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Hajj Crowd Management Using CNN-Based Approach

computers, Materials & Continua 2021年第2期66卷 2183-2197页

作者： Waleed Albattah Muhammad Haris Kaka Khel Shabana Habib Muhammad Islam Sheroz Khan Kushsairy Abdul Kadir Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Electronic Section Universiti Kuala Lumpur British Malaysian InstituteMalaysia Department of Electrical Engineering Onaizah CollegesSaudi Arabia Department of Electrical and Computer Engineering International Islamic UniversityMalaysia

Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every *** to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in *** is considered as the world’s largest human *** makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are *** paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during *** image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech *** core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee *** algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential ***,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of *** aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and *** utility of these results lies in producing appropriate signals for proving helpful in monitoring the *** pilgrims from the photos will help the authorities to determine the number of people in certain *** results demonstrate the utility of agent-based modeling for Hajj pilgrims.

关键词： Crowd management CNN approach Hajj

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Thermal Simulation for Magnetic Coupler of Wireless Power Transfer Electric Vehicles by Using Heat Sink and Thermoelectric Cooler

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Progress In Electromagnetics Research M 2024年 126卷 89-98页

作者： Farooq, Umar Shafique, Shahryar Asif, Muhammad Arslan, Muhammad Wongjom, Poramed Ullah, Rizwan Zhilenkov, Anton Mobayen, Saleh Pijitrojana, Wanchai Electrical and Computer Engineering Department Thammasat School of Engineering Thammasat University Rangsit Campus Klong Luang Pathum Thani 12120 Thailand Ministry of IT & Telecommunications Islamabad Pakistan Department of Electrical Engineering University of Science and Technology Main Campus Bannu23200 Pakistan Department of Mechanical Engineering University of Engineering and Technology Lahore Pakistan Division of Physics Faculty of Science and Technology Thammasat University Rangsit Campus Klongluang Pathum Thani 12120 Thailand Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating temperature of the magnetic couplers. Addressing this, this study introduces a novel approach employing heat sink and thermoelectric cooler technologies to mitigate temperature rise in magnetic couplers, which is particularly crucial for high-power applications. Utilizing ANSYS simulation, the study evaluates a WPT high-power application coil model with a total output power of 2 KW and an 18 cm air gap, with a 3.5 cm adjacent alignment to enhance thermal performance on both transmitter and receiver sides. Results demonstrate significant thermal enhancement, reducing the temperature of coils from 63◦ C to 54◦ C solely with the heat sink and further down to 48◦ C with the combined implementation of both heat sink and thermoelectric cooler. These measures effectively dissipate heat from the coils into the surrounding air, ensuring system efficiency and stability while facilitating functionality of system components. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Heat sinks

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Green Leaf Disease Detection Using Raspberry Pi and Random Forest Machine Learning Algorithm: An Automated Approach for Precision Agriculture 10

Green Leaf Disease Detection Using Raspberry Pi and Random F...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Ranganathan, Chitra Sabapathy Basha, Amjath Ali Jakul Vijayalakshmi, A. Mohankumar, N. Sujatha, S. Sasikala, C. Mphasis Corporation Tamil Nadu Chennai India University of Technology and Applied Sciences Computer Engineering Electrical and Electronic Section Department of Engineering Ibra Oman Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharashtra Nagpur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350363104

Farm production and food security are affected by green leaf diseases. Modern disease detection technologies may discover plant health issues early. This work uses Raspberry Pi, a cheap and flexible single-board computer, along the advanced Random Forest machine learning technique to identify a green leaf disease. To identify crop diseases, the study uses automated, non-invasive technologies. From labeled images of good and sickly leaves, the algorithm extracts color histograms and texture, and form. It feeds a Random Forest classifier these properties for classifying data with high dimensions. Several green leaf diseased plant species were examined. This method detected plant health concerns with high precision. Movement and low cost make the Raspberry Pi ideal for remote and resource-limited agriculture, increasing farming accuracy. Helping farmers and users identify and develop green leaf diseases improves efficient farming. This methodology has the potential to improve crop management, optimize pesticide usage, and increase agricultural productivity. These results might contribute to the development of advanced automatic disease detection techniques to address green leaf diseases in modern agriculture. © 2024 IEEE.

