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Comparative Analysis of image Segmentation Techniques 1st

Comparative Analysis of Image Segmentation Techniques

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1st International conference on Advanced Communication and Computational Technology, ICACCT 2019

作者： Saxena, Snehil Jain, Sidharth Tripathi, Saurabh Gupta, Kapil Department of Computer Applications National Institute of Technology Kurukshetra India

ISBN: (纸本)9789811553400

image segmentation is a prominent task done in computer vision. image thresholding is one such technique in image segmentation. Thresholding is a method of categorizing image intensities into two classes and on the basis of that yielding an image which is a binary image, and ideally also has all the fine details of region of interest which an image should have for analysis. image thresholding is widely used as it reduces the computational cost of processing the image and makes processing feasible in real-world applications like medical imaging, object detection, recognition task, character recognition, etc. This paper dwells into the depth of thresholding techniques to know which technique can perform better on all kinds of images so as to extract region of interest. We found out that not every technique is good for all cases, Otsu’s global thresholding is a promising and faster way to segment and generate a binary image, but works well with images having negligible noise and region of interest already being very much clear in the original image, whereas applying methods like Otsu’s thresholding on sliced blocks of images and then merging them or applying moving averages (sliding windows) on images having noise which is distributed in a specific region of image, moving averages gave result better on images which have distributed gradient noise. Whereas the hybrid technique used are a combination of global and local thresholding. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Designing a 3D parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems 27th

Designing a 3D Parallel Memory-Aware Lattice Boltzmann Algor...

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27th International European conference on parallel and distributed Computing (Euro-Par)

作者： Fu, Yuankun Song, Fengguang Purdue Univ Indianapolis IN USA Indiana Univ Purdue Univ Indianapolis IN 46202 USA

ISBN: (纸本)9783030856656;9783030856649

Lattice Boltzmann method (LBM) is a promising approach to solving Computational Fluid Dynamics (CFD) problems, however, its nature of memory-boundness limits nearly all LBM algorithms' performance on modern computer architectures. This paper introduces novel sequential and parallel 3D memory-aware LBM algorithms to optimize its memory access performance. The introduced new algorithms combine the features of single-copy distribution, single sweep, swap algorithm, prism traversal, and merging two temporal time steps. We also design a parallel methodology to guarantee thread safety and reduce synchronizations in the parallel LBM algorithm. At last, we evaluate their performances on three high-end manycore systems and demonstrate that our new 3D memory-aware LBM algorithms outperform the state-of-the-art Palabos software (which realizes the Fuse Swap Prism LBM solver) by up to 89%.

关键词： Lattice Boltzmann method Memory-aware algorithms parallel numerical methods Manycore systems

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Developing A parallel Program For image Similarity Search Using Hashing methods

Developing A Parallel Program For Image Similarity Search Us...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International conference on

作者： Yasmeen Aldossary Majeed Ebrahim Abdallah Alqaddoumi MSc. in Big Data Science and Analytics College of Science University of Bahrain College of Information Technology College of Science University of Bahrain

ISBN: (纸本)9781665490597

Nowadays, images play an essential role in people’s lives as they document their memories and affairs. Therefore, a large number of images have been generated in different storage. As a result, there was a need to search for similar images in this colossal quantity. While enormous data processing requires speed in execution, it needs the parallel processing method. This study aims to develop a program to search for similar images using the image hashing methods, the Average Hashing, and the Difference Hashing, then to compare their performance. In addition, it aims to explore the effect of using parallelization on the speedup and efficiency of the program. The first part’s results stated that the performance of the Average Hashing and the Difference Hashing methods was affected by the type of data used, despite their effectiveness in detecting identical images. As for the second part, the parallel program reduced the execution time by almost half using two workers, while the best speedup value was when using eight workers. In terms of efficiency, it achieved the highest results with a value greater than 0.9 when using two workers.

关键词： Industries Data analysis parallel processing Business

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A distributed Storage System for System Logs Based on Hybrid Compression Scheme

