scholarly journals Parameterized interpolation for fusion of multidimensional signals of various resolutions

2020 ◽  
Vol 44 (3) ◽  
pp. 436-440
Author(s):  
M.V. Gashnikov
Keyword(s):  
Local Features ◽  
Computational Experiments ◽  
Interpolation Error ◽  
Optimization Scheme ◽  
Signal Fusion ◽  
Interpolation Algorithms ◽  
Multidimensional Signals ◽  
Interpolation Procedure

Parameterized interpolation algorithms are adapted to fusion of multidimensional signals of various resolutions. Interpolating functions, switching rules for them and local features are specified, based on which the interpolating function is selected at each point of the signal. Parameterized interpolation algorithms are optimized based on minimizing the interpolation error. The recurrent interpolator optimization scheme is considered for the situation of inaccessibility of interpolated samples at the stage of setting up the interpolation procedure. Computational experiments are carried out to study the proposed interpolators for fusion of real multidimensional signals of various types. It is experimentally confirmed that the use of parameterized interpolators allows one to increase the accuracy of signal fusion.

scholarly journals Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression

2019 ◽  
pp. 23-30
Author(s):  
A I Maksimov ◽  
M V Gashnikov
Keyword(s):  
Dimension Reduction ◽  
Parameter Space ◽  
Compression Ratio ◽  
Space Dimension ◽  
Computational Experiments ◽  
Parameter Range ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

We propose a new adaptive multidimensional signal interpolator for differential compression tasks. To increase the efficiency of interpolation, we optimize its parameters space by the minimum absolute interpolation error criterion. To reduce the complexity of interpolation optimization, we reduce the dimension of its parameter range. The correspondence between signal samples in a local neighbourhood is parameterized. Besides, we compare several methods for such parameterization. The developed adaptive interpolator is embedded in the differential compression method. Computational experiments on real multidimensional signals confirm that the use of the proposed interpolator can increase the compression ratio.

scholarly journals Adaptive interpolation based on optimization of the decision rule in a multidimensional feature space

2020 ◽  
Vol 44 (1) ◽  
pp. 101-108
Author(s):  
M.V. Gashnikov
Keyword(s):  
Decision Tree ◽  
Decision Rule ◽  
Feature Space ◽  
Computational Experiments ◽  
Arbitrary Form ◽  
Multidimensional Signals ◽  
Adaptive Interpolation ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

An adaptive multidimensional signal interpolator is proposed, which selects an interpolating function at each signal point by means of the decision rule optimized in a multidimensional feature space using a decision tree. The search for the dividing boundary when splitting the decision tree vertices is carried out by a recurrence procedure that allows, in addition to the search for the boundary, selecting the best pair of interpolating functions from a predetermined set of functions of an arbitrary form. Results of computational experiments in nature multidimensional signals are presented, confirming the effectiveness of the adaptive interpolator.

scholarly journals Adaptive interpolation of multidimensional signals for compression on board an aircraft

2019 ◽  
pp. 97-102
Author(s):  
N I Glumov ◽  
M V Gashnikov
Keyword(s):  
Compression Ratio ◽  
Real World ◽  
Adaptive Algorithm ◽  
Computational Experiments ◽  
Interpolation Algorithm ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Adaptive Interpolation ◽  
Hierarchical Compression ◽  
Adaptive Interpolator

We consider the compression of multidimensional signals on the aircraft board. We describe the data of such signals as a hypercube, which is "rotated" in a special way. To compress this hypercube, we use a hierarchical compression method. As one of the stages of this method, we use an adaptive interpolation algorithm. The adaptive algorithm automatically switches between different interpolating functions at each signal point. We perform computational experiments in real-world multidimensional signals. Computational experiments confirm that the use of proposed adaptive interpolator allows increasing (up to 31%) the compression ratio of the “rotated” hypercube corresponding to multidimensional hyperspectral signals.

scholarly journals Interpolation based on context modeling for hierarchical compression of multidimensional signals

2018 ◽  
Vol 42 (3) ◽  
pp. 468-475 ◽  
Author(s):  
M. V. Gashnikov
Keyword(s):  
Arbitrary Dimension ◽  
Computational Experiments ◽  
Context Modeling ◽  
Interpolation Algorithm ◽  
Optimal Parameters ◽  
Compression Method ◽  
Local Neighborhood ◽  
Multidimensional Signals ◽  
Hierarchical Compression

Context algorithms for interpolation of multidimensional signals in the compression problem are researched. A hierarchical compression method for arbitrary dimension signals is considered. For this method, an interpolation algorithm based on the context modeling is proposed. The algorithm is based on optimizing parameters of the interpolating function in a local neighborhood of the interpolated sample. At the same time, locally optimal parameters found for more decimated scale signal levels are used to interpolate samples of less decimated scale signal levels. The context interpolation algorithm is implemented programmatically as part of a hierarchical compression method. Computational experiments have shown that using a context interpolator instead of an average interpolator makes it possible to significantly improve the efficiency of hierarchical compression.

Link Weight Optimization Scheme for Link Reinforcement in IP Networks

2017 ◽  
Vol E100.B (3) ◽  
pp. 417-425 ◽  
Author(s):  
Stephane KAPTCHOUANG ◽  
Hiroki TAHARA ◽  
Eiji OKI
Keyword(s):  
Ip Networks ◽  
Weight Optimization ◽  
Optimization Scheme ◽  
Link Weight

MEASUREMENT AND COMPUTATIONAL EXPERIMENTS WITHIN NEWBORN'S BRAIN COOLING PROCESS

2018 ◽  
Author(s):  
Dominika Bandoła ◽  
Andrzej J. Nowak ◽  
Ziemowit Ostrowski ◽  
Marek Rojczyk ◽  
Wojciech Walas
Keyword(s):  
Computational Experiments ◽  
Brain Cooling ◽  
Cooling Process

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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