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Journal articles on the topic 'Operator Execution Algorithms'

Author: Grafiati

Published: 6 September 2023

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1

MARION-POTY, VIRGINIE, and SERGE MIGUET. "DATA ALLOCATION STRATEGIES FOR PARALLEL IMAGE PROCESSING ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 04 (1995): 615–34. http://dx.doi.org/10.1142/s0218001495000237.

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This paper discusses several data allocation strategies used for the parallel implementation of basic imaging operators. It shows that depending on the operator (sequential or parallel, with regular or irregular execution time), the image data must be partitioned in very different manners: The square sub-domains are best adapted for minimizing the communication volume, but rectangles can perform better when we take into account the time for constructing messages. Block allocations are well adapted for inherently parallel operators since they minimize interprocessor interactions, but in the cas

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Guo, Wenyan, Mingfei Wu, Fang Dai, and Yufan Qiang. "Improved Environmental Stimulus and Biological Competition Tactics Interactive Artificial Ecological Optimization Algorithm for Clustering." Biomimetics 8, no. 2 (2023): 242. http://dx.doi.org/10.3390/biomimetics8020242.

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An interactive artificial ecological optimization algorithm (SIAEO) based on environmental stimulus and a competition mechanism was devised to find the solution to a complex calculation, which can often become bogged down in local optimum because of the sequential execution of consumption and decomposition stages in the artificial ecological optimization algorithm. Firstly, the environmental stimulus defined by population diversity makes the population interactively execute the consumption operator and decomposition operator to abate the inhomogeneity of the algorithm. Secondly, the three diff

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Nobre, Tiago, Silvia Regina Vergilio, and Aurora Pozo. "Reducing Interface Mutation Costs with Multiobjective Optimization Algorithms." International Journal of Natural Computing Research 3, no. 3 (2012): 21–40. http://dx.doi.org/10.4018/jncr.2012070102.

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To reduce mutation test costs, different strategies were proposed to find a set of essential operators that generates a reduced number of mutants without decreasing the mutation score. However, the operator selection is influenced by other factors, such as: number of test data, execution time, number of revealed faults, etc. In fact this is a multiobjective problem. For that, different good solutions exist. To properly deal with this problem, a selection strategy based on multiobjective algorithms was proposed and investigated for unit testing. This work explores the use of such strategy in th

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Lougmiri, Zekri. "A New Progressive Method for Computing Skyline Queries." Journal of Information Technology Research 10, no. 3 (2017): 1–21. http://dx.doi.org/10.4018/jitr.2017070101.

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Skyline queries are important in many fields, especially for decision making. In this context, objects or tuples of databases are defined according to some numerical and non numerical attributes. The skyline operator acts on the numerical ones. The algorithms that implements this skyline operator are genrally of progressive or non progressive. The progressive ones return the skyline operator during its execution while non preogressive alogrithms return the result at the end of its execution. This paper presents a new progressive algorithm for computing the skyline points. This algorithm is bas

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Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION." International Journal of Research -GRANTHAALAYAH 6, no. 6 (2018): 346–56. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1379.

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This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the

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Khamprapai, Wanida, Cheng-Fa Tsai, and Paohsi Wang. "Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases." Applied Sciences 10, no. 20 (2020): 7264. http://dx.doi.org/10.3390/app10207264.

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Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study,

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Ebrahimi, Maryam, Parviz Dinari, Mohamad Samaei, Rouhollah Sohrabi, and Soheil Sherafatianfini. "A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty." Discrete Dynamics in Nature and Society 2022 (September 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/2648936.

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Production system design has lots of restrictions and complex assumptions that cause difficulty in decision making. One of the most important of them is the complexity of the relationship between man and machine. In this regard, operator learning is recognized as an effective element in completing tasks in the production system. In this research, a mathematical model for scheduling the parallel machines in terms of job degradation and operator learning is presented. As one of the most important assumptions, the sequence-dependent setup time is of concern. In other words, jobs are processed seq

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García, José, Gino Astorga, and Víctor Yepes. "An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics." Mathematics 9, no. 3 (2021): 225. http://dx.doi.org/10.3390/math9030225.

