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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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EdgeBoost: Confidence Boosting for Resource Constrained Inference via Selective Offloading

EdgeBoost: Confidence Boosting for Resource Constrained Infe...

引用

International conference on distributed Computing in Sensor Systems (DCOSS)

作者： Naina Said Olaf Landsiedel Kiel University Germany

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This paper introduces EdgeBoost, a selective input offloading system designed to overcome the challenges of limited computational resources on edge devices. EdgeBoost trains and calibrates a lightweight model for deployment on the edge and, in addition, deploys a large, complex model on the cloud. During inference, the edge model makes initial predictions for input samples, and if the confidence of the prediction is low, the sample is sent to the cloud model for further processing otherwise, we accept the local prediction. Due to careful calibration, EdgeBoost reduces the communication cost by 55%, 29% and 20% for CIFAR-100, imageNet and Stanford Cars datasets, respectively, when compared to an cloud-only solution while achieving on par classification accuracy. Finally, EdgeBoost also achieves comparable accuracy to the state-of-the-art routing-based methods without the need for hosting the router on the edge.

关键词： Cloud computing Accuracy Costs image edge detection Computational modeling Neural networks Predictive models

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Dual-Branch Brain Tumor Segmentation Algorithm by Fusing CNNs and Mambas

Dual-Branch Brain Tumor Segmentation Algorithm by Fusing CNN...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Yang Luo Ruihua Liu Yangyang Zou School of Artificial Intelligence Chongqing University of Technology Chongqing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Due to the varying shapes and sizes, blurred boundaries, and unstable positions of brain tumors in magnetic resonance imaging, extracting both local and global features from the images is crucial for accurate segmentation of tumor regions. Existing deep learning methods have insufficient ability to model long-range dependencies in 3D medical images and face challenges in fusing global and local features. To effectively extract and utilize both global and local features, a dual-branch parallel brain tumor segmentation model combining CNNs and Mambas named MamFuse is proposed. MamFuse consists of two branches: one branch employs Mamba module, built on a selective state-space model (SSM), to model long-range dependencies, while the other branch utilizes the convolutional module to extract local information. Additionally, we introduced an innovative fusion module, GAFusion, to integrate multi-level features from both branches. Experimental results on the BraTS21 dataset show that the proposed network outperforms other state-of-the-art segmentation networks, achieving more accurate brain tumor segmentation.

关键词： Solid modeling Accuracy Three-dimensional displays Shape Brain tumors Feature extraction Brain modeling State-space methods Convolutional neural networks Medical diagnostic imaging

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An Application Agglomerative clustering for Computer Graphics processing

An Application Agglomerative clustering for Computer Graphic...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： Wuhui Tang Zhangzhou City Vocational College Zhangzhou China

Computer graphics is the software efficacy which is majorly utilized in every social sector and it is depends on an existing as well as newly designed programs. An idea of computer graphics integrates the large number of current areas, from easy graphic drawing to the design of multiple images of actual existence, developing new projects based on the image by the utilization of programming tool. The hierarchical clustering is the most popular clustering algorithm which is utilized to understand the dataset structure. This proposes the utilization of high-end computer graphics to for performing the Agglomerative Hierarchical Clustering (AHC) by Compute Unified Device Architecture (CUDA) of NVIDIA. This research identified the capabilities of CUDA as well as proposed method to effectively control the data with the graphics hardware of data intense, independent, iterative or tedious persistence approaches like AHC. This research efficiently describes the greater parallel as well as internally distributed CUDA program structure. The experimental results show the proposed method attains the improved speed of 25 to 60 times against the CPU implementation by the utilization micro array gene expressions.

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Hemo-FS-SAM2: Few-Shot Tuning on Hemorrhage Segmentation of Segment Anything Model 2

Hemo-FS-SAM2: Few-Shot Tuning on Hemorrhage Segmentation of ...

