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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Biomedical Data Management and Analytics in IOMT 8th

Biomedical Data Management and Analytics in IOMT

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Niha, K. Surendiran, B. Amutha, S. School of Computer Science and Engineering VIT Vellore Campus Tamil Nadu Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India School of Computer Science and Engineering VIT Chennai Campus Chennai India

ISBN: (纸本)9789819732449

The IoMT is a device that works with different healthcare systems to help develop networking technologies. It reduces the unnecessary burden of visiting the hospital. Instead, it enables the transfer of medical related data with physicians through a secured network. Smart devices, such as wearable and medical monitors are used for healthcare use in the IoMT network. Telehealth and other services are adapted in homes and hospitals in other real-time locations. Approximately in the globe, 80% of healthcare have implemented IoT (Internet of Things) technologies and 20% are expected to do the same in the future. This paper focuses on four topics: (1) recent advances on Internet of medical things, (2) highlights the emerging data fusion process for data preparation, (3) discusses the life cycle of sensor devices and their protocols, and (4) processing data with different techniques and theories. In a nutshell, this paper gives a comprehensive overview of IoMT research advancement, application, data management, and analytics techniques along with the current research challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Voice assistance to blind people by detection of surrounding objects, activities, and humans

Voice assistance to blind people by detection of surrounding...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Kambham, Suhas Preetham Varman, Raahul Ramya, Tadepalli Sri Valli Eniyan, M.K. Tangavel, Senthil Kaumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Mathematics and Computer Science Ashland University United States

ISBN: (纸本)9798331510299

Various systems were developed to assist those with vision impairments and enhance their standard of life. Regrettably, the majority of these systems have restricted functionality. We decided to build a model that detects all possible surrounding things and guides them through voice description. Our main objective is to detect the obstacles in the person's pathway and warn them using voice description [1]. The model can assist blind people from the surrounding obstacles which exist in their pathway. The primary goal of the model is to identify the nearby obstacles - vehicles, animals, persons, objects, etc, in the person's pathway and guide or warn them using voice description. Our proposed system integrates advanced real-time obstacle detection and voice-guided navigation using a novel combination of DepthNet module with convolutional LSTM layers, enhancing spatial context awareness compared to traditional models. This approach, along with fine-tuning on diverse datasets such as MS COCO and BDD100K, ensures robust performance in varied environments and conditions. The real-time guidance capability is further improved by the integration of optical flow algorithms, which accurately estimate motion in dynamic scenes. This comprehensive solution significantly enhances the assistive technology for those with visual impairments. © 2024 IEEE.

关键词： Obstacle detectors

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Scalable Transactional Stream Processing on Multicore Processors

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4254-4269页

作者： Zhao, Jianjun Mao, Yancan Yang, Zhonghao Liu, Haikun Zhang, Shuhao Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore 119077 Singapore Nanyang Technological University 639798 Singapore

Transactional stream processing engines (TSPEs) are central to modern stream applications handling shared mutable states. However, their full potential, particularly in adaptive scheduling, remains largely unexplored. We present MorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e., planning, scheduling, and execution), MorphStream enables adaptive scheduling under varying workload characteristics. Building on this foundation, MorphStream is further enhanced with support for non-deterministic state access, employing a stateful task precedence graph to handle undefined read/write sets at runtime while guaranteeing transaction semantics. Additionally, MorphStream incorporates a generalized framework for managing window-based operations, enabling efficient tracking and maintenance of overlapping windows using multi-versioned state management. These extensions enhance the system’s ability to process dynamic and irregular workloads. Experimental results demonstrate up to 3.4 times higher throughput and 69.1% lower latency compared to state-of-the-art TSPEs, validating its scalability and adaptability in real-world streaming scenarios. © 1989-2012 IEEE.

关键词： Multicore programming

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A QOE-AWARE STRATEGY FOR ENERGY SAVING IN A FOG-BASED ENVIRONMENTAL MONITORING system

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International Journal of Electrical engineering 2023年第2期30卷 43-52页

作者： Haung, Yieh-Ran Yin, Chia-Ling Liu, Jenchi Department of Computer Science and Engineering Yuan Ze University Taiwan Electronic and Optoelectronic System Research Laboratories Industrial Technology Research Institute Taiwan

In the Fog-based Environmental Monitoring system (FEMS), the frequency at which Internet of Thing (IoT) devices upload monitoring data and the frequency at which users obtain monitoring data can highly affect energy consumption, data timeliness, and user quality of experience (QoE). How to reduce energy consumption while achieving high data timeliness and user QoE is not only important but also challenging. We propose a QoE-aware strategy to schedule the frequency at which users obtain monitoring data and the frequency at which IoT devices upload monitoring data. The QoE-aware strategy sets the changing tolerance (CT) of monitoring data according to the requirements of different applications in different environments. CT indicates the sensitivity of the application to the change of the environmental data. Only when the changing rate of monitoring data exceeds the CT value set, the IoT device uploads monitoring data to the fog server. Besides, to meet the requirements of reducing data traffic and achieving high data timeliness, users can set different values of QoE Index (QI), i.e., the achievement rate for timely access, according to different environmental requirements. With the QI value, the fog server can calculate the optimal time interval (i.e., access period, AP) for users to access monitoring data. Consequently, by setting the values of CT, QI, and AP according to the requirements of different applications in different environments, the QoE-aware strategy is able to achieve the lowest energy consumption and the highest data timeliness. © 2023, Chinese Institute of Electrical engineering. All rights reserved.

