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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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Numerical limits in the integration of Vlasov-Poisson equation for Cold Dark Matter

Numerical limits in the integration of Vlasov-Poisson equati...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Luca Cappelli Giuseppe Murante Stefano Borgani Dipartimento di Fisica Universita’ di Trieste INAF-O.A.Ts Trieste Italy INAF-O.A.Ts Trieste Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The Vlasov-Poisson systems of equations (VP) describes the evolution of a distribution of collisionless particles under the effect of a collective-field potential. VP is at the basis of the study of the gravitational instability of cosmological density perturbations in Dark-Matter (DM), but its range of application extends to other fields, such as plasma physics. In the case of Cold Dark Matter, a single velocity is associated with each fluid-element (or particle), the initial condition presents a stiff discontinuity. This creates problems such as diffusion or negative distribution function when a grid based method is used to solve VP. In this work we want to highlight this problem, focusing on the technical aspects of this phenomenon. By comparing different finite volume methods and a spectral method we observe that, while all integration schemes preserve the invariants of the system (e.g, energy), the physical observable of interest, i.e., the density, is not correctly reproduced. We thus compare the density obtained with the different Eulerian integration schemes with the result obtained from a reference N -body method. We point out that the most suitable method to solve the VP system for a self-gravitating system is a spectral method.

关键词： Dark matter Perturbation methods Focusing Numerical simulation Mathematical models Plasmas Physics Finite volume methods Distribution functions

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AffViT: Fast Affine Medical image Registration with Convolutional Vision Transformer 21st

AffViT: Fast Affine Medical Image Registration with Convolu...

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21st Pacific Rim International conference on Artificial Intelligence, PRICAI 2024

作者： Shen, Chaomin Huang, Hao Zhou, Zhongyi School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9789819601219

Affine registration is a key component in a wide range of medical image processing frameworks and is extensively applied in clinical settings. Nevertheless, recent works often emphasize the integration of affine and deformable registration, resulting in the standalone performance of affine registration being underexplored. Despite its clinical significance, affine registration faces two major challenges: it involves larger transformations compared to deformable registration, and it requires fast runtime for high-resolution 3D medical images to meet real-time clinical demands. To address these challenges, we introduce a Transformer block into the CNN-based affine registration network to effectively model the long-range dependencies in high-level features. This integration also preserves the CNN’s ability to capture local features while avoiding the high computational cost of a full Transformer architecture. Moreover, for the CNN stage, we use a Siamese network to extract low-level features for fixed and moving images. We also add parallel convolution branches to enhance the receptive field of convolution operations. Experiments on common and practical affine registration tasks, i.e., template-matching normalization and atlas-based registration, demonstrate that our method achieves higher registration accuracy than the state-of-the-art methods while being 10 times faster and having only half of the model parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Template matching

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Proceedings of SPIE: parallel and distributed methods for image processing II

Proceedings of SPIE: Parallel and distributed methods for im...

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parallel and distributed methods for image processing II

The proceedings contain 15 papers from the conference on parallel and distributed methods for image processing II. The topics discussed include: parallel DSP with memory and I/O processors;analog VLSI implementation of a morphological associative memory;real-time parallel video image processing on a PC cluster;thread concept for automatic task parallelization in image analysis;new parallel vision environment in heterogeneous networked computing and toolkit for parallel image processing.

关键词： Computer vision

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Character Recognition Using Convolutional Neural Networks Based on CUDA

Character Recognition Using Convolutional Neural Networks Ba...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Set Paing Tun Applied Mathematics and Artificial Intelligence Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-based parallel data processing, and presents experimental results derived from the MNIST dataset. The study highlights that implementing CUDA drastically reduces processing time while maintaining a high level of predictive accuracy. The findings emphasize the potential of GPU acceleration in handling intensive computational tasks, making it a promising approach for real-time applications in image recognition and machine learning.

