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Modeling and performances assessments of PV/T bifluid hybrid collector: Three cooling modes operation case 4

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Modeling and performances assessments of PV/T bifluid hybrid...

4th International Conference on Electrical and Information Technologies, ICEIT 2020

作者： El Manssouri, Oussama El Fouas, Chaimae Hajji, Bekkay Rabhi, Abdelhamid Tina, Giuseppe Marco Gagliano, Antonio Mohamed First University Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Oujda Morocco Picardie Jules Verne University Modelization Information and Systems Laboratory Amiens France University of Catania DIEEI: Department of Electric Electronic and Computer Engineering Catania Italy

ISBN: (数字)9781728143415

ISBN: (纸本)9781728143415

The objective of the present work is the performance assessments of a bi-fluid photovoltaic/thermal solar collector (PV/T). Based on energy-balance equations, a mathematical model of PV/T hybrid collector has been developed and simulated using Matlab Software. This model takes into account the effect of the three modes of fluid operation under the same PV/T system, namely: the air mode, the water mode, and the simultaneous mode of water and air. The results show that the lowest PV module temperature is observed in the simultaneous mode (air water) compared to others modes. It's maximum value achieved is around 62°C in the bi-fluid mode against 80°C and 73°C respectively in the air mode and the water mode. The maximum efficiencies of 66.97 % (thermal efficiency) and 10.1% (electrical efficiency) are also observed, when both fluids are operated simultaneously compared to others modes. Finally, the working in simultaneous mode is beneficial to transmit more heat, produce more thermal energy and consequently improve the electrical and thermal performances of the PV/T hybrid collector. Moreover, the theoretical model developed is validated, evidencing a good fit between simulation results and the experimental data available in literature. The PV/T collector designed in this study has a variety of applications, as solar drying in agriculture, air conditioning, space heating and hot water supply for domestic applications. © 2020 IEEE.

关键词： Air

PRIPRO:Solution for user profile control and management based on data privacy 15

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PRIPRO:Solution for user profile control and management base...

15th Iberian Conference on Information systems and Technologies, CISTI 2020

作者： Cesconetto, Jonas Silva, Luis A. Valderi Leithardt, R.Q. Caceres, Maria N. Selva, Luis A. Garcia, Nuno M. Valderi Leithardt, R.Q. University of Vale Do Itajaí Laboratory of Embedded and Distributed Systems Brazil88302-901 Brazil University of Salamanca Department of Computer Science 37007 Spain Universidade da Beira Interior Departamento de Informática Portugal6201-001 Portugal

ISBN: (纸本)9789895465903

Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed that allows a wide and intelligent communication between devices. Hardware reduction of IoT devices results in vulnerabilities. Thus, there are numerous concerns regarding the security of user information, since mobile devices are easily trackable over the Internet. Care must be taken regarding the information in user profiles. Mobile devices are protected by a permission-based mechanism, which limits third-party applications from accessing sensitive device resources. In this context, this work aims to present a proposal for materialization of application for the evolution of user profiles in intelligent environments. Having as parameters the parameters presented in the proposed taxonomy. The proposed solution is the development of two applications, one for Android devices, responsible for allowing or blocking some features of the device. And another in Cloud, responsible for imposing the parameters and privacy criteria, formalizing the profile control module (PRIPRO - PRIvacy PROfiles). © 2020 AISTI.

关键词： Taxonomies

Speak with signs: Active learning platform for Greek sign language, English sign language, and their translation

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arXiv 2020年

作者： Papatsimouli, Maria Lazaridis, Lazaros Kollias, Konstantinos-Filippos Skordas, Ioannis Fragulis, George F. Laboratory of Robotics Embedded and Integrated Systems Dept. of Electrical and Computer Engineering University of Western Macedonia Hellas Greece

Sign Language is used to facilitate the communication between Deaf and non-Deaf people. It uses signs-words with basic structural elements such as handshape, parts of face, body or space, and the orientation of the fingers-palm. Sign Languages vary from people to people and from country to country and evolve as spoken languages. In the current study, an application which aims at Greek Sign Language and English Sign Language learning by hard of hearing people and talking people, has been developed. The application includes grouped signs in alphabetical order. The user can find Greek Sign Language signs, English sign language signs and translate from Greek sign language to English sign language. The written word of each sign, and the corresponding meaning are displayed. In addition, the sound is activated in order to enable users with partial hearing loss to hear the pronunciation of each word. The user is also provided with various tasks in order to enable an interaction of the knowledge acquired by the user. This interaction is offered mainly by multiple-choice tasks, incorporating text or video. The current application is not a simple sign language dictionary as it provides the interactive participation of users. It is a platform for Greek and English sign language active learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Audition

Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram

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Neuroscience Informatics 2025年第2期5卷 100197-100197页

