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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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Detecting ugly and derogatory words in Serbian language using a web browser extension 32

Detecting ugly and derogatory words in Serbian language usin...

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32nd Telecommunications Forum, TELFOR 2024

作者： Milakovic, Adrian Jocovic, Vladimir Cincovic, Jelica Micovic, Marko Radenkovic, Uros Draskovic, Drazen University of Belgrade School of Electrical Engineering Department of Computer Science and Information Technology Serbia

ISBN: (纸本)9798350391053

In natural language processing, data acquisition and preprocessing techniques are significant for experiments involving training models on cleaned data. This paper describes the formation of a dataset of ugly and derogatory words in the Serbian language and the development of a web extension to analyze texts in which such words appear and then their censoring. Developing such an add-on is essential in creating a safer and healthier digital environment for young users who are just developing their Internet habits and adults who want to avoid unwanted content. It is crucial to point out that there are few software tools for natural languages with limited resources, such as the Serbian language. This software could analyze the frequency of certain foul words on different sites, which would contribute to a better understanding of the impact of such language on users. © 2024 IEEE.

关键词： Web browsers

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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(Invited) Mobility Enhancement Technology of Extremely Thin Body Ge-On-Insulator Channel MOSFETs

(Invited) Mobility Enhancement Technology of Extremely Thin ...

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11th Symposium on SiGe, Ge, and Related Compounds: Materials, Processing, and Devices - In Memory of Qizhi Liu, held at the PRiME 2024 Meeting

作者： Takagi, Shinichi Han, Xueyang Chen, Chia-Tsong Sumita, Kei Toprasertpong, Kasidit Takenaka, Mitsuru Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan

ISBN: (纸本)9781623326630

An extremely thin body (ETB) channel is mandatory for scaled nano-sheet structures to realize continuous miniaturization of CMOS devices. Here, mobility booster technologies to maintain high channel mobility are strongly needed, because surface roughness scattering significantly reduces mobility in such ETB channels. From this viewpoint, Ge-On-Insulator (GOI) or SiGe-On-Insulator (SGOI) channels are promising because of the low electron and hole effective mass along channel directions. It is theoretically shown that (111) GOI n-MOSFETs can hold high electron mobility even in the body thickness down to 2 nm. Thus, (111) GOI structures and n-MOSFETs are fabricated, and the electrical characteristics are studied. For p-MOSFETs, on the other hand, (110) surface orientation and application of compressive strain are still effective in boosting hole mobility in ETB channels. Here, (110) asymmetric compressive strain SGOI p-MOSFETs are experimentally demonstrated, and the body thickness dependencies of the effective mobility are examined. Copyright © 2024 by The Electrochemical Society. All rights reserved.

关键词： MOSFET devices

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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A General Framework for Human-Drone Interaction under Limited On-board Sensing

A General Framework for Human-Drone Interaction under Limite...

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2023 systems and information engineering Design Symposium, SIEDS 2023

作者： Nayhouse, S. Chadha, S. Hourican, P. Moore, C. Bezzo, N. University of Virginia Departments of Systems and Information Engineering and Electrical and Computer Engineering CharlottesvilleVA22903 United States University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22903 United States

ISBN: (纸本)9798350300642

Recent advancements in unmanned aerial vehicles (UAVs), has allowed their deployment for numerous applications like aerial photography, infrastructure inspection, search and rescue, and surveillance. Despite the potential for full autonomy, many applications still necessitate human operators for navigating complex environments and decision-making. Existing solutions often employ high-precision and simple sensors like 2-D or 3-D LiDAR, which may provide more data than necessary and contribute to increased system complexity and cost. To address these challenges and bridge the gap between full autonomy and human-controlled UAVs, this work develops a shared-autonomy framework for UAVs, leveraging lightweight, low-cost 1-D LiDAR sensors combined with mobility behaviors to obtain performance comparable to more advanced 2-D/3-D LiDAR sensors while minimizing energy, computation overhead, and weight. Our framework includes a novel state machine method that exploits the UAV mobility to compensate for the limitations of 1-D LiDAR sensors, ensuring safety and obstacle avoidance through a physics-based algorithm that transitions between teleoperation and autonomous mode as needed based on environmental conditions and safety-critical issues. Experimental validations on real UAVs demonstrates the effectiveness of this shared autonomy scheme in complex environments, and the system is further generalized to larger UAVs and prototyped with a custom sensor configuration and onboard obstacle avoidance. © 2023 IEEE.

关键词： Drones

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Modeling and Control of an Aerogenerator Utilizing DFIG and Direct Matrix Converter Technology 4

Modeling and Control of an Aerogenerator Utilizing DFIG and ...

