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Smart parking occupancy detection method using smart app

International Journal of Wireless and Mobile Computing 2024年第1期26卷 99-105页

作者： Rahman, Atiqur Ufiteyezu, Emmanuel Department of Computer Science and Engineering University of Chittagong Hathazari Chittagong Bangladesh Electrical and Electronics Department University of Rwanda Kigali Rwanda

Population growth in cities results in a demand for parking lots from an increasing number of automobiles, which frequently contributes to the global problem of traffic congestion. This study presents the smart parking occupancy detection system as a technique to scientifically enhance the existing parking lot, i.e., parking guidance and information method or something else, occupancy detection system. The smart app named rPark is promoted as a cutting-edge method of parking detection. Ultra-high frequency static radio frequency identification chips are placed on the asphalt and cross-examined by Radio Frequency Identification interrogator aerials over the parking areas to initially assess utilisation status. This plan eliminates the majority of the challenges associated with current up-to-the-minute parking usage identification techniques. The parking detection accuracy of rPark, which was tested and used to illustrate an experimental analysis in a real-world outdoor parking space, has been confirmed to be about 100%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Radio frequency identification (RFID)

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Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1307-1319页

作者： Yi Wang Yanxin Liu Mingdong Wang Venkata Dinavahi Jun Liang Yonghui Sun School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Henan Engineering Research Center of Power Electronics and Energy Systems Zhengzhou 450001China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada School of Engineering Cardiff UniversityCardiff CF243AAUK College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China IEEE

With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid *** solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency ***,a threephase unbalanced network’s positive sequence fast estimation model is ***,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss ***,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).

关键词： Dynamic state estimation Kalman filter partial missing measurements power systems smart grid synchronized measurements

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Noninvasive measurement of subcutaneous temperature using active antenna and high gain low noise microwave radiometer

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Journal of Electromagnetic Waves and Applications 2023年第4期37卷 490-509页

作者： Chi, Yu-Jen Huang, Bing-Chao Chiu, Chien-Wen Department of Electrical and Computer Engineering Tamkang University New Taipei Taiwan Department of Electronic Engineering National Ilan University Ilan Taiwan

This paper presents a radiometric thermometer for non-invasive measurement of the internal body temperature. The proposed radiometric thermometer designed in 1.575 GHz band consists of a near-field probe and a high gain Dick radiometer. A phantom based on Agar material was developed to emulate the real tissue of skin and muscle for experiments. The measured results show that the active probe antenna provides additional 12.5 dB gain with noise figure less than 1dB, and the radiometer has a system gain of 50 dB and total noise figure of 1.738 dB at 1.575 GHz. The output voltage measured by the proposed 1.575 GHz microwave radiometer is 2.994 V when the water temperature is set to 25 °C, while it is 3.129 V when the water temperature is set to 80 °C. The study demonstrates that using the active probe connected to the radiometer can identify temperature difference of the body. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Low noise amplifiers

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Job failure prediction in Hadoop based on log file analysis

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International Journal of computers and Applications 2022年第3期44卷 260-269页

作者： Shirzad, Ehsan Saadatfar, Hamid Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran

Hadoop is a popular framework based on MapReduce programming model to allow for distributed processing of large datasets across clusters with various number of computer nodes. Just like any dynamic computational environment, Hadoop has some problems and one of which is unsuccessful execution of MapReduce jobs. Job failures can cause significant resource wasting, performance deterioration, and user dissatisfaction. Therefore, a proactive and predictive management approach could be very useful in Hadoop systems. In this paper, we try to predict the futurity of MapReduce jobs in OpenCloud Hadoop cluster by using its log files. OpenCloud is a research cluster managed by CMU’s Parallel Data Lab which uses Hadoop to process big data. We first tried to study the log files and analyze the relationship between the jobs, resources, and workload characteristics and the failures in order to discover the effective features for the prediction process. After recognizing the job failure patterns, some popular machine learning algorithms are deployed to predict the success/failure status of the jobs before they start to execute. Eventually, we compared the learning methods and showed that the C5.0 algorithm had the best results with an accuracy of 91.37%, a recall of 74.43%, and a precision of 80.31%. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： MapReduce

