Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computervision, natural language processing, automated speec...
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Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computervision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems (RTSs) community address the strict timing requirements of ML/DL technologies to include in RTSs. This article will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy versus execution time tradeoff policies of ML algorithms, and security and privacy of learning-based algorithms in real-time IoT systems.
It is one of the most critical technologies for unmanned electric locomotives to detect the obstacles in front of their operation quickly and accurately, which is of great significance for the safe operation of electr...
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It is one of the most critical technologies for unmanned electric locomotives to detect the obstacles in front of their operation quickly and accurately, which is of great significance for the safe operation of electric locomotives Aiming at the problems of current computervision detection methods, such as error warning, low detection accuracy, and slow detection speed, an obstacle intelligent detection method for unmanned electric locomotives based on an improved YOLOv3 (YOLOv3-4L) algorithm is proposed. The obstacle image data set of the electric locomotive running area is constructed to provide a testing environment for various obstacle detection algorithms. In the network structure, the darknet-53 feature extraction network is simplified, and the four-scale detection structure is formed by adding the shallow layer detection scale to the detection layer, which can improve the detection speed and accuracy of the algorithm for obstacles in front of the locomotive. Distance intersection over union loss function and Focal loss function are adopted to redesign the loss function of the target detector to further improve the detection accuracy of the algorithm. Traditional computervisiontechniques such as perspective transformation, sliding window, and least square cubic polynomial are used to detect the track lines. By finding the area where the track was located and extending a certain distance to the outside of the track, the dangerous area of electric locomotive running is obtained. The improved YOLOv3 algorithm is utilized to detect obstacles, and only the types and positions of obstacles coincident with dangerous areas are output. The experimental results show that the traditional computervisiontechniques such as perspective transformation, sliding window, and least square cubic polynomial can detect not only straight track but also curved track, which makes up for the shortcomings of the Hough transforms in detecting curved tracks. Compared with the original
Distracted driving is a significant issue that has sparked extensive research in detection and mitigation methods, with previous studies exploring physiological sensors but finding them intrusive, leading to the rise ...
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Multi-object tracking (MOT) is an active area of research in computervision that is extensively applied in various domains, including but not limited to video surveillance, security, and intelligent transportation. T...
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Cataract, the leading cause of global blindness, represents a focal concern within the field of blindness prevention. Its diagnosis primarily relies on the observation of lens opacification under slit-lamp examination...
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With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this techn...
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The proceedings contain 23 papers. The special focus in this conference is on Simulation Tools and techniques. The topics include: KNxsim: Simulator Tool for KNx Home Automation Training by Means of Gro...
ISBN:
(纸本)9783031575228
The proceedings contain 23 papers. The special focus in this conference is on Simulation Tools and techniques. The topics include: KNxsim: Simulator Tool for KNx Home Automation Training by Means of Group Addresses;Development of a 3D Visualization Interface for Virtualized UAVs;test-Driven Simulation of robots Controlled by Enzymatic Numerical P Systems Models;PySPN: An Extendable Python Library for Modeling & Simulation of Stochastic Petri Nets;replacing Sugarscape: A Comprehensive, Expansive, and Transparent Reimplementation;Generative AI with Modeling and Simulation of Activity and Flow-Based Diagrams;wildfire Risk Mapping Based on Multi-source Data and Machine Learning;an intelligent Ranking Evaluation Method of Simulation Models Based on Graph Neural Network;simulation of Drinking Water Infrastructures Through Artificial Intelligence-Based Modelling for Sustainability Improvement;spatio-Temporal Speed Metrics for Traffic State Estimation on Complex Urban Roads;integrating Efficient Routes with Station Monitoring for Electric Vehicles in Urban Environments: Simulation and Analysis;comparing the Efficiency of Traffic Simulations Using Cellular Automata;multi-agent Simulation for Scheduling and Path Planning of Autonomous intelligent Vehicles;ECG Pre-processing and Feature Extraction Tool for intelligent Simulation Systems;OTOVIRT: An Image-Guided Workflow for Individualized Surgical Planning and Multiphysics Simulation in Cochlear Implant Patients;Adaptive Sharing of IoT Resources Through SDN-Based Microsegmentation of Services Using Mininet;UAV-Assisted Wireless Communications: An Experimental Analysis of A2G and G2A Channels;trajectory-Aware Rate Adaptation for Flying Networks;rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning;RateRL: A Framework for Developing RL-Based Rate Adaptation algorithms in ns-3;on the Analysis of Computational Delays in Reinforcement Learning-Based Rate Adaptation algorithms.
Due to the advancements in digital technology, the fifth industrial revolution, also known as aquaponics, has brought changes in the traditional manufacturing and industrial processes. The primary objective of the ind...
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Chest radiography is a significant diagnostic tool used to detect diseases afflicting the chest. The automatic detection techniques associated with computervision are being adopted in medical imaging research. Over t...
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Chest radiography is a significant diagnostic tool used to detect diseases afflicting the chest. The automatic detection techniques associated with computervision are being adopted in medical imaging research. Over the last decade, several remarkable advancements have been made in the field of medical diagnostics with the application of deep learning techniques. Various automated systems have been proposed for the rapid detection of pneumonia from chest x-rays. Although several algorithms are currently available for pneumonia detection, a detailed review summarizing the literature and offering guidelines for medical practitioners is lacking. This study will help practitioners to select the most effective and efficient methods from a real-time perspective, review the available datasets, and understand the results obtained in this domain. It will also present an overview of the literature on intelligent pneumonia identification from chest x-rays. The usability, goodness factors, and computational complexities of the algorithms employed for intelligent pneumonia identification are analyzed. Additionally, this study discusses the quality, usability, and size of the available chest x-ray datasets and techniques for coping with unbalanced datasets. A detailed comparison of the available studies reveals that the majority of the applied datasets are highly unbalanced and limited, providing unreliable results and rendering methods that are unsuitable for large-scale use. Large-scale balanced datasets can be obtained via smart techniques, such as generative adversarial networks. Current literature has indicated that deep learning-based algorithms achieve the best results for pneumonia classification with an accuracy of 98.7%, a sensitivity of 0.99, and a specificity of 0.98. The higher accuracy offered by deep-learning algorithms in addition to their appropriate class balancing techniques serves as a good reference for further research.
Considering the varying advantages, disadvantages, and implementation difficulties of current indoor positioning algorithms, this paper conducts a comparative analysis of common UWB ranging methods. The Two-Way Rangin...
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