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479 Results Found

  • Article
  • Open Access
6 Citations
3,091 Views
32 Pages

11 November 2020

This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy...

  • Article
  • Open Access
2 Citations
1,677 Views
26 Pages

A Massively Parallel SMC Sampler for Decision Trees

  • Efthyvoulos Drousiotis,
  • Alessandro Varsi,
  • Alexander M. Phillips,
  • Simon Maskell and
  • Paul G. Spirakis

2 January 2025

Bayesian approaches to decision trees (DTs) using Markov Chain Monte Carlo (MCMC) samplers have recently demonstrated state-of-the-art accuracy performance when it comes to training DTs to solve classification problems. Despite the competitive classi...

  • Article
  • Open Access
1 Citations
556 Views
20 Pages

Hybrid Drive Simulation Architecture for Power Distribution Based on the Federated Evolutionary Monte Carlo Algorithm

  • Dongli Jia,
  • Xiaoyu Yang,
  • Wanxing Sheng,
  • Keyan Liu,
  • Tingyan Jin,
  • Xiaoming Li and
  • Weijie Dong

24 October 2025

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This...

  • Article
  • Open Access
13 Citations
5,322 Views
21 Pages

26 November 2021

Resampling is a well-known statistical algorithm that is commonly applied in the context of Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian dynamic models. As the models become more complex and accurate, the ru...

  • Article
  • Open Access
1 Citations
1,989 Views
24 Pages

12 November 2024

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capac...

  • Article
  • Open Access
2 Citations
3,068 Views
23 Pages

17 July 2023

The LU factorization of very large sparse matrices requires a significant amount of computing resources, including memory and broadband communication. A hybrid MPI + OpenMP + CUDA algorithm named SuperLU3D can efficiently compute the LU factorization...

  • Article
  • Open Access
7 Citations
2,640 Views
33 Pages

19 June 2023

In this work, we introduce a scalable and efficient GPU-accelerated methodology for volumetric particle advection and finite-time Lyapunov exponent (FTLE) calculation, focusing on the analysis of Lagrangian coherent structures (LCS) in large-scale di...

  • Review
  • Open Access
18 Citations
4,236 Views
23 Pages

Acoustic Emissions during Structural Changes in Shape Memory Alloys

  • Dezső László Beke,
  • Lajos Daróczi,
  • László Zoltán Tóth,
  • Melinda Kalmárné Bolgár,
  • Nora Mohareb Samy and
  • Anikó Hudák

9 January 2019

Structural changes (martensitic transformation, rearrangements of martensitic variants) in shape memory alloys have an intermittent character that is accompanied by the emission of different (thermal, acoustic, and magnetic) noises, which are fingerp...

  • Article
  • Open Access
11 Citations
5,384 Views
18 Pages

Parallel Improvements of the Jaya Optimization Algorithm

  • Héctor Migallón,
  • Antonio Jimeno-Morenilla and
  • Jose-Luis Sanchez-Romero

18 May 2018

A wide range of applications use optimization algorithms to find an optimal value, often a minimum one, for a given function. Depending on the application, both the optimization algorithm’s behavior, and its computational time, can prove to be...

  • Feature Paper
  • Article
  • Open Access
9 Citations
2,667 Views
21 Pages

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

  • Alejandro García-Monzó,
  • Héctor Migallón,
  • Antonio Jimeno-Morenilla,
  • José-Luis Sánchez-Romero,
  • Héctor Rico and
  • Ravipudi Venkata Rao

A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuris...

  • Article
  • Open Access
1 Citations
710 Views
19 Pages

17 August 2025

Distributed permutation flowshop scheduling is an NP-hard problem that has become a hot research topic in the fields of optimization and manufacturing in recent years. Multimodal optimization finds multiple global and local optimal solutions of a fun...

  • Article
  • Open Access
7 Citations
2,594 Views
14 Pages

A Parallel Multiobjective PSO Weighted Average Clustering Algorithm Based on Apache Spark

  • Huidong Ling,
  • Xinmu Zhu,
  • Tao Zhu,
  • Mingxing Nie,
  • Zhenghai Liu and
  • Zhenyu Liu

31 January 2023

Multiobjective clustering algorithm using particle swarm optimization has been applied successfully in some applications. However, existing algorithms are implemented on a single machine and cannot be directly parallelized on a cluster, which makes i...

