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نتیجه جستجو - Parallel computation

تعداد مقالات یافته شده: 22
ردیف عنوان نوع
1 Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022
Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of tumors.
keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron
مقاله انگلیسی
2 Post-Quantum Blockchain-Based Data Sharing for IoT Service Providers
به اشتراک گذاری داده های مبتنی بر بلاک چین پسا کوانتومی برای ارائه دهندگان خدمات اینترنت اشیا-2022
Quantum technologies have made significant advances and are likely to lead to important security challenges and threats to networks in the near future. On the other hand, sharing the huge amount of data from the Internet of Things (IoT) in the context of data as a service could provide new revenue streams for infrastructure providers and service providers. However, post-quantum computing exposes the entire data sharing ecosystem to a new set of security risks. In this article, we propose a novel blockchain-based system for data sharing in the post-quantum era. The proposed system facilitates data sharing among multiple organizations while meeting compliance and regulatory requirements via private blockchain. We implemented the proposed architecture and information flow using three blockchain networks (namely Hyperledger Fabric, Ethereum, and Quorum) and selected NTRU as our quantum resistant security algorithm (QRSA) to compare the parallelization performance of Toom-Cook’s and Karatsuba’s computation methods. Experimental results show that parallel computation has a positive impact when the security level of QRSAs is lowered, and the transaction time savings is almost 50 percent in favor of Quorum. Finally, we outline the main challenges and potential solutions.
مقاله انگلیسی
3 Post-Quantum Era in V2X Security: Convergence of Orchestration and Parallel Computation
دوران پسا کوانتومی در امنیت V2X: همگرایی ارکستراسیون و محاسبات موازی-2022
Along with the potential emergence of quantum computing, safety and security of new and complex communication services such as automated driving need to be redefined in the post-quantum era. To ensure reliable, continuous, and secure operation of these scenarios, quantum-resistant security algorithms (QRSAs) that enable secure connectivity must be integrated into the network management and orchestration systems of mobile networks. This article explores a roadmap study of post-quantum era convergence with cellular connectivity using the Service & Computation Orchestrator (SCO) framework for enhanced data security in radio access and backhaul transmission with a particular focus on vehicle-to-everything services. Using NTRU as a QSRA, we show that the parallelization performance of the Toom-Cook and Karatsuba computation methods can vary based on different CPU load conditions through extensive simulations, and that the SCO framework can facilitate the selection of the most efficient computation for a given QRSA. Finally, we discuss the evaluation results, identify the current standardization efforts, and present possible directions for the coexistence of post-quantum and mobile network connectivity through an SCO framework that leverages parallel computing.
مقاله انگلیسی
4 Classical Computer Assisted Analysis of Small Multiqudit Systems
تحلیل کامپیوتری کلاسیک سیستم‌های چند کیوبیتی کوچک-2022
Quantum computation model is regarded as a model which can overcome barriers in calculations efficiency of problems which appear in modern science. In spite of hardware development, in particular a recent emergence of several different physical installations of the pioneering quantum machines, the contemporary and numerical analysis of problems concerning quantum computing is very important. In the first part of this article, some useful computing techniques for quantum registers processed by a quantum circuits are presented. Applied classical parallel computational techniques are utilised to shorten the whole computational time. New methods of processing state vectors for qudits and density matrices are presented, indicating which operations may be performed in parallel in the context of the implementation of local unitary operations. There is also shown, how to use the reduction operation in parallel implementation of the von Neumann measurement by performing local measurements on a system of qudits. In addition to the purely technical results as described above, the paper includes also a bunch of purely theoretical results which substitute a solid mathematical ground for the computations performed with the help of the computational routines as described in Section III. In particular, a discussion concerning general multi-qudit quantum states through the prism of Entropy and Negativity measures of entanglement included in has been presented. Additionally, the notion of the total entanglement has been introduced. For certain classes of popular multiqudit states, the introduced deficits of entanglement defined with the use of von Neumann Entropy and Negativity have been discussed. In particular, by the use of Gram matrix technique, the corresponding deficits of entanglement in the analysed states have been computed in an explicite way. Additionally, some new results on AME states for some multiqudit systems are also included in Section V. The last part of the article presents some numerical experiments on multi-qudit entanglement and determination of total entanglement values for convex combinations of GHZ and W states. Some details concerning technical nature of results are included in the attached Appendix.
