Demand side management (DSM) has become one of the major concerns of the smart grids to cope with the penetration of renewable energy. The availability of new communication technologies can enhance the resilient opera...
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This article adds to the emergent body of research that examines the potential of 6G as a platform that can combine wired and wireless sensing modalities. We apply vision transformers (ViTs) in a distributed fiber-opt...
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This article adds to the emergent body of research that examines the potential of 6G as a platform that can combine wired and wireless sensing modalities. We apply vision transformers (ViTs) in a distributed fiber-optic sensing system to evaluate road traffic parameters in smart cities. Convolutional neural networks (CNNs) are also assessed for benchmarking. The experimental setup is based on a direct-detection phase-sensitive optical time-domain reflectometer (phi-OTDR) implemented using a narrow linewidth source. The monitored fibers are buried on the university campus, creating a smart city environment. Backscattered traces are consolidated into space-time matrices, illustrating traffic patterns and enabling analysis through imageprocessing algorithms. The ground truth is established by traffic parameters obtained by processing video camera images monitoring the same street using the YOLOv8 model. The results indicate that ViTs outperform CNNs for estimating the number of vehicles and the mean vehicle speed. While a ViT necessitates a significantly larger number of parameters, its complexity is similar to that of a CNN when considering multiply-accumulate operations and random access memory usage. The processed dataset has been made publicly available for benchmarking.
Blind or visually impaired people face numerous hurdles in their day today life and rely on the assistance of others. The majority of them rely on a cane or a dog for assistance to notice the obstacles in their path. ...
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In recent years, the field of emotion recognition has witnessed significant advancements, with applications spanning from healthcare monitoring to human-computer interaction. However, deploying such systems on resourc...
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Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic...
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This paper presents a novel development of a real-time evaluation system for facial asymmetry. Using this tool, it is expected possibly to assess the severity of facial asymmetry based on local and global comparisons ...
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When a scene to be photographed is behind the glass medium, the photo often suffers from reflection artifacts, which compromise the integrity of the image content. Existing methods for solving this problem are ineffec...
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A tunnel boring machine (TBM) generates rock chips during excavation, which are crucial for assessing surrounding rock integrity, enhancing excavation efficiency, and evaluating cutter wear. However, traditional metho...
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A tunnel boring machine (TBM) generates rock chips during excavation, which are crucial for assessing surrounding rock integrity, enhancing excavation efficiency, and evaluating cutter wear. However, traditional methods struggle to identify small rock chips, chips submerged in soil or water, and chips in stacked states. This paper proposes a convolutional neural network (CNN)-based method for directly recognizing the particle size distribution from rock chip images. A dataset of 2520 rock chip images representing 84 particle-size distributions was collected in a laboratory environment. By comparing various CNN architectures and hyperparameters, an optimal model was obtained with a mean absolute error (MAE) of 1.66 x 10-2 and R2 of 0.923 on the test set. The results demonstrate that the proposed method enables the real-time recognition of particle size distribution using rock chip images, which has the potential to significantly improve intelligent auxiliary excavation technology in TBMs.
Efficient super-resolution networks have been more well-known in recent years due to their effectiveness in increasing image resolution with the least amount of processing cost. This research introduces a novel lightw...
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This research focuses on the detection of diseases on apple tree branches using artificial intelligence (AI). The aim of this study is to develop an intelligent system that can automatically detect and segment disease...
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