引用

Advanced Algorithms and Control Engineering (ICAACE), International conference on

作者： Xi Wang Peng Qiao Yong Dou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Surgical hemorrhage is a common occurrence in surgeries. Accurate segmentation of hemorrhage regions is important for surgical navigation and post-operative assessment. Some segmentation models focused on medical images, but its performance on hemorrhage data is limited. Besides, there is an essential challenge to annotate a large number of hemorrhage data, and previous segmentation methods struggle with complex hemorrhage characteristics like unclear boundaries and scattered targets. The Segment Anything Model 2 (SAM2) shows an significant zero-shot ability in general image segmentation, and is generalizable to fine-tuned on downstream tasks. However, it often faces limitations in hemorrhage segmentation task that lacking annotations. This paper proposes a fine-tuning approach for SAM2, significantly improving its performance on hemorrhage segmentation tasks with limited data. Our method provides better segmentation performance on few-shot hemorrhage data than SAM and SAM2 based models.

关键词： image segmentation Adaptation models Accuracy Annotations Shape Surgery Data models Hemorrhaging Tuning Biomedical imaging

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Subsampling Decomposition based k-Space Refinement for Accelerated MRI Reconstruction

Subsampling Decomposition based k-Space Refinement for Accel...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaoyu Qiao Weisheng Li Bin Xiao Yuping Huang Lijian Yang Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In accelerated MRI reconstruction problem, directly recovering all the missing k-space data from undersampled measurements is highly ill-posed and often leads to suboptimal performance. To address the problem, we propose a novel deep unfolding network (DUN) with subsampling decomposition (SD) based k-space refinement to mitigate the ill-posedness. Our method employs a parallel network architecture with a primary branch unfolded by gradient descent-inspired optimization process (GD-PB) for reconstruction. Additionally, we introduce an SD-based auxiliary branch (SD-AB) that decompose the inverse problem into moderately corrupted subproblems. We design a novel subsampling mask predictor that captures both global and local spatial correlations in k-space, ensuring the SD-AB effectively preserves the most well-reconstructed subsets as reliable region (RR). The RR in SD-AB is used to periodically refine the intermediate outputs of the GD-PB, achieving improved accuracy. Experimental results reveal that our method significantly outperforms conventional and SD-based DUN techniques, achieving superior PSNR and SSIM results compared with cutting-edge methods.

关键词： Correlation Inverse problems Magnetic resonance imaging Signal processing Network architecture Reliability engineering Sensors Speech processing image reconstruction Optimization

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Discrimination of active false targets in distributed multiple-radar system

Discrimination of active false targets in distributed multip...

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2021 International conference on Signal image processing and Communication, ICSIPC 2021

作者： Zhao, Shanshan Liu, Ziwei Zhang, Xian Shi, Junnan Nanjing University of Posts and Telecommunications Nanjing China Shanghai Radio Equipment Research Institute Shanghai China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

An active false targets discrimination method is proposed in distributed multiple-radar system to counter deception jamming, which is based on the correlation coefficient between different received signal vectors. The false targets generated by one jammer are highly correlated, but the radar target is uncorrelated with either other targets or false targets. Clustering analysis is applied to discriminate targets. The targets in the cluster, whose target number exceeds the threshold, are judged as false targets. Compared with existing methods, no prior information is required here. Simulation results corroborate the feasibility of the proposed discrimination method. © 2021 SPIE.

关键词： Jamming

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U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention

U-shaped Dual Attention Transformer: An Efficient Transforme...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International conference on

作者： Zhaoyuan Zhai Peng Qiao Rongchun Li Zhen Zhou National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Transformer-based methods have demonstrated remarkable performance on image super-resolution tasks. Due to high computational complexity, researchers have been working to achieve a balance between computation costs and performance. Restormer has achieved commendable balance by utilizing global channel attention. However, the performance is limited by insufficient local pixel reconstruction. In this paper, we propose a U-shaped Dual Attention Transformer (UDAT) with local-global receptive field, addressing the limitation of Restormer in local pixel reconstruction. We propose a dense window channel attention to enhance the local feature representation, more efficient in computational complexity. Experiments demonstrate that our UDAT achieve superior performance compared with Restormer on benchmark datasets, surpassing Restormer by 0.57 dB on the Urban100 dataset. On-par with SwinIR, our method reduces computational complexity by 3.2 times and improves inference speed by 2 times, achieving a better balance between computational costs and performance.

关键词： Visualization Superresolution Noise reduction Transform coding Transformers image restoration Computational complexity Spatial resolution image reconstruction Robots

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A Graph Computing based Power System Transient Analysis Software

A Graph Computing based Power System Transient Analysis Soft...

