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Motion Detection Methods Applied on RGB-D Images for Vehicle Classification on the Edge Computing

IEEE Internet of Things Journal 2025年第12期12卷 19631-19645页

作者： Micko, Kristian Papcun, Peter Technical University in Košice Faculty of Electrical Engineering and Informatics Department of Cybernetics and Artificial Intelligence Košice Slovakia

Intelligent Transportation Systems rely on data processing methods in real-time conditions. There are many methods to process the data. However, the progress of hardware computational power forces us to reassess the effectiveness of some methods. The first idea for discussion is whether cloud or fog computing is necessary to process computer Vision methods in real-time conditions. The second idea for discussion is whether a depth map gained from the 2D image monocular estimation is suitable as an extra feature to process. Another scientific question is the categorization of the vehicles based on 3D data by extracted volume or height features. This approach could be useful for ToF camera output. This study simulates the output of a ToF camera with a resolution higher than VGA via the monocular depth map estimation with the Convolutional Neural Network model MiDaS. This article proposes the computational architectures for data processing between SBCs with various computational power. Data processing includes obtaining, feature extraction, and classification via various methods. These methods are image loading, background subtraction, shadow removal, monocular depth estimation, pointcloud calculation, 3D convex hull, and volume thresholding. Volume thresholding is a reliable approach for categorization into light and heavy vehicles. Background subtractors connected with K-Means are reliable for vehicle detection without shadows. © 2014 IEEE.

关键词： Fog computing

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Survey of Resources Allocation Techniques with a Quality of Service (QoS) Aware in a Fog Computing Environment

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computers, Materials & Continua 2023年第7期76卷 1291-1308页

作者： Wan Norsyafizan WMuhamad Kaharudin Dimyati Muhammad Awais Javed Suzi Seroja Sarnin Divine Senanu Ametefe School of Electrical Engineering College of EngineeringUniversity Teknologi Mara40450Shah AlamSelangorMalaysia Department of Electrical Engineering Faculty of EngineeringUniversity Malaya50603KualaLumpurMalaysia Department of Electrical and Computer Engineering COMSATS University Islamabad45550Pakistan

The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud ***,suitable and effective applications could be performed to satisfy the applications’latency *** allocation techniques are essential aspects of fog networks which prevent unbalanced load *** resource management techniques can improve the quality of service *** to the limited and heterogeneous resources available within the fog infrastructure,the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT *** has been limited research on resource management strategies in fog networks in recent years,and a limited systematic review has been done to compile these *** article focuses on current developments in resource allocation strategies for fog-IoT networks.A systematic review of resource allocation techniques with the key objective of enhancing QoS is *** involved in conducting this systematic literature review include developing research goals,accessing studies,categorizing and critically analysing the *** resource management approaches engaged in this article are load balancing and task offloading *** the load balancing approach,a brief survey of recent work done according to their sub-categories,including stochastic,probabilistic/statistic,graph theory and hybrid techniques is provided whereas for task offloading,the survey is performed according to the destination of task *** load balancing and task-offloading approaches contribute significantly to resource management,and tremendous effort has been put into this critical ***,this survey presents an overview of these extents and a comparative ***,the study discusses ongoing research issues and potential future directio

关键词： Resource management task offloading load balancing QoS latency energy consumption

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RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

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A Single-Pass Noise Covariance Estimation Algorithm in Nonswitching Multiple-Model Adaptive Kalman Filters for Nonstationary Systems

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Journal of Advances in Information Fusion 2024年第1期19卷 3-19页

作者： Kim, Hee-Seung Bienkowski, Adam Zhang, Lingyi Pattipati, Krishna R. Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States

