Platoon formation focuses on effectively coordinating the speeds of vehicles within a group,with automatic speed adjustments for each vehicle to maintain a desired *** implementation of appropriate control techniques ...
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Platoon formation focuses on effectively coordinating the speeds of vehicles within a group,with automatic speed adjustments for each vehicle to maintain a desired *** implementation of appropriate control techniques in platooning is crucial to achieve efficient vehicle coordination to facilitate seamless communication and synchronization among *** platoon functions as a cluster,where vehicles within the platoon are treated as *** study presents an idea for implementing clustering strategies in a platoon with a focus on achieving string stability by decreasing disturbances and variations in vehicle speed and *** also involves an indepth analysis of clustering algorithms to identify the most suitable approach for integration into vehicle platooning,specifically for network analysis *** investigation of various control techniques and clustering algorithms aims to optimize the performance and functionality of platooning systems contributing to the advancement of wireless-connected autonomous vehicles and their transformative potential in transportation.
Alzheimer's disease is a neurological disorder. Research on early detection and classification using machine learning techniques has become essential in recent years. This paper focuses on developing a Convolution...
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A capacity of foreseeing price fluctuations in bitcoin with exceptionally precise is very worthwhile to investigators and funding sources. However, as the cryptocurrency market is nonlinear, it can be challenging to d...
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Ancient Tamil letters hold significant historical, archaeological, and linguistic value. This study explores various deep learning techniques employed to recognize handwritten Tamil characters in ancient palm leaf man...
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Detecting small objects in aerial imagery, particularly from UAVs, presents unique challenges due to the reduced size of targets, complex backgrounds, and scale variations. Despite advancements in deep learning and im...
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A significant portion of people have suffered from a form of Parkinson's disease (PD), widely attributed to be the second most frequently diagnosed form of neurological illness that significantly impairs motor and...
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This research explores the potential of Data Mining in Education, particularly in the capability to predict student performance and identify the factors that influence academic outcomes. Employing the UC Irvine Studen...
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Malware detection and classification are crucial in cybersecurity to protect systems and networks against malicious attacks. This study introduces a proficient image-based method employing convolutional neural network...
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This paper presents a Model-Based Design(MBD)approach for the design and control of a customized manipulator intended for drilling and position-ing of dental implants accurately with minimal human *** performing an in...
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This paper presents a Model-Based Design(MBD)approach for the design and control of a customized manipulator intended for drilling and position-ing of dental implants accurately with minimal human *** performing an intra-oral surgery for a prolonged duration within a limited oral cavity,the tremor of dentist's hand is *** a result,wielding the drilling tool and inserting the dental implants safely in accurate position and orientation is highly challenging even for experienced ***,we introduce a customized manipulator that is designed ergonomically by taking in to account the dental chair specifications and anthropomorphic data such that it can be readily mounted onto the existing dental *** manipulator can be used to drill holes for dental inserts and position them with improved accuracy and ***-more,a thorough multi-body motion analysis of the manipulator was carried out by creating a virtual prototype of the manipulator and simulating its controlled movements in various *** overall design was prepared and validated in simulation using Solid works,MATLAB and Simulink through Model Based Design(MBD)*** motion simulation results indicate that the manipulator could be built as a prototype readily.
Human action recognition is a vital aspect of computer vision, with applications ranging from security systems to interactive technology. Our study presents a comprehensive methodology that employs multiple feature ex...
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Human action recognition is a vital aspect of computer vision, with applications ranging from security systems to interactive technology. Our study presents a comprehensive methodology that employs multiple feature extraction and optimization techniques to enhance the accuracy and efficiency of human action identification. The video input was divided into four distinct elements: RGB images, optical flow information, spatial saliency maps, and temporal saliency maps. Each component was analyzed independently using advanced computer vision algorithms. The process involves utilizing various algorithms and techniques to extract meaningful information from the visual data. The Farneback algorithm was employed to examine the optical flow, whereas Canny edge detection was used to assess spatial prominence. Additionally, frame comparison helps to identify motion-based prominence. These processed elements provide a comprehensive representation of both spatial and temporal information. The extracted data were then input into distinct pretrained deep learning models. Specifically, Inception V3 was used for RGB frames and optical flow analysis, ResNetV2 processed spatial saliency maps, and DenseNet-121 handled motion saliency maps. The input data are processed separately by these networks, each of which extracts specific features that are suited to their respective modalities. This feature extraction process ensures the comprehensive capture of both static and dynamic elements in video data. Subsequently, sequence modeling and classification were performed using a gated recurrent unit (GRU) that incorporated an attention mechanism. This mechanism dynamically highlights the most significant temporal segments, improving the capacity of the model to comprehend intricate human actions within video sequences. To enhance the efficiency of the model, we implemented the Grasshopper optimization algorithm to optimize the feature selection and classification stages, thus maximizing the u
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