  • Article
  • Open Access
12 Citations
4,432 Views
13 Pages

Privacy amplification is an indispensable procedure for key generation in the quantum key distribution system and the physical layer key distribution system. In this paper, we propose a high-speed privacy amplification algorithm that saves hardware m...

  • Article
  • Open Access
2 Citations
1,709 Views
17 Pages

30 November 2022

Large-scale inverse problems that require high-performance computing arise in various fields, including regional air quality studies. The paper focuses on parallel solutions of an emission source identification problem for a 2D advection–diffus...

  • Feature Paper
  • Article
  • Open Access
5 Citations
2,067 Views
16 Pages

2 December 2022

Public goods games have been extensively studied to determine the mechanism behind cooperation in social dilemmas. Previous public goods games based on particle swarm algorithms enabled individuals to integrate their past best strategies with the cur...

  • Article
  • Open Access
55 Citations
5,642 Views
22 Pages

1 June 2020

The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the p...

  • Article
  • Open Access
5 Citations
4,240 Views
31 Pages

Efficient Group K Nearest-Neighbor Spatial Query Processing in Apache Spark

  • Panagiotis Moutafis,
  • George Mavrommatis,
  • Michael Vassilakopoulos and
  • Antonio Corral

Aiming at the problem of spatial query processing in distributed computing systems, the design and implementation of new distributed spatial query algorithms is a current challenge. Apache Spark is a memory-based framework suitable for real-time and...

  • Article
  • Open Access
12 Citations
3,629 Views
26 Pages

The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and mat...

  • Article
  • Open Access
1 Citations
1,839 Views
11 Pages

Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms

  • Hasan Raza,
  • Ishtiaq Ahmad,
  • Noor M. Khan,
  • Waseem Abbasi,
  • Muhammad Shahid Anwar,
  • Sadique Ahmad and
  • Mohammed A. El-Affendi

5 December 2022

The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed...

  • Article
  • Open Access
1,717 Views
30 Pages

10 October 2023

Cloudlet networks are an emerging distributed data processing paradigm, which contain multiple cloudlets deployed beside base stations to serve local user devices (UDs). Each cloudlet is a small data center with limited memory, in which multiple virt...

  • Article
  • Open Access
1,759 Views
24 Pages

Memory Management Strategies for Software Quantum Simulators

  • Gilberto Díaz,
  • Luiz Steffenel,
  • Carlos Barrios and
  • Jean Couturier

9 September 2025

Software quantum simulators are essential tools for designing and testing quantum algorithms on classical computing architectures, especially given the current limitations of physical quantum hardware. This work focuses on studying and evaluating mem...

  • Article
  • Open Access
5 Citations
2,877 Views
19 Pages

Modeling and Analysis of Dekker-Based Mutual Exclusion Algorithms

  • Libero Nigro,
  • Franco Cicirelli and
  • Francesco Pupo

Mutual exclusion is a fundamental problem in concurrent/parallel/distributed systems. The first pure-software solution to this problem for two processes, which is not based on hardware instructions like test-and-set, was proposed in 1965 by Th.J. Dek...

  • Article
  • Open Access
1 Citations
970 Views
20 Pages

9 February 2025

Balancing efficiency and accuracy is often challenging in the numerical solution of three-dimensional (3D) point source acoustic wave equations for layered media. To overcome this, an efficient solution method in the spatial-wavenumber domain is prop...

  • Article
  • Open Access
4 Citations
1,214 Views
26 Pages

1 December 2024

Inthis paper, a distributed semi-supervised partial multi-label learning (dS2PML) algorithm is proposed, which can be used to address the problem of distributed classification of partially multi-labeled data and unlabeled data. In this algorithm, we...

  • Article
  • Open Access
2 Citations
1,182 Views
17 Pages

5 January 2025

Online learning is a framework for processing and learning from sequential data in real time, offering benefits such as promptness and low memory usage. However, it faces critical challenges, including concept drift, where data distributions evolve o...

