The paper aims to propose a distributed clustering method for High performance computing (HPC) models and, its application for medical imageprocessing. The communication cost is one of the great challenges, which min...
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The paper aims to propose a distributed clustering method for High performance computing (HPC) models and, its application for medical imageprocessing. The communication cost is one of the great challenges, which minimizes the scalability of parallel and distributed computing models. Indeed, it reduces significantly the performance of HPC systems where these models are assigned to be implemented. In this paper, we present a new distributed k-means method which integrates virtual paralleldistributed computing model with a low communication cost mechanism. The k-means method is performed as a distributed service within a cooperative micro-services team which uses asynchronous communication mechanism based on AMQP protocol. We design and implement a parallel and distributed HPC application for MRI image segmentation assigned to be deployed on cloud. Experimental results show that the proposed method (DSCM) and its assigned model reach high degree of scalability. We expect this clustering approach to provide scalable HPC applications for big data clustering.
For traffic monitoring systems it is desirable to have a low-cost, low-maintenance, and reliable system to facilitate continuous and safe traffic flow. In line with this, we introduce distributed acoustic sensing (DAS...
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ISBN:
(纸本)9781728191423
For traffic monitoring systems it is desirable to have a low-cost, low-maintenance, and reliable system to facilitate continuous and safe traffic flow. In line with this, we introduce distributed acoustic sensing (DAS) for traffic monitoring applications. In DAS systems, series of light pulses are transmitted along a fiber-optic cable and the back-scattered light, which is affected by the mechanical strain of the fiber-optic cables due to ground vibrations, is measured and analyzed. With fiber-optic cables installed parallel to a highway where ground vibrations are induced by passing vehicles, traffic information such as vehicle flow and average speeds can be estimated from the DAS signals. In this paper, we present an algorithm based on imageprocessingmethods that estimates traffic information from image representations generated from 1-minute segments of DAS signals. We tested the results of the algorithm on DAS data recording on a real highway against reference traffic data measured by road-side sensors.
Machine vision systems used in modern industrial complexes, based on the analysis of multi and hyperspectral imaging. The transition to implementing the "Industry 4.0" program is not possible when using one ...
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ISBN:
(数字)9781510645974
ISBN:
(纸本)9781510645974;9781510645967
Machine vision systems used in modern industrial complexes, based on the analysis of multi and hyperspectral imaging. The transition to implementing the "Industry 4.0" program is not possible when using one type of data. The first control system used only the visible range image. They made it possible to analyze the trajectories of movement of objects, control product quality, carry out security functions (control of perimeter crossing), etc. The development of new industrial robotic cells and processing complexes using cognitive functions implying the receipt, analysis, and processing of heterogeneous data. The construction of a unified information field, which allows performing multidimensional operations with data, allows increasing the speed of decision-making and the implementation of automated robot-human systems at the level of an assistant working in a unified workspace. The use of machine vision systems analyzing information received in: visible (shape, the trajectory of movement, position of objects, etc.);near-infrared range (data is similar to visible, allows operation in dusty, foggy, low light conditions);far-infrared range - thermal (plotting temperature gradients, identifying areas of overheating);ultraviolet range (analysis of ionization sources, corona discharges, static charges, tags);X-ray and microwave ranges (analysis of the surface and internal structure of objects, allow the identification of defects);range and 3D sensors (construction of volumetric figures, analysis of the relative position of objects and their interaction), etc. Data analysis is often performed not by a single camera but by a group of sensors located not in a single housing. Primary data integration reduces the number of information channels while maintaining the functionality and accuracy of the analysis. The article discusses creating fusion images obtained by industrial sensors into a combined image containing joint data. Combining multi and hyperspectral imaging makes i
Worldwide, Type ii Diabetes Mellitus (T2DM) is a major condition whose incidence is rising. It is a public health issue as well as the silent killer of humanity. Its diverse aetiology, which includes genetic predispos...