INDEX TERMS: Quantum computing | quantum circuits | entanglement | entropy | Schmidt decomposition | parallel computations.
مقاله انگلیسی
5 Parallelizing pairings on Hessian elliptic curves
موازی سازی جفتی بر روی منحنی بیضوی هسیان-2019
This paper considers the computation of the Ate pairing on the Hessian model of elliptic curves. Due to the many important properties making the model attractive in cryptography, we compute for the first time the Ate pairing on this model and show how both the Tate and the Ate pairings can be parallelized on this curve. We wrote codes in the Sage software to ensure the correctness of formulas in this work.
Keywords: Hessian curves | Tate and ate pairings | Parallel computation
مقاله انگلیسی
6 Peak operation of hydropower system with parallel technique and progressive optimality algorithm
بهره برداری از سیستم نیروی برق آبی با تکنیک موازی و الگوریتم بهینه سازی پیشرفته-2018
With the rapid economic growth in recent years, the peak operation of hydropower system (POHS) is becoming one of the most important optimization problems in power system. However, the rapid expansion of system scale, refined management and operational constraints has greatly increased the optimization difficult of POHS. As a result, it is of great importance to develop effective methods that can ensure the computational efficiency of POHS. The progressive optimality algorithm (POA) is a commonly used technique for solving hydropower operation problem, but its execution time still grows sharply with the increasing number of hydropower plants, making it difficult to satisfy the efficiency requirement of POHS. To address this problem, a novel efficient method called parallel progressive optimality algorithm (PPOA) is presented in this paper. In PPOA, the complex problem is firstly divided into several two-stage optimization subproblems, and then the classical Fork/Join framework is used to realize parallel computation of subproblems, making a significant improvement on execution efficiency. The simulations in a real-world hydropower system demonstrate that as compared with the standard POA, PPOA can use abundant multi-core resources to reduce execution time while keeping the quality of solution, providing a new alternative to solve the complex hydropower peak operation problem.
Keywords: Hydropower reservoirs | Peak operation | Progressive optimality algorithm | Fork/Join framework | Parallel computing | Curse of dimensionality
مقاله انگلیسی
7 A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing
مدل تحمل نظارت بر گسل چند عامل بر اساس یک خوشه GPU برای پردازش داده های بزرگ-2018
High-performance computing clusters are widely used in large-scale data mining applica tions, and have higher requirements for persistence, stability and real-time use and sre therefore computationally intensive. To support large-scale data processing, we design a multi-factor real-time monitoring fault tolerance (MRMFT) model based on a GPU clus ter. However, the higher clock frequency of GPU chips results in excessively high energy consumption in computing systems. Moreover, the ability to support a long-lasting high temperature operation varies greatly between different GPUs owing to the individual dif ferences between the chips. In this paper, we design a GPU cluster energy consumption monitoring system based on wireless sensor networks (WSNs) and propose an energy con sumption aware checkpointing (ECAC) for high energy consumption problems with the following two advantages: the system sets checkpoints according to actual energy con sumption and the device temperature to improve the utilization of checkpoints and re duce time cost; and it exploits the parallel computing features of CPU and GPU to hide the CPU detection overhead in GPU parallel computation, and further reduce the time and energy consumption overhead in the fault tolerance phase. Using ECAC as the constraint and aiming for a persistent and reliable operation, the dynamic task migration mechanism is designed, and the reliability of the cluster is greatly improved. The theoretical analysis and experiment results show that the model improves the persistence and stability of the computing system while reducing checkpoint overhead.
