Morphological processing of binary images using reconfigurable computing environments

Shashev, Dmitriy; Shidlovskiy, Stanislav

doi:10.3103/s8756699015030036

Cited by 11 publications

(6 citation statements)

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“…Moreover, we understand that our method cannot be a winner in many cases if to compare to powerful and very complex methods as [1,2,6,7,13,14,22]. …”

Section: Results Of Modelingmentioning

confidence: 99%

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

et al. 2016

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Abstract. The new method for combining semisoft thresholding rules during wavelet-based data compression of images with multiplicative noise is suggested. The method chooses the best thresholding rule and the threshold value using the proposed criteria which provide the best nonlinear approximations and take into consideration errors of quantization. The results of computer modeling have shown that the suggested method provides relatively good image quality after restoration in the sense of some criteria such as PSNR, SSIM, etc.

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“…Moreover, we understand that our method cannot be a winner in many cases if to compare to powerful and very complex methods as [1,2,6,7,13,14,22]. …”

Section: Results Of Modelingmentioning

confidence: 99%

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

et al. 2016

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“…In the loop feedback parameters [4,5] of fuzzy decision trees proposed to adapt based on the stochastic gradient algorithm by traversing back from leaf to root nodes. With this strategy in the course of adaptation parameters, the hierarchical structure of fuzzy decision trees intact, and the neural network back-propagation algorithm directly on the structure of fuzzy decision trees improves the accuracy of training without compromising interpretability.…”

Section: Resultsmentioning

confidence: 99%

A Synergistic Effect in the Measurement of Neuro-Fuzzy System

2016

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Abstract. We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules "If ... then".

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“…They include the issues related to the training process necessary for class discrimination. ITR (Information transfer rate) is one of the system evaluation metrics that combine both performance and acceptance aspects [10][11][12][13].…”

Section: Training Process and Itr (Information Transfer Rate)mentioning

confidence: 99%

Evolution of the Brain Computing Interface (BCI) and Proposed Electroencephalography (EEG) Signals Based Authentication Model

Ramzan

Shidlovskiy

2018

MATEC Web Conf.

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toggleTechs (a creativity platform), Islamabad, PakistanAbstract. With current advancements in the field of Brain Computer interface it is required to study how it will affect the other technologies currently in use. In this paper, the authors motivate the need of Brain Computing Interface in the era of IoT (Internet of Things), and analyze how BCI in the presence of IoT could have serious privacy breach if not protected by new kind of more secure protocols. Security breach and hacking has been around for a long time but now we are sensitive towards data as our lives depend on it. When everything is interconnected through IoT and considering that we control all interconnected things by means of our brain using BCI (Brain Computer Interface), the meaning of security breach becomes much more sensitive than in the past. This paper describes the old security methods being used for authentication and how they can be compromised. Considering the sensitivity of data in the era of IoT, a new form of authentication is required, which should incorporate BCI rather than usual authentication techniques.

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Morphological processing of binary images using reconfigurable computing environments

Cited by 11 publications

References 1 publication

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

A Synergistic Effect in the Measurement of Neuro-Fuzzy System

Evolution of the Brain Computing Interface (BCI) and Proposed Electroencephalography (EEG) Signals Based Authentication Model

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