Background When a user walks freely in an unknown virtual scene and searches for multiple dynamic targets,the lack of a comprehensive understanding of the environment may have a negative impact on the execution of vir...
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Background When a user walks freely in an unknown virtual scene and searches for multiple dynamic targets,the lack of a comprehensive understanding of the environment may have a negative impact on the execution of virtual reality *** studies can help users with auxiliary tools,such as top view maps or trails,and exploration guidance,for example,automatically generated paths according to the user location and important static spots in virtual ***,in some virtual reality applications,when the scene has complex occlusions,and the user cannot obtain any real-time position information of the dynamic target,the above assistance cannot help the user complete the task more *** We design a virtual camera priority-based assistance to help the user search dynamic targets *** of forcing users to go to destinations,we provide an optimized instant path to guide them to places where they are more likely to find dynamic targets when they ask for *** assume that a certain number of virtual cameras are fixed in virtual scenes to obtain extra depth maps,which capture the depth information of the scene and the locations of the dynamic *** method automatically analyzes the priority of these virtual cameras,chooses the destination,and generates an instant path to assist the user in finding the dynamic *** method is suitable for various virtual reality applications that do not require manual supervision or *** A user study is designed to evaluate the proposed *** results indicate that compared with three conventional navigation methods,such as the top-view method,our method can help users find dynamic targets more *** advantages include reducing the task completion time,reducing the number of resets,increasing the average distance between resets,and reducing user task *** We presented a method for improving dynamic target searching efficiency in virtual scenes by virtual cam
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balanci...
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Most recent works in the field of grammatical error correction (GEC) rely on neural machine translation-based models. Although these models boast impressive performance, they require a massive amount of data to proper...
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The automatic detection and diagnosis of colorectal lesions during colonoscopy are vital for the prevention and treatment of colorectal cancer. To date, numerous ROI-based neural network models have been proposed to c...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
The automatic detection and diagnosis of colorectal lesions during colonoscopy are vital for the prevention and treatment of colorectal cancer. To date, numerous ROI-based neural network models have been proposed to classify colonoscopic lesions. However, they commonly encounter two main issues: 1) Their performance is limited by the dataset size and the diversity of lesion types included; 2) They tend to directly extract deep features from the original Region of Interests (ROIs) of the lesion, neglecting features of the lesion’s edge background as well as detailed texture and color information. To address these issues, we first propose a publicly available colonoscopic lesion classification dataset that includes all major categories of intestinal lesions. Secondly, we innovatively utilize additional colonoscopy video detection data with consistency between frames to enhance the lesion classification model by leveraging an online-target network. Simultaneously, to better mine the characteristics of lesions in data, we propose an ROI broaden strategy and a deep-fusion module, enabling the model to learn lesion-related information such as lesion edges, morphology, texture, and color. Experiments show that our method outperforms existing supervised and semi-supervised state-of-the-art methods, demonstrating the effectiveness of our approach. The GitHub link for our work is https://***/Zoe-TAN/EBM-polyp-cls.
Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attent...
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Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attention field(AF),a range within visual *** AF reshapes the distribution of activity throughout the visual pathway,including the early visual cortex(EVC)and all areas along the dorsal and ventral visual cortical pathways[2,3].Importantly,the effect of attention depends not only on the spatial position of the AF but also on its size[4].
Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how conce...
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Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how concerned they are about it. The way we live today is different from how it was in the past because of our working environment and the lack of opportunities to interact with people. WHO estimates that about 10 billion individuals perish each year due to Alzheimer's disease since not been appropriately diagnosed and treated. An advanced Machine Learning-based recognition system design performs in this research work. The proposed model has been designed based on the human being EEG dataset(testing) and the Kaggle Alzheimer dataset (training). The developed intelligent system will be updated with data collection and pre-processing through the Random Forest machine model to locate our health condition and recognize daily routine changes. Only by developing a machine-based approach will we be able to discover Alzheimer's patients who previously had no idea that they had the illness and are now unable to reverse the progression of their condition. The designed application has attained a sensitivity of 93.01%, an accuracy of 91.32%, a recall of 97.28%, and a detection rate of 98.91%. The implemented application is beneficial to investigators and surgeons for easy treatment. The proposed RFO Machine learning design competes with present technology and outperforms methodology.
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis to support clinical decision-making and future intervention strategies. Resting-state functional magnetic r...
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Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issu...
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ISBN:
(数字)9798350351255
ISBN:
(纸本)9798350351262
Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm.
Today,resource depletion threatens a number of resource-based cities in *** ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resou...
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Today,resource depletion threatens a number of resource-based cities in *** ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted *** 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to *** results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe ***,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety *** top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest *** 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.
EEG is widely used in the field of brain computer interfaces. It has the advantages of being non-invasive, easy acquisition, and so on. However, multi-channel EEG signals will bring noise interference and redundant in...
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EEG is widely used in the field of brain computer interfaces. It has the advantages of being non-invasive, easy acquisition, and so on. However, multi-channel EEG signals will bring noise interference and redundant information while improving the resolution, and channel selection can reduce noise signals to obtain more real signals. Therefore, appropriate channel selection is very necessary for BCI system application. This paper proposes a Fisher score calculation method based on OVR-CSP features for channel selection and uses a transformer to classify. The experimental results show that the proposed method achieves 85.54% accuracy in selecting 16.55 channels on the public dataset BCI IV IIa.
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