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Frontiers of Power and Energy (ACFPE), Asian conference on

作者： Jiangang Lu Jun Tan Wenxin Guo Ruifeng Zhao Huijuan Tan Tian Lan Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Guangzhou China

ISBN: (数字)9798350367676

ISBN: (纸本)9798350378467

With the increasing penetration of inverter based renewable energy and distributed energy storages in nowadays’ power systems, the system status and operation conditions are hard to track with traditional scheduling-based power system transient analysis approach, and more fast and efficient transient analysis methods are needed to address the power system dynamic security problems. This paper proposed a graph computing based power system transient analysis approach to achieve efficient and accurate solution for power system stability analysis. To overcome the data management challenges, a spatiotemporal graph model is constructed for efficient data management in power system transient analysis. Then, a graph computing based transient analysis method is proposed to leverage the parallel computing power to speed up the computational process. Finally, a power system transient analysis software is developed based on the graph computing based transient analysis method to validate the proposed approach.

关键词： Analytical models Renewable energy sources Computational modeling Power system stability parallel processing Software Data models Spatiotemporal phenomena Security Transient analysis

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No-reference stereo image quality evaluation method integrating monocular and binocular features 23

No-reference stereo image quality evaluation method integrat...

引用

Proceedings of the 4th International conference on Artificial Intelligence and Computer Engineering

作者： Jinxin Bai Xiaojuan Hu Changchun University of Science and Technology School of Computer Science and Technology China

ISBN: (纸本)9798400708831

In recent years, the widespread adoption of 3D technology has created a need for accurate and efficient stereoscopic image quality assessment methods. Such methods aim to automatically supervise and optimize stereoscopic image and video processing systems. Based on the characteristics of human vision, we propose a method that integrates monocular and binocular features. Initially, monocular quality-sensitive features are extracted separately for the left and right views through two parallel CNN sub-networks. Subsequently, a third CNN sub-network extracts binocular quality-sensitive features for stereo image pairs. Quality scores are then computed based on both monocular and binocular features, and the final predicted quality score is obtained through quality fusion. The proposed method is validated on the Waterloo dataset, achieving a PLCC value of 0.979 and SROCC value of 0.975 on Waterloo P-I, and a PLCC value of 0.973 and SROCC value of 0.970 on Waterloo P-II. The results demonstrate that the predicted scores obtained by the proposed method exhibit excellent consistency with subjective evaluation scores.

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Improved Approach for Effective Covid-19 Virus Detection Using Hybrid Deep Learning Approaches

Improved Approach for Effective Covid-19 Virus Detection Usi...

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Smart Electronics and Communication Systems (ISENSE), International conference on

作者： J Manikandan R Kiruthiga R Subashini Information Technology St. Joseph's College of Engineering Chennai India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

The COVID-19 pandemic has created an urgent need for fast and accurate diagnostic methods. This study introduces a deep learning approach for classifying chest X-ray images to detect COVID-19, using the LIDC-IDRI dataset. To ensure high-quality and uniform input, pre-processing was carried out according to the guidelines of the Radiological Society of North America (RSNA). The processes used here comprised of resizing, normalization, data augmentation, noise reduction, histogram equalization, and cropping. These are all followed for the sake of standardizing the image quality to make training of the model more reliable. Two parallel architectures of CNNs, namely ResNet-34 and HRNet, have been chosen to take advantage of the benefits of different architectures. ResNet-34 reduces the problem of vanishing gradient, while the HRNet keeps high-resolution features, which improves the quality of classification. Training the two networks in parallel enabled us to enhance both accuracy and robustness in distinguishing positives from negatives of COVID-19 cases. It was performed based on the performance metrics in terms of accuracy, precision, recall, and F1-score for each model, and the experiment proved that our method is capable of classifying COVID-19 cases from chest X-rays. Results demonstrate the significant role of advanced pre-processing and parallel CNN architectures in improving diagnostic precision, highlighting AI's potential to support medical imaging and enhance COVID-19 diagnosis accuracy during a pandemic. This study underlines the importance of structured pre-processing and parallel deep learning models as essential components in developing effective diagnostic tools for large-scale health crises.

关键词： COVID-19 Deep learning Training Histograms Accuracy Pandemics Noise reduction parallel architectures X-ray imaging Biomedical imaging

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