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arXiv 2023年

作者： Eckhoff, Moritz Knobbe, Dennis Zwirnmann, Henning Swikir, Abdalla Haddadin, Sami Technical University of Munich Germany TUM School of Computation Information and Technology Department of Computer Engineering Chair of Robotics and Systems Intelligence Germany Germany Technische Universität Dresden Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University Albaida Libya

One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Although several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully hand-crafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and realtime compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Collision avoidance

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Welcome to 2023 9th International Conference on Signal Processing and Communication (ICSC)

2023 9th International Conference on Signal Processing and C...

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2023 9th International Conference on Signal Processing and Communication, ICSC 2023 2023年 vi-vii页

作者： Whittow, William G. Kurinec, Santosh K. Sharma, Satish K. Lall, Brejesh Pachori, Ram Bilas Deb, Sujay Karandikar, Abhay Sachan, Amit Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University LEICS LE11 3TU United Kingdom IEEE Electrical & Microelectronics Engineering Rochester Institute of Technology New York United States Department of Electrical and Computer Engineering San Diego State University United States Bharti School of Telecom Technology & Management India Department of Electrical Engineering Indian Institute of Technology Delhi India Department of Electronics and Communication Engineering Indraprastha Institute of Information Technology Delhi India The Department of Science & Technology Indian Institute of Technology Kanpur India Media Intelligence Jio India

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Life Test Plan Optimization for Electrical Insulation During Temperature Load: A Semi-Optimal Approach

Life Test Plan Optimization for Electrical Insulation During...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Rahul Vishnoi Sanjeev Kumar Verma Vishal Sharma K. Yuvaraj S. Gopinath Deepika Dongre Department of ECE TMU Department of Applied Sciences Invertis University Bareilly Uttar Pradesh Film & Television Studies School of Media IIMT University Meerut Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore Vishwakarma Institute of Information Technology Pune

Considering that the Arrhenius equation between stress condition and lifetime is valid and that the proportional lifespan at an excessive pressure reflects some reliable statistical properties. It is essential to demonstrate the highest diagnostic strategy from the perspective of effectiveness under such living scenarios. Knowing the semi-optimal test plan, where effectiveness is similar to that of the maximum one and test conditions are straightforward, is additionally helpful. The goal of minimization is to determine the ideal amount of test subjects with each test stressors, and we assume that there are three sample stress levels. The root-average-squared error for the duration in use circumstance serves as the benchmark for optimization problems. We were using the hyperparameters in an actual trial situation to adjust for realism. If predictability of the Characteristic curve is necessary, we have ended up finding that there is just a subtle distinction between both the optimal results obtained and the mainstream test result when making comparisons it to the data achieved that use the mainstream tester, in which test samples are equitably represented to each experiment anxiety levels. The traditional testing phase is one of the partially ideal test plans, in our opinion. The agreement between the theory and computer findings has been examined.

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Unveiling the Efficacy of Unani Medicine in Female Disorders Through Machine Learning: Current Challenges and Opportunities 20

Unveiling the Efficacy of Unani Medicine in Female Disorders...

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20th International computer Conference on Wavelet Active media technology and information Processing, ICCWAMTIP 2023

作者： Sultana, Arshiya Akhtar, Faijan Bin Heyat, Md Belal Singh, Priya Parveen, Saba Kumar, Shailendra Sayeed, Eram Akhtar, Soha Singh, Bandana Rahmani, Khaleequr Li, Jianping National Institute of Unani Medicine Ministry of AYUSH Department of Amraze Niswan Wa Ilmul Qabalat Karnataka Bengaluru India School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Engineering Westlake University CenBRAIN Neurotech Center of Excellence Zhejiang Hangzhou China Dreamvcssels Technology UP India College of Electronic and Information Engineering Shenzhen University Guangdong Shenzhen China Integral University Department of Electronics and Communication Engineering Lucknow India Government Co-ed Sarvodaya Vidyalaya Madanpur Khadar New Delhi India Hamdard Public School New Delhi India School of Media and Communication Shenzhen University Shenzhen China National Institute of Unani Medicine Ministry of AYUSH Department of Ilmul Saidla Karnataka Bengaluru India

