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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是141-150 订阅

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Geometrically reconfigurable Silk-Based Electronic Implants 38

Geometrically Reconfigurable Silk-Based Electronic Implants

引用

38th IEEE international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Ni, Siyuan Zhu, Ziyi Yan, Zhiwen Liang, Zhengyu Jiang, Jianbo Zou, Dujuan Yang, Huiran Zhou, Zhitao Sun, Liuyang Tao, Tiger H. Qian, Yun Wei, Xiaoling Liu, Keyin State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China Shanghai Sixth People's Hospital Shanghai Jiao Tong University School of Medicine China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China ShanghaiTech University China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Jiangxi Nanchang China Guangdong Institute of Intelligence Science and Technology Zhuhai Guangdong Hengqin China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

ISBN: (纸本)9798331508890

We report a geometrically reconfigurable silk-based electronic implant designed for peripheral nerve monitoring. Metallic conductive structures were fabricated on the surface of a geometrically reconfigurable membrane using Micro-Electro-Mechanical systems (MEMS) technology. By controlling the cutting angles of the membrane, we achieved water-responsive self-wrapping spiral electrodes, which exhibit excellent geometric adaptability to nerve tissues. In vitro tests demonstrate that our electrodes possess good mechanical, biocompatibility, and electrical properties. In vivo applications confirmed that our electrodes can effectively record compound nerve action potentials (CNAP). Our approach offers a new method for developing implantable electronic devices that require mechanical and geometrical adaptability. © 2025 IEEE.

关键词： Silk

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Decentralized Target Localization in Distributed MIMO Radars 16

Decentralized Target Localization in Distributed MIMO Radars

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Qiu, Qinhui Li, Jie Xi, Feng School of Electronic and Optical Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781510689251

This paper addresses the problem of target estimation in distributed MIMO radars, focusing on the challenge of requiring a robust fusion center to process all the collected data. To overcome this limitation, we propose a solution based on the Alternating Direction Method of Multipliers (ADMM) that eliminates the need for a centralized fusion center. By leveraging the sparsity of target location information within the detection range, the target localization problem is reformulated as a sparse recovery problem, which is then efficiently solved using ADMM. The key innovation of the proposed approach is the transformation of the average value computation, typically required in centralized methods, into a consensus averaging problem, and subsequently into a consensus optimization problem. This transformation allows for fully decentralized processing, where each node only communicates with its neighboring nodes to reach a consensus on the estimation of target location. Simulation results demonstrate that the proposed distributed algorithm achieves performance comparable to that of semi-distributed algorithms while fully exploiting the advantages of decentralization. © 2025 SPIE.

关键词： Consensus algorithm

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Machine Learning for Signal Processing: Kernel-extreme Learning Machines with Augmented Complex-valued and Mixture Correntropy Techniques 16

Machine Learning for Signal Processing: Kernel-extreme Learn...

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Lin, Bingqing Wang, Yan Guan, Yunhe Qian, Guobing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781510689251

In this paper, we propose novel machine learning algorithms for signal processing applications based on kernel-extreme learning machines (KELM) and its variants. Specifically, we focus on the use of complex domain and mixture correntropy techniques to enhance the performance and generalization ability of KELM. Two models are derived by augmenting a complex-valued extreme learning machine with kernel functions, minimizing human intervention while enhancing accuracy. Furthermore, we extend the mixture correntropy-based kernel extreme learning machine (MCKELM) to complex domains, resulting in the MCKCELM algorithm, capable of solving prediction problems in complex domains. To further improve the performance of MCKCELM, we apply an augmented matrix to derive two variant models, and demonstrate their convergence through experiments. All models presented in this paper are regularized models specifically designed for signal processing tasks. © 2025 SPIE.

关键词： Adversarial machine learning

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IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 IEEE.

关键词： Electronic health record

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AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Tidke, Bharat Pragadeeswaran, S. Lakshmi, R. Poornima Bhuvaneswari, G. Manikandan, G. Murugan, S. University Nagpur Campus Pune India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering college Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Kavaraipettai Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities. © 2025 IEEE.

