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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Sorin Pavel Simona Moldovanu Dorel Aiordachioaie Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

This paper presents a case study related to emotion recognition based on human thermal image processing. Three states are considered for human faces: normal, sad, and happy. The thermal images are pre-processed for important feature selection and extraction by using the random forest (RF) classifier. All and selected features set is used as input to a feedforward backpropagation neural network (FFBPN), with binary outputs. The input images are organized in pairs, i.e., normal with sad and normal with happy. The preliminary results show particularly good classification accuracy.

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Evaluating Gaussianity of heterogeneous fractional Brownian motion

arXiv

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arXiv 2025年

作者： Balcerek, Michal Pacheco-Pozo, Adrian Wylomańska, Agnieszka Krapf, Diego Department of Electrical and Computer Engineering School of Biomedical Engineering Colorado State University Fort CollinsCO80523 United States Faculty of Pure and Applied Mathematics Hugo Steinhaus Center Wroclaw University of Science and Technology Wroclaw50-370 Poland

Heterogeneous diffusion processes are prevalent in various fields, including the motion of proteins in living cells, the migratory movement of birds and mammals, and finance. These processes are often characterized by time-varying dynamics, where interactions with the environment evolve, and the system undergoes fluctuations in diffusivity. Moreover, in many complex systems anomalous diffusion is observed, where the mean square displacement (MSD) exhibits non-linear scaling with time. Among the models used to describe this phenomenon, fractional Brownian motion (FBM) is a widely applied stochastic process, particularly for systems exhibiting long-range temporal correlations. Although FBM is characterized by Gaussian increments, heterogeneous processes with FBM-like characteristics may deviate from Gaussianity. In this article, we study the non-Gaussian behavior of switching fractional Brownian motion (SFBM), a model in which the diffusivity of the FBM process varies while temporal correlations are maintained. To characterize non-Gaussianity, we evaluate the kurtosis, a common tool used to quantify deviations from the normal distribution. We derive exact expressions for the kurtosis of the considered heterogeneous anomalous diffusion process and investigate how it can identify non-Gaussian behavior. We also compare the kurtosis results with those obtained using the Hellinger distance, a classical measure of divergence between probability density functions. Through both analytical and numerical methods, we demonstrate the potential of kurtosis as a metric for detecting non-Gaussianity in heterogeneous anomalous diffusion processes. © 2025, CC BY.

关键词： Brownian movement

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BloodCell-Net: A lightweight convolutional neural network for the classification of all microscopic blood cell images of the human body

arXiv

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arXiv 2024年

作者： Mondal, Sohag Kumar Talukder, Md Simul Hasan Aljaidi, Mohammad Sulaiman, Rejwan Bin Tushar, Md Mohiuddin Sarker Alsuwaylimi, Amjad A. Electrical and Electronic Engineering Khulna University of Engineering and Technology Bangladesh Khulna Khulna Bangladesh E-12/A Agargaon Dhaka1207 Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer science and Technology Northumbria University United Kingdom Electrical and Electronic Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University Bangladesh Department of Information Technology College of Computing and Information Technology Northern Border University Arar Saudi Arabia

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.25% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier's performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split t

关键词： Blood

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Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization

Optimizing Resource Allocation in Mobile Edge Computing for ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sudhakar AVV Mohan M K. Maharajan Mohammed I. Habelalmateen N. Naga Saranya Department of Electrical and Electronics Engineering SR University Telangana India Department of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Bangalore Krishnankoil India Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor Malaysia Department of Computer Technical Engineering College of Technical Engineering the Islamic University Najaf Iraq Department of Computer Applications Saveetha College of Liberal Arts and Science SIMATS Chennai India

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge Computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques—more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms—are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32.

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Passivity of nonlinear time-delay systems

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IFAC-PapersOnLine 2023年第2期56卷 8568-8573页

作者： Yu Kawano Saeed Ahmed Michele Cucuzzella Jacquelien M.A. Scherpen Graduate School of Advanced Science and Engineering Hiroshima University Higashi-hiroshima 739-8527 Japan Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen The Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

This paper presents passivity-based control of nonlinear systems with retarded delays in the state. To this end, we first show that the standard passivity concept can naturally be generalized to time-delay systems, which readily implies that a feedback interconnection (with or without communication delays) of passive time-delay systems is also passive. Then, we propose a storage functional for passivity analysis and further use it for stability analysis of controlled-passive time-delay systems. In particular, invoking an invariance principle for retarded functional differential equations, we show that a passive time-delay system can always be stabilized by a static output feedback controller under a delayed version of the zero-state detectability assumption.

关键词： Passivity nonlinear systems time-delay systems retarded functional differential equations stabilizing control

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An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation

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Heliyon 2024年第21期10卷 e39902页

作者： Moustafa, Ghareeb Alnami, Hashim Ginidi, Ahmed R. Shaheen, Abdullah M. Department of Electrical and Electronic Engineering College of Engineering and Computer Science Jazan University P.O. Box114 Jazan 45142 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Suez University P.O. Box: 43221 Suez Egypt

Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. Kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results. © 2024

关键词： Kepler optimization algorithm Local Escaping Operator Practical solar modules PV parameters extraction

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Vitality Detection with FMCW Radar Based SAR Imaging Technique at 24GHz

Vitality Detection with FMCW Radar Based SAR Imaging Techniq...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Haris Ahmad Saleem Shahid Musab Hameed Wolfgang Bosch Syed Muzahir Abbas Department of Electrical and Computer Engineering Air University Islamabad Pakistan Institute of Microwave and Photonic Engineering Graz University of Technology Graz Austria Institute of Electronics Graz University of Technology Graz Austria Faculty of Science and Engineering School of Engineering Macquarie University Sydney NSW Australia

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

Identifying human targets in synthetic aperture radar (SAR) imaging is crucial for security and rescue ap-plications, but it poses range and accuracy challenges. This paper presents a vital-SAR-imaging method developed using a frequency-modulated continuous-wave (FMCW) radar system. A measurement setup is developed to capture reflections from human breathing induced chest movements. The Intermediate Frequency (IF) is processed through an audio port of the PC sound card. The acquired data is processed and matched with synchronization pulse and used to generate SAR images. The experimental results showcase the accuracy and extended range in identifying human targets useful for localization applications.