This paper presents a single-pass stochastic gradient descent (SGD) algorithm for estimating unknown noise covariances. The proposed algorithm is designed for nonswitching multiple-model adaptive Kalman filters, where the noise covariances can occasionally jump up or down by an unknown magnitude. Compared to our previous batch estimation or multipass decision-directed estimation methods, the proposed algorithm has the advantage of reading measurement data exactly once, leading to a significant improvement in computational efficiency and practicality. Moreover, the algorithm achieves an acceptable level of root mean square error (RMSE) in state estimates, making it suitable for real-time industrial applications. The proposed algorithm utilizes recursive fading memory estimates of the sample cross-correlations of the innovations and employs the root mean square propagation (RMSprop) accelerated SGD algorithm. The combination of these techniques enables the algorithm to achieve high accuracy in estimating the unknown noise covariances while maintaining superior computational efficiency over iterative batch methods. The paper presents a comparative evaluation of the proposed method on several test cases, which demonstrate its effectiveness in terms of computational efficiency and estimation accuracy. Overall, the proposed algorithm offers a promising approach for real-time noise covariance estimation in multiple-model adaptive Kalman filters. © 2024 JAIF.

关键词： Adaptive filters

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Encryption of medical data based on blockchain and multi-chaotic maps

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Multimedia Tools and Applications 2025年第17期84卷 17871-17892页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In this work, the SHA-256 mapper of the blockchain has been utilized to secure medical data from brute-force attacks. The uniform distribution and lower correlation of the encrypted data are achieved using the multi-chaotic in the encryption approach. Also, the circular shift method is employed in the approach to avoid differential attacks. The encryption approach is applied to secure the patient information, medical signals (ECG and EEG), and medical images (Grayscale, RGB, and Hyperspectral). The performance of the proposed approach has been analyzed for twenty signals and fifty medical images. Performance metrics such as MSE, SNR, PSNR, entropy, histogram, variance, chi-square, NPCR, UACI, and SSIM have been considered to check the resilience of the proposed approach against various types of attacks. The proposed approach satisfies the requirements of performance metrics by achieving higher MSE values with lower SNR and PSNR values for the encrypted data. Furthermore, the proposed methodology attains an entropy approaching 8, signifying robust randomness and resistance against entropy attacks. The SSIM values for encrypted images are close to 0, indicating significant dissimilarity from the original images. The NPCR values surpassing 99.60% and UACI exceeding 33.40% demonstrate resilience against differential attacks in the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Optimized Temporary Frequency Support for Wind Power Plants Considering Expanded Operational Region of Wind Turbines

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 51-64页

作者： Zhengyang Hu Bingtuan Gao Zhao Xu Sufan Jiang School of Electrical Engineering Southeast UniversityNanjing210096China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996 USA

Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated operations remain insufficiently investigated,highlighting the potential for further improvement of the frequency *** paper proposes a bi-level optimized temporary frequency support(OTFS)strategy for a *** implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine(WT)controllers and the central WPP ***,to exploit the frequency support capability of WTs,the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT *** approach synergizes the WTs'temporary frequency support with the secondary frequency control of synchronous generators,enabling WTs to release more kinetic energy without causing a secondary frequency ***,a model predictive control strategy is developed for the WPP *** strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy ***,comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.

关键词： Active power control model predictive control optimized temporary frequency support wind power plant wind turbine

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Gkillas, Athanasios Simos, Michael Angelos Makris, Christos University of Patras Department of Electrical and Computer Engineering Patras Greece University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350318067

YouTube is a popular online video-sharing and social media platform, hosting diverse content for all ages. The platform is gradually gaining ground against traditional media, with major turnover increasingly turning there due to videos and creators' influence, especially in younger audiences. Some of the videos may attain a large number of views often accompanied by many comments. Comments, often focus not only on the content of the video but also on several other aspects and topics. In this paper, we focus on the sentiment analysis of video comments, using a variety of methods we explore the correlation between user comments' sentiment polarity score and the popularity of the relevant video. Some adversities of machine learning or neural network model approach to the task include linguistic irregularities, typing errors, niche vocabulary, sarcastic and ambiguous content, or unrelated events influencing the commented themes. Unlike previous works [1], [2], our focus is centered on leveraging comment sentiment polarity for deriving predictions of the popularity and success of the respective videos. Our methods depend on lexicon-based sentiment analysis approaches like VADER and TextBlob but also on neural network architectures including BERT and RoBERTa. Our experimental analysis resulted in promising results, setting a baseline for this application theme. © 2023 IEEE.