关键词： Fog

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Emotional Intelligence and Responsible Social Media Behavior 3

Emotional Intelligence and Responsible Social Media Behavior

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Gita Gayatri, Nyoman Ayu Anggraini Kusumajati, Dian Bina Nusantara University School of Computer Science Department of Computer Science Jakarta Indonesia Bina Nusantara University Character Building Development Center School of Information System Jakarta Indonesia

ISBN: (纸本)9798350367492

The rapid development of today's world has seen the pervasive use of social media platforms, which significantly affects human behavior. Emotional intelligence plays a crucial role in this context. It includes self-awareness, recognizing one's own emotions, managing emotions, recognizing the emotions of others, and interacting effectively with others, thereby influencing how individuals navigate the digital world. Social media serves as a digital territory where various emotional signals are expressed through text, emojis, and interactions. This study aims to determine the impact of emotional intelligence on the responsible use of social media among students at Bina Nusantara University and its implications for achieving the Sustainable Development Goals (SDGs), particularly the Quality Education indicator. The findings reveal a positive correlation between self- awareness and social media usage. This suggests that the students in the study can recognize their emotions and understand their moods when using social media, which affects their behavior. The study underscores the importance of incorporating emotional intelligence development into educational programs to enhance awareness and promote responsible behavior in the use of digital media in the digital age. © 2024 IEEE.

关键词： Ambient intelligence

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Adaptive Hypergraph Network for Trust Prediction 40

Adaptive Hypergraph Network for Trust Prediction

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Xu, Rongwei Liu, Guanfeng Wang, Yan Zhang, Xuyun Zheng, Kai Zhou, Xiaofang School of Computing Macquarie University Australia School of Computer Science and Engineering University of Electronic Science and Technology of China China Hong Kong University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually complicated rather than pairwise only. Hypergraphs offer a flexible approach to modeling these complex high-order correlations (not just pairwise connections), since hypergraphs can leverage hyperedeges to link more than two nodes. However, most hypergraph-based methods are generic and cannot be well applied to the trust prediction task. In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations. AHNTP utilizes Motif-based PageRank to capture high-order social influence information. In addition, it constructs hypergroups from both node-level and structure-level attributes to incorporate complex correlation information. Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network (GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user embeddings, facilitating trust relationship prediction. To enhance model generalization and robustness, we introduce a novel supervised contrastive learning loss for optimization. Extensive experiments demonstrate the superiority of our model over the state-of-the-art approaches in terms of trust prediction accuracy. © 2024 IEEE.

关键词： Multilayer neural networks

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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Optimized Convolutional Neural Network at the IoT edge for image detection using pruning and quantization

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Multimedia Tools and Applications 2025年第9期84卷 5435-5455页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of Electronics and Communication Engineering REVA University Bangalore India

Most real-time computer vision applications heavily rely on Convolutional Neural Network (CNN) based models, for image classification and recognition. Due to the computationally and memory-intensive nature of the CNN model, it’s challenging to deploy on resource-constrained Internet of Things (IoT) devices to enable Edge intelligence for real-time decision-making. Edge intelligence requires minimum inference latency, memory footprint, and energy-efficient model. This work aims to develop an energy-efficient deep learning accelerator using a 3-stage pipeline: Training, Weight-pruning, and Quantization to reduce the model size and optimize the resources. First, we employ YOLOv3, a CNN architecture to detect objects in an image on the trained data. In addition, a sparse network of YOLO has been created by using pruning, which helps to improve the network’s performance and efficiency by reducing the computational requirements. Finally, we utilize 8-bit quantization to reduce the precision of the weights and activations, in a neural network. The evaluation of our proposed model shows that combining pruning and 8-bit quantization improves the efficiency and performance of the model. While pruning shows a decline of 80.39% in model parameters. The combination of 8-bit quantization results in an improvement in inference latency by 22.72% compared to existing SQuantization approach and a reduction of energy consumption by 29.41%. © The Author(s) 2024.

关键词： Convolutional neural networks

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High-Performance Chip Design With Parallel Architecture for Magnetic Field Imaging system

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-13页

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Lin, Chun-Hung Yuan Ze University Department of Computer Science and Engineering Taoyuan320315 Taiwan National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung912301 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan International Games System Company Ltd. New Taipei City248 Taiwan

To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetoresistance (AMR) sensors. The core controller of the proposed system was designed using a pipelined parallelization algorithm and implemented on a high-speed field-programmable gate array (FPGA) in a fully hardware-based architecture. The designed MFI system can achieve parallel acquisition of magnetic field components from the 16-channel AMR sensor array, parallel data processing, parallel data transmission, and parallel computations in a single cycle of only 4 ms. Compared with typical detection systems, the proposed solution not only exhibits superior performance but can also provide dynamic visual representations of sensed information. © 1963-2012 IEEE.

关键词： Parallel architectures

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