This letter investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback...
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Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast...
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Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to g...
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The accurate acquisition of multiview fetal cardiac ultrasound images is very important for the diagnosis of fetal congenital heart disease (FCHD). However, these manual clinical procedures have drawbacks, e.g., varyi...
The accurate acquisition of multiview fetal cardiac ultrasound images is very important for the diagnosis of fetal congenital heart disease (FCHD). However, these manual clinical procedures have drawbacks, e.g., varying technical capabilities and inefficiency. Therefore, exploring automatic recognition method for multiview images of fetal heart ultrasound scans is highly desirable to improve prenatal diagnosis efficiency and accuracy. In this work, we propose an improved multi-head self-attention mechanism called IMSA combined with residual networks to stably solve the problem of multiview identification and anatomical structure localization. In details, IMSA can capture short- and long-range dependencies from different subspaces and merge them to extract more precise features, thus making use of the correlation between fetal heart structures to make view recognition more focused on anatomical structures rather than disturbing regions, such as artifacts and speckle noises. We validate our proposed method on fetal cardiac ultrasound imaging datasets from a single center and 38 multicenter studies and the results outperform other state-of-the-art networks by 3%-15% of F1 scores in fetal heart six standard view *** Relevance— This technology has great potential in assisting cardiologists to complete the automatic acquisition of multi-section fetal echocardiography images.
Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and co...
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A workflow is proposed for the detection of unauthorized persons in an ATM. Certain assumptions, including the context of identification are made by training their features and storing the datasets. However, not every...
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A workflow is proposed for the detection of unauthorized persons in an ATM. Certain assumptions, including the context of identification are made by training their features and storing the datasets. However, not every individual will be a suspect. Suspicious activity on ATMs that are located in remote areas and to reduce the risk of fraudulent transactions. such as using another person's card to withdraw money etc., Various video survey and image processing have been discussed in relation to surveillance methods. It discusses the various processes, preprocessing, classification, feature extraction and the corresponding video processing methods.
The development of artificial intelligence(AI)and the mining of biomedical data complement each *** the direct use of computer vision results to analyze medical images for disease screening,to now integrating biologic...
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The development of artificial intelligence(AI)and the mining of biomedical data complement each *** the direct use of computer vision results to analyze medical images for disease screening,to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries,the boundaries of both are constantly expanding,and their connections are becoming ***,the theme of the 2024 Annual Quantitative Biology Conference is set as“Biomedical data and AI”,and was held in Chengdu,China from July 15 to 17,2024.
We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in a...
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ISBN:
(数字)9798350348811
ISBN:
(纸本)9798350348828
We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.
Nowadays, modeling exercises on software development objects are conducted in higher education institutions for information technology. Not only are there many defects such as missing elements in the models created by...
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Research backs up the common perception that the years preceding up to college are the most difficult, showing that students report higher levels of discomfort and worse self-esteem during this switch. Positive person...
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ISBN:
(数字)9798331540364
ISBN:
(纸本)9798331540371
Research backs up the common perception that the years preceding up to college are the most difficult, showing that students report higher levels of discomfort and worse self-esteem during this switch. Positive personality qualities such as optimism, hope, and happiness were examined in this system along with 215 first-semester Israeli university students' ratings of functional impairment, psychological distress, and self-esteem. The three stages that make up this suggested method are data preprocessing, model training, and feature selection. In order to process data that would be infeasible to process without data preprocessing, the data can be adjusted to meet the specifications of each data mining technique. Beginning with different forms of feature classification or aggregation and progressing to individual activity levels, several levels of feature granularity were investigated in order to employ a feature selection technique. The proposed approach used an AOA-CNN-XGBoost for the whole model training phase. This novel method outperforms CNN and AOA with an average accuracy of 89.27%.
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