关键词： Sentiment analysis

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CTRL: Clustering Training Losses for Label Error Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4121-4135页

作者： Yue, Chang Jha, Niraj K. Princeton University Department of Electrical and Computer Engineering PrincetonNJ08540 United States

In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models trained on such datasets do not generalize well. Thus, detecting the label errors can significantly increase their efficacy. We propose a novel framework, called CTRL11CTRL is open-source: https://***/chang-yue/ctrl. (Clustering TRaining Losses for label error detection), to detect label errors in multiclass datasets. It detects label errors in two steps based on the observation that models learn clean and noisy labels in different ways. First, we train a neural network (NN) using the noisy training dataset and obtain the loss curve for each sample. Then, we apply clustering algorithms to the training losses to group samples into two categories: cleanly labeled and noisily labeled. After label error detection, we remove samples with noisy labels and retrain the model. Our experimental results demonstrate state-of-the-art error detection accuracy on both image and tabular datasets under labeling noise. We also use a theoretical analysis to provide insights into why CTRL performs so well. © 2020 IEEE.

关键词： Neural networks

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Detecting Braess-like Paradoxes in Cascade Failures via Markov Chains 63

Detecting Braess-like Paradoxes in Cascade Failures via Mark...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Materassi, Donatello Tomsovic, Kevin University of Minnesota Department of Electrical Engineering and Computer Engineering United States Clemson University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

The article wants to draw attention to the potential occurrence of Braess-like phenomena in the context of cascade failures, where certain networked system configurations, which might appear more resilient than others because of the presence of additional redundancies, actually exhibit an unexpected higher degree of fragility. To elucidate and attempt to quantify this counterintuitive phenomenon, the article introduces an approach that leverages basic elements of the theory of Markov chains. Indeed, if it is possible to estimate the probabilities of local failures within the system, a comprehensive Markov Embedding Chain can be defined. This chain not only can capture the interplay of sequences of failure events, but can also provide a means to detect and analyze the latent brittleness inherent in seemingly resilient systems. An intrinsic advantage of this approach lies in its model-agnostic nature, allowing its application across a wide array of infrastructures and distributed systems irrespective of the actual underlying failure model. © 2024 IEEE.

关键词： Markov chains

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Physical-Layer Identification of Wireless IoT Nodes Through PUF-Controlled Transmitter Spectral Regrowth

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IEEE Transactions on Microwave Theory and Techniques 2024年第2期72卷 1045-1055页

作者： Zhou, Qiang He, Yan Yang, Kaiyuan Chi, Taiyun Rice University Department of Electrical and Computer Engineering HoustonTX77005 United States

Securing low-power Internet-of-Things (IoT) sensor nodes is a critical challenge for the widespread adoption of IoT technology due to their limited energy, computation, and storage resources. As an alternative to the traditional wireless security solution based on cryptography, there has been growing interest in RF physical-layer security, which promises a lower overhead and energy cost. In this work, we demonstrate energy-efficient physical-layer identification, a.k.a., RF fingerprinting, designed specifically for resource-constrained IoT nodes. To enhance the identification performance beyond prior demonstrations using off-the-shelf radios, we propose a minor modification to the radio frontend by integrating a digital physically unclonable function (PUF). The PUF controls the transmitter (TX) spectral regrowth as the RF fingerprint (RFF), enhancing its uniqueness and identification space beyond solely relying on transistor intrinsic process variations. As a proof of concept, a 2.4-GHz physical-layer identification is implemented in the GlobalFoundries 45-nm CMOS SOI process. It achieves 4.7-dBm output power and 36% efficiency, which are comparable to state-of-the-art low-power 2.4-GHz power amplifiers (PAs). Additionally, it demonstrates significant improvement in RFF reliability, uniqueness, and identification space over prior physical-layer identification demonstrations. The identification rate and security performance of the proposed approach under different attack models are also discussed. © 1963-2012 IEEE.

关键词： Power amplifiers

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