The growing number of objects in the Low Earth Orbit is becoming increasingly concerning to astronomers and space missions - particularly due to the limitations in tracking accuracy due to the availability and placeme...
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The growing number of objects in the Low Earth Orbit is becoming increasingly concerning to astronomers and space missions - particularly due to the limitations in tracking accuracy due to the availability and placements of surveillance sensors. This paper connects to this issue, presenting the development of an open-source, ray-traced, signal-level pulsed radar simulator for use in exploring and planning space monitoring solutions without using real-time data feeds. The simulator was also developed around the use of the NVIDIA® OptiX™ engine to accommodate its ray-tracing features and accelerate performance. This was designed to aid astronomers and researchers in space situational awareness applications through the simulation of radar designs for orbital surveillance experiments. The developed tool was also compared against a more streamlined application that used point-model approximations for quick-look simulations, and the trade-offs between both simulators were evaluated. While the use of ray tracing resulted in significant speed costs, it was found that the algorithm also introduced more realistic results relative to point-model simulations - providing various advantages in scenarios involving shadowing and multiscatter.
Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental se...
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YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tas...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.
Emotion recognition is one of the most fascinating emerging fields of research. It’s useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots’ ability to see an...
Emotion recognition is one of the most fascinating emerging fields of research. It’s useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots’ ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person’s emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives.
Modern in-memory databases are typically used for high-performance workloads, therefore they have to be optimized for small memory footprint and high query speed at the same time. Data compression has the potential to...
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In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physi...
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Monocular 3D human mesh estimation is an ill-posed problem, characterized by inherent ambiguity and occlusion. While recent probabilistic methods propose generating multiple solutions, little attention is paid to obta...
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ISBN:
(数字)9798350353006
ISBN:
(纸本)9798350353013
Monocular 3D human mesh estimation is an ill-posed problem, characterized by inherent ambiguity and occlusion. While recent probabilistic methods propose generating multiple solutions, little attention is paid to obtaining high-quality estimates from them. To address this limitation, we introduce ScoreHypo, a versatile framework by first leveraging our novel HypoNet to generate multiple hy-potheses, followed by employing a meticulously designed scorer, ScoreNet, to evaluate and select high-quality esti-mates. ScoreHypo formulates the estimation process as a re-verse denoising process, where HypoNet produces a diverse set of plausible estimates that effectively align with the im-age cues. Subsequently, ScoreNet is employed to rigorously evaluate and rank these estimates based on their quality and finally identify superior ones. Experimental results demon-strate that HypoNet outperforms existing state-of-the-art probabilistic methods as a multi-hypothesis mesh estimator. Moreover, the estimates selected by ScoreNet significantly outperform random generation or simple averaging. Notably, the trained ScoreNet exhibits generalizability, as it can effectively score existing methods and significantly reduce their errors by more than 15%. Code and models are available at ht tps: / /xy02- 05. gi thub. io/ScoreHypo.
We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subs...
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Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with t...
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Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models. Trained on large-scale dataset to bridge the gap between different modalities, foundation models facilitate contextual reasoning, generalization, and prompt capabilities at test time. The predictions of these models can be adjusted for new tasks by augmenting the model input with task-specific hints called prompts without requiring extensive labeled data and retraining. Capitalizing on the advances in computer vision, medical imaging has also marked a growing interest in these models. With the aim of assisting researchers in navigating this direction, this survey intends to provide a comprehensive overview of foundation models in the domain of medical imaging. Specifically, we initiate our exploration by providing an exposition of the fundamental concepts forming the basis of foundation models. Subsequently, we offer a methodical taxonomy of foundation models within the medical domain, proposing a classification system primarily structured around training strategies, while also incorporating additional facets such as application domains, imaging modalities, specific organs of interest, and the algorithms integral to these models. Furthermore, we emphasize the practical use case of some selected approaches and then discuss the opportunities, applications, and future directions of these large-scale pre-trained models, for analyzing medical images. In the same vein, we address the prevailing challenges and research pathways associated with foundational models in medical imaging. These encompass the areas of interpretability, data management, computational requirements, and the nuanced issue of contextual comprehension. Finally, we gather the over-viewed studies with their available open-source implementations at our GitHub. We aim
Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resourc...
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