Motion estimation using learning automata

Damerchilu, Bahman; Norouzzadeh, Mohammad Sadegh; Meybodi, Mohammad Reza

doi:10.1007/s00138-016-0788-0

Cited by 17 publications

(3 citation statements)

References 31 publications

(34 reference statements)

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“…Future work intends to apply these novel designs of LA to deep learning and real-life challenging problems such as training deep neural networks and clustering [4] [5], financial portfolio management [6], adaptive recommender system [7], resourceefficient cloud computing and cost-efficient resource allocations [8]- [12], wireless network design and management [13]- [14], stochastic queuing systems [15], machine vision [16] and optimization of cooperative tasks [17], where efficient LA with fast convergence rate and assured 3-optimality or real-time reaction for each iteration are required.…”

Section: Discussionmentioning

confidence: 99%

A New Class of Learning Automata: The Probabilistically-Switch-Action-on-Failure Automaton Part 2 – Simulations

Aggarwal¹,

Liu²,

Levitin³

2022

Preprint

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<div> <div> <div> <p>In this part of the two-part paper, simulations of Probabilistically-Switch-Action-on-Failure learning automaton (PSAFA) are presented in various stationary and non-stationary environments. The PSAFA is a novel fixed structure stochastic automaton (FSSA) framework, and its analytical model is presented in detail in Part 1 of this paper set. The key differentiating feature of this automaton is that it allows action switching in every state. We anticipate that this feature attributes PSAFA dynamic properties that make certain aspects of its performance superior to other FSSA that do not possess this property.</p> <p>In this paper, simulations of the PSAFA in comparison with other FSSA are considered in two types of environments: a stationary environment (with fixed penalty probabilities) and a non-stationary environment, where the penalty probabilities are changing in time periodically as a sinusoidal function. In both cases the simulation demonstrates a dramatic difference in performance for these types of learning automata. The PSAFA shows its huge advantage in adaptability that leads to a better performance for the length of the simulation up to 30,000-150,000 steps. Only for very long stationary conditions Tsetlin automata outperforms PSAFA. In the case of sinusoidal modulations, the PSAFA tremendously outperforms other types of FSSA for all modulation frequencies and for all depths D>3. The performance of PSAFA does not deteriorate with increasing modulation frequency, while other FSSA are not resilient to that increase. </p> </div> </div> </div>

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Section: Discussionmentioning

confidence: 99%

A New Class of Learning Automata: The Probabilistically-Switch-Action-on-Failure Automaton Part 2 – Simulations

Aggarwal¹,

Liu²,

Levitin³

2022

Preprint

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“…In recent years, LAs have been successfully applied as optimization tools in various applications with unknown, dynamic, and complex environments, such as image processing 40 , optimization 41 , clustering 42 , community detection 43 , cellular networks 44 , wireless sensor networks 45 , queuing systems 46 , grid systems 47 , cloud computing 48 and complex social networks 49 .…”

Section: Related Workmentioning

confidence: 99%

A new stochastic diffusion model for influence maximization in social networks

Rezvanian

Vahidipour

Meybodi

2023

Sci Rep

Self Cite

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Most current studies on information diffusion in online social networks focus on the deterministic aspects of social networks. However, the behavioral parameters of online social networks are uncertain, unpredictable, and time-varying. Thus, deterministic graphs for modeling information diffusion in online social networks are too restrictive to solve most real network problems, such as influence maximization. Recently, stochastic graphs have been proposed as a graph model for social network applications where the weights associated with links in the stochastic graph are random variables. In this paper, we first propose a diffusion model based on a stochastic graph, in which influence probabilities associated with its links are unknown random variables. Then we develop an approach using the set of learning automata residing in the proposed diffusion model to estimate the influence probabilities by sampling from the links of the stochastic graph. Numerical simulations conducted on real and artificial stochastic networks demonstrate the effectiveness of the proposed stochastic diffusion model for influence maximization.

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“…Several works are developed to review and compare some or all these algorithms [2]- [4]. Other works are conducted to enhance these algorithms, such as those in [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Motion Estimation Approach for Video Coding: Direct Path for Block Matching (DPBM)

Fouad¹,

Mansour²

2018

IJCA

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The paper proposes a new approach to detect the matched Macroblock in the reference frame for motion estimation in video codec. According to the fact that the content of a MB is correlated and also the surrounding within a small area, the algorithm detects the shortest direct path to the matched Macroblock. This will be performed by tracking the gradual decrease of the differences of the compared Macroblocks. We start from the corresponding initial position at the predicted frame in the reference frame according to a novel and simple criteria. The proposed algorithm is developed and evaluated using benchmark video sequences of various types, achieving computation reduction up to 98%.

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Motion estimation using learning automata

Cited by 17 publications

References 31 publications

A New Class of Learning Automata: The Probabilistically-Switch-Action-on-Failure Automaton Part 2 – Simulations

A New Class of Learning Automata: The Probabilistically-Switch-Action-on-Failure Automaton Part 2 – Simulations

A new stochastic diffusion model for influence maximization in social networks

A Novel Motion Estimation Approach for Video Coding: Direct Path for Block Matching (DPBM)

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