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The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the

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Fan, Yi, Minghui Cai, Naiqi Li, and Yongmei Liu. "A First-Order Interpreter for Knowledge-Based Golog with Sensing based on Exact Progression and Limited Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 734–42. http://dx.doi.org/10.1609/aaai.v26i1.8230.

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While founded on the situation calculus, current implementations of Golog are mainly based on the closed-world assumption or its dynamic versions or the domain closure assumption. Also, they are almost exclusively based on regression. In this paper, we propose a first-order interpreter for knowledge-based Golog with sensing based on exact progression and limited reasoning. We assume infinitely many unique names and handle first-order disjunctive information in the form of the so-called proper+ KBs. Our implementation is based on the progression and limited reasoning algorithms for proper+ KBs

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GURMAN, Ivan, Pavlo MOTORNYI, Viktor CHESHUN, Andrii DZHULIY, and Vitalii CHORNENKYI. "ALGORITHM OF SYNTHESIS OF DISCRETE TESTS BASED ON EVOLUTIONARY THEORIES." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (2022): 40–43. http://dx.doi.org/10.31891/2307-5732-2022-309-3-40-43.

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The complexity of modern digital components and devices as objects of diagnosis, in most cases, makes it impossible for experts to synthesize tests manually in the time allowed to solve the problem of organizing diagnostic tests. This determines the relevance of the development and implementation of intelligent methods and tools for automated synthesis of test sequences. One of the promising options for the synthesis and optimization of discrete tests is the solution of this problem based on the theory of evolution or genetic algorithms. The paper describes an algorithm for the synthesis of di

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Suzuki, Hideaki. "An Approach to Biological Computation: Unicellular Core-Memory Creatures Evolved Using Genetic Algorithms." Artificial Life 5, no. 4 (1999): 367–86. http://dx.doi.org/10.1162/106454699568827.

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A novel machine language genetic programming system that uses one-dimensional core memories is proposed and simulated. The core is compared to a biochemical reaction space, and in imitation of biological molecules, four types of data words (Membrane, Pure data, Operator, and Instruction) are prepared in the core. A program is represented by a sequence of Instructions. During execution of the core, Instructions are transcribed into corresponding Operators, and Operators modify, create, or transfer Pure data. The core is hierarchically partitioned into sections by the Membrane data, and the data

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Abdel-Basset, Mohamed, Reda Mohamed, Waleed Abd Abd Elkhalik, Marwa Sharawi, and Karam M. Sallam. "Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution." Mathematics 10, no. 21 (2022): 4049. http://dx.doi.org/10.3390/math10214049.

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Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms s

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Chen, Yingguo, Ming Chen, Jun Wen, Yingwu Chen, and Wen Xiang. "An Adaptive Large Neighborhood Search Algorithm for the Satellite Data Transmission Scheduling Problem." International Journal of Aerospace Engineering 2020 (September 16, 2020): 1–12. http://dx.doi.org/10.1155/2020/5243749.

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Satellite establishes a satellite ground station to satellite link with a ground station to complete data transmission. However, in China, satellite ground stations only exist within the country. With the rapid increase in the number of satellites, effectively dispatching satellite communications and maximizing the performance of ground stations are necessary. We propose a heuristic adaptive large neighborhood search algorithm (H-ALNS) to solve the satellite data transmission scheduling (SDTS) problem. The algorithm includes two heuristic rules for generating the initial scheme and the conflic

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Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Yan Zeng Shu-Wen Yao, Ke Zhao Yan Zeng, Yan-An Guo Ke Zhao, and Li Li Yan-An Guo. "Research of the Service Composition Optimization Model Based on Petri Net." 電腦學刊 32, no. 5 (2021): 101–14. http://dx.doi.org/10.53106/199115992021103205008.