关键词： Deep learning Training Energy consumption Graphics processing units parallel processing Real-time systems Energy efficiency Convolutional neural networks Character recognition Optimization

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Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Boosting Performance of Iterative Applications on GPUs: Kern...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Jonah Ekelund Stefano Markidis Ivy Peng KTH Royal Institute of Technology Sweden

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Graphics processing Units (GPUs) have become the standard in accelerating scientific applications on heterogeneous systems. However, as GPUs are getting faster, one potential performance bottleneck with GPU-accelerated applications is the overhead from launching several fine-grained kernels. CUDA Graph addresses these performance challenges by enabling a graph-based execution model that captures operations as nodes and dependence as edges in a static graph. Thereby consolidating several kernel launches into one graph launch. We propose a performance optimization strategy for iteratively launched kernels. By grouping kernel launches into iteration batches and then unrolling these batches into a CUDA Graph, iterative applications can benefit from CUDA Graph for performance boosting. We analyze the performance gain and overhead from this approach by designing a skeleton application. The skeleton application also serves as a generalized example of converting an iterative solver to CUDA Graph, and for deriving a performance model. Using the skeleton application, we show that when unrolling iteration batches for a given platform, there is an optimal size of the iteration batch, which is independent of workload, balancing the extra overhead from graph creation with the performance gain of the graph execution. Depending on workload, we show that the optimal iteration batch size gives more than $1.4 \times$ speed-up in the skeleton application. Furthermore, we show that similar speed-up can be gained in Hotspot and Hotspot3D from the Rodinia benchmark suite and a Finite-Difference Time-Domain (FDTD) Maxwell solver.

关键词： Graphics processing units Performance gain Boosting Skeleton Iterative methods Kernel Time-domain analysis Optimization Standards Finite difference methods

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FedSe: Group-Based Sequential Training Strategies for Mitigating Label Skew in Federated Learning

FedSe: Group-Based Sequential Training Strategies for Mitiga...

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

作者： Ketu Qiao Yi Wang Baoquan Wang Zhengdong Luo Xi Zhou Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Urumqi China University of Chinese Academy of Sciences Beijing China Xinjiang Laboratory of Minority Speech and Language Information Processing Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) has emerged as a promising approach for distributed machine learning, enabling clients to collaboratively train models without sharing their data. However, existing FL methods continue to face challenges when dealing with non-IID data, particularly under conditions of extreme label skew. This divergence among client models can lead to a significant degradation in the accuracy of the global model. To address this critical issue, we introduce the FedSe approach, which incorporates two novel components: homogeneous grouping and sequential training. First, clients are grouped based on the distribution of data labels to ensure that each group contains a balanced representation of all labels. Second, task models are trained sequentially within these groups while training occurs in parallel across groups. The final step involves aggregating the models through averaging. Extensive experimental results on several datasets with label skew demonstrate the effectiveness of FedSe, and its results surpass current state-of-the-art methods.

关键词： Training Degradation Federated learning Signal processing algorithms distributed databases Signal processing Data models Speech processing Faces Convergence

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Hybrid Quantum Model for Digital Media processing for Metal Surface Defect

Hybrid Quantum Model for Digital Media Processing for Metal ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Joy Saha Shreyansh Jain Roopa M Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

processing Digital media such images is a crucial step for any image classification task such as surface defect detection or any deep learning task which include Digital Media processing. Ensuring the detail capturing of each and every feature of the input image from each and every pixel is must in order to build a robust system with minimal error or no error. While it may not be possible and causes the struggle for classical computing methods to efficiently capture and process complex patterns and subtle details. Utilising quantum computing for digital image processing gives an advantage over classical methods as it operates on high dimensional space and leverages quantum parallelism to explore numerous possibilities simultaneously. This enable the extraction of more complex features that might be otherwise missed improving the model’s capability to flag defects with greater accuracy and efficiency. Quantum computing’s potential to enhance feature extraction can significantly boost the performance of classification models, leading to dependable and precise surface defect detection.

关键词： Deep learning Analytical models Quantum computing Computational modeling Digital images Media parallel processing Feature extraction Surface treatment Defect detection

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Automated Identification of Traffic Accidents in images and Videos Employing Advanced Deep Learning methods 25

Automated Identification of Traffic Accidents in Images and ...