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Radio Frequency Characterization of Gold Nanoparticles with 3D Printed U-Cavity Sensor

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IEEE Sensors Journal 2025年第12期25卷 21067-21075页

作者： Kattel, Bibek Vashisth, Priyavrat Syed, Azeemuddin Hutchcraft, Winn Elliott Tanner, Eden E. L. University of Mississippi Electrical & Computer Engineering UniversityMS38677 United States University of Mississippi Department of Chemistry & Biochemistry UniversityMS38677 United States Penn State Erie Behrend College Electrical & Computer Engineering PA16510 United States

This paper presents a novel radio frequency characterization of gold nanoparticles based on their geometric shapes with a 3D printed U-cavity sensor. Traditional methods for nanoparticle characterization, such as spectroscopy and scanning electron microscopy, are costly, time-consuming, and require specialized equipment. In contrast, the fabricated U-cavity sensor offers a rapid and cost-effective method for discerning nanoparticles based on their morphology. The sensor features an embedded U-cavity within its substrate, designed to contain the test liquid. To streamline the fabrication process, the substrate with the embedded U-cavity was fabricated in a single step using an additive manufacturing process. The various distinctly shaped nanoparticles were injected into the cavity, and return loss measurements were performed. The study’s findings revealed that distinct gold nanoparticle shapes induced unique spectral signatures, enabling accurate characterization based on their geometric shapes. The variation in the resonance characteristics of the sensor resulting from the presence of AuNPs is termed effective shift throughout the paper. As the geometric symmetry of the nanoparticles increased from cylindrical to quasi-spherical to spherical, a more pronounced effective shift was observed. This novel U-cavity microfluidic sensor demonstrates cost-effectiveness, ease of fabrication, and rapid characterization, highlighting the potential for nanoparticle characterization using radio frequency methods. © 2001-2012 IEEE.

关键词： Gold nanoparticles

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AdaFedProx: A Heterogeneity-Aware Federated Deep Reinforcement Learning for Medical Image Classification

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1432-1441页

作者： Sahoo, Pranab Tripathi, Ashutosh Saha, Sriparna Mondal, Samrat Singh, Jyoti Prakash Sharma, Bhisham Computer Science and Engineering Department Indian Institute of Technology Patna India Electrical and Electronics Engineering Department The Rajiv Gandhi Institute of Petroleum Technology India Indian Institute of Technology Computer Science and Engineering Department Patna India National Institute of Technology Computer Science and Engineering Department Patna India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India

In the realm of smart healthcare, vast amounts of valuable patient data are generated worldwide. However, healthcare providers face challenges in data sharing due to privacy concerns. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without direct access to patient data. This decentralized approach utilizes data from diverse sources, resulting in a globally learned model with satisfactory performance across individual sites. However, federated training encounters challenges such as "system heterogeneity"in client specifications and "data heterogeneity"in imbalanced data distribution. Standard FL methods become unstable and require extensive hyperparameter tuning for optimal performance when facing heterogeneous clients, posing challenges in real-world applications. This research introduces a novel heterogeneity-aware federated learning approach named AdaFedProx to address the performance degradation resulting from heterogeneity. AdaFedProx utilizes a reinforcement learning-driven strategy, introducing a tailored proximal term into the objective function to incorporate partial client information in heterogeneous settings. Dynamically determining the proximal term based on individual client states, including critical characteristics like data distribution, system specification, and performance feedback, empowers AdaFedProx to make regularized decisions throughout the federated training. AdaFedProx consistently outperforms four state-of-the-art methods on three medical datasets across diverse heterogeneity settings (0%, 50%, and 75% straggler). The code can be found at https://***/Pranabiitp/AdaFedProx. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Physical Beam Sharing for Communications With Multiple Low Earth Orbit Satellites

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IEEE Transactions on Signal Processing 2024年 72卷 2783-2798页