作者： Ramesh, Janjhyam Venkata Naga Quraishi, Aadam Aoudni, Yassine Mudhafar, Mustafa Nimma, Divya Bansal, Monika Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh 522302 India M.D. Research Intervention Treatment Institute Houston TX United States Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 Sfax 3038 Tunisia Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala 56001 Iraq Department of Anesthesia Techniques and Intensive Care Al-Taff university college Kerbala 56001 Iraq Data Analyst in UMMC United States Department of Computer Science SSD Women's Institute of Technology Bathinda India

Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation. © 2025 The Author(s)

关键词： ECG Healthcare HRV Non-invasive brain stimulation tNIR

PFV2: Packet fragmentation with variable size and vigorous mapping in time-sensitive networking

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Journal of systems Architecture 2025年 166卷

作者： Wenyan Yan Bin Fu Dongsheng Wei Renfa Li Yixue Lei Yuhang Jia Guoqi Xie Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha 410082 Hunan China Center for Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing 401120 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems School of Computer and Artificial Intelligence Xiangnan University Chenzhou 423000 Hunan China Future Network Laboratory Tencent Beijing 100080 China

With the rapid advancement of intelligent automobiles, the ever-growing communication data put forward high bandwidth and low latency requirements. To meet these requirements, automotive Original Equipment Manufacturers (OEMs) widely adopt domain-centralized Electrical/Electronic (E/E) architecture. In this architecture, Time-Sensitive Networking (TSN) is expected to serve as the backbone network because of its high bandwidth and deterministic communication. TSN uses the Gate Control List (GCL) to divide its time length into multiple time slots, and the time intervals of these time slots are non-uniform (equal or unequal). Different flows have varying requirements for time slot sizes. To enhance the acceptance ratio of Time-Triggered (TT) flows through GCL time slot allocation, packet fragmentation (i.e., flow fragmentation) is introduced into TSN in the recent study. The state-of-the-art packet fragmentation solution divides one un-schedulable TT flow into multiple equal-sized packets (i.e., equal-sized time slots). In other words, these fixed-size packets are difficult to be mapped into the time slots with different sizes. This study develops a Packet Fragmentation with Variable-size and Vigorous-mapping (PFV2) technique based on the following three innovations: (1) we implement variable-size packet fragmentation, which iteratively divides the un-schedulable TT flow into smaller packets and then dynamically reschedules these packets; (2) we implement the vigorous mapping solution from packets to time slots by deeply searching for available time slots within the flow’s deadline; and (3) we verify PFV2 based on the LS1028A with Cortex-A72 (i.e., the NXP automotive-grade development board). PFV2 improves the acceptance ratio by up to 20.18% and bandwidth utilization by up to 7.024% compared with the state-of-the-art solution. The theoretical and practical co-verification experiments demonstrate that the PFV2 can effectively improve the flow acceptance ratio and outpe

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Finite time convergence of pinning synchronization with a single nonlinear controller

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arXiv 2021年

作者： Chen, Tianping Lu, Wenlian Liu, Xiwei The School of Computer Sciences/Mathematics Fudan University Shanghai200433 China The Centre for Computational Systems Biology School of Mathematical Sciences Fudan University China Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai200092 China

Optimisation of a siamese neural network for real-time energy efficient object tracking

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TechRxiv

TechRxiv 2020年

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, CC BY-NC-SA.

关键词： Field programmable gate arrays (FPGA)

Optimisation of a siamese neural network for real-time energy efficient object tracking

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arXiv 2020年

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations - from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. Copyright © 2020, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

Continual Learning in the Presence of Repetition

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arXiv 2024年

作者： Hemati, Hamed Pellegrini, Lorenzo Duan, Xiaotian Zhao, Zixuan Xia, Fangfang Masana, Marc Tscheschner, Benedikt Veas, Eduardo Zheng, Yuxiang Zhao, Shiji Li, Shao-Yuan Huang, Sheng-Jun Lomonaco, Vincenzo van de Ven, Gido M. Institute for Computer Science University of St. Gallen Rosenbergstrasse 30 St. Gallen9000 Switzerland Department of Computer Science University of Bologna Via dell’Università 50 Cesena47521 Italy The University of Chicago 5801 S Ellis Ave Chicago60637 United States Argonne National Laboratory 9700 S Cass Ave Lemont60439 United States Graz University of Technology Rechbauerstraße 12 Graz8010 Austria TU Graz - SAL Dependable Embedded Systems Lab Silicon Austria Labs Graz8010 Austria Know-Center GmbH Sandgasse 36/4 Graz8010 Austria MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science University of Pisa Piano Secondo Largo Bruno Pontecorvo 3 Pisa56127 Italy Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 Leuven3001 Belgium

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design. © 2024, CC BY.

关键词： Contrastive Learning