关键词： Radar remote sensing Accuracy Surveillance Radar detection Radar imaging Radar polarimetry Robustness

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A Slot Antenna Array With Low Cross-Polarization Level Based on a Novel Substrate Integrated Coaxial Line Feeding Structure

A Slot Antenna Array With Low Cross-Polarization Level Based...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Shuhao Shen Kang Zhou Chunli Dong School of Electrical Information and Electronics Engineering Shanghai Jiao Tong University Shanghai China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

A slot antenna array with low cross-polarization level based on a novel substrate integrated coaxial line (SICL) feeding structure is proposed in this paper. The longitudinal slots are etched on the top layer as the radiation part, thus a broadside radiation characteristic can be achieved. Two sets of SICL are arranged on both sides to feed the quasi-substrate integrated double line (SIDL), and the length of the quasi-SIDL is adjusted to obtain the optimal slot etching scheme. A slot antenna array covering 27.1-27.6 GHz is designed and verified. The simulated results show that the peak gain is more than 8.6 dBi, while the cross-polarization level is better than 43.9 dB. The excellent radiation performance makes it a strong competitor for millimeter-wave (mm-wave) wireless applications.

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Quantum-Inspired Resource Optimization for 6G Networks: A Survey

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IEEE Communications Surveys and Tutorials 2024年

作者： Butt, Muhammad Omair Waheed, Nazar Duong, Trung Q. Ejaz, Waleed Lakehead University Department of Electrical Engineering ON Canada Faculty of Computing Information Systems Higher Colleges of Technology United Arab Emirates Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

The Internet of things (IoT) drives an exponential surge in computing and communication devices. Consequently, it triggers capacity, coverage, interference, latency, and security issues in the existing communication networks. The forthcoming sixth-generation (6G) networks will address these issues by providing comprehensive solutions. In particular, quantum communication technology can potentially address the challenges of 6G networks. However, its implementation requires substantial infrastructure upgrades. Therefore, the quantum-inspired (QI) techniques offer an intermediate resort due to their ability to utilize the classical communication infrastructure for design and implementation. Hence, we review QI techniques in this survey that address radio resource optimization challenges across various communication aspects, including channel assignment, reconfigurable intelligent surfaces, spectrum sensing, unmanned aerial vehicle-assisted networks, and related areas. The analysis explores diverse aspects, including objectives, constraints, problem characterization, proposed solutions, and lessons learnt. Research indicates that QI techniques offer advantages such as faster convergence and reduced complexity, providing promising solutions to complex optimization problems in communication networks. Furthermore, we identify the future directions, research gaps, and ongoing challenges from the QI radio resource optimization dataset. © 1998-2012 IEEE.

关键词： Quantum communication

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Deploying YOLOV10 for Affordable Real-Time Handgun Detection

Deploying YOLOV10 for Affordable Real-Time Handgun Detection

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Nikola Žigulić Matko Glučina Domagoj Frank Ivan Lorencin Zoran Šverko Dario Matika Faculty of Engineering University of Rijeka Rijeka Croatia Department of Engineering Istrian University of Applied Sciences Pula Croatia Department of Computer Science and Informatics University North Koprivnica Croatia Faculty of Informatics Juraj Dobrila University of Pula Pula Croatia Department of Electrical engineering University North Varaždin Croatia

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

The development of artificial intelligence is one of the most significant technological innovations that contributes to humanity with its characteristics and facilitates, secures, and improves everyday life. However the challenge arises when the programmer-engineer learns the algorithm of artificial intelligence to perform the proposed task, that is, the challenge lies in the issue of available hardware resources. Artificial intelligence algorithms perform many human-impossible tasks, such as detection and counting, i.e. calculating the interrelationships of individual objects, segmentation of tumors and other malignant diseases, i.e. tissues, classification of specific states of classes of a scene, and many other similar technologies. This research paper examines the possibility of implementing the You Only Look Once algorithm of the tenth generation on certain devices such as an affordable Raspberry Pi, and will discuss the advantages and disadvantages of changes in detection parameters, i.e. inferences to the applied model. In addition, a mock-up of the device will be shown, which will serve to provide timely information about criminals and suspicious persons who possess firearms in different situations, such as normal weather conditions in a populated place or in shops where petty robberies are frequent. The testing will be done using recorded videos in real-time scenarios. Finally, real-time inference or detection in real-time will be tested and the actions that Raspberry will perform will be simulated. The results indicate that the optimal model achieved a precision of 0.938, recall of 9.863, mAP50 of 0.91, and mAP50-95 of 0.739. This was achieved using an image size of 640, IOU threshold of 0.7, confidence threshold of 0.6, and training for 600 iterations with the Stochastic Gradient Descent optimizer, without augmentations, and employing the ONNX inference format.

关键词： YOLO Training Technological innovation Real-time systems Hardware Classification algorithms Delays Videos Tumors Testing

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