On estimates of computational complexity and error of the fast algorithm in the vortex methods

We have addressed blind deconvolution in a multichannel framework. Recently, a robust solution to this problem based on a Bayesian approach called sparse multichannel blind deconvolution (SMBD) was proposed in the literature with interesting results. However, its computational complexity can be high. We have proposed a fast algorithm based on the minimum entropy deconvolution, which is considerably less expensive. We designed the deconvolution filter to minimize a normalized version of the hybrid [Formula: see text]-norm loss function. This is in contrast to the SMBD, in which the hybrid [Formula: see text]-norm function is used as a regularization term to directly determine the deconvolved signal. Results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework. Similar results were also obtained in a real marine data set for both techniques.

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Fast algorithm for calculating autocorrelation function in code synthesis tasks by enumerative technique

Issues of radio electronics ◽

10.21778/2218-5453-2021-1-13-18 ◽

2021 ◽

Vol 1 (1) ◽

pp. 13-18

Author(s):

A. V. Korobeinikov

Keyword(s):

Computational Complexity ◽

Autocorrelation Function ◽

Linear Dependence ◽

Fast Algorithm ◽

Binary Code ◽

Code Combination ◽

Current Code

The fast algorithm for calculating the autocorrelation function (ACF) of a binary code is developed in relation to the problem of synthesizing codes with a given ACF by enumerative technique. The algorithm is applicable for any duration of the N code. The computational complexity of calculating the ACF is 2N multiplication operations and 2N addition operations. The linear dependence of computational complexity on the duration of the N code is noted. To calculate the ACF of the newly created code combination, the previous code, its ACF, and the index of the changed code element are used. The condition of applicability of the algorithm is that the search of code combinations must be performed by changing only one element in the current code combination. An enumerative technique is proposed that allows a complete enumertion of all 2N existing combinations by sequentially changing the code combination of just one element.

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FAST STEREO IMAGE DEPTH ESTIMATION ALGORITHM BASED ON CHANGE DETECTION METHODS IN CAMERAS FIELD OF VIEW

Issues of radio electronics ◽

10.21778/2218-5453-2018-8-94-98 ◽

2018 ◽

pp. 94-98

Author(s):

A. V. Khamukhin ◽

V. V. Kuzmina

Keyword(s):

Computational Complexity ◽

Fast Algorithm ◽

Moving Objects ◽

Depth Estimation ◽

Estimation Algorithm ◽

Video Stream ◽

Field Of View ◽

Stereo Image ◽

Detection Methods ◽

Image Depth

Stereo image depth estimation algorithms have not been widely used in CCTV systems yet, since either their accuracy is low or their computational complexity is high, which prevents implementation of these algorithms due to economic limitations on the cost of equipment used in security systems. In this paper, a new fast algorithm is proposed for reconstructing the depth of stereo images, which is used for reliable event identification in the CCTV systems cameras field of view. This real-time video stream processing method is based on the use of a fast algorithm for detecting changes in the cameras scene in the combination with SGBM depth estimation algorithm, which processes only image areas containing scene changes. The proposed method of significant computational complexity reduction of the depth estimation algorithm makes possible to acquire information about the distance from the cameras to moving objects in the field of view and use this information as an additional feature, that helps to reduce the operational cost and to improve the reliability of CCTV systems.

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A Fast Algorithm for Vector ARMA Parameter Estimation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4475 ◽

2012 ◽

Vol 433-440 ◽

pp. 4475-4481

Author(s):

Zhi Qing Chen ◽

You Shen Xia

Keyword(s):

Parameter Estimation ◽

Computational Complexity ◽

Fast Algorithm ◽

Linear Equations ◽

Estimation Problem ◽

Parameter Estimation Problem ◽

Model Technique

In this paper, a fast algorithm for vector autoregressivemoving-average (ARMA) parameter estimation under noise environments is proposed. Based on an equivalent AR parameter model technique and a Yule-Walker equation technique, solving the parameter estimation problem of the VARMA model is well converted into solving linear equations. Therefore, the proposed algorithm has a lower computational complexity and a faster speed than conventional algorithms. Application examples with application to Lorenz systems confirm that the proposed algorithm can obtain a good solution.

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Current results in fast algorithm computational complexity analysis, computer architecture design, and VLSI hardware advances with applications to digital signal processing

Digital Signal Processing ◽

10.1016/1051-2004(91)90091-x ◽

1991 ◽

Vol 1 (1) ◽

pp. 27-30

Author(s):

Jim Schroeder

Keyword(s):

Signal Processing ◽

Computational Complexity ◽

Digital Signal Processing ◽

Computer Architecture ◽

Fast Algorithm ◽

Complexity Analysis ◽

Digital Signal ◽

Architecture Design ◽

Analysis Computer

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Efficient Construction of the Equation Automaton

Algorithms ◽

10.3390/a14080238 ◽

2021 ◽

Vol 14 (8) ◽

pp. 238

Author(s):

Faissal Ouardi ◽

Zineb Lotfi ◽

Bilal Elghadyry

Keyword(s):

Computational Complexity ◽

Fast Algorithm ◽

Time Complexity ◽

Regular Expression ◽

Finite Automata ◽

Fast Computation ◽

Minimization Algorithm ◽

Deterministic Finite Automata ◽

Efficient Construction ◽

Time And Space Complexity

This paper describes a fast algorithm for constructing directly the equation automaton from the well-known Thompson automaton associated with a regular expression. Allauzen and Mohri have presented a unified construction of small automata and gave a construction of the equation automaton with time and space complexity in O(mlogm+m2), where m denotes the number of Thompson automaton transitions. It is based on two classical automata operations, namely epsilon-removal and Hopcroft’s algorithm for deterministic Finite Automata (DFA) minimization. Using the notion of c-continuation, Ziadi et al. presented a fast computation of the equation automaton in O(m2) time complexity. In this paper, we design an output-sensitive algorithm combining advantages of the previous algorithms and show that its computational complexity can be reduced to O(m×|Q≡e|), where |Q≡e| denotes the number of states of the equation automaton, by an epsilon-removal and Bubenzer minimization algorithm of an Acyclic Deterministic Finite Automata (ADFA).

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