  • Article
  • Open Access
11 Citations
2,700 Views
16 Pages

A Complex Model via Phase-Type Distributions to Study Random Telegraph Noise in Resistive Memories

  • Juan E. Ruiz-Castro,
  • Christian Acal,
  • Ana M. Aguilera and
  • Juan B. Roldán

16 February 2021

A new stochastic process was developed by considering the internal performance of macro-states in which the sojourn time in each one is phase-type distributed depending on time. The stationary distribution was calculated through matrix-algorithmic me...

  • Article
  • Open Access
9 Citations
2,627 Views
15 Pages

24 January 2023

This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, Inclán and Tiao’s iterated cumulativ...

  • Article
  • Open Access
9 Citations
6,289 Views
29 Pages

Algorithms and Data Structures for Sparse Polynomial Arithmetic

  • Mohammadali Asadi,
  • Alexander Brandt,
  • Robert H. C. Moir and
  • Marc Moreno Maza

We provide a comprehensive presentation of algorithms, data structures, and implementation techniques for high-performance sparse multivariate polynomial arithmetic over the integers and rational numbers as implemented in the freely available Basic P...

  • Article
  • Open Access
1,529 Views
21 Pages

Packet classification is a core function of network devices for providing advanced services, with the key challenge being to optimize classification speed while maintaining low memory usage. So far, many have proposed software-based packet classifica...

  • Article
  • Open Access
8 Citations
6,227 Views
12 Pages

JAMPI: Efficient Matrix Multiplication in Spark Using Barrier Execution Mode

  • Tamas Foldi,
  • Chris von Csefalvay and
  • Nicolas A. Perez

The new barrier mode in Apache Spark allows for embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage does not depend on any other tasks in the same stage, and hence i...

  • Review
  • Open Access
7 Citations
2,127 Views
21 Pages

Review of Nonlocal-in-Time Damping Models in the Dynamics of Structures

  • Vladimir Sidorov,
  • Marina Shitikova,
  • Elena Badina and
  • Elena Detina

10 July 2023

In the present paper, the nonlocal-in-time damping models, called “damping-with-memory” models, are reviewed. Since such models do not involve the distribution along the longitudinal coordinate, they are easily adjustable for the FEM (Fin...

  • Article
  • Open Access
2,416 Views
14 Pages

Towards Building a Distributed Virtual Flow Meter via Compressed Continual Learning

  • Hasan Asy’ari Arief,
  • Peter James Thomas,
  • Kevin Constable and
  • Aggelos K. Katsaggelos

15 December 2022

A robust–accurate estimation of fluid flow is the main building block of a distributed virtual flow meter. Unfortunately, a big leap in algorithm development would be required for this objective to come to fruition, mainly due to the inability...

  • Feature Paper
  • Article
  • Open Access
5 Citations
2,575 Views
18 Pages

To plan the work of power generation equipment, it is necessary to ensure that the power supply is sufficient and to achieve the minimum cost to ensure the safety and economy of the microgrid. Based on back propagation neural network–local mean...

  • Article
  • Open Access
1 Citations
2,144 Views
23 Pages

Relieving Compression-Induced Local Wear on Non-Volatile Memory Block via Sliding Writes

  • Kailun Jin,
  • Yajuan Du,
  • Mingzhe Zhang,
  • Zhenghao Yin and
  • Rachata Ausavarungnirun

27 February 2023

Due to its non-volatility and large capacity, NVM devices gradually take place at various levels of memories. However, their limited endurance is still a big concern for large-scale data centres. Compression algorithms have been used to save NVM spac...

  • Article
  • Open Access
9 Citations
3,249 Views
15 Pages

A Low-Power RRAM Memory Block for Embedded, Multi-Level Weight and Bias Storage in Artificial Neural Networks

  • Stefan Pechmann,
  • Timo Mai,
  • Julian Potschka,
  • Daniel Reiser,
  • Peter Reichel,
  • Marco Breiling,
  • Marc Reichenbach and
  • Amelie Hagelauer

20 October 2021

Pattern recognition as a computing task is very well suited for machine learning algorithms utilizing artificial neural networks (ANNs). Computing systems using ANNs usually require some sort of data storage to store the weights and bias values for t...