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4th International Conference on Innovative Research in Applied Science, engineering and Technology, IRASET 2024

作者： Blouh, Fatima-Ezzahra Bezza, Mohamed El Myasse, Ilyass Fst Of Mohammedia Laboratory Of Electronic Electrotechnical Automatic And Traitement Of Information Department Of Electrical Engineering Casablanca Morocco Enset Of Mohammedia Laboratory Of Electrical Engineering And Intelligent Systems Department Of Electrical Engineering Casablanca Morocco

ISBN: (纸本)9798350309508

This paper explores the control challenges associated with wind energy conversion systems (WECS) incorporating a doubly fed induction generator (DFIG) powered by advanced converters. With the increasing prominence of power electronics, particular emphasis is placed on the utilization of a new generation of converters, specifically the AC/AC matrix converter. This converter offers direct AC/AC power conversion, bidirectional power flow, and nearly sinusoidal input and output waveforms. The study presented in this paper focuses on assessing the performance of a wind turbine equipped with a DFIG and powered by a matrix converter, which employs the maximum power point tracking technique to get the maximum power obtainable. The studied system is evaluated within the dq coordinates. The proposed control scheme is implemented, and its efficacy is assessed through simulation results using MatLab/Simulink. © 2024 IEEE.

关键词： Matrix converters

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An IoMT-Based Incremental Learning Framework with a Novel Feature Selection Algorithm for Intelligent Diagnosis in Smart Healthcare

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 370-383页

作者： Sai, Siva Bhandari, Kartikey Singh Nawal, Aditya Chamola, Vinay Sikdar, Biplab Department of Computer Science and Information Systems Pilani Campus Pilani333031 India Microsoft Campus Gachibowli Telangana Hyderabad500032 India Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India Pilani Campus Pilani333031 India National University of Singapore Department of Electrical and Computer Engineering Cluny Road Singapore117583 Singapore

Several recent research papers in the Internet of Medical Things (IoMT) domain employ machine learning techniques to detect data patterns and trends, identify anomalies, predict and prevent adverse events, and develop personalized patient treatment plans. Despite the potential of machine learning techniques in IoMT to revolutionize healthcare, several challenges *** conventional machine learning models in the IoMT domain are static in that they were trained on some datasets and are being used for real-time inferencing data. This approach does not consider the patient's recent health-related data. In the conventional machine learning models paradigm, the models must be re-trained again, even to incorporate a few sets of additional samples. Also, since the training of the conventional machine learning models generally happens on cloud platforms, there are also risks to security and privacy. Addressing these several issues, we propose an edge-based incremental learning framework with a novel feature selection algorithm for intelligent diagnosis of patients. The approach aims to improve the accuracy and efficiency of medical diagnosis by continuously learning from new patient data and adapting to patient conditions over time, along with reducing privacy and security issues. Addressing the issue of excessive features, which might increase the computational burden on incremental models, we propose a novel feature selection algorithm based on bijective soft sets, Shannon entropy, and TOPSIS(Technique for Order Preference by Similarity to Ideal Solution). We propose two incremental algorithms inspired by Aggregated Mondrian Forests and Half-Space Trees for classification and anomaly detection. The proposed model for classification gives an accuracy of 87.63%, which is better by 13.61% than the best-performing batch learning-based model. Similarly, the proposed model for anomaly detection gives an accuracy of 97.22%, which is better by 1.76% than the best-performing b

关键词： Patient treatment

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Kinmen Wind Lion Face Generation Based on the Deep Convolutional Generative Adversarial Network 13

Kinmen Wind Lion Face Generation Based on the Deep Convoluti...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Wu, Chia-Huan Zhao, Yu-Xiang Chou, Cheng-Hou Hsieh, Yi-Zeng National Taiwan University of Science and Technology Department of Electrical and Computer Engineering Taiwan National Quemoy University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350355079

Under the advancements of science and technology at present, artificial intelligence has become widely applied in daily life. Hence, deep learning has attracted much attention in recent years and has been widely used in recognition, classification and generation. The results generated by generative adversarial networks have become increasingly realistic and are now being applied to human face generation and reconstruction. Among the various technologies, the deep convolutional generative adversarial network (DCGAN) is more widely used. Based on the trained DCGAN of human and cat faces, this paper collected Kinmen wind lion data by YOLOv3 and crawler technology and transferred the data to the DCGAN of wind lions through network-based transfer learning to generate complete wind lion faces. The experimental process consisted of four parts. The wind lion dataset was directly used to train the DCGAN in the first two parts, and the DCGAN of the human and cat faces was used for transfer learning in the second two parts. According to the results, the training effect of human face-based transfer learning was the best and was the closest to Kinmen's wind lions. © 2024 IEEE.

关键词： Generative adversarial networks

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