseases, leaf appearances, and lighting variations, improving its robustness and accuracy. Additionally, AWS IoT integration allows real-time monitoring and alerts, further empowering farmers with timely disease detection. The decentralized nature of this approach not only enhances disease detection in guava crops but also fosters data privacy, ensuring farmers' information remains secure. © 2025 IEEE.

关键词： Sensitive data

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IoT Based Varicose Vein Diagnosis system and Therapy 2

IoT Based Varicose Vein Diagnosis System and Therapy

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Shubha, B. Ambalika Hegde, K. Amruta, H. Ananya Deeksha, B.V. Ashwini, K. NMAM Institute of Technology Dept. of E and C Eng. Karnataka 574110 India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Varicose veins are common among old age people. Varicose veins are inflamed and tangled veins on the legs. Blood is carried from the heart to the body via arteries, with veins returning blood to the heart, necessitating the return of blood from the legs against gravitational forces. Damage to vein valves leads to varicose veins, a condition linked to factors such as overweight, lack of exercise, hormonal fluctuations during pregnancy, and prolonged standing. This condition can cause significant discomfort and pain. Massage and exercise are effective in improving blood flow and alleviating symptoms. This project proposes the constant monitoring of physiological conditions using sensor technology and the alleviation of symptoms through vibration and cooling therapy. The integration of heart rate, SpO2, temperature, and muscle activity allows for the measurement of physiological indicators. This real-time analysis, facilitated through the Blynk app, enables symptom detection, and discomfort is alleviated and blood circulation is enhanced through controlled vibration and cooling therapy. © 2025 IEEE.

关键词： Heart

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Smart Grid Management with AI Driven Analytics in Solar Energy

Smart Grid Management with AI Driven Analytics in Solar Ener...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Timande, Devanshi Agade, Nishant Gudadhe, Amit Faculty of Engineering & Technology Department of Artificial Intelligence and Data Science Wardha India Faculty of Engineering & Technology Wardha India

ISBN: (纸本)9798331505745

This paper presents an AI-driven framework for real-time smart grid optimization tailored specifically for solar energy integration. The proposed system features a multi-layered architecture that integrates data acquisition from solar irradiance sensors, smart meters, and meteorological sources with advanced pre-processing techniques to ensure data quality and fusion. Using advanced deep learning architectures, such as Long Short-Term Memory networks for forecasting solar irradiance and regression methodologies for predicting load, our proposed framework dynamically adjusts energy dispatch and demand response measures to address the inherent variability associated with solar power. Additionally, a combined strategy that incorporates reinforcement learning is implemented to improve real-time grid stability and operational efficiency, and explainable artificial intelligence methods are used to provide clarity in decision-making processes. Simulation studies conducted on an enhanced IEEE 24-bus test system have shown to reduce forecast error by up to 30% and prevent the occurrence of grid instability events by about 40% in contrast with traditional methods. These results are promising for AI-based analytics since it promises to enhance smart grid functionality and enable more sustainable use of renewable resources. Further research will develop this model with additional renewable energy sources and increase cybersecurity. © 2025 IEEE.

关键词： Reinforcement learning

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The integration of Ideological and Political Content in College English Teaching from the Perspective of VR technology 24

The Integration of Ideological and Political Content in Coll...

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Proceedings of the 2024 2nd International conference on Information Education and Artificial Intelligence

作者： Yaru Cheng Guofeng Cui English School Jilin International Studies University Changchun Jilin China Changchun Qiming Lingdian Automotive Electronics Co. Changchun Jilin China

ISBN: (纸本)9798400711732

This study explores the application of virtual reality (VR) technology in the ideological and political education of English courses in colleges and universities, with the aim of improving students’ vocabulary memorization, oral expression and acceptance of ideological and political content. Using the method of controlled experiments, the experimental group was placed in an immersive VR environment and compared with the control group in terms of multidimensional teaching effect, and its significant advantages in improving students’ learning effect and ideological and political identity were verified through data analysis. The results of the study show that VR technology is superior to traditional teaching methods in building interactivity and sense of context, confirming its potential for application in ideological and political education, and providing an important reference for the subsequent innovation of education mode.

关键词： College English Teaching

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