Environmental phenomena affect our everyday life greatly. This included flexible structures the resources of life, including the pure water and air of our planet. The word climate is characterized as a platform that p...
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ISBN:
(纸本)9798350351491;9798350351484
Environmental phenomena affect our everyday life greatly. This included flexible structures the resources of life, including the pure water and air of our planet. The word climate is characterized as a platform that provides numerous facilities for several atmospheric applications in the areas of water allocation, poor air quality, meteorological conditions, radioactive detection, wastewater treatment, catastrophic event as well as many other predisposing factors. Monitoring system, modeling and management provide a better knowledge of key development and environmental changing management methods. Over time, the problem of emissions has increased owing to a number of reasons, such as increasing population and usage of cars, urban development, which has a significant impact on workers' health. This article proposes AI and IoT for the predictions and modeling of the environmental quality assessment system. The Artificial Intelligence method can evaluate data extremely efficiently and take accurate choices on services in many kinds. The IoT gateway includes the entire application software from sensor level to environmental information systems. The test data suggest that the new approach offers an efficient means of analyzing environmental information over a lengthy period of time.
In the label manufacturing industry, accurately identifying label positioning defects is of great significance for ensuring product quality. However, traditional defect detection techniques have faced significant chal...
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ISBN:
(纸本)9798350350920
In the label manufacturing industry, accurately identifying label positioning defects is of great significance for ensuring product quality. However, traditional defect detection techniques have faced significant challenges due to the diversity of label materials and the complexity of imaging conditions. In recent years, semantic segmentation technology has made breakthrough progress in the field of image processing, providing innovative solutions to the aforementioned issues. This study proposes a semantic segmentation framework that integrates ResNet50 and Global Context (GCNet) modules, aiming to accurately identify label positioning defects. Through the training of a deep learning model, this framework achieves precise delineation of the label area and effectively determines positioning defects. The experiment was conducted using a dataset composed of 1653 manually annotated images, and the results revealed that the proposed algorithm has significant advantages in terms of segmentation accuracy and algorithm robustness. The algorithm developed in this study demonstrates high efficiency in detecting label positioning defects, providing an innovative technical approach for automated quality control systems.
With the development of image recognition technology and computer technology, artificial intelligence is more and more applied in people's production and life. In addition, the internet of Things technology can be...
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In the era of intelligent information technology of 5G, smart buildings, promoted by the internet of Things, enhance the ability of indoor monitoring and equipment management. The acquisition and transmission of Archi...
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Intelligent Transport systems, such as self-driving automobiles, represent a prime example of applications utilizing traffic sign recognition technology. The increasing demand for autonomous vehicles highlights a sign...
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The rapid development of the internet of Things (IoT) is fundamentally transforming various industries, with many IoT systems increasingly driven by artificial intelligence (AI). Advances in AI introduce new changes a...
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With the rapid advancement of generative models, image detectors for AI-generated content have become an increasingly necessary technology in computer vision, attracting significant attention from researchers. This te...
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ISBN:
(纸本)9798350349405;9798350349399
With the rapid advancement of generative models, image detectors for AI-generated content have become an increasingly necessary technology in computer vision, attracting significant attention from researchers. This technology aims to detect whether an image is naturally generated by imaging systems (e.g., digital cameras) or generated by advanced AI techniques. Despite the promising performance achieved by recent fake detection methods, they are typically trained on millions of redundant images with similar characteristics, leading to inefficient training. Furthermore, the performances of existing detectors often deteriorate when the training datasets are imbalanced. To address these challenges, we propose a novel AI-generated image detector based on dynamic aggregation and information compression with the Wasserstein distance. Experimental results show that our proposed method significantly outperforms state-of-the-art models that generalize across different generative models, with an increase of +1.86% average accuracy and +0.14% average precision, while substantially reducing the training time. On imbalanced datasets, our proposed method leads to a +14.46% accuracy improvement, clearly demonstrating its robustness on imbalanced datasets.
Deep neural network-basedimagesignal processing (ISP-DNN) improves image quality with techniques such as demosaicing, but these models pose substantial computational and memory challenges when implemented on CMOS im...
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ISBN:
(纸本)9798350349641;9798350349634
Deep neural network-basedimagesignal processing (ISP-DNN) improves image quality with techniques such as demosaicing, but these models pose substantial computational and memory challenges when implemented on CMOS image sensors, particularly due to the high-resolution inputs that increase memory requirements for activations. Layer fusion reduces memory usage by combining consecutive processing steps, yet it increases computational demands, a critical issue in resource-limited on-sensor environments. To address these challenges, we introduce ISP2DLA, an automated deep learning accelerator design framework that balances computational and memory demands for on-sensor ISP. This framework optimizes hardware designs by adjusting line buffer sizes and the number of MAC units, reducing gate counts by 14-79% across two ISP-DNN models, thus enabling efficient on-sensor ISP model inference within constrained resources.
Sign language is the primary means of communication for the deaf and mute community. The regional and national differences in sign language have led to barriers between different sign language systems, making communic...
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Acoustic signal processing holds significant promise for real-time fish feeding intensity estimation in aquaculture. Unlike traditional methods reliant on visual cues or sensor data, acoustic analysis provides valuabl...
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