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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Ahn, Seokho Kim, Hyungjin Lee, Euijong Seo, Young-Duk Inha University Department of Electrical and Computer Engineering Korea Republic of Chungbuk National University School of Computer Science Korea Republic of

ISBN: (纸本)9798350324136

The crucial part of IoT-controlled devices is the collection of accurate data. However, manufacturers often use low-cost sensors to make everyday home devices affordable, which can compromise accuracy. Therefore, we introduce a novel framework designed to improve the calibration performance of low-cost sensors incorporated into these devices. Applying this framework to home appliances makes it possible to calibrate low-cost sensors with inference speeds comparable to linear models while achieving accuracies similar to those of deep learning models. Specifically, the framework offers a selection of three different model variants, each considering factors such as implementation difficulty, calibration accuracy, or inference speed. Experimental findings indicate that our framework exhibits superior performance in both general-purpose and embedded hardware, highlighting its potential applicability to everyday home devices such as IoT-controlled appliances. © 2024 IEEE.

关键词： deep learning home device Internet of Things sensor calibration

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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DON'T JUDGE BY THE LOOK: TOWARDS MOTION COHERENT VIDEO REPRESENTATION 12

DON'T JUDGE BY THE LOOK: TOWARDS MOTION COHERENT VIDEO REPRE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Yitian Bai, Yue Wang, Huan Wang, Yizhou Fu, Yun Department of Electrical and Computer Engineering Northeastern University United States Khoury College of Computer Science Northeastern University United States

Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice. In this study, we investigate the effect of hue variance in the context of video understanding and find this variance to be beneficial since static appearances are less important in videos that contain motion information. Based on this observation, we propose a data augmentation method for video understanding, named Motion Coherent Augmentation (MCA), that introduces appearance variation in videos and implicitly encourages the model to prioritize motion patterns, rather than static appearances. Concretely, we propose an operation SwapMix to efficiently modify the appearance of video samples, and introduce Variation Alignment (VA) to resolve the distribution shift caused by SwapMix, enforcing the model to learn appearance invariant representations. Comprehensive empirical evaluation across various architectures and different datasets solidly validates the effectiveness and generalization ability of MCA, and the application of VA in other augmentation methods. Code is available at https://***/BeSpontaneous/MCA-pytorch. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Object recognition

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RIS-Aided MIMO Downlink Transmission for Ultradense LEO Satellite-Terrestrial Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15304-15318页

作者： Zhang, Xin Qin, Xiaohan Zhang, Zitian Cai, Lin X. Zhou, Haibo Zhuang, Weihua Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou310018 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms. © 2014 IEEE.

关键词： Integer programming

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Robustness of generative AI detection: adversarial attacks on black-box neural text detectors

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International Journal of Speech Technology 2024年第4期27卷 861-874页

作者： Fishchuk, Vitalii Braun, Daniel Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of High-tech Business and Entrepreneurship University of Twente Enschede Netherlands

The increased quality and human-likeness of AI generated texts has resulted in a rising demand for neural text detectors, i.e. software that is able to detect whether a text was written by a human or generated by an AI. Such tools are often used in contexts where the use of AI is restricted or completely prohibited, e.g. in educational contexts. It is, therefore, important for the effectiveness of such tools that they are robust towards deliberate attempts to hide the fact that a text was generated by an AI. In this article, we investigate a broad range of adversarial attacks in English texts with six different neural text detectors, including commercial and research tools. While the results show that no detector is completely invulnerable to adversarial attacks, the latest generation of commercial detectors proved to be very robust and not significantly influenced by most of the evaluated attack strategies. © The Author(s) 2024.

关键词： Generative adversarial networks

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A New Switched Reluctance Motor with Embedded Permanent Magnets for Transportation Electrification 50

A New Switched Reluctance Motor with Embedded Permanent Magn...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Davarpanah, Gholamreza Mohammadi, Sajjad Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering and Computer Science MIT Cambridge United States

ISBN: (纸本)9781665464543

A new three-phase hybrid-excited multi-tooth switched reluctance motor with embedded permanent magnets is proposed, capable of achieving higher torque density for transportation electrification applications. Operating principles and design considerations are discussed. A magnetic equivalent circuit is developed. Finite element method is employed in the field analysis. The advantages of the proposed topology over existing designs for switched reluctance motors and flux switching motors are presented. Finally, the optimized design is prototyped to experimentally confirm the design and simulation results. © 2024 IEEE.