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26th International conference on distributed Computing and Networking, ICDCN 2025

作者： Kumari, Babli Dhir, Renu Chatterjee, Siddhartha Jain, Suchi Computer Science and Engineering Dr B R Ambedkar National Institute of Technology Punjab Jalandhar144011 India Computer Science and Engineering College of Engineering and Management Kolaghat West Bengal Purba Medinipur721171 India

ISBN: (纸本)9798400710629

An accident detection is essential since it ensures emergency response and raises the bar for road safety in general. In this paper, A CNN (Convolutional Neural Network) based collision recognition technique using the Tensorflow framework has been proposed. Deep learning has been used to automatically pick up information and identify important characteristics from pictures or videos taken by in-car cameras or roadside cameras. By utilizing a CNN architecture, model can effectively identify various accident-related patterns and distinguish them from normal traffic scenes. To train the CNN model, a large-scale dataset consisting of both accident and non-accident samples. The dataset is carefully labelled to ensure accurate ground truth annotations. During the training phase, data augmentation techniques has been employed to enhance the generalization ability of the model and mitigate overfitting. This technique has been used such as random crop, platooning and rotation to artificially enhance training diversity samples. For cropping, flipping and rotation of the images we have used image augmentation which is a function of imageDataGenerator. After training, we evaluated the efficiency of our model on a separate test set, consisting of previously unseen accident and non-accident instances. Our model achieved 98% accuracy and robustness, demonstrating its effectiveness in accident detection. An OTP verification feature has been integrated to further enhance the system's functionality. This OTP verification confirms that emergency responders reach the accident location, adding a layer of security and ensuring follow-through in emergency responses. Overall, this study showcases the potential of deep learning techniques, specifically Convolutional Neural Networks, in addressing the crucial task of accident detection. This proposed method can integrate with the Computing Classification System (CCS), ensuring efficient categorization and real-time processing of data for acc

关键词： Convolutional neural networks

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MAITFuse:Multi-Dimension Adaptive Interaction Transform Network For Infrared-visible image Fusion

MAITFuse:Multi-Dimension Adaptive Interaction Transform Netw...

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

作者： Yabin Sun Wentai Lei Ziyi Zhang Jiongchang Liu Chenxu Li Tao Zhang Artificial Intelligence Jiangnan University Wu Xi China Communications Engineering Central South University Chang Sha China School of Electronic Information Central South University Chang Sha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performance. However, existing methods only optimize along a single dimension and struggle to simultaneously capture the complex dependencies between spatial and channel dimensions. To address this problem, we propose a novel multi-dimensional adaptive interaction transformer network, named as MAITFuse, to enhance the multilevel information expression and detail retention capabilities of images. We design a Multi-Dimensional Feature Extraction (MDFE) module to extract features across spatial and channel dimensions in parallel, and introduce a novel weighted cross-attention fusion method to integrate multi-dimensional information effectively. Experimental results show that, compared to existing fusion methods, our proposed method achieves superior fusion performance across various datasets.

关键词： Adaptive systems Transforms Signal processing Feature extraction Transformers Acoustics Data mining Speech processing image fusion Visual perception

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Harnessing CUDA-Q’s MPS for Tensor Network Simulations of Large-Scale Quantum Circuits

Harnessing CUDA-Q’s MPS for Tensor Network Simulations of L...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Gabin Schieffer Stefano Markidis Ivy Peng KTH Royal Institute of Technology Sweden

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Quantum computer simulators are an indispensable tool for prototyping quantum algorithms and verifying the functioning of existing quantum computer hardware. The current largest quantum computers feature more than one thousand qubits, challenging their classical simulators. Statevector quantum simulators are challenged by the exponential increase of representable quantum states with respect to the number of qubits, making more than fifty qubits practically unfeasible. A more appealing approach for simulating quantum computers is adopting the tensor network approach, whose memory requirements fundamentally depend on the level of entanglement in the quantum circuit, and allows simulating the current largest quantum computers. This work investigates and evaluates the CUDA-Q tensor network simulators on an Nvidia Grace Hopper system, particularly the Matrix Product State (MPS) formulation. We compare the performance of the CUDA-Q state vector implementation and validate the correctness of MPS simulations. Our results highlight that tensor network-based methods provide a significant opportunity to simulate large-qubit circuits, albeit approximately. We also show that current GPUaccelerated computation cannot fully utilize GPU efficiently in the case of MPS simulations.

关键词： Tensors Quantum algorithm Quantum entanglement Computational modeling Qubit Graphics processing units Quantum state Vectors Integrated circuit modeling Quantum circuit

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