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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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Science China(Information Sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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Ultrasonic-based Passenger Boarding Determination System for Trams

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Journal of institute of Control, Robotics and Systems 2023年第4期29卷 308-313页

作者： Lee, Seung-Cheol Kwak, Jae-Ho Hwang, Hyeon-Chyeol Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Advanced Railroad Vehicle Division Korea Railroad Research Institute Korea Republic of Smart Tram Research Department Korea Railroad Research Institute Korea Republic of

In this paper, a system that can determine whether a passenger is on the tram by using only the ultrasonic generator inside the tram vehicle and the built-in microphone of the smartphone was proposed. Due to the probabilistic uncertainty of radio waves, determining whether a passenger is on the tram by using existing local wireless communication methods (e.g., WI-FI and BLE) is difficult when a passenger is near the boundary between the interior and exterior of the vehicle. To overcome this problem, in this study, ultrasonic signal, which has excellent shielding property between obstacles or barriers, was used to determine the interior or exterior of the vehicle. The door of the vehicle is closed, and the indoor ultrasonic generator emits ultrasonic waves of a specific frequency only under certain driving conditions. We developed a smartphone app based on the discrete Fourier transform algorithm to determine whether a passenger is on the tram. We installed the system on a tram of the Osong Tram Test Line and evaluated its effectiveness. © ICROS 2023.

关键词： Smartphones

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Optimal Volume-Sensitive Bounds for Polytope Approximation 39

Optimal Volume-Sensitive Bounds for Polytope Approximation

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39th International Symposium on Computational Geometry, SoCG 2023

作者： Arya, Sunil Mount, David M. Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9783959772730

Approximating convex bodies is a fundamental question in geometry and has a wide variety of applications. Consider a convex body K of diameter ∆ in Rd for fixed d. The objective is to minimize the number of vertices (alternatively, the number of facets) of an approximating polytope for a given Hausdorff error Ε. It is known from classical results of Dudley (1974) and Bronshteyn and Ivanov (1976) that (Equation presented) vertices (alternatively, facets) are both necessary and sufficient. While this bound is tight in the worst case, that of Euclidean balls, it is far from optimal for skinny convex bodies. A natural way to characterize a convex object's skinniness is in terms of its relationship to the Euclidean ball. Given a convex body K, define its volume diameter ∆d to be the diameter of a Euclidean ball of the same volume as K, and define its surface diameter (Equation presented) analogously for surface area. It follows from generalizations of the isoperimetric inequality that (Equation presented). Arya, da Fonseca, and Mount (SoCG 2012) demonstrated that the diameter-based bound could be made surface-area sensitive, improving the above bound to (Equation presented). In this paper, we strengthen this by proving the existence of an approximation with (Equation presented) facets. This improvement is a result of the combination of a number of new ideas. As in prior work, we exploit properties of the original body and its polar dual. In order to obtain a volume-sensitive bound, we explore the following more general problem. Given two convex bodies, one nested within the other, find a low-complexity convex polytope that is sandwiched between them. We show that this problem can be reduced to a covering problem involving a natural intermediate body based on the harmonic mean. Our proof relies on a geometric analysis of a relative notion of fatness involving these bodies. © Sunil Arya and David M. Mount;licensed under Creative Commons License CC-BY 4.0.

关键词： Approximation algorithms

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Digital-Twin Architecture of a Spiking Neuron using Carbon Nanotube Field Effect Transistors

Digital-Twin Architecture of a Spiking Neuron using Carbon N...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Williams, Shelby Kirtonia, Prosen Khalil, Kasem Bayoumi, Magdy Univ. of Louisiana at Lafayette Center for Advanced Computer Studies LafayetteLA70504 United States University of Louisiana at Lafayette Dept. of Electrical and Computer Engr. LafayetteLA70504 United States Univ. of Mississippi Department of Electrical and Computer Engineering OxfordMS38677 United States Assiut University Department of Electrical Engineering Assiut71515 Egypt

