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50th International conference on parallel processing Workshop, ICPP 2021 - Proceedings

50th International Conference on Parallel Processing Worksho...

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50th International conference on parallel processing Workshop, ICPP 2021

ISBN: (纸本)9781450384414

The proceedings contain 38 papers. The topics discussed include: automated arrhythmia detection using Hilbert-Huang transform based convolutional neural network;self-stabilization with selfish agents;new evacuation guidance using augmented reality for emergency rescue evacuation support system (ERESS);analysis on nursing care activity related stress level for reduction of caregiving workload;warp-centric k-nearest neighbor graphs construction on GPU;explaining the classification performance of supervised and semi-supervised methods for automated sparse matrix format selection;an intelligent parallel distributed streaming framework for near real-time science sensors and high resolution medical images;and intra- and inter- layer transformation to reduce memory traffic for CNN computation.

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Recognition of micro-objects with adaptive models of image processing in a parallel computing environment 14

Recognition of micro-objects with adaptive models of image p...

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14th International Scientific and Technical conference on Applied Mechanics and Systems Dynamics, AMSD 2020

作者： Jumanov, I.I. Djumanov, O.I. Safarov, R.A. Samarkand State University 15 University Boulevard Samarkand Uzbekistan

Scientific and methodological foundations for optimizing the processing of micro-objects, in particular, pollen grains, have been developed on the basis of models and methods of preliminary information processing with mechanisms for filtering, identifying, and using textural, specific characteristics, and geometric features of images. The efficiency of image filtering mechanisms is investigated based on the use of statistical control rules, adaptive two-threshold control, trend functions, and control of the contour description error, traditional detectors of random noise, and interference. The mechanisms of image identification using biquadratic, orthogonal algebraic polynomials, 4th order splines are proposed. A technique for optimizing image identification by finding a function by its integrals in a family of straight lines based on the Mellin and Fourier transforms has been developed. Combined models with mechanisms for smoothing reference points, segmentation of contours, filtering with detection of changes in dynamics, optimization by the conjugate gradient method, time relaxation have been built. A software complex for the recognition and classification of images of pollen grains in C ++ has been developed, which includes testing identification algorithms based on the Daubechies spline function, orthogonal algebraic polynomials of 4, 8 orders in the parallel computing environment "CUDA". © Published under licence by IOP Publishing Ltd.

关键词： Fourier transforms

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Deep Contrastive Patch-Based Subspace Learning for Camera image Signal processing

Deep Contrastive Patch-Based Subspace Learning for Camera Im...

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Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Yunhao Yang Yi Wang Chandrajit Bajaj University of Texas at Austin Austin USA

Camera image Signal processing(ISP) pipelines can get appealing results in different image signal processing tasks. Nonetheless, the majority of these methods, including those employing an encoder-decoder deep architecture for the task, typically utilize a uniform filter applied consistently across the entire image. However, it is natural to view a camera image as heterogeneous, as the color intensity and the artificial noise are distributed vastly differently, even across the two-dimensional domain of a single image. Varied Moire ringing, motion blur, color-bleaching, or lens-based projection distortions can all potentially lead to a heterogeneous image artifact filtering problem. In this paper, we present a specific patch-based, local subspace deep neural network that improves Camera ISP to be robust to heterogeneous artifacts (especially image denoising). We call our three-fold deep-trained model the Patch Subspace Learning Autoencoder (PSL-AE) 1 1 Oursource code is publiclyavailable at https://***/CVC-Lab/Patch-Subspace-Learning-Autoencodd.. The PSL-AE model does not make assumptions regarding uniform levels of image distortion. Instead, it first encodes patches extracted from noisy a nd clean image pairs, with different artifact types or distortion levels, by contrastive learning. Then, the patches of each image are encoded into corresponding soft clusters within their suitable latent sub-space, utilizing a prior mixture model. Furthermore, the decoders undergo training in an unsupervised manner, specifically trained for the image patches present in each cluster. The experiments highlight the adaptability and efficacy through enhanced heterogeneous filtering, both from synthesized artifacts but also realistic SIDD image pairs.

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Lightweight Human Gesture Recognition Using Multimodal Features

Lightweight Human Gesture Recognition Using Multimodal Featu...

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European Signal processing conference (EUSIPCO)

作者： Anestis Christidis Christos Papaioannidis Ioannis Mademlis Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Human gesture recognition is a very important tool in human-computer or human-robot interaction. In many cases, such algorithms may need to be executed on systems with limited computational capabilities, due to size or weight constraints, introducing restrictions that can impede gesture recognition performance. This paper proposes a gesture recognition method that is based on a very simple and lightweight Deep Neural Network (DNN) architecture, suitable for embedded execution. In order to achieve increased accuracy without a large computational/memory overhead, the proposed method utilizes as input both full 2D human body skeletons and image patches extracted from regions of interest (e.g., around human arms) in each video frame. These two input types are processed in parallel by separate modules and the corresponding features are fused before being exploited for gesture recognition. Reliance on 2D skeleton sequences allows the utilization of a lightweight DNN architecture, while the image patches convey rich semantic information that enhances gesture recognition performance. This approach is unlike existing similar methods, which only exploit skeleton sequences. Experimental evaluation indeed shows increased recognition accuracy, indicating that the proposed method offers a reliable solution for human gesture recognition on embedded systems.

