Medical imageprocessing provides the information regarding the detection of brain tumor. imageprocessing techniques are used to find out the brain tumor with the help of various steps. The steps are image acquisitio...
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Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, wh...
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Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.
Due to the high cost of image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA *** address this, this paper proposes a novel end-to-end blind...
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Due to the high cost of image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA *** address this, this paper proposes a novel end-to-end blind IQA method: ***, we first analyze the causal mechanisms in IQA tasks and construct a causal graph to understand the interplay and confounding effects between distortion types, image contents, and subjective human ***, through shifting the focus from correlations to causality, Causal-IQA aims to improve the estimation accuracy of image quality scores by mitigating the confounding effects using a causality-based optimization *** optimization strategy is implemented on the sample subsets constructed by a Counterfactual Division process based on the Backdoor *** experiments illustrate the superiority of Causal-IQA. Copyright 2024 by the author(s)
To effectively realize the reasonable obstacle avoidance of the detection robot, VGG based obstacle discrimination method is proposed. Above all, the image captured by the robot is input into the multi-layer convoluti...
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Structured illumination microscopy(SIM)has emerged as a promising super-resolution fluorescence imaging technique,offering diverse configurations and computational strategies to mitigate phototoxicity during real-time...
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Structured illumination microscopy(SIM)has emerged as a promising super-resolution fluorescence imaging technique,offering diverse configurations and computational strategies to mitigate phototoxicity during real-time imaging of biological *** efforts to enhance system frame rates have concentrated on processing algorithms,like rolling reconstruction or reduced frame reconstruction,or on investments in costly sCMOS cameras with accelerated row readout *** this article,we introduce an approach to elevate SIM frame rates and region of interest(ROI)coverage at the hardware level,without necessitating an upsurge in camera expenses or intricate ***,parallel acquisition-readout SIM(PAR-SIM)achieves the highest imaging speed for fluorescence imaging at currently available detector *** using the full frame-width of the detector through synchronizing the pattern generation and image exposure-readout process,we have achieved a fundamentally stupendous information spatial-temporal flux of 132.9 MPixels·s^(−1),9.6-fold that of the latest techniques,with the lowest SNR of−2.11 dB and 100 nm ***-SIM demonstrates its proficiency in successfully reconstructing diverse cellular organelles in dual excitations,even under conditions of low signal due to ultra-short exposure ***,mitochondrial dynamic tubulation and ongoing membrane fusion processes have been captured in live COS-7 cell,recorded with PAR-SIM at an impressive 408 *** posit that this novel parallel exposure-readout mode not only augments SIM pattern modulation for superior frame rates but also holds the potential to benefit other complex imaging systems with a strategic controlling approach.
Accurate and timely information on the spatiotemporal distribution of crops is essential for sustainable agricultural practices and ensuring food security. The significant challenges persist in accurately classifying ...
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Accurate and timely information on the spatiotemporal distribution of crops is essential for sustainable agricultural practices and ensuring food security. The significant challenges persist in accurately classifying crop types in highly fragmented cropland regions characterized by small field sizes, complex landscapes, and highly frequent cloud cover. This study presents a novel classification workflow designed to generate archaic/historic and reliable land cover (LC) maps from integrating time series data from multiple EO sources—Sentinel-1, Sentinel-2, and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM)—with the Random Forest (RF) classifier and cloud computing technology. To the evaluate the effectiveness of this approach, Northeast (NE) Thailand was selected as a case study region, focusing on the classification of 14 crop types between 2021 and 2023. Different combinations of EO datasets and a RF classifier were evaluated using a substantial dataset of 13,453 reference points. The crop type/LC transitions from 2021 to 2023 were then analysed and a temporal transfer model was employed to map historical crop fields. The combined all EO datasets in this work achieved high overall accuracy and F1 scores (>85 %) with the high spatial consistency of crop fields when compared to the use of combined both datasets. Results demonstrated the high potential and excellent efficiency of the RF, utilising an extensive reference dataset and the continuous temporal monthly information of gap-filled data. The most dominant crops were rice, followed by cassava, sugarcane and rubber trees throughout the three study years. The transfer learning RF model proved effective in mapping historical crop types and LC even when ground data was limited. Transitions of 7,287 km2 (∼5%) appeared from 2021 to 2022, with major crop decreases in rice and sugarcane. From 2022 to 2023, cropland changes totaled 8,466 km2 (∼6%), primarily as reductions
Cancer lesion segmentation plays a vital role in breast cancer diagnosis and treatment planning. As creating labels for large medical image datasets can be time-consuming, laborious and error prone, a framework is pro...
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—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while re...
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Advances in neuroscience have suggested that addiction is not only an ongoing dynamic transaction between the person, their behavior, and the environment, but a disturbance of neurotransmitters in neurons involved in ...
In this paper, a fall detection system consisting of a thermopile imaging array with 80*64 pixels and a Raspberry Pi 3 has been developed. First, the thermal images captured by the hardware system are processed to eli...
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ISBN:
(数字)9781728153179
ISBN:
(纸本)9781728153186
In this paper, a fall detection system consisting of a thermopile imaging array with 80*64 pixels and a Raspberry Pi 3 has been developed. First, the thermal images captured by the hardware system are processed to eliminate fixed interferences and identify the human body. Then, the real height of the human body is estimated from the original height in the thermal images. Finally, after smoothing the fluctuation of the real height, fall events are detected according to the relative variations of the smoothed height. Our experiments show that the newly developed system and imageprocessing algorithm can achieve much better performance on fall detection than other systems based on infrared sensors or sensor arrays.
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