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Cloud service systems bring together a wide variety of flexible and scalable mass services. The service combination scheme which can meet the user&rsquo;s needs is provided to the user through the flexible and changing service mode of service combination technology. However, service combination failures often occur. In view of the above problems, this paper establishes the cloud service combination optimization model. Firstly, it is proposed that the Service Combination Optimization Petri Net models and analyzes the service selection and combination. In order to avoid local convergence, co

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García, José, Paola Moraga, Broderick Crawford, Ricardo Soto, and Hernan Pinto. "Binarization Technique Comparisons of Swarm Intelligence Algorithm: An Application to the Multi-Demand Multidimensional Knapsack Problem." Mathematics 10, no. 17 (2022): 3183. http://dx.doi.org/10.3390/math10173183.

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In order to minimize execution times, improve the quality of solutions, and address more extensive target situations, optimization techniques, particularly metaheuristics, are continually improved. Hybridizing procedures are one of these noteworthy strategies due to their wide range of applications. This article describes a hybrid algorithm that combines the k-means method to produce a binary version of the cuckoo search and sine cosine algorithms. The binary algorithms are applied on the NP-hard multi-demand multidimensional knapsack problem. This problem is of particular interest because it

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Agnes Shifani, S., G. Ramkumar, V. Nanammal, and R. Thandaiah Prabu. "Exploration of Morphological Procedure on the Recognition of Fundus Image." Journal of Computational and Theoretical Nanoscience 17, no. 5 (2020): 2014–20. http://dx.doi.org/10.1166/jctn.2020.8841.

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A gainful fuzzy k-means clustering algorithm under Morphological Image Processing (MIP) is performed. Image processing is one of quickly developing examination territory nowadays and now it is particularly coordinated with all identified with science field. Image Processing can be utilized for breaking down various restorative and MRI Image to get the uncommon and anomaly in the image. Image segmentation manages segmentation of vein segmentation algorithm utilizing fundus Image. In this task, this segmentation is done utilizing k-means clustering and c-means clustering algorithm and Morphologi

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Adamski, Mirosław, Andrzej Żyluk, and Marcin Chodnicki. "Analysis of Methods and Control Systems of Unmanned Platforms." Journal of KONBiN 51, no. 4 (2021): 143–56. http://dx.doi.org/10.2478/jok-2021-0048.

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Abstract The key aspect affecting the safety of routing and the unmanned platform mission execution is the autonomy of control systems. To achieve the mission goal, control algorithms supported by advanced sensors have to estimate the obstacle location. Moreover, it is needed to identify potential obstacles, as well as algorithms for trajectory planning in two or three dimensions space. The use of these algorithms allows to create an intelligent object that performs tasks in difficult conditions in which communication between the platform and the operator is constricted. The article mainly foc

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Li, Xiang, Nuozhou Sun, Yunqian Luo, and Mingyu Gao. "SODA: A Set of Fast Oblivious Algorithms in Distributed Secure Data Analytics." Proceedings of the VLDB Endowment 16, no. 7 (2023): 1671–84. http://dx.doi.org/10.14778/3587136.3587142.

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Cloud systems are now a prevalent platform to host large-scale big-data analytics applications such as machine learning and relational database. However, data privacy remains as a critical concern for public cloud systems. Existing trusted hardware could provide an isolated execution domain on an untrusted platform, but also suffers from access-pattern-based side channels at various levels including memory, disks, and networking. Oblivious algorithms can address these vulnerabilities by hiding the program data access patterns. Unfortunately, current oblivious algorithms for data analytics are

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García, José, José Lemus-Romani, Francisco Altimiras, et al. "A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem." Mathematics 9, no. 20 (2021): 2611. http://dx.doi.org/10.3390/math9202611.

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Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This proble

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Иванова, Е. В., and Л. Б. Соколинский. "Using Intel Xeon Phi coprocessors for execution of natural join on compressed data." Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie), no. 4 (December 18, 2015): 534–42. http://dx.doi.org/10.26089/nummet.v16r450.