关键词： Permanent magnets

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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

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Neural Computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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ESSPI as a Tool for STEM Education in Energy Backup System Design 54

ESSPI as a Tool for STEM Education in Energy Backup System D...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Alvarez, Natanael Batista Acosta, Brian E. Vázquez Nazario, Jesús F. Montalvo Lopez Cardalda, Guillermo Ortiz-Rivera, Eduardo González, Alanis M. Colón University of Puerto Rico Department of Computer Science and Engineering Mayaguez Puerto Rico University of Puerto Rico Department of Electrical and Computer Engineering Mayaguez Puerto Rico

ISBN: (纸本)9798350351507

This research-to-practice full paper helps users prepare for blackouts and power outages by presenting a cutting-edge tool for STEM outreach. In an emergency, loads are prioritized according to the Energy Storage System Prioritization Index (ESSPI). It evaluates loads based on recovery time, power consumption, and ranking;it will soon be available as a web application. It provides a cost analysis and prioritizing index for building backup systems. By dividing loads into categories such as important, necessary, discretionary, non-essential, and disposable, the ESSPI tool is being used as an agile system design tool. Case studies show how adaptable it is to many environments, from homes to grid systems, improving energy management. Furthermore, the application educates users about renewable energy by offering cost and size assessments, specifically for Photovoltaic (PV) installations. Based on user inputs such as location, number of hours of sunlight, and length of backup, the app estimates system costs and battery size and optimizes the size of the PV system by taking shading and panel efficiency into account. To sum up, this creative tool and its intuitive online application emphasize STEM education and mark major advancements in backup system design and emergency preparedness. It helps users make cost-effective decisions by promoting knowledge of solar energy and battery technology, which is beneficial for researchers and aficionados of renewable energy sources. © 2024 IEEE.

关键词： Cost benefit analysis

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Multiple Redundant Weighted Clustered Scheme with Dynamic Weights Adjustment for Damaged S-UAV 29

Multiple Redundant Weighted Clustered Scheme with Dynamic We...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Khayat, Grace Mavromoustakis, Constandinos X Batalla, Jordi Mongay Pitsillides, Andreas Department of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University of Cyprus Department of Computer Science Cyprus Department of Electrical & Electronic Engineering Science South Africa

ISBN: (纸本)9798350377644

Swarm of UAVs (S-UAVs) refers to an assembly of unmanned aerial vehicles (UAVs) working together to accomplish prearranged missions. In emergency scenarios, such as a fire, any UAV is susceptible to damage. Among the routing algorithms that S-UAVs utilize the most frequently is clustering, where UAVs are divided into clusters. Each cluster consists of a cluster head (CH) and cluster members (CM). The selection of the CH is a topic of continuous research because of its crucial significance in inter-cluster communication. We propose a clustered weighted method with dynamic weight modification and redundancy to ensure end-to-end communication despite non-functional CH. Interspace, speed, and performance indicators are combined into a weighted metric that is used to select the CH, redundant CHs, and CMs. The suggested technique optimizes UAV role selection by dynamically and autonomously adjusting the weights. Based on the results of the executed simulation, this is a promising strategy that minimizes data loss in an emergency situation and minimizes delay. © 2024 IEEE.

关键词： Vehicle routing

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iStream: A Flexible Container-Based Testbed for Multimedia Streaming 6

iStream: A Flexible Container-Based Testbed for Multimedia S...

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6th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2023

作者： Akbari, Navid Majdabadi, Reza Hedayati Ansari, Akram Wang, Mea Krishnamurthy, Diwakar University of Calgary Department of Computer Science Canada University of Calgary Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350307818

Multimedia streaming is growing at a phenomenal rate, especially with the emergence of augmented/virtual reality applications and the growing demand for YouTube-like services. This stimulates research in all aspects of streaming systems, including but not limited to real-time and resource-efficient transcoding, server efficiency, network optimization, and client adaptation. Research and development of any part of the system require repetitive and tedious tasks in setting up the rest of the system for validation and evaluation of new ideas. This not only complicates the research work but also challenges the reproducibility and sharing of the research results. Existing proposed testbeds all have a specific focus and do not address all components of multimedia streaming. In this paper, we propose iStream, a flexible testbed for multimedia streaming system that integrates various components relevant to such systems. The components are plug-&-playable and customizable to compose various streaming systems and setups. iStream provides multimedia system researchers and developers with a flexible playground to explore their innovative ideas. Along with the design, we present a diverse set of case studies demonstrating the wide-range use of iStream. © 2023 IEEE.

关键词： Testbeds

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