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arXiv 2025年

作者： Gao, Yunquan Zhang, Zhiguo Donta, Praveen Kumar Dehury, Chinmaya Kumar Wang, Xiujun Niyato, Dusit Zhang, Qiyang School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China Department of Computer and Systems Sciences Stockholm University Stockholm106 91 Sweden Institute of Computer Science University of Tartu IISER Berhampur India College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Computer Science School Peking University Beijing100876 China

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving a growing demand to enable their capabilities on mobile devices. However, existing mobile inference frameworks are often rely on a single processor to handle each model’s inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNNs accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Several works have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an optimal subgraph partitioning strategy offline, considering both hardware support of operations and scheduling granularity, while employing a processor-state-aware scheduling algorithm that dynamically balances workloads based on real-time operational conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results show that, compared to vanilla inference frameworks, ADMS reduced multi-DNN inference latency by 4.04×.MSC Codes 68T07, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

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New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking

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computers, Materials & Continua 2022年第12期73卷 4595-4609页

作者： Deepak Sukheja Javaid Ahmad Shah G.Madhu K.Sandeep Kautish Fahad A.Alghamdi Ibrahim.S.Yahia El-Sayed M.El-Kenawy Ali Wagdy Mohamed Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia Department of Mathematics Dr.APJ Abdul Kalam University Indore(M.P)India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia LBEF Campus Kathmandu44600Nepal Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Laboratory of Nano-Smart Materials for Science and Technology(LNSMST) Department of PhysicsFaculty of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Nanoscience Laboratory for Environmental and Biomedical Applications(NLEBA) Semiconductor Lab.Department of PhysicsFaculty of EducationAin Shams UniversityRoxyCairo 11757Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Sciences Engineering The American University in CairoEgypt

Efficient decision-making remains an open challenge in the research community,and many researchers are working to improve accuracy through the use of various computational *** this case,the fuzzification and defuzzification processes can be very *** is an effective process to get a single number from the output of a fuzzy *** defuzzification as a center point of this research paper,to analyze and understand the effect of different types of vehicles according to their *** this paper,the multi-criteria decision-making(MCDM)process under uncertainty and defuzzification is discussed by using the center of the area(COA)or ***,to find the best solution,Hurwicz criteria are used on the defuzzified ***-making technique is proposed using Hurwicz criteria for triangular and trapezoidal fuzzy *** proposed technique considers all types of decision makers’perspectives such as optimistic,neutral,and pessimistic which is crucial in solving decisionmaking problems.A simple case study is used to demonstrate and discuss the Centroid Method and Hurwicz Criteria for measuring risk attitudes among *** significance of the proposed defuzzification method is demonstrated by comparing it to previous defuzzification procedures with its application.

关键词： Defuzzification decision-making fuzzy numbers Hurwicz multicriteria decision-making ranking order

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Toward accurate thermal modeling of phase change material based photonic devices

arXiv

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arXiv 2023年

作者： Aryana, Kiumars Kim, Hyun Jung Popescu, Cosmin-Constantin Vitale, Steven Bae, Hyung Bin Lee, Taewoo Gu, Tian Hu, Juejun NASA Langley Research Center HamptonVA23666 United States Department of Materials & Science Engineering Massachusetts Institute of Technology CambridgeMA United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA United States KAIST Analysis Center Korea Advanced Institute of Science and Technology Yuseong-gu Daejeon34141 Korea Republic of Materials Research Laboratory Massachusetts Institute of Technology CambridgeMA United States

Reconfigurable or programmable photonic devices are rapidly growing and have become an integral part of many optical systems. The ability to selectively modulate electromagnetic waves through electrical stimuli is crucial in the advancement of a variety of applications from data communication and computing devices to environmental science and space explorations. Chalcogenide-based phase change materials (PCMs) are one of the most promising material candidates for reconfigurable photonics due to their large optical contrast between their different solid-state structural phases. Although significant efforts have been devoted to accurate simulation of PCM-based devices, in this paper, we highlight three important aspects which have often evaded prior models yet having significant impacts on the thermal and phase transition behavior of these devices: the enthalpy of fusion, the heat capacity change upon glass transition, as well as the thermal conductivity of liquid-phase PCMs. We further investigated the important topic of switching energy scaling in PCM devices, which also helps explain why the three above-mentioned effects have long been overlooked in electronic PCM memories but only become important in photonics. Our findings offer insight to facilitate accurate modeling of PCM-based photonic devices and can inform the development of more efficient reconfigurable optics. © 2023, CC BY.

