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International conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Fahad Ahmad Muhammad H. Ahmed Yumna Adnan Department of Electrical Engineering University of Gujrat Gujrat Pakistan

ISBN: (数字)9781837243150

image processing is a vast field of study and has gained significant importance in recent years. image fusion and denoising a re most important methods in the field of image processing. The project develops an image processing system on a Field-Programmable Gate Array (FPGA) platform that uses the Undecimated Discrete Wavelet Transform (UDWT) for image fusion and denoising. By merging several images into a single better image and using sophisticated wavelet techniques to reduce noise the aim is to improve image quality. UDWT preserves image details by maintaining high-frequency components without down sampling. Reconstructing denoised images thresholding to remove noise and dividing images into wavelet coefficients are the steps in the process that culminate in the creation of a superior composite image. Due to its ability to process data in parallel the FPGA platform is preferred over conventional CPU-based techniques for processing large amounts of data quickly. Suitable for real-time applications, this method takes advantage of the intrinsic parallelism of FPGA to process data quickly and efficiently. According to the results the FPGA-based system performs faster and produces better images than traditional techniques making it a reliable option for high-performance image processing application.

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Crop Classification methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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MAP image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model

MAP Image Recovery with Guarantees using Locally Convex Mult...

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

作者： Jyothi Rikhab Chand Mathews Jacob Department of Electrical and Computer Engineering University of Iowa IA USA Department of Electrical and Computer Engineering University of Virginia VA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose a multi-scale deep energy model that is strongly convex in the local neighbourhood around the data manifold to represent its probability density, with application in inverse problems. In particular, we represent the negative log-prior as a multi-scale energy model parameterized by a Convolutional Neural Network (CNN). We restrict the gradient of the CNN to be locally monotone, which constrains the model as a Locally Convex Multi-Scale Energy (LC-MuSE). We use the learned energy model in image-based inverse problems, where the formulation offers several desirable properties: i) uniqueness of the solution, ii) convergence guarantees to a minimum of the inverse problem, and iii) robustness to input perturbations. In the context of parallel Magnetic Resonance (MR) image reconstruction, we show that the proposed method performs better than the state-of-the-art convex regularizers, while the performance is comparable to plug-and-play regularizers and end-to-end trained methods.

关键词： Manifolds Inverse problems Perturbation methods Magnetic resonance Signal processing Robustness Data models Convolutional neural networks Speech processing image reconstruction

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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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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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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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A large-scale lychee image parallel classification algorithm based on spark and deep learning

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 230卷

作者： Xiao, Yiming Wang, Jianhua Xiong, Hongyi Xiao, Fangjun Huang, Renhuan Hong, Licong Wu, Bofei Zhou, Jinfeng Long, Yongbin Lan, Yubin South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou Peoples R China South China Agr Univ Guangdong Lab Lingnan Modern Agr Guangzhou Peoples R China South China Agr Univ Natl Ctr Int collaborat Res precis Agr Aviat Pesti Guangzhou Peoples R China

Accurate and rapid classification of large-scale lychee images is crucial for collecting germplasm resources and studying the characteristics of different lychee varieties, and it requires the construction of accurate classification models and the design of rapid classification algorithms. However, the current deep learning-based classification methods for lychee images are unable to simultaneously meet the processing requirements of accuracy and timeliness in large-scale lychee image classification. To address the problem above, this paper proposes a largescale parallel classification algorithm for lychee images based on Spark and deep learning. Specifically, first, the T_ECBAM_ResNetS-34 model architecture was designed and trained using a self-built dataset covering ten types of lychee images and the PyTorch deep learning framework, which improved the accuracy of model classification;Second, the model inference algorithm trained by PyTorch was restructured, utilizing Apache Spark RDD and broadcast variables and data structures to implement data partitioning and model parallel computation across nodes. The experimental results show that the method proposed in this paper surpasses existing technologies in both classification accuracy and the speed of large-scale lychee image classification.

关键词： Litchi Classification Apache Spark ResNet-34 distributed Model Inference

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