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High Performance Stingray: fast spectral timing for all

High Performance Stingray: fast spectral timing for all

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Matteo Bachetti Eleonora Veronica Lai Daniela Huppenkothen Guglielmo Mastroserio Matteo Lucchini Osservatorio Astronomico di Cagliari Istituto Nazionale di Astrofisica Cagliari Italy Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam the Netherlands Dipartimento di Fisica Università degli Studi di Milano Milano Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Celestial objects are known to be change in brightness over time, driven by a diverse combination of physical processes, whose time scales range from sub-milliseconds to billions of years. Stingray is an open-source Python package that brings advanced time series analysis techniques to the astronomical community, with a focus on high-energy astrophysics, but built on top of general-purpose classes and methods that are designed to be easily adapted and extended to other use cases. We describe the work being done to adapt Stingray to the analysis of large data archives. In particular, we measure the performance and scalability of Stingray and use parallel computing to speed up selected parts of the code.

关键词： Codes Atmospheric measurements Astrophysics Scalability Time series analysis Brightness parallel processing Particle measurements Extraterrestrial measurements Timing

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liu, Mingyu Wang, Yijie Zhou, Xiaohui Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors. © 2025 IEEE.

关键词： Anomaly detection Graph structure learning Multivariate time series Window size selection

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AFMA-Track: Adaptive Fusion of Motion and Appearance for Robust Multi-object Tracking 27th

AFMA-Track: Adaptive Fusion of Motion and Appearance for ...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Liao, Wei Luo, Lei Zhang, Chunyuan College of Computer Science and Technology National University of Defence Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9783031784439

Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distinctive appearance representations, the use of appearance and motion cues is often confined to simplistic association techniques. For instance, fixed weights are commonly employed to combine the intersection-over-union (IoU) matrix and appearance similarity matrix, yielding an association cost matrix. To harness the full potential of motion and appearance cues across diverse scenarios, we propose an innovative approach that dynamically balances motion and appearance cues based on scene and object information during the association process. Furthermore, we introduce a new mechanism for updating appearance representations, effectively mitigating noise introduced by occlusion. Our method demonstrates state-of-the-art performance on the MOT17 and MOT20 test sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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Relevance-Based Multi-User Data Compression for Fronthaul Rate Reduction in Cell-Free Massive MIMO Systems

Relevance-Based Multi-User Data Compression for Fronthaul Ra...

引用

14th International ITG conference on Systems, Communications and Coding, SCC 2025

作者： Danaee, Alireza Hassanpour, Shayan Wubben, Dirk Dekorsy, Armin University of Bremen Department of Communications Engineering Germany

ISBN: (纸本)9798331522896

In the Cell-Free massive Multiple-Input MultipleOutput (CF-mMIMO) systems, a large number of distributed users are simultaneously served by multiple Radio Access Points (RAPs). In the uplink, each RAP receives noisy observations from several users and must locally compress these signals before forwarding them to the corresponding Central processing Unit (CPU) via multiple fronthaul channels, each subject to a rate limitation. The challenge is to design the compressed signals at the RAPs such that the received signals at the CPU retain as much information as possible about the users (to be retrieved). To address this, we adopt compression techniques based on the Information Bottleneck (IB) principle to design local quantizers at the RAPs by ensuring an efficient balance between the informativity and compactness of the compressed signals. We discuss here two different compression schemes: one that processes the signals independently across fronthaul links and another that leverages the side information from previously retrieved signals at the CPU. By using side information, the latter generally provides a better tradeoff between the compression efficiency and performance, albeit with an increased complexity. Through numerical simulations, we demonstrate the effectiveness of both IB-based schemes compared to the conventional compression methods, showing their potential for improving fronthaul rate efficiency and overall system performance in typical digital transmission scenarios. © 2025 IEEE.

关键词： parallel processing systems

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Research of Latent Video Stream Compression methods for FPV Control of UAVs 27th

Research of Latent Video Stream Compression Methods for FP...

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27th International conference on distributed Computer and Communication Networks, DCCN 2024

作者： Chenskiy, A. Berezkin, Aleksandr Vivchar, R. Kirichek, Ruslan Kukunin, D. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Bolshevikov Avenue 22 St. Petersburg Russia

ISBN: (纸本)9783031808524

The efficiency of video stream transmission links between an unmanned aerial vehicle and its operator in mobile and hybrid orbital-terrestrial communication networks directly depends on solving the problem of compressing video stream frames, while ensuring the quality of the restored image. One of the methods of frame compression is the use of variational autoencoders to transfer the latent space obtained during the processing of individual frames. The present paper is devoted to the research of how effectively different algorithms can compress quantized latent space of variational autoencoder VQ-f16 from Stable Diffusion repository. The system of quantized latent space compression algorithms efficiency indicators and their description are presented. A comparative analysis of the efficiency of quantized latent space compression algorithms is conducted. The results of analyzing the efficiency of quantized latent space compression algorithms are presented and recommendations for improving their efficiency are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Unmanned aerial vehicles (UAV)

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Application of Various Novel Computer image processing (CIP) Algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Xian Du Shanghai Institute of Visual Arts Shanghai China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm.

关键词： Training image coding Art image analysis Translation image edge detection Digital images Semantics Media Feature extraction

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Undecimated discrete wavelets transform-based image fusion and de-noising in FPGA

Undecimated discrete wavelets transform-based image fusion a...

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

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

引用

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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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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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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