Worldwide, Type ii Diabetes Mellitus (T2DM) is a major condition whose incidence is rising. It is a public health issue as well as the silent killer of humanity. Its diverse aetiology, which includes genetic predisposition and environmental influences, may offer a distinctive trait in the population that is useful for early diagnosis. As a result, researchers are seeking novel approaches to its early diagnosis and treatment. Now, manual methods are used to measure and compare the axial triradius (ATD) angles of both palms using a Goniometer for the early prediction of T2DM. The creation of datasets containing individuals' left and right palm prints is the primary objective of this project, which aims to develop a model that would assist in the early prediction of T2DM. The model would have the option to track down the distinction in the ATD points in the two palms and foresee assuming that the singular will be determined to have T2DM in the future. A total of 390 instances were collected out of which 130 are diabetic and 260 were non-diabetic patients. It consists of various image pre-processing techniques and delta-point detection methods. The model would be able to predict the results if the user simply uploaded images of both palms. The image Thresholding and Segmentation Method was used to evaluate the best features and the crossing number method was used to automate the model, indicating that the model achieved an accuracy of 91.02% in predicting the ATD angle. Using biomarkers, this project can be implemented in the health sector for early detection of Type ii Diabetes Mellitus.
We introduce SMT-D, a tool for portfolio-based distributed SMT solving. We propose a general architecture consisting of two main components: (i) solvers extended with the capability of sharing and importing informatio...
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ISBN:
(数字)9783854480655
ISBN:
(纸本)9798331524784
We introduce SMT-D, a tool for portfolio-based distributed SMT solving. We propose a general architecture consisting of two main components: (i) solvers extended with the capability of sharing and importing information on the fly while solving; and (ii) a central manager that orchestrates and monitors solvers while also deciding which information to share with which solvers. We introduce new information-sharing strategies based on the idea of maximizing the amount of useful diversity in the system. We show that on hard benchmarks from recent related work, SMT-D instantiated with the cvc5 SMT solver achieves significant speed-up over sequential performance, is competitive with existing portfolio approaches, and contributes a number of unique solutions.
The proceedings contain 16 papers. The special focus in this conference is on Signal and imageprocessing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Ide...
ISBN:
(纸本)9789813369658
The proceedings contain 16 papers. The special focus in this conference is on Signal and imageprocessing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Identification in Spoken Language Mismatch Condition: An Experimental Study;Ultrasound image Classification Using ACGAN with Small Training Dataset;preface;Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study;assessment of Eyeball Movement and Head Movement Detection Based on Reading;using Hadoop Ecosystem and Python to Explore Climate Change;a Brief Review of Intelligent Rule Extraction Techniques;the Effect of Different Feature Selection methods for Classification of Melanoma;intelligent Hybrid Technique to Secure Bluetooth Communications;parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional;a Fog-Based Retrieval of Real-Time Data for Health Applications;differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval;qutrit-Based Genetic Algorithm for Hyperspectral image Thresholding.
This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastruct...
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This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastructure-agnostic distributed and parallel applications. In Kulla, applications, dependencies and environment settings, are mapped with construction units called Kulla-Blocks. A parallel programming model enables developers to couple those interoperable structures for creating constructive structures named Kulla-Bricks. In these structures, continuous dataflow and parallel patterns can be created without modifying the code of applications. methods such as Divide&Containerize (data parallelism), Pipe&Blocks (streaming), and Manager/Block (task parallelism) were developed to create Kulla-Bricks. Recursive combinations of Kulla instances can be grouped in deployment structures called Kulla-Boxes, which are encapsulated into VCs to create infrastructure-agnostic parallel and/or distributed applications. Deployment strategies were created for Kulla-Boxes to improve the IT resource profitability. To show the feasibility and flexibility of this model, solutions combining real-world applications were implemented by using Kulla instances to compose parallel and/or distributed system deployed on different IT infrastructures. An experimental evaluation based on use cases solving satellite and medical imageprocessing problems revealed the efficiency of Kulla model in comparison with some traditional state-of-the-art solutions. (C) 2020 Published by Elsevier Inc.
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 ...
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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.
In this paper, a content-based video retrieval (CBVR) system called Bounded Coordinate of Motion Histogram version 2 (BCMH v2) was processed on a distributed computing platform by using Apache Hadoop framework and a r...
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This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This ch...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual imageprocessing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate state-of-the-art performance.
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