ISBN: (纸本)9798350367942

The rapid advancements in nanotechnology and neuromorphic engineering have paved the way for developing high-performance computational models that mimic biological neural networks. This paper presents a novel digital-twin architecture for a spiking neuron, leveraging the exceptional properties of Carbon Nanotube Field Effect Transistors (CNFETs). The proposed architecture aims to emulate the dynamic behavior of biological neurons with high fidelity, providing a robust platform for simulation and analysis. By integrating CNFETs, we achieve significant improvements in lowest-energy usage and highest-spiking frequency, compared to traditional silicon-based technologies. Furthermore, the digital twin not only replicates the electrical characteristics of a spiking neuron but also facilitates advanced functionalities such as adaptive learning, fault tolerance, and self-optimization. Experimental results demonstrate the potential of CNFET-based neurons in enhancing the performance of neuromorphic systems, offering promising applications in artificial intelligence, cognitive computing, and complex system modeling. Integrating digital twin technology with CNFETs allows for precise control and manipulation of spiking behavior, enabling the development of more sophisticated and efficient neural models. This research underscores the transformative impact of combining digital twin technology with cutting-edge nanomaterials, setting a new benchmark for future explorations in the field. The findings highlight the importance of continued research and development in this area, as it holds the promise of revolutionizing the way we design and implement neuromorphic systems, pushing the boundaries of what is possible in artificial intelligence and beyond. © 2024 IEEE.

关键词： Carbon nanotube field effect transistors

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Tensorial Evolutionary Computation for Spatial Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 154-166页

作者： Si-Chao, Lei Xiaolin, Xiao Yue-Jiao, Gong Yun, Li Jun, Zhang the School of Computer Science and Engineering South China University of Technology Guangzhou510006 China the Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen610056 China the School of Computer Science South China Normal University Guangzhou510006 China the Department of Electrical and Electronic Engineering Hanyang University Seoul15588 Korea Republic of

—Spatial optimization problems (SOPs) refer to a class of problems where the decision variables require spatial organization. Existing methods based on evolutionary algorithms (EAs) fit conventional evolutionary operators by flattening the spatial representations of SOPs to one dimension, which hence loses crucial spatial structures of the potential solutions. To address this issue, this article proposes a tensorial evolutionary algorithm (TEA) by formulating a tensor space of the SOPs, where the population of candidate solutions is represented as a third-order tensor. Accordingly, we design a tensor-product crossover operator and a two-phase mutation consisting of a local convolutional mutation operator and a global Gaussian mutation operator in the tensor space. These operators are all implemented based on tensor operations. Under the consideration of spatial context on individual solutions, we devise three types of evolutionary tensors, namely, the crossover tensor, the convolutional mutant tensor, and the Gaussian mutant tensor for the three tensorial evolutionary operators correspondingly. The resultant TEA is tested using two typical real-world SOPs, namely, the facility layout problem and the image steganography problem. Comparative experimental studies validate the effectiveness and robustness of TEA. © 2024 IEEE.

关键词： Evolutionary algorithms

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Detecting Persons in River Quarry Operations: Aerial Surveillance for Flood Pre-Disaster Monitoring 15

Detecting Persons in River Quarry Operations: Aerial Surveil...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Madulara, Rochelle F. Aleluya, Earl Ryan M. Pao, Jeanette C. Clar, Steve E. Alagon, Francis Jann A. Salaan, Carl John O. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

During the flood disaster preparedness procedure, warning dissemination followed by response decision-making and implementation is usually time-consuming for disaster preparedness teams. Some inaccessible areas that are also prone to flooding is a crucial challenge for the disaster preparedness team in disseminating the early warnings, which may result in endangering the lives of people living in those areas. Moreover, the disaster preparedness team is dedicated to overseeing and managing river quarrying activities, as these are areas where workers or individuals are at high risk of flooding disasters. In this study, an Unmanned Aerial Vehicle (UAV) with Person Detection System that can assist the disaster preparedness team for flood pre-disaster monitoring was introduced. The study investigated the performance of advanced deep learning models, particularly on You Only Look Once, for accurate person detection utilizing UAV imagery focusing in a river-quarry setting. A novel approach to data collection was also presented, specialized datasets were created for capturing river quarry-specific scenarios and used this in facilitating realistic model training. Two models were evaluated in this study, the YOLOv5 model and YOLOv8 model, through their precision, recall, F1-score, and mAP performance. The trained YOLOv5 model showed better performance with 97.70% precision, 94.10% recall, 95.10% F1-Score, and 64.50% mAP than the YOLOv8 model. The significant difference of 2.00% for precision, 3.80% for recall, 3.00% for F1-score, and 0.40% for mAP, between YOLOv5 model and YOLOv8 model, provided beneficial findings for optimizing model selection for person detection using UAV imagery considering other areas needed for pre-disaster monitoring. © 2023 IEEE.