关键词： Sustainable city

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Advancements in BLDC Motor Technology for Sustainable Electric Mobility 3

Advancements in BLDC Motor Technology for Sustainable Electr...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Zaineb, Azra Naresh, Jella Sydu, Shabbier Ahmed Chenchireddy, Kalagotla Jagan, Vadthya Ganesh, Lukka Bhanu Geethanjali College of Engineering and Technology Dept. of Eee Hyderabad India St.Peter's Engineering College Eee Dept. Telangana Hyderabad India E&e Section University of Technology and Applied Sciences AI-Musannah Oman Vbit Eee Deptt. Telangana Hyderabad India Vignan Instituteo of Technology and Science Dept. of Eee India

ISBN: (纸本)9798331509675

Brushless DC (BLDC) motors have emerged as a cornerstone in the propulsion systems of electric vehicles (EVs), offering high efficiency, compact size, and reliable operation. This paper explores the latest advancements in BLDC motor technology and their implications for sustainable electric mobility. Innovations in magnetic materials, power electronics, and control algorithms have enhanced motor efficiency, power density, and reliability, making them pivotal for advancing electric vehicle technology. The adoption of rare-earth-free magnetic materials addresses supply chain and environmental concerns, while cutting-edge control algorithms like model predictive control and artificial intelligence enable precise motor management. Improved thermal management systems and lightweight designs have further contributed to higher energy efficiency and extended vehicle range. Moreover, the integration of BLDC motors with renewable energy systems and vehicle-to-grid (V2G) technology underscores their role in promoting a sustainable and resilient energy ecosystem. Despite these advancements, challenges such as cost optimization, scalability, and durability remain critical. This paper highlights the multidisciplinary efforts required to overcome these hurdles and outlines future directions in BLDC motor research and development. By fostering collaboration across industries and academia, the continued evolution of BLDC motor technology will ensure its critical role in the global transition to sustainable electric mobility. © 2025 IEEE.

关键词： Predictive control systems

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AI-Powered Face Authentication System for Web and Native Apps 2

AI-Powered Face Authentication System for Web and Native App...

引用

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

作者： Roy, Monideepa Datta, Sujoy Khan, Muhit Paroi, Methu Hasan, Mehedi School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (纸本)9798331505745

In a period where security breaches and password-related issues are prevalent, the demand for more secure and user-friendly authentication methods has become paramount. This paper introduces a novel approach to web application authentication using facial recognition technology. The system allows users to register and log in to the web app using their unique facial features, offering a secure and convenient alternative to traditional password-based authentication methods. Through the integration of facial detection and recognition algorithms, coupled with a user-friendly interface, this paper aims to provide a seamless and robust authentication experience for users across various platforms. © 2025 IEEE.

关键词： Face recognition

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Secure Healthcare Monitoring: algorithms and Techniques for Heart Disease Detection and Data Privacy 1

Secure Healthcare Monitoring: Algorithms and Techniques for ...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Poonam Mallapur, Sujata V. Sharnbasva University Department of Computer Science and Engineering Kalaburagi India Sharnbasva University Department of Artificial Intelligence & Machine Learning Kalaburagi India

ISBN: (纸本)9798331529246

This survey offers the review of Healthcare Monitoring systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope. © 2025 IEEE.

关键词： Differential privacy

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Machine learning algorithm for brain stroke detection 4th

Machine learning algorithm for brain stroke detection

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Thakur, Sakshi Puri, Renu Virk, Ishpreet Singh Department of Computer Science and Engineering Chandigarh Engineering College – CGC Landran SAS Nagar India

ISBN: (纸本)9781032911571

Early brain stroke detection is essential for improved results and prompt treatments. Standared techniques work suggests a novel method for machine learning that makes use of CT or MRI scans images. In orderly to train a model using these type of classification algorithms like SVM, Random Forest, or CNN, knn a dataset/model of both healthy and stroke-affected brains must be *** type of Features like intensity, texture, and shape must then be extracted. This methodology has potential uses in modern healthcare systems and in the diagnosis of other brain disorders. It shows promise in enhancing the accuracy and efficiency of stroke detection. © 2025 the Author(s).

关键词： Computerized tomography

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Approximate Message Passing for Spatial Modulation with Structured Sparsity 14

Approximate Message Passing for Spatial Modulation with Stru...

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14th international ITG conference on systems, Communications and Coding, SCC 2025

作者： Franz, Daniel Kuhn, Volker University of Rostock Faculty of Computer Science and Electrical Engineering Germany

ISBN: (纸本)9798331522896

Multiple antennas at transmitter and receiver have significantly improved the performance of wireless communications systems. Traditionally, space-time coding, beamforming, or spatial multiplexing are applied to achieve diversity, SNR, and multiplexing gains, respectively. A different concept is pursued with Generalized Spatial Modulation (GSM). It conveys information through the selection of active antennas from a set of available antennas in addition to the transmission of conventional modulation symbols. This results in sparse transmit vectors, and detection algorithms known from compressed sensing can be applied. In this paper, Vector Approximate Message Passing (VAMP) is considered for the detection of GSM signals in massive Multiple-Input Multiple-Output (MIMO) systems. Its performance is improved by introducing structured redundancy in space or in time. In order to exploit this redundancy, the VAMP algorithm has to be extended by an additional graph layer. The resulting performance is evaluated by analyzing the error rates and the throughput obtained from Monte Carlo simulations. © 2025 IEEE.

关键词： Space time codes

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