ISBN: (纸本)9798350318982

In the past decade, the healthcare sector has seen a noteworthy increase in the application of artificial intelligence (AI) technologies across various therapeutic domains. AI-powered technologies have been increasingly used in the context of female diseases within Unani medicine. This study unveils the efficacy of Unani medicine in female diseases through machine learning. This review abides by the preferred reporting items for systematic reviews and meta-analysis extensions for scoping reviews. Studies published after January 1, 2000, were included in 2000 to 2023. A total of 402 full-length articles were searched from the PubMed and Science direct. Two studies from published articles and two from the proposed dataset exclusively focused on the efficacy of Unani medicine, along with one cross-sectional study. This review demonstrates that this research has been conducted using various machine-learning techniques to detect the efficacy of Unani treatment in female disoder. Machine learning was also applied in a cross-sectional study on abnormal vaginal discharge and Mizaj. Machine learning models were used. While research on machine learning models in Unani herbal medicine is in its preliminary stages, the findings from the selected research suggest that ML methods can enhance the services provided by Unani practitioners to their patients. Nevertheless, future advancements should involve the application of diverse ML models across a broad spectrum of Unani treatments. © 2023 IEEE.

关键词： Machine learning

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A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

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Discover Data 2024年第1期2卷 1-39页

作者： Adewole, Kayode S. Alozie, Emmanuel Olagunju, Hawau Faruk, Nasir Aliyu, Ruqayyah Yusuf Imoize, Agbotiname Lucky Abdulkarim, Abubakar Imam-Fulani, Yusuf Olayinka Garba, Salisu Baba, Bashir Abdullahi Hussaini, Mustapha Oloyede, Abdulkarim A. Abdullahi, Aminu Kanya, Rislan Abdulazeez Usman, Dahiru Jafaru Department of Computer Science University of Ilorin Ilorin Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Department Information and Media Studies Faculty of Communication Bayero University Kano Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunications Engineering Kampala International University Kampala Uganda Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Software Engineering Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Cyber Security Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Economics Faculty of Social and Management Sciences Kafin Hausa Nigeria Department: University Medical Services Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Computer Science and Information Technology Baze University Abuja Nigeria Department: International Law and Jurisprudence Faculty of Law Bayero University Kano Nigeria

A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural mod

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Capacitor Minimization in Analog Synapses for Efficient and Compact Binarized SNNs

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and Systems for Artificial Intelligence 2025年第2期2卷 114-125页

作者： Mikail Yayla Ming-Liang Wei Simon Thomann Albi Mema Chia-Lin Yang Jian-Jia Chen Hussam Amrouch Design Automation for Embedded Systems Group TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Department of the Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Technical University of Munich (TUM) Munich Germany TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Munich Germany Department of Computer Science and Information Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Computer Science TU Dortmund University Dortmund Germany Lamarr Institute Dortmund Germany

Accelerating the immense workloads of Neural Networks (NNs) is a critical challenge, and analog computing-based approaches present a promising solution. Among these, the Integrate-and-Fire (IF) Spiking Neural Network (SNN) stands out for its potential in efficient neural computation. However, achieving high inference accuracy through precise multiply-accumulate (MAC) operations necessitates a large membrane capacitor—224 to 547 times larger than the synapse array at the 14nm technology node—resulting in prohibitive area costs. Additionally, the large capacitor size introduces higher energy consumption and longer access times. Thus, a key research challenge is maintaining accuracy while mitigating the costs associated with the large membrane capacitor. In this work, we propose a HW/SW Codesign method, called CapMin, for capacitor size minimization in analog computing IF-SNNs. CapMin minimizes the capacitor size by reducing the number of spike times needed for accurate operation of the HW, based on the absolute frequency of MAC level occurrences in the SW. To increase the operation of IF-SNNs to current variation, we propose the method CapMin-V, which trades capacitor size for protection based on the reduced capacitor size found in CapMin. In our experiments, CapMin achieves more than a 14 $\times$ reduction in capacitor size along with a 34% reduction in energy consumption over the state of the art, while CapMin-V achieves increased variation tolerance in the IF-SNN operation, requiring only a small increase in capacitor size.