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В статье описывается сопроцессор баз данных для высокопроизводительных кластерных вычислительных систем с многоядерными ускорителями, использующий распределенные колоночные индексы с интервальной фрагментацией. Работа сопроцессора рассматривается на примере выполнения операции естественного соединения. Параллельная декомпозиция естественного соединения выполняется на основе использования распределенных колоночных индексов. Предложенный подход позволяет выполнять реляционные операции на кластерных вычислительных системах без массовых обменов данными. Приводятся результаты вычислительных экспери

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Sundaram, Ramakrishnan, Ravichandran KS, Premaladha Jayaraman, and Venkatraman B. "Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach." Mathematics 7, no. 2 (2019): 169. http://dx.doi.org/10.3390/math7020169.

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A hybrid segmentation algorithm is proposed is this paper to extract the blood vesselsfrom the fundus image of retina. Fundus camera captures the posterior surface of the eye and thecaptured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinalhaemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysisof vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the fieldof ophthalmology. It is derived from the literature review that no unique segmentation algorithm issuitable for image

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Sosimi, Adeyanju, Folorunso Oladimeji Ogunwolu, and Taoreed Adegbola. "A Makespan Optimization Scheme for NP-Hard Gari Processing Job Scheduling Using Improved Genetic Algorithm." Journal of Industrial Engineering 2014 (April 6, 2014): 1–10. http://dx.doi.org/10.1155/2014/628640.

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An optimization scheme for minimizing makespan of Gari processing jobs using improved initial population Genetic Algorithm (GA) is proposed. GA with initial population improved by using job sequencing and dispatching rules of First Come First Served (FCFS), Shortest Processing Time (SPT), Longest Processing Time (LPT), and Modified Johnson’s Algorithm for m-machines in order to obtain better schedules than is affordable by GA with freely generated initial population and by individual traditional sequencing and dispatching rules was used. The traditional GA crossover and mutation operators as w

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Guerrero, José L., Antonio Berlanga, and José M. Molina. "A Guided Mutation Operator for Dynamic Diversity Enhancement in Evolutionary Strategies." International Journal of Natural Computing Research 4, no. 2 (2014): 20–39. http://dx.doi.org/10.4018/ijncr.2014040102.

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Diversity in evolutionary algorithms is a critical issue related to the performance obtained during the search process and strongly linked to convergence issues. The lack of the required diversity has been traditionally linked to problematic situations such as early stopping in the presence of local optima (usually faced when the number of individuals in the population is insufficient to deal with the search space). Current proposal introduces a guided mutation operator to cope with these diversity issues, introducing tracking mechanisms of the search space in order to feed the required inform

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Xing, Lining, Rui Wu, Jiaxing Chen, and Jun Li. "Knowledge-Based Evolutionary Optimizing Makespan and Cost for Cloud Workflows." Mathematics 11, no. 1 (2022): 38. http://dx.doi.org/10.3390/math11010038.

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Workflow scheduling is essential to simultaneously optimize the makespan and economic cost for cloud services and has attracted intensive interest. Most of the existing multi-objective cloud workflow scheduling algorithms regard the focused problems as black-boxes and design evolutionary operators to perform random searches, which are inefficient in dealing with the elasticity and heterogeneity of cloud resources as well as complex workflow structures. This study explores the characteristics of cloud resources and workflow structures to design a knowledge-based evolutionary optimization operat

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Li, Hongbo, Zhe Xu, and Wenchao Wei. "Bi-Objective Scheduling Optimization for Discrete Time/Cost Trade-Off in Projects." Sustainability 10, no. 8 (2018): 2802. http://dx.doi.org/10.3390/su10082802.