关键词： computer Vision Green Leaf Plant Disease Detection Precision Agriculture raspberry pi

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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Energy and cost benefits of advanced lighting technology applications in traffic light systems

Energy and cost benefits of advanced lighting technology app...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MEDPOWER 2024)

作者： Alexis Polycarpou Marios S. Georgiou Department of Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus Electromechanical Section Nicosia Municipality Nicosia Cyprus

ISBN: (数字)9781837242689

Following a pilot project, which concluded in 2016, the replacement of old energy consuming Traffic Light Systems (TLS) of Nicosia Municipality were the subject of an investigation with regards to the energy saving potential and most importantly the payback period of the investment, through presumptions. This paper presents the results obtained of the new efficient technology implementation, with regards to the energy saving that materialised in the Nicosia Municipality. Investigation is presented in an attempt to determine whether the proposed solution and calculated capital employed had the expected energy usage reduction, return on investment, and payback period. The estimated cost benefit of the installed systems compared to the actual benefits varies significantly, after factors such as maintenance and electricity costs are considered.

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Omega-3 in the Subjective Betterment of Mood and Sleep Disturbances Induced by Hormone Therapy in Women with Breast Cancer: A Pilot, Randomized, Double-Blinded, Placebo-Controlled Clinical Trial

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Sleep and Vigilance 2024年第1期8卷 39-47页

作者： Nourozi, Behzad Naghsh, Erfan zadeh, Sahar Esmaeil Mehrzad, Valiollah Darakhshandeh, Ali Moghaddas, Azadeh Department of Clinical Pharmacy and Pharmacy Practice Faculty of Pharmacy Isfahan University of Medical Sciences Isfahan Iran Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Department of Internal Medicine Oncology and Hematology Section Faculty of Medicine Isfahan University of Medical Sciences Isfahan Iran

Background: This study was performed to investigate the effect of omega-3 administration on the treatment of mood and sleep problem induced by menopausal complications due to hormone therapy in patients with breast cancer. Methods and Patients: A pilot, randomized, double-blinded and placebo-controlled clinical trial was designed in oncology-hematology outpatient’s clinic of Omid Hospital, Isfahan, Iran. First, 60 patients were screened by Hospital Anxiety and Depression Scale (HADS) for any non-identified baseline mood problems and then divided into either intervention group which received 2 g’ omega-3 daily for 4 weeks or placebo group which received identical supplement. Then, the patients were considered to assess by center for epidemiological studies—depression scale (CES-D), profile of mood states (POMS), and Pittsburgh sleep quality index (PSQI) questionnaires at the baseline and after 4-week follow-up. Results: The mean scores of CES-D (P = 0.002), POMS (P = 0.03), and PSQI (P = 0.04) were significantly lower in the intervention group than the control group. In the intervention group, mean scores of CES-D (P < 0.001), POMS (P < 0.001), and PSQI (P = 0.003) were significantly lower after intervention than pre-intervention. Mean changes in scores of CES-D (P = 0.01), POMS (P = 0.001), and PSQI (P = 0.02) were significantly higher in the intervention group than the control group. Conclusion: Our findings revealed that omega-3 supplementation have the potential to reduce mood problems complications induced by hormone therapy in breast cancer patients, and it could also improve sleep quality items in terms of subjective sleep quality, delay in falling asleep, sleep delay, and sleep disturbance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2023.