关键词： Phase change materials

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A Complex Brain Learning Skeleton Comprising Enriched Pattern Neural Network System for Next Era Internet of Things

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Journal of Healthcare engineering 2023年第1期2023卷

作者： Venkatesh, J. Shukla, Prashant Kumar Ahanger, Tariq Ahamed Maheshwari, Manish Pant, Bhasker Hemamalini, R. Rani Halifa, Awal Center for System Design Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur522502 India College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Science & Applications Makhanlal Chaturvedi National University of Journalism and Communication Madhya Pradesh Bhopal India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of Electrical and Electronics Engineering St. Peter's Institute of Higher Education and Research Tamil Nadu Chennai India Kwame Nkrumah University of Science and Technology Kumasi Ghana

The Internet of Things (IoT) is now growing dramatically on various levels and helps to digitize various vital industries quickly. The most difficult obstacle for BCIs to overcome is the fact that not everyone has the same brain. Every new session requires the BCI to learn from the user's brain, which is accomplished via the use of machine learning. However, this learning process is time-consuming. Calibration time refers to the amount of time it takes for the BCI to adapt to the user's brain in order to properly categorize their thoughts and determine their meaning. The patient has had to wait an arduous and tiresome length of time for the system to be completely functioning up until now because of this calibration, which may take up to 20-30 minutes. The aim of this paper was to find a way to decrease the amount of time required for calibration to the smallest amount feasible. In the first section of this paper, a first effort is made to determine the optimum number of features required for the BCI to operate reasonably, taking into consideration all of the calibration data provided. When the results were averaged across five participants, the percentage of properly identified thoughts was just 67.15 percent. Transfer learning was used in order to improve the performance of the BCI while simultaneously decreasing the calibration time. It is feasible to decrease the amount of calibration required for the categorization of thoughts coming from a new target subject by using knowledge collected from previously recorded subjects to the greatest extent possible in transfer learning. It was determined that existing methods were superior, and a new methodology was created that required just 24 seconds of calibration data while accurately identifying 86.8% of the thoughts. In order to alleviate mental stress and anger, the system suggested fits effectively with a deep learning network. This paper proposes a brain learning framework that uses a neural network model that is

关键词： Calibration

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Web-enabled Vision-based Person Counting System for Smart Space Management

Web-enabled Vision-based Person Counting System for Smart Sp...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Jasgurpreet Singh Chohan Zamen Latef Naser Hayder Sharif Laith H. A. Fezaa Krishna Kant Dixit Yashwant Singh Bisht University Center for Research and Development Chandigarh University Mohali Punjab India National University of Science and Technology Dhi Qar Iraq Medical Technical College Al-Farahidi University Iraq Dept. of Computer Engineering and Applications GLA University Mathura India Department of Mechanical Engineering Uttaranchal Institute of Technology Dehradun India

Efficient space management has emerged as a crucial issue in the era of smart spaces and IoT-enabled surroundings. In this study, an innovative method is proposed to solve the difficulties of real-time occupancy monitoring and resource optimization in various public and private venues. The system makes use of an ESP32-CAM module with a built-in camera to capture real-time images of the monitored region. The recorded images are analyzed using computer vision techniques and algorithms to track and detect people entering and leaving the area. The basis of the system is the person counting algorithm, which delivers precise and immediate occupancy statistics. The system allows users to log in from any location and monitor space occupancy in real time. The study was conducted based on the performance and accuracy of the system under various circumstances, indicating its appropriateness for a range of applications, from offices and retail outlets to public spaces and smart homes. The importance of this research is found in its contribution to the subject of smart space management, which is still in its infancy but where data-driven choices are essential for maximizing resource usage, guaranteeing social distance compliance, and improving user experiences. This web-enabled vision-based person counting system can help advance smart space management in the context of the Internet of Things (IoT) and smart cities as a creative and useful solution. In the end, the study provides a thorough description outlining its goals, approach, salient characteristics, and prospective effects on smart space management.