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In sustainable project management, time and cost are two critical factors affecting the success of a project. Time/cost trade-offs in projects accelerate the execution of some activities by increasing the amount of non-renewable resources committed to them and therefore shorten the project duration. The discrete time/cost trade-off problem (DTCTP) has been extensively studied during the past 20 years. However, due to its complexity, the DTCTP—especially the DTCTP curve problem (DTCTP-C)—has only been solved for relatively small instances. To the best of our knowledge, there is no computational

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Salman, Ayed A., Kishan Mehrotra, and Chilukuri K. Mohan. "Adaptive Linkage Crossover." Evolutionary Computation 8, no. 3 (2000): 341–70. http://dx.doi.org/10.1162/106365600750078817.

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Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will more quickly lead a genetic algorithm (GA) towards better results. In many problems, crucial knowledge is not found in individual components, but in the interrelations between those components. For such problems, we develop an interrelation (linkage) based crossover operator that has the advantage of liberating GAs from the constraints imposed by the fixed representations generall

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Liu, Chunsheng, and Chunping Ren. "Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator." Journal of Electrical and Computer Engineering 2019 (May 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/9594301.

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Aiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted to reconstruct the input coal-rock infrared images, so as to reduce the noise interference, and then, the reconstructed image is transformed by gray level. Finally, we consider the frequency characteristics and long memory properties of fractional differential, the classical first-order Sobel and se

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Zuhtuogullari, Kursat, Novruz Allahverdi, and Nihat Arikan. "A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems." Computational and Mathematical Methods in Medicine 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/587564.

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The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crosso

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Ramirez-Atencia, Cristian, and David Camacho. "Extending QGroundControl for Automated Mission Planning of UAVs." Sensors 18, no. 7 (2018): 2339. http://dx.doi.org/10.3390/s18072339.

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Unmanned Aerial Vehicles (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In

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Minnetti, Elisa, Paolo Chiariotti, Nicola Paone, et al. "A Smartphone Integrated Hand-Held Gap and Flush Measurement System for in Line Quality Control of Car Body Assembly." Sensors 20, no. 11 (2020): 3300. http://dx.doi.org/10.3390/s20113300.

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This paper presents the design and the characterization of a portable laser triangulation measurement system for measuring gap and flush in the car body assembly process. Targeting Human in the Loop (HILT) operations in the manufacturing sector, and in line with the vision of human empowerment with Industry 4.0 technologies, the instrument embeds features to ease operators’ activity and compensate possible misuse that could affect the robustness and the quality of data acquired. The device is based on a smartphone integrated with a miniaturized laser triangulation system installed in a cover.

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Piskunov, M. A., and D. V. Adamov. "Kinematic analysis of connection of hydraulic cylinders of a forwarder crane under execution of defined motion of grapple." Traktory i sel hozmashiny 83, no. 4 (2016): 42–47. http://dx.doi.org/10.17816/0321-4443-66153.

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The article deals with the kinematic analysis of forwarder crane; it determines the parameters of extension of hydraulic-cylinder rods for execution of defined trajectory of grapple motion. The current systems of forwarder crane control do not allow an operator to realize optimal trajectories and speed ability of motion of crane links. This disadvantage can be partly corrected, for example, with the use of automatic control of crane motion in certain phases of technological cycle. The analysis of matched connection of hydraulic cylinders of a typical forwarder crane is presented in the article

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Mohiuddin, Khalid, Mohamed Nadhmi Miladi, Mohiuddin Ali Khan, et al. "Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency." Wireless Communications and Mobile Computing 2022 (September 5, 2022): 1–17. http://dx.doi.org/10.1155/2022/6151168.

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Mobile learning (m-learning) adoption has increased and shall be demonstrated superior performance by implementing related computing paradigms, such as IoT, edge, mobile edge, fog, AI, and 5G. Mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations while executing real-time applications. This study investigates the state-of-the-art m-learning architectures, determines a layered m-learning-MCA obtaining numerous benefits of related computing paradigms, and expands m-learning functional structure. It evaluates m-learning performance across the four physical

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Smirnova, Ekaterina, and Boris Spassky. "Organization of supervisory control in scenarios of extreme robotics." Robotics and Technical Cybernetics 8, no. 4 (2020): 245–58. http://dx.doi.org/10.31776/rtcj.8401.

