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29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Cao, Yuan Meng, Fanlei Wu, Xiangyu Wang, Zijie Ocean University of China School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350330717

Hashing is frequently used in approximate nearest neighbor search due to its storage and search efficiency. On account of the bottleneck of traditional learning to hash methods, deep-based learning to hash has gained quite a popularity among researchers recently. While such methods show a promising performance gain by utilizing deep neural networks into its end-to-end training process to generate compact binary codes, the intrinsic connections between components make it unfeasible to optimize the architecture significantly. Subject to noise interference and absence of similarity labels of training data, normal unsupervised deep models even carry a noticeable deviation at representation learning stage. By integrating Generative Adversarial Network, this paper presents a novel architecture for generating compact hash codes from an extended set derived from original images while using the benefit of similarity prediction. Extensive experiments conducted on three benchmarks, NUS-WIDE, CIFAR-10, and MS-COCO illustrate that our method outperforms state-of-the-art image retrieval models and generates high-quality binary hash codes seamlessly. © 2023 IEEE.

关键词： generative model hashing image retrieval unsupervised learning

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Accelerating Traditional Object Detection methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Satellite image Classification using Convolutional Neural Network with Huber Loss Function for Land Cover Mapping in Forest Ecosystem

Satellite Image Classification using Convolutional Neural Ne...

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2024 International Conference on distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Al-Fatlawy, Ramy Riad Raviprakash, P. Vybhavi, Gummagatta Yajaman Jagadish, Sripelli Sundaram, S Meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India School of Computer Science & Artificial Intelligence Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350375442

Satellite image classification for land cover involves determining high-resolution imagery to recognize and classify various types of land covers. This process assists in monitoring forest health, managing resources, and assessing biodiversity effectively. However, accurately classifying images for land cover is difficult because of diverse and complex nature of forest environments and varying image qualities. In this research, the Convolutional Neural Network with Huber Loss Function (CNN-HLF) is proposed to classify the satellite images accurately for landcover mapping in forest ecosystem. The CNN-HLF effectively manage the variability and complexity of forest environments which improves the performance of land cover. Initially, the Eurosat dataset is used to determine the CNN-HLF performance. Resizing and image sharpening is applied to adjust the size and enhance the clarity of features. DenseNet201 is employed for extracting features effectively from pre-processed images. The proposed CNN-HLF achieves a high accuracy of 99.87% using Eurosat dataset compared to existing methods like GoogleNet with pre-processing and Lightweight parallel CNN (LPCNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration 29

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

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29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Xiaoyang Chen, Wenjie Zhou, Zihao Duan, Zhuowen East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm's execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks. © 2023 IEEE.

关键词： Deep Learning Defect Detection FPGA parallel Computing

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25th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

25th International Conference on Parallel and Distributed Co...

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25th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

ISBN: (纸本)9789819642069

The proceedings contain 55 papers. The special focus in this conference is on parallel and distributed Computing: Applications and Technologies. The topics include: A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude Control;Securing Energy Transactions for Electric Vehicles: The Blockchain Approach and Encrypted NFTs;optimizing Task Allocation in Heterogeneous Agent Manufacturing Systems;MPG: Multi-modal Personal Health Graph for Alzheimer’s Disease Diagnosis;SMAC: A Secure Multi-authority Access Control Scheme with Attribute Unification for Fog Enabled IoT in E-Health;Convolutional Neural Networks Parameter Training for SCM Algorithm Based on Hausdorff Difference;handling Non-stationarity with Distribution Shifts and Data Dependency in Time Series Forecasting;the Two-Stage Stochastic Facility Location Game;regularized Non-monotone γ-weakly Submodular Maximization;fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning;WaitIO-Hybrid: Communication for Coupling MPI Programs Among Heterogeneous Systems;the Material Delivery Route Prediction Method Based on Deep Reinforcement Learning;privacy-Preserving in Medical image Analysis: A Review of methods and Applications;research on Task Migration Problem Based on Link Uncertainty in Adversarial Scenarios;optimizing Production Component Scheduling in Multivariate Industrial Networks with Dynamic Changes in Production Costs;multi-agent Collaboration for Time-Sensitive Tasks in Multiple Networked Adversarial Scenarios;containerized Data-Flow processing for Scalable Real-Time Analytics on Edge Devices;fast Approximation for Scheduling Malleable Jobs on parallel Batch Machines with Rejection;real-Time and In-Situ Temperature Profiling for Determining Detonation of White Dwarf Mergers;accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives;Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams;long-Term and Periodicity-Aware Spatio

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Fast parallel Bayesian Network Structure Learning 36

Fast Parallel Bayesian Network Structure Learning

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36th IEEE International parallel and distributed processing Symposium (IEEE IPDPS)

作者： Jiang, Jiantong Wen, Zeyi Mian, Ajmal Univ Western Australia Dept Comp Sci & Software Engn Nedlands WA Australia