关键词： Capacitors Circuits Accuracy Capacitance Micromechanical devices Costs Neurons Integrated circuit modeling Spiking neural networks Hardware

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AI-DRIVEN DAY-TO-DAY ROUTE CHOICE

arXiv

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arXiv 2024年

作者： Wang, Leizhen Duan, Peibo He, Zhengbing Lyu, Cheng Chen, Xin Zheng, Nan Yao, Li Ma, Zhenliang Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge United States Chair of Transportation Systems Engineering Technical University of Munich Munich Germany School of Civil Engineering The University of Queensland Brisbane Australia Department of Civil Engineering Monash University Melbourne Australia School of Computer Science and Engineering Southeast University Nanjing China Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden

Understanding travelers’ route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently, Large Language Models (LLMs) have emerged as a promising alternative, demonstrating remarkable ability to replicate human-like behaviors across various fields. Despite this potential, their capacity to accurately simulate human route choice behavior in transportation contexts remains doubtful. To satisfy this curiosity, this paper investigates the potential of LLMs for route choice modeling by introducing an LLM-empowered agent, "LLMTraveler." This agent integrates an LLM as its core, equipped with a memory system that learns from past experiences and makes decisions by balancing retrieved data and personality traits. The study systematically evaluates the LLMTraveler’s ability to replicate human-like decision-making through two stages of day-to-day (DTD) congestion games: (1) analyzing its route-switching behavior in single origin-destination (OD) pair scenarios, where it demonstrates patterns that align with laboratory data but cannot be fully explained by traditional models, and (2) testing its capacity to model adaptive learning behaviors in multi-OD scenarios on the Ortuzar and Willumsen (OW) network, producing results comparable to Multinomial Logit (MNL) and Reinforcement Learning (RL) models. Additionally, the study assesses lightweight, open-source LLMs, highlighting their effectiveness in route choice simulation and their potential as cost-effective alternatives to more advanced closed-source models. These experiments demonstrate that the framework can partially replicate human-like decision-making in route choice while providing natural language explanations for its decisions. This capability offers valuable insights for transportation poli

关键词： Reinforcement learning

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1-Bit Reconfigurable Phase Shifter for mm-Wave Antenna Beam-Steering Applications

1-Bit Reconfigurable Phase Shifter for mm-Wave Antenna Beam-...

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Antennas and Propagation Society International Symposium

作者： Asif Bilal Abdul Quddious Atsushi Kanno Tetsuya Kawanishi Marco A. Antoniades Stavros Iezekiel EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universität Dresden Dresden Germany Nagoya Institute of Technology Aichi Japan National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

A 1-bit reconfigurable phase shifter for 28 GHz mm-wave beam steering antennas is proposed. The design uses two beam-lead GaAs PIN diodes loaded on a microstrip transmission line in parallel, to provide two discrete phases for the transmitted signal. When the diodes are switched OFF, no shift in the phase of the transmitted signal is observed while a phase shift of ~90° is achieved when the diodes are switched ON. The total length of the phase shifter is $\sim\lambda /2$ . The compact size, low cost, reduced complexity and easy adaptation in antenna and phased array technology makes it a suitable candidate for 5G mm-wave beam steering applications.

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Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

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SN computer Science 2020年第4期1卷 1-15页

作者： Shinde, Gitanjali R. Kalamkar, Asmita B. Mahalle, Parikshit N. Dey, Nilanjan Chaki, Jyotismita Hassanien, Aboul Ella Department of Computer Engineering Smt. Kashibai Navale College of Engineering Maharashtra Pune India Department of Communication Media and Information Technologies Aalborg University Copenhagen Denmark Department of Information Technology Techno International New Town Kolkata India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Faculty of Computers and Information Information Technology Department Cairo University Giza Egypt

COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data COVID-19 Epidemic Forecasting models Machine learning method Pandemic Prediction

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Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy

arXiv

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arXiv 2023年

作者： Alharbi, Raed Chan-Olmsted, Sylvia Chen, Huan Thai, My T. Computer and Information Science and Engineering Department University of Florida GainesvilleFL32611 United States Department of Media Production Management and Technology University of Florida GainesvilleFL32611 United States Department of Advertising University of Florida GainesvilleFL32611 United States

Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunately, there are many factors involving in deciding whether to take the vaccine, especially from the cultural point of view. To obtain these goals, we design a novel culture-aware machine learning (ML) model, based on our new data collection, for predicting vaccination willingness. We further analyze the most important features which contribute to the ML model's predictions using advanced AI explainers such as the Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP). These analyses reveal the key factors that most likely impact the vaccine adoption decisions. Our findings show that Hispanic and African American are most likely impacted by cultural characteristics such as religions and ethnic affiliation, whereas the vaccine trust and approval influence the Asian communities the most. Our results also show that cultural characteristics, rumors, and political affiliation are associated with increased vaccine rejection. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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