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The article describes the general principles of supervisory control organization in extreme robotics scenarios with different levels of autonomy. It is shown that increasing the level of autonomy of the robot allows reducing the operator’s load due to the division of labor between man and machine and to the implementation of the supervisory control mode, the essence of which is the decomposition of the mission into scenarios, which describe algorithms for solving functionally completed tasks and consists of sequence of simple technological operations. Such scenarios are executed automatically.

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Khosravi, Faramarz, Alexander Rass, and Jürgen Teich. "Efficient Computation of Probabilistic Dominance in Multi-objective Optimization." ACM Transactions on Evolutionary Learning and Optimization 1, no. 4 (2021): 1–26. http://dx.doi.org/10.1145/3469801.

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Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach

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Lee, Jongseok, Ribin Balachandran, Konstantin Kondak, et al. "Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities." Field Robotics 3, no. 1 (2023): 323–67. http://dx.doi.org/10.55417/fr.2023010.

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This paper presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot’s workspace as well as a haptic guidance to its remotely located operator. To realize this, multiple sensors, namely, a LiDAR, cameras, and IMUs are utilized. For processing of the acquired sensory data, pose estimation pipelines are devised for industrial objects of both known and unknown geometries. We further propose an activ

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Sun, Xue, Ping Chou, Chao-Chin Wu, and Liang-Rui Chen. "Quality-Oriented Study on Mapping Island Model Genetic Algorithm onto CUDA GPU." Symmetry 11, no. 3 (2019): 318. http://dx.doi.org/10.3390/sym11030318.

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Genetic algorithm (GA), a global search method, has widespread applications in various fields. One very promising variant model of GA is the island model GA (IMGA) that introduces the key idea of migration to explore a wider search space. Migration will exchange chromosomes between islands, resulting in better-quality solutions. However, IMGA takes a long time to solve the large-scale NP-hard problems. In order to shorten the computation time, modern graphic process unit (GPU), as highly-parallel architecture, has been widely adopted in order to accelerate the execution of NP-hard algorithms.

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Salhi, Dhai Eddine, Abelkamel Tari, and Mohand Tahar Kechadi. "Using E-Reputation for Sentiment Analysis." International Journal of Cloud Applications and Computing 11, no. 2 (2021): 32–47. http://dx.doi.org/10.4018/ijcac.2021040103.

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In a competitive world, companies are looking to gain a positive reputation through these clients. Electronic reputation is part of this reputation mainly in social networks, where everyone is free to express their opinion. Sentiment analysis of the data collected in these networks is very necessary to identify and know the reputation of a companies. This paper focused on one type of data, Twits on Twitter, where the authors analyzed them for the company Djezzy (mobile operator in Algeria), to know their satisfaction. The study is divided into two parts: The first part was the pre-processing p

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Mu`ñoz, Daniel M., Diego F. Sanchez, Carlos H. Llanos, and Mauricio Ayala-Rincón. "Tradeoff of FPGA Design of a Floating-point Library for Arithmetic Operators." Journal of Integrated Circuits and Systems 5, no. 1 (2010): 42–52. http://dx.doi.org/10.29292/jics.v5i1.309.

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Many scientific and engineering applications require to perform a large number of arithmetic operations that must be computed in an efficient manner using a high precision and a large dynamic range. Commonly, these applications are implemented on personal computers taking advantage of the floating-point arithmetic to perform the computations and high operational frequencies. However, most common software architectures execute the instructions in a sequential way due to the von Neumann model and, consequently, several delays are introduced in the data transfer between the program memory and the

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Zapridou, Eleni, Ioannis Mytilinis, and Anastasia Ailamaki. "Dalton." Proceedings of the VLDB Endowment 16, no. 3 (2022): 491–504. http://dx.doi.org/10.14778/3570690.3570699.