Keywords: Big data processing ، GPU cluster ، Persistence computing ، Energy consumption ، Fault tolerance ، Energy consumption aware heckpointing ، Task migration
مقاله انگلیسی
8 A Big Data Scale Algorithm for Optimal Scheduling of Integrated Microgrids
الگوریتم مقیاس داده های بزرگ برای زمانبندی بهینه میکرو شبکه های یکپارچه-2018
The capability of switching into the islanded operation mode of microgrids has been advocated as a viable solution to achieve high system reliability. This paper proposes a new model for the microgrids optimal scheduling and load curtailment problem. The proposed problem determines the optimal schedule for local generators of microgrids to minimize the generation cost of the associated distribution system in the normal operation. Moreover, when microgrids have to switch into the islanded operation mode due to reliability considerations, the optimal generation solution still guarantees for the minimal amount of load curtailment. Due to the large number of constraints in both normal and islanded operations, the formulated problem becomes a large-scale optimization problem and is very challenging to solve using the centralized computational method. Therefore, we propose a decomposition algorithm using the alternating direction method of multipliers that provides a parallel computational framework. The simulation results demonstrate the efficiency of our proposed model in reducing generation cost, as well as guaranteeing the reliable operation of microgrids in the islanded mode. We finally describe the detailed implementation of parallel computation for our proposed algorithm to run on a computer cluster using the Hadoop MapReduce software framework.
Index Terms: Alternating direction method of multipliers (ADMM), big data, Hadoop, integrated microgrid, islanded operation, load curtailment, MapReduce
مقاله انگلیسی
9 Parallel algorithms for fitting Markov arrival processes
الگوریتم های موازی برای متناسب سازی فرآیندهای ورود مارکوف-2018
The fitting of Markov arrival processes (MAPs) with the expectation–maximization (EM) algorithm is a computationally demanding task. There are attempts in the literature to reduce the computational complexity by introducing special MAP structures instead of the general representation. Another possibility to improve the efficiency of MAP fitting is to reformulate the inherently serial classical EM algorithm to exploit modern, massively parallel hardware architectures. In this paper we present three different EM-based fitting procedures that can take advantage of the parallel hardware (like Graphics Processing Units, GPUs) and apply a special MAP structure, the Erlang distributed-continuous-time hidden Markov chain (ER-CHMM) structure for reducing the computational complexity. All the proposed parallel algorithms have their strengths: the first one traverses the samples only once per iteration, the second one is memory efficient (far more than the classical serial algorithm), and the third one has exceptionally low execution times. These procedures are compared with the standard serial forward–backward procedure for performance comparison. The new algorithms are orders of magnitudes faster than the standard serial procedure, while (depending on the variant) using less memory.
keywords: Markov arrival process |Traffic model fitting |EM algorithm |Parallel computation |GPU
مقاله انگلیسی
10 Dynamic Adaptation to Environmental Changes of Optical Virtual Networking and Cloud Computing Systems for Tightly Coupling Big Data and Peripheral Computer Resources
سازگاری پویا با تغییرات محیطی شبکه های مجازی نوری و سیستم های محاسبات ابری برای جمع آوری داده های بزرگ و منابع کامپیوتری محیطی-2018
Recently, the use of big data has attracted attention as a profitable business strategy, and is expected to keep increasing in the future. In contrast to existing ways of big data analysis based on centralized computing environment such on a few huge data centers, we advocate a distributed and parallel computation environment aiming at fine-grained cloud computing, which includes so-called edge computing. In the advocated environment, it is assumed that many users, which own big data to be analyzed, dynamically participate in the network to request computer resources and leave after finishing their analyses. In such a dynamic and realistic environment, this paper improves the proximity of computer resources to big data by applying virtualized network in which nodes with each big data and the corresponding computer resources are mutually connected by proper optical paths. Optical path arrangement is periodically updated for new users, without affecting other users currently using computer resources. Moreover, a resource assignment algorithm suitable for such dynamic changes is also proposed to achieve fairness in terms of the network distance between big data and computer resources, and effective load balancing among resource suppliers. We evaluate its effectiveness by computer simulation.
مقاله انگلیسی
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