ISBN: (纸本)9781665481069

Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require performing a large number of conditional independence (CI) tests. The learning process is very time-consuming, especially for high-dimensional problems, which hinders the adoption of BNs to more applications. Existing works attempt to accelerate the learning process with parallelism, but face issues including load unbalancing, costly atomic operations and dominant parallel overhead. In this paper, we propose a fast solution named Fast-BNS on multi-core CPUs to enhance the efficiency of the BN structure learning. Fast-BNS is powered by a series of efficiency optimizations including (i) designing a dynamic work pool to monitor the processing of edges and to better schedule the workloads among threads, (ii) grouping the CI tests of the edges with the same endpoints to reduce the number of unnecessary CI tests, (iii) using a cache-friendly data storage to improve the memory efficiency, and (iv) generating the conditioning sets on-the-fly to avoid extra memory consumption. A comprehensive experimental study shows that the sequential version of Fast-BNS is up to 50 times faster than its counterpart, and the parallel version of Fast-BNS achieves 4.8 to 24.5 times speedup over the state-of-the-art multi-threaded solution. Moreover, Fast-BNS has a good scalability to the network size as well as sample size.

关键词： Schedules Uncertainty Instruction sets Scalability Memory management parallel processing Dynamic scheduling

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A Differential Privacy image Publishing Method Based on Wavelet Transform 22nd

A Differential Privacy Image Publishing Method Based on Wave...

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22nd International Conference on parallel and distributed Computing, Applications and Technologies (PDCAT 2021)

作者： Zhang, Guifen Wei, Hangui Ge, Lina Qin, Xia Guangxi Univ Nationalities Sch Artificial Intelligence Nanning Peoples R China Guangxi Univ Nationalities Key Lab Network Commun Engn Nanning Peoples R China

ISBN: (纸本)9783030967727;9783030967710

image is an important information-bearing medium with many important attributes. If the image data is released directly, personal privacy will be compromised. This paper aims at how to use the method of differential privacy to protect the privacy of image data and make the image data have high usability. In this paper, a WIP method based on wavelet change is proposed. Firstly, wavelet transform is used to compress the image. Then, noise is added to the main features after transformation to obtain the published image satisfying the differential privacy. It solves the problem of low usability of large images and the problem that Fourier transform cannot deal with abrupt signal. Experimental results show that compared with similar methods in the frequency domain, the denoised image obtained by the proposed WIP method is more distinguishable and the information entropy is closer to the original image. The accuracy is 10% higher than other methods. Compared with other frequency-domain methods for image differential privacy protection, the proposed WIP method has higher usability and robustness.

关键词： image processing Differential privacy Privacy protection The wavelet transform

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QGIP: A Framework Bridging Quantum Grayscale image processing and Applications

QGIP: A Framework Bridging Quantum Grayscale Image Processin...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Xilong Che Jiale Zhang Shuo Chen Shun Peng Juncheng Hu College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2 n × 2 n after a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2 n ) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal.

关键词： Training distributed processing Quantum algorithm image coding Gray-scale parallel processing image restoration Proposals image storage Time complexity

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A fast and fully distributed method for region-based image segmentation Fast distributed region-based image segmentation

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JOURNAL OF REAL-TIME image processing 2021年第3期18卷 793-806页

作者： Mazouzi, Smaine Guessoum, Zahia Skikda Univ 20 Aout 1955 Comp Sci Dept Skikda Algeria Univ Reims Sorbonne Univ CReSTIC Reims Lip6 Paris France

distributed and parallel computing techniques allow fast image processing, namely when these techniques are applied at the low and the medium level of a vision system. In this paper, a collective and distributed method for image segmentation is introduced and evaluated. The method is modeled as a multi-agent system, where the agents aim to collectively produce a region-based segmentation. Each agent starts searching for an acceptable region seed by randomly jumping within the image. Next, it performs a region growing around its position. Thus, several agents find themselves within the same homogeneous region and are organized in a graph where two agents are connected if they are within the same region. So, a unifying of the labels in a same region is collaboratively performed by the agents themselves. The proposed method was experimented on real range images from the ABW dataset and the Object Segmentation Database (OSD) one, and the obtained results were compared to those of some well-referenced methods from the literature. The evaluation results show that the proposed method provides fast and accurate image segmentation, allowing it to be deployed for real-time vision systems.

关键词： image segmentation Region growing Pixel labeling distributed computing Real-time computer vision systems

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image Compression and Reconstruction Based on Quantum Network

Image Compression and Reconstruction Based on Quantum Networ...

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： J. Xun Qin Liu Shan Huang Andi Chen W. Shengjun National Laboratory of Solid State Microstructure School of Physics and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Institute for Brain Sciences and Kuang Yaming Honors School Nanjing University Nanjing China Hefei National Laboratory University of Science and Technology of China Hefei China

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.

关键词： Training distributed processing image coding Accuracy Conferences Quantum mechanics Reconstruction algorithms

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