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IEEE Conference on Vehicular Technology (VTC)

作者： Anders Enqvist Özlem Tuğfe Demir Cicek Cavdar Emil Björnson Department of Computer Science KTH Royal Institute of Technology Kista Sweden Department of Electrical-Electronics Engineering TOBB University of Economics and Technology Ankara Türkiye

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, we examine the energy efficiency (EE) of a base station (BS) with multiple antennas. We use a state-of-the-art power consumption model, taking into account the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number of antennas as the optimization variables. We provide novel closed-form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna. We present a novel algorithm that jointly optimizes these variables to achieve maximum EE, while fulfilling constraints on the variable ranges. We also discover a new relationship between the radiated power and the passive transceiver power consumption. We provide analytical insight into whether using maximum power or bandwidth is optimal and how many antennas a BS should utilize.

关键词： Vehicular and wireless technologies Power demand Wireless networks Transmitting antennas Signal processing algorithms Bandwidth Signal processing

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Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling

arXiv

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arXiv 2023年

作者： Jiao, Ruochen Wang, Yixuan Liu, Xiangguo Zhan, Simon Sinong Huang, Chao Zhu, Qi The Department of Electrical and Computer Engineering Northwestern University IL United States The School of Electronics and Computer Science The University of Southampton United Kingdom

Trajectory generation and trajectory prediction are two critical tasks in autonomous driving, which generate various trajectories for testing during development and predict the trajectories of surrounding vehicles during operation, respectively. In recent years, emerging data-driven deep learning-based methods have shown great promise for these two tasks in learning various traffic scenarios and improving average performance without assuming physical models. However, it remains a challenging problem for these methods to ensure that the generated/predicted trajectories are physically realistic. This challenge arises because learning-based approaches often function as opaque black boxes and do not adhere to physical laws. Conversely, existing model-based methods provide physically feasible results but are constrained by predefined model structures, limiting their capabilities to address complex scenarios. To address the limitations of these two types of approaches, we propose a new method that integrates kinematic knowledge into neural stochastic differential equations (SDE) and designs a variational autoencoder based on this latent kinematics-aware SDE (LK-SDE) to generate vehicle motions. Experimental results demonstrate that our method significantly outperforms both model-based and learning-based baselines in producing physically realistic and precisely controllable vehicle trajectories. Additionally, it performs well in predicting unobservable physical variables in the latent space. Copyright © 2023, The Authors. All rights reserved.

关键词： Kinematics

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Reducing Computation Requirements for Autonomous Mobile Robots on Low Powered Embedded Systems

Reducing Computation Requirements for Autonomous Mobile Robo...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Abhinav Pathak Aditya Singhal Adit Tewari Aadish Jain V. Kalaichelvi Department of Computer Science Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani

Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computer vision techniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots.

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Topological Signal Processing and Learning: Recent Advances and Future Challenges

arXiv

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arXiv 2024年

作者： Isufi, Elvin Leus, Geert Beferull-Lozano, Baltasar Barbarossa, Sergio Lorenzo, Paolo Di Faculty of Electrical Engineering Mathematics and computer Science Delft University of Technology Delft Netherlands SIGIPRO Department Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to algebraic tool for modeling the structure via nodes and edges capturing their interactions, leading to the establishment of the fields of graph signal processing (GSP) and graph machine learning (GML). Key graph-aware methods include Fourier transform, filtering, sampling, as well as topology identification and spatiotemporal processing. Although versatile, graphs can model only pairwise dependencies in the data. To this end, topological structures such as simplicial and cell complexes have emerged as algebraic representations for more intricate structure modeling in data-driven systems, fueling the rapid development of novel topological-based processing and learning methods. This paper first presents the core principles of topological signal processing through the Hodge theory, a framework instrumental in propelling the field forward thanks to principled connections with GSP-GML. It then outlines advances in topological signal representation, filtering, and sampling, as well as inferring topological structures from data, processing spatiotemporal topological signals, and connections with topological machine learning. The impact of topological signal processing and learning is finally highlighted in applications dealing with flow data over networks, geometric processing, statistical ranking, biology, and semantic communication. © 2024, CC BY.

关键词： Network theory (graphs)

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Accelerated Ageing Test and Lifetime Prediction of MOSFETs 23

Accelerated Ageing Test and Lifetime Prediction of MOSFETs

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23rd European Microelectronics and Packaging Conference and Exhibition, EMPC 2021

作者： Maxa, Jacob Nowottnick, Mathias Department of Electrical Engineering and Computer Science Institute of Applied Electronics Circuits University of Rostock Rostock Germany

ISBN: (纸本)9780956808677

An active power cycling test-bed for a power MOSFET is presented in this publication. This setup is used to create temperature dependent load cycles of 24 parallel devices. After a passive calibration step the drain-to-source voltage is used to estimate the junction temperature. The devices are then heated until a predefined value is reached. The setup is power cycled until at least 20 channels failed and then analysed using metallographic cross-sections. Furthermore, this setup records the thermal impedance of each channel over time. With these data it is possible to detect and predict a solder fatigue (crack, void) during operation. Finally after all load cycles the results were evaluated by Weibull plots and a lifetime estimation for other temperatures is given in the way of a stress-life curve. The results suggest a comparable lifetime compared to ceramic-based components, if the substrate does not have an influence as a source of faults. © 2021 2021 23rd European Microelectronics and Packaging Conference and Exhibition, EMPC 2021. All rights reserved.

关键词： Power MOSFET

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Sentiment Analysis in Indian Sub-continent during COVID-19 Second Wave using Twitter Data 9

Sentiment Analysis in Indian Sub-continent during COVID-19 S...