关键词： Breast cancer Mood problem Omega-3 Sleep quality

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric Pattern Recognition System Using Multi Resolution Approach 3

FPGA Implementation of Textural Feature Extractor and Distan...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Hariprasath, S. Santhi, M. Prabakar, T.N. Koushick, V. Aravind, P. Gunasekaran, T. Saranathan College of Engineering Department of Ece Trichy India Sri Sairam College of Engineering Department of Ece Bangalore Anekal India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Mattu University Department of Electrical and Computer Engineering Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9798350322842

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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TRISAT-R: FLIGHT EXPERIENCE IN MEO

TRISAT-R: FLIGHT EXPERIENCE IN MEO

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2024 Small Satellites Systems and Services Symposium, 4S 2024

作者： Gonzales, Lorenzo Benamar, Kenza Santin, Giovanni Danzeca, Salvatore Kramberger, Iztok Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia Noordwijk2200 AG Netherlands Noordwijk2200 AG Netherlands Section Leader BE-CEM-EPR CERN Esplanade des Particules 1 Geneva 231211 Switzerland

ISBN: (纸本)9781510688940

This paper highlights the technical achievements and presents the in-flight experience of one of the first successful 3U nanosatellite mission in medium earth orbit (MEO) in Europe, TRISAT-R. TRISAT-R is an institutional non-commercial nanosatellite mission primed by the University of Maribor under the contract with ESA and in cooperation with CERN and Slovenian company SkyLabs. The primary mission objective is measurement of ionizing radiation and characterization of the space environment, consolidating the environmental models by comparing the simulated high energy particles particle environment with the measured data in orbit. The radiation measurements are conducted with several scientific payloads to monitor the Single Event Effects and Total Ionizing Dose. The secondary mission objectives are several In-Orbit demonstrations to show new solutions for nanosatellite component miniaturization, testing new design concepts, and demonstrating new Radiation Hardened by Design mitigation techniques for protection of high-performance and high-density electronic components, and targeting the upcoming era of Artificial Intelligence in space applications. Additionally, the satellite was equipped with two highly miniaturized experimental COTS cameras capturing the black Sun effect, which is a consequence of pixel spillover due to Sun’s energy saturation. These cameras captured a view of the Earth from MEO orbit by the 3U nanosatellite. © 2025 The Authors.

关键词： Radiation hardening

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A Robust Bio-inspired Color Detection Algorithm Under Low/High Light and Shadow for UAV Applications

A Robust Bio-inspired Color Detection Algorithm Under Low/Hi...

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IEEE Latin American Robotics Symposium, LARS

作者： Viviane S. S. Freitas Fabio Suim Chagas Thiago J. M. Baldivieso Daniel Morais Marcos P. S. Campanha Andre Rotava Paulo F. F. Rosa Defense Engineering Section Military Institute of Engineering Rio de Janeiro Brazil Computer Engineering Section Military Institute of Engineering Rio de Janeiro Brazil Electrical Engineering Section Military Institute of Engineering Rio de Janeiro Brazil

This project presents a bio-inspired color detection algorithm designed to keep good performance in scenarios where the light conditions are variable and challenging. It is based on the theory of opposite colors, studied in the context of human vision, where the ability to detect colors is associated with a neural response to contrast. To validate the algorithm, we have performed three experiments under adverse luminosity conditions in an indoor environment. Then, we compared the processed images analyzing them in two color spaces - YCrCb and CIEXYZ. As a result, we identified that the YCrCb space provided superior responses and allowed the detection of all eight colors chosen for the experiments.

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ViWise: Fusing Visual and Wireless Sensing Data for Trajectory Relationship Recognition

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ACM Transactions on Internet of Things 2023年第4期4卷 1-29页

作者： Wu, Fang-Jing Lai, Sheng-Wun Sou, Sok-Ian TU Dortmund University Otto-Hahn-Str. 6 Dortmund44227 Germany Department of Computer Science and Information Engineering National Taiwan University No. 1 Section 4 Roosevelt Rd Da’an District Taipei10617 Taiwan Department of Electrical Engineering National Cheng Kung University No. 1 Daxue Rd. East Dist. Tainan701401 Taiwan

People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. However, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%. © 2023 Copyright held by the owner/author(s)

关键词： Object detection

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