作者： He, Yan-Yin Tsai, Shang-Ho Poor, H. Vincent Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Physical (analog) beamforming is expected to become an important technique in Low Earth Orbit (LEO) satellite transmission in upcoming 6G communications. To build dense networks via LEO satellites and decrease deployment expenses, the corresponding satellites should have minimal hardware, low computational complexity, and limited power consumption. Additionally, issues such as different propagation delays, Doppler effects, long feedback delays, and serious attenuation caused by bad weather should also be addressed in LEO satellite communications. This work proposes a novel physical beam sharing scheme for multiple-satellite systems that enables a satellite to simultaneously serve multiple users using only one Radio-Frequency (RF) chain, consequently leading to a significant reduction in hardware complexity and power consumption. In this case, all users can achieve full multiplexing gain. Furthermore, we propose two ways of optimizing for this novel scheme. One is maximizing the throughput of all users, and the other is minimizing the LEO satellite system's power consumption. For the problems, we propose both solutions using instantaneous Channel State Information (iCSI) and long-term statistical Channel State Information (sCSI). Simulation results demonstrated that the proposals significantly outperform conventional schemes in terms of throughput and power consumption, and the performance of the proposed sCSI solutions is close to that of iCSI ones. © 1991-2012 IEEE.

关键词： Electric power utilization

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Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration

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Journal of engineering and Applied Science 2024年第1期71卷 1-18页

作者： Kumawat, Manoj Onaolapo, Adeniyi Sharma, Gulshan Barutcu, Ibrahim Cagri Adefarati, Temitope Bansal, Ramesh Department of Electrical Engineering National Institute of Technology Delhi Delhi India Department of Electrical Engineering Technology University of Johannesburg Johannesburg2006 South Africa Department of Electricity and Energy Çölemerik V.H.S. Hakkari University Hakkari30000 Turkey Department of Electrical and Electronics Engineering Federal University Oye Ekiti Oye Ekiti Nigeria Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electric Electronic and Computer Engineering University of Pretoria Pretoria South Africa

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. © The Author(s) 2024.

关键词： Smart power grids

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The Effect of Imperfect Channel-Sensing for Low Power Wide Area Networks with Listen-Before-Talk

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IEEE Internet of Things Journal 2025年第12期12卷 20512-20529页

作者： Hu, Yangqian Seo, Jun-Bae Jin, Hu Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of Gyeongsang National University Department of Information and Communication Engineering Tongyeong53064 Korea Republic of Henan Normal University College of Computer and Information Engineering Xinxiang453007 China Hanyang University School of Electrical Engineering Ansan15588 Korea Republic of

This study investigates ALOHA with Listen-Before-Talk (LBT) to enhance the scalability of Low-Power Wide Area Networks (LPWANs), such as LoRa. The LBT allows devices to sense the channel prior to accessing so that it can mitigate interference by preventing devices from transmitting during ongoing transmissions. However, its effectiveness is compromised by inherent imperfections in channel sensing, such as false negatives and false positives. A false negative occurs when devices incorrectly find the channel idle while it is actually in use. Thus, this leads devices to unintended interferences with ongoing transmissions. A false positive arises when the channel is erroneously sensed as busy, despite the fact that it is free. This deprives devices of access opportunities. This work analyzes the impact of these imperfections of LBT on the performance of ALOHA in terms of throughput, access delay, and system stability. Additionally, we propose an online backoff control algorithm to optimize system performance under imperfect LBT. The results show that even when devices falsely identify the channel as idle or mistakenly detect it as busy nearly half the time, the throughput still outperforms that of ALOHA without LBT. The proposed backoff control algorithm is also shown to be essential to maximize the throughput in the presence of sensing errors. To demonstrate our analysis and algorithm, we incorporate LoRa's physical layer parameters into simulations and validate the results accordingly. © 2014 IEEE.

关键词： Low power electronics

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Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview 17

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Aliyev, Ilkin Svoboda, Kama Adegbija, Tosiron Fellous, Jean-Marc University of Arizona Department of Electrical & Computer Engineering TucsonAZ United States University of Arizona Departments of Psychology and Biomedical Engineering TucsonAZ United States

ISBN: (纸本)9798331530471

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuro-morphic systems that fully exploit the computational advantages of sparse SNNs. © 2024 IEEE.

关键词： Neural network models

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