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9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Bl, Meghana Midha, Sanskriti Murthy Oruganti, V Ramana Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering India

ISBN: (纸本)9781665432405

In Indian sub-continent COVID-19 second wave started in early March 2021 and its effect was more lethal than the first wave, the confirmed cases and the death rate was higher than in the first wave. Unlike the national lockdown in 2020, this year different states have started imposing lockdown like restrictions spanning April-June 2021. This paper investigates the sentiments of the people using twitter messages during early period of the second wave. Two-weeks data is manually annotated and several machine learning models were built. The best performing models were used to predict sentiments for the next 2-3 weeks and analysis is presented. Predictions of public, commercial libraries were also analysed in the same context. © 2021 IEEE.

关键词： COVID-19

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User-Centric Cell-Free Massive MIMO With RIS-Integrated Antenna Arrays

User-Centric Cell-Free Massive MIMO With RIS-Integrated Ante...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Özlem Tuğfe Demir Emil Björnson Department of Electrical-Electronics Engineering TOBB University of Economics and Technology Ankara Türkiye Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Cell-free massive MIMO (multiple-input multiple-output) is a promising network architecture for beyond 5G systems, which can particularly offer more uniform data rates across the coverage area. Recent works have shown how reconfigurable intelligent surfaces (RISs) can be used as relays in cell-free massive MIMO networks to improve data rates further. In this paper, we analyze an alternative architecture where an RIS is integrated into the antenna array at each access point and acts as an intelligent transmitting surface to expand the aperture area. This approach alleviates the multiplicative fading effect that normally makes RIS-aided systems inefficient and offers a cost-effective alternative to building large antenna arrays. We use a small number of antennas and a larger number of controllable RIS elements to match the performance of an antenna array whose size matches that of the RIS. In this paper, we explore this innovative transceiver architecture in the uplink of a cell-free massive MIMO system for the first time, demonstrating its potential benefits through analytic and numerical contributions. The simulation results validate the effectiveness of our proposed phase-shift configuration and highlight scenarios where the proposed architecture significantly enhances data rates.

关键词： Wireless communication Array signal processing Architecture Transmitting antennas Signal processing algorithms Massive MIMO Reconfigurable intelligent surfaces Arrays Uplink Antenna arrays

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A New Three-Phase High Performance H8 Filter Clamped Transformer-less Inverter for Grid-tied Photovoltaic Systems 4

A New Three-Phase High Performance H8 Filter Clamped Transfo...

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4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Biswas, Mohua Biswas, Shuvra Prokash Islam, Md. Rabiul Department of Electronics and Telecommunication Engineering Rajshahi6204 Bangladesh School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia

ISBN: (纸本)9781728199511

Renewables such as solar, wind, tide energies are the new route for alternative power sources in spite of fossil fuels or other nonrenewable energies. From the last decade, converting the variable solar dc power to constant ac power has become a challenge. This paper underpins a novel three phase transformer-less grid connected photovoltaic inverter with the control blocks applicable for all types of grid voltage ranges. This novel three-phase inverter topology not only contains very lower reduced leakage current as well as less total harmonic distortion (THD) but also improved power quality. Filter clamping approach has been owned to eliminate extra leakage current through the ground path. Besides unipolar sinusoidal pulse width modulation (SPWM) method has been used to control the power switches. As a result, the common mode voltage has been held to constant, which is also responsible for reducing leakage current to 25.42 mA. The leakage current suppression has been analyzed in details with simulation results. The switches did not affect the efficiency of this inverter so much but reduced the leakage current. Switching losses and conduction losses are very low. The THD of Grid voltage of this proposed topology is recorded 0.94% likewise, grid current THD is measured as 0.42%. To check the performance of this proposed inverter more accurately, input voltage as well as load amount of this inverter have been changed instantly in the middle of simulation. This topology has been implemented and tested via MATLAB/Simulink platform. © 2021 IEEE.

关键词： Leakage currents

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Utilizing Machine Learning Techniques for Identifying and Diagnosing Integrated Circuit Faults

Utilizing Machine Learning Techniques for Identifying and Di...

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Science, Technology, engineering and Management (ICSTEM), International Conference on

作者： G. Nixon Samuel Vijayakumar Vipin Sharma S. Pradeep Pratheeba C T. Suresh Balakrishnan Ramprasad Jat Department of Physics R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Mechanical Engineering Medi-Caps University Indore Madhya Pradesh India Department of Electronics and Communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Nandha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Engineering Government Engineering College Jhalawar Rajasthan India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The findings of the research that was carried out present a method that is driven by machine learning, which may be utilizedto conduct thermal testing of integrated circuits. To carry out this method, the current temperature distribution is compared to a reference distribution that is suitable for the energy state of the system. This comparison is made to ensure that the procedure is carried out smoothly. In addition, as part of this research endeavor, an investigation is being conducted into a methodology for the positioning of temperature sensors. This placement approach is advised for discovering and resolving issues that are associated with integrated circuits (ICs), and it is recommended that it be used. Our strategy for failure localization makes use of the average temperatures in some sub-areas of the integrated circuit, in addition to the interconnections that stand between those temperatures. This allows us to pinpoint the exact location of the failure. The results of the simulations that were performed for the sensor placement strategy, defect detection, and localization are presented in this research. The findings regarding the efficiency of the testing strategy are displayed through the utilization of statistical approaches to display the findings.

关键词： Temperature sensors Location awareness Temperature measurement Temperature distribution Sensor placement Integrated circuit interconnections Machine learning

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