关键词： Visualization Accuracy Gesture recognition Computer architecture Artificial neural networks Signal processing Feature extraction Transformers Skeleton Reliability

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parallelDualSPHysics: supporting efficient parallel fluid simulations through MPI-enabled SPH method 22

ParallelDualSPHysics: supporting efficient parallel fluid si...

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Proceedings of the 51st International conference on parallel processing

作者： Sifan Long Xiao-Wei Guo Xiaokang Fan Chao Li Kelvin Wong Ran Zhao Yi Liu Sen Zhang Canqun Yang College of Computer National University of Defense Technology China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology China College of Computer Central South University China

ISBN: (纸本)9781450397339

Smoothed Particle Hydrodynamics (SPH) is a classical mesh-free particle method which has been successfully applied in the field of Computational Fluid Dynamics (CFD). Its advantages over traditional mesh-based methods have made it very popular in simulating problems involving large deformation and free-surface flow. The high computational cost of the SPH method has obstructed its vast application. A lot of research effort has been devoted to accelerating the SPH method using GPU and multi threading. However, developing efficient parallel SPH algorithms on modern high-performance computers (HPCs) remains significantly challenging, especially for simulating real-world engineering problems involving hundreds of millions of particles. In this paper, we proposed an MPI-enabled parallel SPH algorithm and developed the parallelDualSPHysics1, an open-source software supporting efficient parallel fluid simulations. Based on an efficient domain decomposition scheme, the essential data structure and algorithms of DualSPHysics were refactored to build the parallel version. For collaborating with evenly distributed particles on a distributed-memory HPC system, the parallel particle interaction and particle update modules were introduced, which enabled the SPH solver to synchronize computations among multiple processors using MPI. In addition, the redesigned pre-processing and post-processing capabilities of the parallelDualSPHysics supported the applications of this software in a wide range of areas. Real-life test cases with up to 120 million particles were simulated and analyzed on a modern HPC system. The results showed that the parallel efficiency of parallelDualSPHysics exceeds 90 with up to 1024 CPU cores. It indicated that parallelDualSPHysics has the potential for large-scale engineering applications.

关键词： Computational Fluid Dynamics Smoothed Particle Hydrodynamics parallelDualSPHysics parallel Computing

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Multi-objective evolutionary based feature selection supported by distributed multi-label classification and deep learning on image/video data 21

Multi-objective evolutionary based feature selection support...

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22nd International Middleware conference, Middleware 2021

作者： Karagoz, Gizem Nur Delft University of Technology Delft Netherlands

ISBN: (纸本)9781450391559

We live in an era in which a myriad of computer systems produce immense amounts of (raw) data every day. This big data must be processed efficiently to gain valuable and hidden knowledge. Complex processing pipelines need to be designed for filtering out irrelevant data, also for efficient data mining and machine learning methods must be used to discover useful correlations in the big data. The purpose of this PhD research is the implementation of multi-objective evolutionary-based dimensionality reduction on a high volume of image/video data with the support of distributed multi-label classification algorithms. © 2021 Owner/Author.

关键词： Big data

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An SoC FPGA-based Integrated Real-time image Processor for Uncooled Infrared Focal Plane Array

An SoC FPGA-based Integrated Real-time Image Processor for U...

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Euromicro Symposium on Digital System Design

作者： Hyun Woo Oh Cheol-Ho Choi Jeong Woo Cha Hyunmin Choi Joon Hwan Han Jung-Ho Shin Infra Technology R&D Center Hanwha Systems Seongnam Republic of Korea

This paper presents an integrated image processor architecture designed for realtime interfacing and processing of high-resolution thermal video obtained from an uncooled infrared focal plane array (IRFPA) utilizing a modern system-on-chip field-programmable gate array (SoC FPGA). Our processor provides a one-chip solution for incorporating non-uniformity correction (NUC) algorithms and contrast enhancement methods (CEM) to be performed seamlessly. We have employed NUC algorithms that utilize multiple coefficients to ensure robust image quality, free from ghosting effects and blurring. These algorithms include polynomial modeling-based thermal drift compensation (TDC), two-point correction (TPC), and runtime discrete flat field correction (FFC). To address the memory bottlenecks originating from the parallel execution of NUC algorithms in realtime, we designed accelerators and parallel caching modules for pixel-wise algorithms based on a multi-parameter polynomial expression. Furthermore, we designed a specialized accelerator architecture to minimize the interrupted time for runtime FFC. The implementation on the XC7Z020CLG400 SoC FPGA with the QuantumRed VR thermal module demonstrates that our image processing module achieves a throughput of 60 frames per second (FPS) when processing 14-bit 640×480 resolution infrared video acquired from an uncooled IRFPA.