A Distributed Storage System for System Logs Based on Hybrid...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Baoming Chang Fengxi Zhou Zhaoyang Wang Yu Wen Boyang Zhang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Modern enterprises are facing a massive threat from Advanced Persistent Threats (APTs), which have risen to be one of the most dangerous challenges in recent years. Since system logs capture the complex causality dependencies between system entities, they have become the primary data source for countering APTs. However, as modern computer systems get more complicated, system logs can pile up in large quantities. Besides, APTs are sophisticated and persistent cyber attacks that can remain hidden in the target for a long time and constantly steal private data. System logs need to be collected and stored for a long duration to enable a complete analysis of APTs. Such a vast amount of log data is challenging for enterprises to store and manage. There are two mainstream solutions for reducing storage overhead. Data compression methods provide an intuitive idea. However, they are designed for general text and lack optimization for system logs. Another solution considers log reduction, which removes redundant system events recorded in system logs by predefined rules. Unfortunately, they are tailored for specific kinds of redundant information, resulting in limited applicability. Realizing that these two solutions adopt two distinct perspectives to reduce storage overhead, they are complementary. Data compression methods shrink the size of log data from their binary form. Log reduction starts from the semantic information of system logs and removes redundant information to reduce storage overhead. Combining both methods maximizes storage efficiency. In this paper, we propose a distributed storage system based on a hybrid compression scheme. To address the above deficiencies, we first identify and merge redundant system events by analyzing and tracing the information flow rather than based on rules. Then, we apply log parsing to preprocess log entries for further storage efficiency. Besides, we design a distributed architecture to optimize compression and eliminate repeated

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Estimation of Stochastic Time Lags between Data Sources in distributed Production Facilities Based on Cross-Correlated Signals 22

Estimation of Stochastic Time Lags between Data Sources in D...

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22nd IEEE International conference on Industrial Technology (ICIT)

作者： Zumsande, Johannes Kortmann, Karl-Philipp Ortmaier, Tobias Wielitzka, Mark Leibniz Univ Hannover Inst Mechatron Syst Hannover Germany

ISBN: (纸本)9781728157306

The increasing degree of automation in modern industrial production is accompanied by a growing number of data sources located in the shop floor at the machines and products. methods of data mining allow to make statements and predictions about the machine processing hidden in heterogeneous data. In condition monitoring, however, large and often indefinite time delays exist between the collected process data and quality measurements at distributed work stations, which make these analyses difficult. In order to estimate the time lags, a probabilistic directed graph can be used. Parameters of such a graph are the sojourn times of the production goods in the stations. To identify these times, cross-correlation and transfer entropy can be applied to signals with known collinearity. To compare the two methods, both simulated and real-world data of an industrial return sand cooler are used. For evaluation, we propose a novel method for image-based time delay distribution measurement in bulk material processing by use of a fluorescent contrast medium. A dependence of the estimation result on the signal's statistic is shown. Nevertheless, without knowledge of the signal model a statement about the magnitude and scattering of the stochastic time delay can be made.

关键词： Production systems lagged signals cross-correlation transfer entropy Gaussian processes

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AdaDiffAD: Adaptively Segmenting Diffusion Models for Time Series Anomaly Detection in Dynamic JointCloud Environment

AdaDiffAD: Adaptively Segmenting Diffusion Models for Time S...

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International conference on parallel and distributed Systems (ICPADS)

作者： Chao Ma Lin Yi Linjiang Zhou Zepeng Wang Xiaochuan Shi Weiping Zhu School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Time series anomaly detection is one kind of critical time series analytical tasks, which is widely applied to various real-world applications. Recently, the diffusion models have shown promising performance on time series imputation for anomaly detection. And the high computing requirements of the diffusion models naturally extend their computation paradigm from the centralized computing to the cloud computing, and then further to JointCloud computing which allows the diffusion models to be deployed across multiple clouds. However, the JointCloud computing paradigm faces the challenge of task offloading over multiple nodes across multiple clouds. Unfortunately, most of existing task offloading methods overlook the dynamic nature of network conditions among clouds. To address this issue, we propose a time series anomaly detection approach named AdaDiffAD by adaptively segmenting diffusion models in JointCloud environment with dynamic network conditions. Specifically, we design a task offloading strategy by segmenting the denoising process of the diffusion model onto both the edge clouds and central clouds, and utilizing the edge cloud results directly for anomaly detection when the network condition is not ideal. By conducting comprehensive experiments on seven datasets, the experimental results demonstrate that our proposed AdaDiffAD always achieves lower time consumption while maintaining competitive anomaly detection performance compared to the state-of-the art without adaptive task offloading strategy.

关键词： Cloud computing image edge detection Computational modeling Time series analysis Noise reduction Packet loss Diffusion models Robustness Imputation Anomaly detection

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Optimization method for the parallel algorithm for finding faces in graphic images

Optimization method for the parallel algorithm for finding f...

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2021 International conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2021

作者： Miroshnikov, A.S. Berko, I.A. Berko, A.A. Department of Informatics and Computing North Caucasian Institute of Mining and Metallurgy Vladikavkaz Russia Department of Industrial Power Supply North Caucasian Institute of Mining and Metallurgy Vladikavkaz Russia

ISBN: (纸本)9781728145877

In computer-human interaction, many applications use automatic face detection that is a complex problem in image processing. The purpose of the research is to design the parallel version of the Viola-Jones object detection framework (VJOD) and to optimize it with the help of high-performance Symmetric Multiprocessing (SMP) systems to achieve a minimum operation time. The article discusses the VJOD in more detail. and also methods for optimizing the parallelization of this algorithm. Although the VJOD can be implemented to detect various objects, it is mainly used to detect faces in an image or in a video stream. Result: The face detection system based on VJOD is built and tested. Efficiency of the approach is illustrated with examples. © 2021 IEEE.