  • Article
  • Open Access
4 Citations
3,096 Views
22 Pages

Recommendation Model Based on a Heterogeneous Personalized Spacey Embedding Method

  • Qunsheng Ruan,
  • Yiru Zhang,
  • Yuhui Zheng,
  • Yingdong Wang,
  • Qingfeng Wu,
  • Tianqi Ma and
  • Xiling Liu

8 February 2021

The traditional heterogeneous embedding method based on a random walk strategy does not focus on the random walk fundamentally because of higher-order Markov chains. One of the important properties of Markov chains is stationary distributions (SDs)....

  • Article
  • Open Access
2 Citations
1,672 Views
20 Pages

19 July 2023

Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficienc...

  • Article
  • Open Access
8 Citations
2,173 Views
15 Pages

13 September 2023

The 3D inversion algorithm for gravity data based on a smooth model constraint has been proven to yield a reasonable density distribution. However, as the amount of observed data and model parameters increases, the algorithm experiences issues with h...

  • Article
  • Open Access
49 Citations
7,879 Views
18 Pages

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

  • Ziyang He,
  • Xiaoqing Zhang,
  • Yangjie Cao,
  • Zhi Liu,
  • Bo Zhang and
  • Xiaoyan Wang

17 April 2018

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algo...

  • Article
  • Open Access
4 Citations
4,744 Views
9 Pages

The distribution of the sum of negative binomial random variables has a special role in insurance mathematics, actuarial sciences, and ecology. Two methods to estimate this distribution have been published: a finite-sum exact expression and a series...

  • Article
  • Open Access
6 Citations
2,028 Views
20 Pages

24 April 2024

The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of vari...

  • Article
  • Open Access
6 Citations
4,905 Views
12 Pages

A Parallel Algorithm for Matheuristics: A Comparison of Optimization Solvers

  • Martín González,
  • Jose J. López-Espín and
  • Juan Aparicio

21 September 2020

Metaheuristic and exact methods are one of the most common tools to solve Mixed-Integer Optimization Problems (MIPs). Most of these problems are NP-hard problems, being intractable to obtain optimal solutions in a reasonable time when the size of the...

  • Article
  • Open Access
9 Citations
3,123 Views
14 Pages

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the e...

  • Article
  • Open Access
6 Citations
4,014 Views
20 Pages

Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem

  • Claudia Cavallaro,
  • Carolina Crespi,
  • Vincenzo Cutello,
  • Mario Pavone and
  • Francesco Zito

1 February 2024

This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-obj...

  • Article
  • Open Access
2 Citations
2,101 Views
21 Pages

10 August 2023

Service mesh is gaining popularity as a microservice architecture paradigm due to its lightness, transparency, and scalability. However, fully releasing configurations to the data plane during the business development phase can result in noticeable p...

  • Article
  • Open Access
4 Citations
2,188 Views
19 Pages

6 April 2023

With the development of various information and communication technologies, the amount of big data has increased, and distributed file systems have emerged to store them stably. The replication technique divides the original data into blocks and writ...

  • Article
  • Open Access
7 Citations
6,315 Views
19 Pages

10 September 2016

Sustainability research faces many challenges as respective environmental, urban and regional contexts are experiencing rapid changes at an unprecedented spatial granularity level, which involves growing massive data and the need for spatial relation...

  • Article
  • Open Access
2,481 Views
24 Pages

25 September 2021

Super points detection plays an important role in network research and application. With the increase of network scale, distributed super points detection has become a hot research topic. The key point of super points detection in a multi-node distri...

  • Article
  • Open Access
2 Citations
3,086 Views
21 Pages

A DFT-Based Running Time Prediction Algorithm for Web Queries

  • Oscar Rojas,
  • Veronica Gil-Costa and
  • Mauricio Marin

4 August 2021

Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ra...

  • Article
  • Open Access
7 Citations
3,315 Views
24 Pages

25 November 2022

The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much la...

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