Hartmann wavefront sensor is applied widely in adaptive optics systems. Considering the real-time performance requirements and processing amount of Hartmann sensors in computing the centroids of points, this article p...
Hartmann wavefront sensor is applied widely in adaptive optics systems. Considering the real-time performance requirements and processing amount of Hartmann sensors in computing the centroids of points, this article proposes an FPGA-based real-time image processing and centroid extraction system. The system adopts FPGA as the core processor and uses the connected domain labeling algorithm and the gray centroid method to complete the centroid calculation. In addition, the calculation results are output simultaneously through multiple DAs to drive the device under test to achieve image wavefront restoration. At the same time, the image data is uploaded to the host computer in real-time through the Ethernet interface. The system has excellent performance in real-time image processing, image output quality, and system control stability. Experiments show that it is an efficient technical means of providing real-time wavefront processing measurement for the Hartmann sensor in various application scenarios, such as adaptive optical correction.
Water covers approximately 71% of the earth's surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms,...
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Monitoring the state of semiconductor equipment is crucial for ensuring optimal performance and preventing downtime. In previous studies, researchers have attempted to derive a health index that represents the overall...
Monitoring the state of semiconductor equipment is crucial for ensuring optimal performance and preventing downtime. In previous studies, researchers have attempted to derive a health index that represents the overall condition of the equipment as a single index. However, these studies have often relied solely on time-series data from each sensor, neglecting other important viewpoints engineers consider when monitoring the equipment. To address this limitation, we propose a multi-view data set specifically designed for semiconductor equipment, which incorporates process, trend, and spatial data. In addition, we present a framework for deriving a hierarchical health index based on a multi-view data set. The hierarchical structure is derived using a hierarchical spectral clustering method, and an autoencoder-based health index is used. We have verified the effectiveness of our approach with real data sets, demonstrating its potential as a valuable tool for monitoring the condition of semiconductor equipment.
Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most a...
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ISBN:
(数字)9798350387315
ISBN:
(纸本)9798350387322
Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’.Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies.
With the development of the Internet of Things (IoT), the amount of global data is increasing rapidly. However, due to the increased burden of the cloud network, it is difficult to implement low-latency and high-effic...
With the development of the Internet of Things (IoT), the amount of global data is increasing rapidly. However, due to the increased burden of the cloud network, it is difficult to implement low-latency and high-efficiency video analysis in the cloud computing mode. To address this issue, this paper proposes an Intelligent Video Analysis System (IVAS) that can execute deep learning algorithms on low-power edge IoT devices, such as face detection and face recognition algorithms. IVAS enables fast, accurate, and real-time inference calculations of intelligent video analysis algorithms, providing an evaluation platform for performance testing, key parameter selection, and result analysis. The experiments based on real-world data confirm that IVAS can achieve good performance in people counting under an edge computing environment.
PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalitie...
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This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML mode...
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In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precis...
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The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigma...
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As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. Convex analysis (CA) is one of the fundamental methods used in anomaly detection, ...
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As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. Convex analysis (CA) is one of the fundamental methods used in anomaly detection, which contributes to the robust approximation of algebra and geometry, efficient computation to a unique global solution, and mathematical optimization for modeling. Despite the essential role and evergrowing research in CA-based anomaly detection algorithms, little work has realized a comprehensive survey of it. To fill this gap, we summarize the CA techniques used in anomaly detection and classify them into four categories of density estimation methods, matrix factorization methods, machine learning methods, and the others. The theoretical background, sub-categories of methods, typical applications as well as strengths and limitations for each category are introduced. This paper sheds light on a succinct and structured framework and provides researchers with new insights into both anomaly detection and CA. With the remarkable progress made in the techniques of big data and machine learning, CA-based anomaly detection holds great promise for more expeditious, accurate and intelligent detection capacities.
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