关键词： Quarries

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Eggplant Leaf Semantic Segmentation in Aerial Imagery for Precision Agriculture 15

Eggplant Leaf Semantic Segmentation in Aerial Imagery for Pr...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Usman, Nouran M. Aleluya, Earl Ryan M. Clar, Steve E. Alagon, Francis Jann A. Salaan, Carl John O. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Eggplant is one of the prominent vegetable crops in the Philippines. Traditional farming practices, like agrochemical spraying, often lack real-time, high-resolution data crucial for informed decisions. Monitoring vegetation indices is vital for crop growth assessment, health monitoring, and precision agriculture. However, optimizing agrochemical spraying for specific crop needs remains a challenge. Unmanned aerial vehicles (UAV s) offer a transformative solution. RGB cameras, designed for visual imagery, may not provide accurate vegetation index measurements. computer vision, through leaf detection, plays a crucial role in assessing crop health. This study aims to distinguish eggplant leaves from their surroundings. Subsequently, the authors compared two open-source segmentation models, namely U-Net and FPN. Two pre-trained encoders, VGG16 and VGG19, were used in both models to further evaluate their performance. Using a drone-mounted RGB camera, the authors gathered images of mature eggplant leaves. The experimental findings demonstrate the effectiveness of the VGGl6-based FPN architecture for real-time leaf segmentation tasks as it acquires 97.24 % and 96.73 % accuracy with a latency time of 31.5 ms and 32.6 ms, on 0.5 and 0.75 threshold values. The study recommends FPN with VGG16 for its accurate and low-latency performance in time-sensitive leaf segmentation applications. © 2023 IEEE.

关键词： Antennas

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Ground Robot Detection for Collaborative UAV-UGV Alignment and Retrieval Operations 15

Ground Robot Detection for Collaborative UAV-UGV Alignment a...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Pepito, Jamaica Mae L. Aleluya, Earl Ryan M. Bolaybolay, John Mel A. Alagon, Francis Jann A. Clar, Steve E. Pao, Jeanette C. Salaan, Carl John O. Guirnaldo, Sherwin A. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Volcanologists face considerable challenges in ex-ploring and monitoring harsh and dangerous environments like volcanoes. To gather essential data, they use robots, such as drones and mobile robots. However, drones have limited capacity and flight time, while mobile robots face mobility constraints. Combining drones and mobile robots has emerged as an inno-vative technology for long-range monitoring of volcanoes. This approach allows for fast deployment of ground robots using transport drones. However, the retrieval operation of the ground robot is challenging due to inaccurate drone positioning. A visual technique for detecting and aligning the ground robot is crucial during this procedure. In this study, the detection of the ground robot utilizing a drone was investigated. The drone-based detection approach involved (i) capturing ground robot images from various conditions, (ii) training a robust detection model, and (iii) assessing its performance. Furthermore, this study employed the You Only Look Once (YOLOv8) architecture for ground robot detection. Three models were investigated: the plate detection model, the ground robot detection model, and the model for both ground robot and plate detection. The trained ground robot detection model achieved a robust detection performance garnering the precision of 98.9% with a respectable recall of 100%, F1 score of 99.94%, and 98.6% mean average precision, compared to the other two trained models. The trained detection model presented a practical approach, which produced good performance and could predict and segment the ground robot during the alignment procedure for the retrieval operation by the drone. © 2023 IEEE.

关键词： Drones

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Mask, Hairnet, and Handwash Detection System for Food Manufacturing Health and Safety Monitoring 15

Mask, Hairnet, and Handwash Detection System for Food Manufa...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Pepito, Collien Princess C. Aleluya, Earl Ryan M. Alagon, Francis Jann A. Clar, Steve E. Abayan, Jared Jan A. Salaan, Carl John O. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines Department of Electrical Engineering College of Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Food safety is a top concern within food manufacturing facilities, with strict measures in place to ensure the quality and integrity of products. The existing food safety measures typically rely on manual inspections and human vigilance to ensure compliance with safety protocols. These measures, although essential, are prone to human error and limitations in terms of real-time monitoring. Moreover, the global pandemic has underscored the importance of adherence to health and safety guidelines, including the wearing of masks, hairnets, and proper handwashing practices to ensure the well-being of workers and the integrity of food production processes. This research introduces the development of a mask, hairnet, and handwash detection system employing pre-trained deep learning models, specifically YOLOv5 and YOLOv8 (You Only Look Once). Each model was evaluated on various metrics to assess its accuracy and overall performance. The YOLOv8 model showcases outstanding performance in mask and hairnet detection, along with handwash detection. For mask and hairnet detection, it achieved a precision, recall, and F1 score of 100%, and mAP of 99.40%. While handwash detection attained a precision, recall, and F1 score of 100%, and mAP of 92.70%. To provide a comparative analysis between the YOLO models used in the study, the YOLOv5 model was also trained using the same dataset as the YOLOv8 and was evaluated accordingly. Moreover, this study aims to impart significant contributions, particularly in the real-time application of health and safety monitoring within food manufacturing settings. © 2023 IEEE.

关键词： Object detection

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