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      VLSI Implementation of a High-Performance Nonlinear Image Scaling Algorithm

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          Abstract

          This study implements the VLSI architecture for nonlinear-based picture scaling that is minimal in complexity and memory efficient. Image scaling is used to increase or decrease the size of an image in order to map the resolution of different devices, particularly cameras and printers. Larger memory and greater power are also necessary to produce high-resolution photographs. As a result, the goal of this project is to create a memory-efficient low-power image scaling methodology based on the effective weighted median interpolation methodology. Prefiltering is employed in linear interpolation scaling methods to improve the visual quality of the scaled image in noisy environments. By decreasing the blurring effect, the prefilter performs smoothing and sharpening processes to produce high-quality scaled images. Despite the fact that prefiltering requires more processing resources, the suggested solution scales via effective weighted median interpolation, which reduces noise intrinsically. As a result, a low-cost VLSI architecture can be created. The results of simulations reveal that the effective weighted median interpolation outperforms other existing approaches.

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          Urban water resource management for sustainable environment planning using artificial intelligence techniques

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            Accurate Magnetic Resonance Image Super-Resolution Using Deep Networks and Gaussian Filtering in the Stationary Wavelet Domain

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              Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

              Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.

                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2021
                21 July 2021
                : 2021
                : 6297856
                Affiliations
                1Al-Nahrain University, Baghdad, Iraq
                2Universidad Santiago de Cali, Cali, Colombia
                3Ramco Institute of Technology, Rajapalayam, Tamilnadu, India
                4Vins Womens Christian College of Engineering, Kanyakumari, Tamilnadu, India
                Author notes

                Academic Editor: Ayush Dogra

                Author information
                https://orcid.org/0000-0002-4750-8384
                https://orcid.org/0000-0003-4339-2115
                Article
                10.1155/2021/6297856
                8321724
                34336160
                8f32e7d1-aa02-46ac-bf19-a1d472110bbe
                Copyright © 2021 Osamah Ibrahim Khalaf et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 May 2021
                : 6 June 2021
                : 21 June 2021
                Funding
                Funded by: Dirección General de Investigaciones of Universidad Santiago de Cali
                Award ID: 01-2021
                Categories
                Research Article

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