Remote photoplethysmography (rPPG) is a technology that extracts blood volume pulse (BVP) signals by analyzing the wavelength differences of light reflected from a person’s skin using devices such as RGB and infrared...
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Building a hierarchical conceptual structure for smart health care, identifying the effects of distributed ledger technology on clever medical treatment, and then developing a system of applications for smart health c...
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In today’s interconnected world, the number of social media users, such as Twitter, Facebook, and Reddit, has continued to grow. The vast number of users generates a wealth of social data, which holds significant val...
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To apply a solution of the optimal control problem directly to the control object for which model this problem was solved, it is necessary to build a system of motion stabilization along the obtained optimal trajector...
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This study presents a mathematical modeling framework that leverages recurrent neural networks (RNNs), specifically echo state networks (ESNs) and long short-Term memory (LSTM) architectures, for the predictive modeli...
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With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It ...
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Pedestrian trajectory prediction is critical for autonomous driving, crowd analysis, and urban surveillance. To address insufficient interaction modeling in existing methods, we propose two enhancements. First, a fiel...
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ISBN:
(数字)9798331533113
ISBN:
(纸本)9798331533120
Pedestrian trajectory prediction is critical for autonomous driving, crowd analysis, and urban surveillance. To address insufficient interaction modeling in existing methods, we propose two enhancements. First, a field-of-view(FoV)-aware masking mechanism filters irrelevant interactions by dynamically adjusting to pedestrian distances and motion directions. Second, we introduce a more detailed modeling of the influence among pedestrians, replacing the traditional modeling based on reciprocal of distance. This model, integrated with FoV masks to construct sparse adjacency matrices for graph convolution. A temporal convolutional network then predicts trajectories as bivariate Gaussian distributions. Evaluations on ETH and UCY benchmarks demonstrate the effectiveness of our method.
Graph Neural Network (GNN) methods for Dynamic Link Prediction (DLP) have been a very active research area in recent years. DLP extends traditional LP to time-varying graphs that demand models incorporating structural...
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Marketing organization features and strategy implementation have been studied for over 30 years. These include organizational structure, culture, leadership, and processes. HR regulations can motivate marketing profes...
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