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To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability Deciding on a static partitioning for a given set of "hot" keys is not sufficient as these keys are not known in advance, and even worse, the data distribution can change unpredictably. Existing algorithms either optimize for a specific distribution or, in order to adapt, assume a centralized partitioner that processes every incoming tuple and observes the whole workload. However,

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Koutsoukos, Dimitrios, Ingo Müller, Renato Marroquín, Ana Klimovic, and Gustavo Alonso. "Modularis." Proceedings of the VLDB Endowment 14, no. 13 (2021): 3308–21. http://dx.doi.org/10.14778/3484224.3484229.

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The enormous quantity of data produced every day together with advances in data analytics has led to a proliferation of data management and analysis systems. Typically, these systems are built around highly specialized monolithic operators optimized for the underlying hardware. While effective in the short term, such an approach makes the operators cumbersome to port and adapt, which is increasingly required due to the speed at which algorithms and hardware evolve. To address this limitation, we present Modularis , an execution layer for data analytics based on sub-operators , i.e., composable

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Moroz, Serhii, Anatolii Tkachuk, Mykola Khvyshchun, Stanislav Prystupa, and Mykola Yevsiuk. "METHODS FOR ENSURING DATA SECURITY IN MOBILE STANDARDS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 12, no. 1 (2022): 4–9. http://dx.doi.org/10.35784/iapgos.2877.

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The analysis of mobile communication standards is carried out, the functional structure and interfaces of interaction between the structural elements of the cellular network are considered. To understand the principle of communication according to the GSM standard, a block diagram of a mobile switching center (MSC), base station equipment (BSS), control and service center (MCC), mobile stations (MS) is presented. The main algorithms for ensuring the confidentiality and security of mobile subscribers' data, in different types of standards, as well as the vulnerabilities of information flows are

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P., Kiran, and Parameshachari B. D. "Resource-Optimized Selective Image Encryption of Medical Images Using Multiple Chaotic Systems." International Journal of e-Collaboration 18, no. 1 (2022): 1–20. http://dx.doi.org/10.4018/ijec.304379.

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Securing medical images becomes a major concern, to avoid leaking the confidential data. This problem motivated to develop many low computational complexity methods to encrypt these medical images. In this research work, Block Cipher based Region of interest medical image encryption with multiple maps is proposed. Primarily, Region of Interest (ROI) regions and Region of Background (ROB) are extracted with the help of Laplacian edge detection operator. Further important ROI regions are permuted in a circular fashion with the help of Arnold cat map and angle value. Then permuted ROI part is enc

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Barry, Abdoul, Wantao Li, Juan A. Becerra, and Pere L. Gilabert. "Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization." Sensors 21, no. 17 (2021): 5772. http://dx.doi.org/10.3390/s21175772.

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The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most

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Vasilev, Vladimir S., and Alexander I. Legalov. "Loop-invariant Optimization in the Pifagor Language." Modeling and Analysis of Information Systems 25, no. 4 (2018): 347–57. http://dx.doi.org/10.18255/1818-1015-2018-4-347-357.

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The paper considers methods of program transformation equivalent to optimizing the cycle invariant, applied to the functional data-flow model implemented in the Pifagor programming language. Optimization of the cycle invariant in imperative programming languages is reduced to a displacement from the cycle of computations that do not depend on variables that are changes in the loop. A feature of the functional data flow parallel programming language Pifagor is the absence of explicitly specified cyclic computations (the loop operator). However, recurring calculations in this language can be spe

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Andrabi, Umer Mukhtar, M. G. Kanishcheva, and S. N. Stepanov. "OBSERVATION SYSTEM RESOURCE PLANNING IN PRESENCE OF ACCESS CONTROL BASED ON VOLUME OF RESOURCE OCCUPIED BY TRAFFIC FLOWS." T-Comm 16, no. 8 (2022): 54–62. http://dx.doi.org/10.36724/2072-8735-2022-16-8-54-62.