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Faster Depth-First Subgraph Matching on GPUs

Faster Depth-First Subgraph Matching on GPUs

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International conference on Data Engineering

作者： Lyuheng Yuan Da Yan Jiao Han Akhlaque Ahmad Yang Zhou Zhe Jiang Indiana University Bloomington Auburn University USA University of Florida USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Subgraph search problems such as maximal clique enumeration and subgraph matching generate a search-space tree which is traversed in depth-first manner by serial backtracking algorithms that are recursive. Since Jenkins et al. reported the backtracking paradigm to be sub-optimal for GPU acceleration, breadth-first traversal of the search-space tree is widely adopted by GPU algorithms. However, they produce a lot of intermediate subgraphs that exhaust the GPU device memory. Recent works revive the depth-first backtracking paradigm for GPU acceleration, where each warp is a basic processing unit with its own stack in device memory for subgraph backtracking. However, they adopt complicated methods for load balancing that incur a lot of overheads. They also use hardcoded fixed space for stacks that is determined ad-hoc and may lead to inaccuracy when the allocated space is insufficient. In this paper, we use subgraph matching as a case study to propose novel depth-first GPU solutions to address the above problems. Our approach, called T-DFS, decomposes computation into independent tasks that process search-space subtrees, which are managed by an efficient lock-free circular task queue. Tasks are distributed to different warps for parallel processing, and a novel timeout mechanism is used to eliminate straggler tasks to ensure load balancing. We also support flexible and fine-grained dynamic memory allocation for stack spaces to avoid the stack space allocation pitfalls of existing works. Extensive experiments on real graphs show that T-DFS significantly outperforms existing depth-first GPU solutions for the subgraph matching application.

关键词： Backtracking Graphics processing units parallel processing Search problems Load management Dynamic scheduling Data engineering

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HDC-Based Convolutional Neural Network image Inpainting 5

HDC-Based Convolutional Neural Network Image Inpainting

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5th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Wang, Shengbo Wang, Xiuyou Liu, Huaming Bi, Xuehui Fuyang Normal University The School of Computer and Information Engineering Fuyang China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Recently deep learning-based methods have been widely used in various aspects of image processing, with remarkable results for the challenging task of filling large areas of missing images. These methods can generate visually plausible structures and textures, but fuzzy filling can also occur when irregular voids are encountered. This is mainly due to the grid effect caused by unreasonable expansion factor setting in the use of neural network and continuous expansion convolution. As a result, the deep convolution operation cannot effectively obtain the details of the initial feature map, resulting in unreasonable repairing effect. Based on this phenomenon, we propose a convolutional neural network structure based on HDC (hybrid dilated convolution). The first stage consists of ordinary convolution and continuous dilated convolution, and the design of the dilated factor in the continuous dilated convolution is following the HDC method, which makes full use of the underlying feature map information while increasing the perceptual field. The second stage of the network consists of a convolutional branch and a perceptual branch, which are in parallel structure. The perceptual branch generates the filled region by extracting the feature of interest from the known region and generating the filled region based on the feature, after which it is aggregated with the feature map generated by the convolutional branch and fed into the decoder and deconvoluted to produce the restored image. Experimental results show that our method produces higher-quality inpainting results in terms of generating coherence, clarity, and visual confidence. © 2022 IEEE.

关键词： Convolution

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Multi-Terminal Collaborative distributed Garden Planning System Integrating CG Technology

Multi-Terminal Collaborative Distributed Garden Planning Sys...

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Pervasive Computing and Social Networking (ICPCSN), International conference on

作者： Ye Wang Qi Liu School of Art Design and Media Sanda University Shanghai China

The fast distributed garden planning system in colleges and universities is one of the mainstream applications of contemporary computers. The latest traditional methods usually use the GPU full virtualization technology as an entry point to improve the problem of a single user and a single computer, so that a physical computer can shared by multiple users without interfering with each other. With this basis, this research study designs the novel multi-terminal collaborative distributed garden planning system integrating CG technology. Key innovations are: (1) The parallel SSOR-AI preconditioning combined with the CG algorithm is proposed to improve the traditional CG model. (2) The designed multi-terminal collaboration model considers the heterogeneous structure that will be more robust for the complex data. (3) The virtual image model is designed to finish the whole work flow. In the experiment part, the data is collected from Baidu search engine with the size of more than 1000000, the various tests are conducted to validate the performance.

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