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Chip-integrated Spectroscopy Capable of Temperature Retrieval

arXiv

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arXiv 2024年

作者： Du, Yifan Sua, Yong Meng Kumar, Santosh Zhang, Jiuyi Li, Xiangzhi Hu, Yongxiang Ghuman, Parminder Huang, Yuping Department of Physics Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States Center for Quantum Science and Engineering Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States Quantum Computing Inc. 5 Marinview Plaza HobokenNJ07030 United States NASA Langley Research Center HamptonVA23681 United States NASA Earth Science Technology Office GreenbeltMD20771 United States

We demonstrate a chip-integrated emission spectroscope capable of retrieving the temperature of the light sources. It consists of a single photon detector with low dark counts and a sweeping on-chip filter with 2 pm spectral resolution in the visible and near-infrared regimes. With wildfire sensing applications in mind, we test our system with a hollow cathode lamp to simulate the Kline emission, and show how the models of Doppler and collision broadening in the plasma can be used for temperature retrieval. With favorable device parameters, high spectral resolution, and a novel temperature retrieval capability, our technique may find broad applications in environmental monitoring, astrophysics, plasma physics, and so on. Copyright © 2024, The Authors. All rights reserved.

关键词： Infrared devices

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Cross-lingual abstractive summarization with limited parallel resources 59

Cross-lingual abstractive summarization with limited paralle...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Bai, Yu Gao, Yang Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing China Southeast Academy of Information Technology Fujian China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9781954085527

Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the cross-lingual summarization (CLS) task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources. © 2021 Association for Computational Linguistics

关键词： Decoding

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Utilizing Motor-Imagery Brain-computer Interfaces for the Assessment of Developmental Coordination Disorder in Children

Utilizing Motor-Imagery Brain-Computer Interfaces for the As...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kuan-Yi Lee Kong-Yi Chang Hao-Che Hsu Yu-Ting Tseng Chun-Shu Wei Shih-Syun Lin Chun-Hsiang Chuang Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Kinesiology National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Education and Mind Sciences National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder characterized by significant motor difficulties that affect daily life. Current assessment methods primarily focus on behavioral analysis, lacking in neuroscientific metrics for a comprehensive evaluation. This study introduced an electroencephalography-based motor imagery brain-computer interface classification system for evaluating children with DCD. A key of this system was the implementation of entropy-based data screening, which markedly enhanced classification performance. Notably, using mu band power in a support vector machine achieved an accuracy rate of 79.0%. These findings pave the way for developing a tool that could assist professionals in identifying children potentially affected by DCD.

关键词： Support vector machines Measurement Pediatrics Accuracy Motors Brain-computer interfaces engineering in medicine and biology

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Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

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Journal of Neuroscience research 2023年第6期101卷 901-915页

作者： Ng, Hei-Yin Hydra Wu, Changwei W. Huang, Feng-Ying Huang, Chih-Mao Hsu, Chia-Fen Chao, Yi-Ping Jung, Tzyy-Ping Chuang, Chun-Hsiang Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Educational Psychology and Counseling College of Education National Tsing Hua University Hsinchu Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Brain and Consciousness Research Center Taipei Medical University-Shuang Ho Hospital New Taipei Taiwan Department of Education National Taipei University of Education Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Behavioral Sciences Chang Gung University Taoyuan Taiwan Department of Child Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA United States

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation. © 2023 Wiley Periodicals LLC.

关键词： direct directed transfer function effective connectivity emotional regulation MBSR mindfulness

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Electromagnetic spectrum chipless radio frequency identification:A review

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Digital Communications and Networks 2020年第3期6卷 377-388页

作者： Qingjuan Li Rana Sadaf Anwar Huansheng Ning Jun Wang Lingfeng Mao School of Computer and Communication Engineering University of Science and TechnologyBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Beijing Electro-Mechanical Engineering Institute Beijing100074China

Over the past few years,Radio Frequency IDentification(RFID)has stridden significantly due to its adoption in many business and daily life *** empowers end-to-end and item-by-item visibility,letting the customers or decision-makers effectively trail the personnel and resources for improved *** research and development in this area have transformed the inexpensive,miniaturized technology of RFIDs without chip *** Radio Frequency IDentification(CRFID)is a unique wireless technology based on radar principle to sense,identify,and track the *** are simplified and miniaturized tags of traditional RFID,which can widely be used in small and cheap objects/applications in our daily *** paper reviews the definition,principle,and most recent development in *** on the reported researches,the ElectroMagnetic Spectrum(EMS)method is typical,which has been concerned by many RFID ***,this paper reviews different types of EMS CRFID tag and classifies these tags based on structure design emphasizing their significant geometries in comparison with performance *** and coding methods of the EMS CRFID are also elaborated,contrasting the tag capacity and coding ***,we briefly summarize the challenges and limitations in this emerging field and look forward to the prospects of future CRFID.

关键词： CRFID Electromagnetic Tag structure Coding

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