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Due to various recent improvements in cellular technology, communication networks now offer information flows with a wide range of transmission speed and service quality characteristics. These characteristics are more highly noticeable in intelligent networks, particularly when IoT technology is used. Regulation and high-quality transmission of such massive volumes of data present severe issues among virtual operators engaged in the communications sector and utilizing cellular network infrastructure to provide video surveillance services for its consumers. Unfortunately, no single approach exi

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Selvarani, P., and N. Malarvizhi. "Secure data in cloud with multimodal key generation." International Journal of Engineering & Technology 7, no. 1.7 (2018): 27. http://dx.doi.org/10.14419/ijet.v7i1.7.9382.

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Data Security is the Major problem in Cloud Computing. In order to overcome the data security problem the proposed technique utilizes effective data storage using biometric-based cryptographic authentication to support the user authentication for the cloud environment. For user authentication here we are considering iris and fingerprint. Initially the feature values are extracted from the iris and fingerprint using local binary pattern and Minutiae extraction respectively. Local binary pattern operator works with the eight neighbors of a pixel, using the value of this center pixel as a thresho

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Tavarageri, Sanket, Alexander Heinecke, Sasikanth Avancha, Bharat Kaul, Gagandeep Goyal, and Ramakrishna Upadrasta. "PolyDL." ACM Transactions on Architecture and Code Optimization 18, no. 1 (2021): 1–27. http://dx.doi.org/10.1145/3433103.

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Deep Neural Networks (DNNs) have revolutionized many aspects of our lives. The use of DNNs is becoming ubiquitous, including in software for image recognition, speech recognition, speech synthesis, language translation, to name a few. The training of DNN architectures, however, is computationally expensive. Once the model is created, its use in the intended application—the inference task, is computationally heavy too and the inference needs to be fast for real time use. For obtaining high performance today, the code of Deep Learning (DL) primitives optimized for specific architectures by exper

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Triantafyllis, Nikolaos, Ioannis E. Venetis, Ioannis Fountoulakis, Erion-Vasilis Pikoulis, Efthimios Sokos, and Christos P. Evangelidis. "Gisola: A High-Performance Computing Application for Real-Time Moment Tensor Inversion." Seismological Research Letters 93, no. 2A (2021): 957–66. http://dx.doi.org/10.1785/0220210153.

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Abstract Automatic moment tensor (MT) determination is essential for real-time seismological applications. In this article, Gisola, a highly evolved software for MT determination, oriented toward high-performance computing, is presented. The program employs enhanced algorithms for waveform data selection via quality metrics, such as signal-to-noise ratio, waveform clipping, data and metadata inconsistency, long-period disturbances, and station evaluation based on power spectral density measurements in parallel execution. The inversion code, derived from ISOLated Asperities—an extensively used

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Yang, Weiwei, Jing Yang, and Haifeng Song. "The Improved SIFT Algorithm Based on Rectangular Operator and Its Parallel Implementation." Journal of Information Technology Research 12, no. 1 (2019): 1–17. http://dx.doi.org/10.4018/jitr.2019010101.

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The SIFT algorithm is one of the most widely used algorithm which bases on local feature extraction. But it could not meet the requirement of the real-time application due to the high time complexity and low execution efficiency. In order to improve these drawback, the authors optimized the SIFT algorithm by using the Gaussian convolution scale of adaptive scale space. The authors also provided the executive process of the improved SIFT algorithm on the MapReduce programming model and compared its performance in terms of the stand-alone and cluster environment. The experiment result showed tha

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Bannour, Ahmed, Ahmed Harbaoui, and Fawaz Alsolami. "Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks." Electronics 10, no. 22 (2021): 2750. http://dx.doi.org/10.3390/electronics10222750.

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The Global Positioning System (GPS) is not the only way to solve connected objects’ geo-localization problems; it is also possible to use the mobile network infrastructure to geo-locate objects connected to the network, using antennas and signals designed for voice and data transfer, such as the 5th generation network. 5G is considered as a least expensive solution because there is no specific equipment to set up. As long as the object is in an area covered by the network, it connects to the nearest 5G Micro-Cell (MC). Through exchange of signals with the MC node we can locate the object. Curr

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