关键词： Object detection

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Optimizing Cellular Automata Execution by distributed Discrete Event Simulation Techniques 28

Optimizing Cellular Automata Execution by Distributed Discre...

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28th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Giordano, Andrea D'Ambrosio, Donato Kongo, Rocco Spataro, William De Rango, Alessi CNR ICAR Cosenza Italy Univ Calabria Dept Matemat & Comp Sci Cosenza Italy Univ Calabria Dept Environm Engn Cosenza Italy

ISBN: (纸本)9781728165820

Cellular Automata (CA) are parallel models well suited for studying complex systems that are based on local rules of evolution. Notable examples of application are found in fluid-dynamics, crowd simulation, flow-simulation and many more. Nevertheless, CA can be fruitfully exploited as a support in numerical approaches, such as finite element and finite volume methods. Though easily parallelizable by domain partitioning among the nodes of a parallel system, the performance and scalability of cellular automata executed on parallel/distributed machines are limited due to the need of synchronizing nodes at each computational step. With the aim of reducing the synchronization burden, we here present a preliminary study on techniques stemmed from the Discrete Event Simulation field for the optimization of CA on distributed memory architectures. Preliminary results, executed in a distributed memory environment, have shown the usefulness of the considered approach in reducing execution times and therefore in improving the speed up of the parallel execution of the test case.

关键词： MPI Cellular Automata Discrete Event Simulation Lookahead Software Tools Modelling and Simulation

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Tereis: A Package-Based Scheduling in Deep Learning Systems

Tereis: A Package-Based Scheduling in Deep Learning Systems

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International conference on parallel and distributed Systems (ICPADS)

作者： Chen Chen Sen Wang Yingwen Chen Jianchen Han National University of Defense Technology Changsha China The State Information Center of China Beijing China

Deep learning (DL) systems are typically used to accelerate training DL jobs. Training DL models requires feeding mass input data. It takes a long time to transfer training data from the storage nodes to the compute nodes. However, the computational resources of the GPUs are idle during the data transmission period, which results in a waste of computing resources. In DL systems, a large number of short-term jobs are queuing longer than their own execution times. Meanwhile, many multi-GPU jobs are suffering a long-queuing time due to not enough free GPU. To the best of our knowledge, no studies try to use the idle computation resources of GPU in the data transmission *** propose Tereis, a package-based scheduler to make full use of GPU. Tereis predicts a DL job’s execution time and data transmission time, then safely package two jobs on the same GPU. One of the packaged jobs will be completed before the other job ends transferring data, which is what ‘safely’ means. This ‘safe’ Packaging does not cause GPU contention. Tereis also designs multi-level queues to prevent starvation. We implemented Tereis in python on the actual cluster and evaluated its performance. The experiment results show that Tereis decreases the average waiting time by 3.2× to10.5×, and has an improvement of 18% to 42% on makespan compared to other methods. Furthermore, we have made large-scale simulations to explore the sensitivities of Tereis. We find that Tereis performs better in the scenario where the job set has a distribution of large dataset size and jobs are submitted frequently.

关键词： Training Deep learning Sensitivity Computational modeling Graphics processing units Training data Packaging

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Improving Impulse Noise Classification Using Ensemble Learning methods 5th

Improving Impulse Noise Classification Using Ensemble Learni...

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5th International conference on Advanced Computing and Intelligent Engineering, ICACIE 2020

作者： Kumarasamy, Kunaraj Maria Wenisch, S. Balaji, S. Jenifer Suriya, L.J. Jerlin, A. Robert Rajkumar, S. Chennai600034 India

ISBN: (纸本)9789813342989

Medical image denoising is an essential pre-processing step in medical image processing which improves the performance of clinical diagnosis and prognosis. The high level medical image processing algorithms like segmentation, classification etc. works better if the image is denoised appropriately. The main objective of this research work is to find and replace only the corrupted pixels with suitable estimates of pixels in medical images. The other pixels which are not corrupted are left undisturbed, thereby preserving the image quality for proper diagnosis. For the primary task of finding the corrupted pixels, an ensemble of machine learning (EML) classifiers namely Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT or RT) and Random Forest (RF) are used by supervised learning methods. The final classification output is determined by the majority voting of the outputs of each ML classifier which works in parallel. By adopting this method, a classification accuracy of 99.87% is achieved. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pixels

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