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Algorithms
Special Issues
Algorithms in Stochastic Models
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Algorithms in Stochastic Models

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 10631

Special Issue Editors

Dr. Shankarachary Ragi

E-Mail Website
Guest Editor
Department of Electrical Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
Interests: optimal control; decision-making under uncertainty; predictive modeling; data analytics; and optimization in autonomous/semi-autonomous systems
Prof. Dr. Edwin K P Chong

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA
Interests: systems, control, and optimization; wireless networks and cellular systems; computer and communication networks; sensor networks; resource allocation and management; information theory; stochastic optimization and approximation; discrete event systems

Special Issue Information

Dear Colleagues,

Stochastic models are useful in many real-world scenarios to capture their inherent randomness. Examples of their applications include biological processes, financial markets, manufacturing processes, control systems, power grids, and weather prediction. As stochastic models often suffer from significant complexity, we are interested in algorithms to tackle computationally intensive challenges in stochastic models, including decision-making, control, signal processing, optimization, and resource allocation.

This will bring together algorithms, modeling, and numerical studies on algorithms in stochastic models in applications, including (but not limited to) intelligent transportation, smart and connected communities, sensing, smart grids, finance, and telecommunications.

Prof. Dr. Edwin K P Chong
Dr. Shankarachary Ragi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Algorithms in stochastic models
  • Consensus algorithms
  • Optimization
  • Decision making under uncertainty
  • Game-theoretic approaches
  • Smart grids
  • Computer networks
  • Connected autonomous vehicles
  • Finance
  • Telecommunications

Published Papers (2 papers)

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Research

30 pages, 326 KiB  
Article
Phase Congruential White Noise Generator
by Aleksei F. Deon, Oleg K. Karaduta and Yulian A. Menyaev
Algorithms 2021, 14(4), 118; https://doi.org/10.3390/a14040118 - 05 Apr 2021
Cited by 2 | Viewed by 6089
Abstract
White noise generators can use uniform random sequences as a basis. However, such a technology may lead to deficient results if the original sequences have insufficient uniformity or omissions of random variables. This article offers a new approach for creating a phase signal [...] Read more.
White noise generators can use uniform random sequences as a basis. However, such a technology may lead to deficient results if the original sequences have insufficient uniformity or omissions of random variables. This article offers a new approach for creating a phase signal generator with an improved matrix of autocorrelation coefficients. As a result, the generated signals of the white noise process have absolutely uniform intensities at the eigen Fourier frequencies. The simulation results confirm that the received signals have an adequate approximation of uniform white noise. Full article
(This article belongs to the Special Issue Algorithms in Stochastic Models)
12 pages, 393 KiB  
Article
UAV Formation Shape Control via Decentralized Markov Decision Processes
by Md Ali Azam, Hans D. Mittelmann and Shankarachary Ragi
Algorithms 2021, 14(3), 91; https://doi.org/10.3390/a14030091 - 17 Mar 2021
Cited by 16 | Viewed by 3643
Abstract
In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive [...] Read more.
In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms. Full article
(This article belongs to the Special Issue Algorithms in Stochastic Models)
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