This research proposes finger vein recognition system using Local Line Binary Pattern (LLBP) method and Learning Vector Quantization (LVQ). LLBP is is the advanced feature extraction method of Local Binary Pattern (LBP) method that uses a combination of binary values from neighborhood pixels to form features of an image. The straight-line shape of LLBP can extract robust features from the images with unclear veins, it is more suitable to capture the pattern of vein in finger vein image. At the recognition stage, LVQ is used as a classification method to improve recognition accuracy, which has been shown in earlier studies to show better results than other classifier methods. The three main stages in this research are preprocessing, feature extraction using LLBP method and recognition using LVQ. The proposed methodology has been tested on the SDUMLA-HMT finger vein image database from Shandong University. The experiment shows that the proposed methodology can achieve accuracy up to 90%.
The Covid-19 pandemic affects many areas of life, including the tourism sector. Furthermore, it significantly reduced the number of people visiting tourist destinations, and the reduction has helped to improve the environment in the National Park. Therefore, this study aims to present a satellite image classification method using Support Vector Machine to identify changes in the vegetation area of Komodo National Park. The satellite image used was created with Google Earth Pro with a resolution of 1920 x 1280 pixels using data collected in 2019 and 2020 before and during the pandemic. This study focuses on six tourist destinations in Komodo National Park: Loh Liang, Loh Buaya, Padar Island, Kanawa Island, Pink Beach, and Loh Sebita. The image was pre-processed using radiometric calibration, atmospheric correction, and contrast enhancement. The results of the pre-processing showed that segmentation will be performed to distinguish the area between one class and another. Furthermore, the image will be classified into five classes using the Support Vector Machine, including Soil, Vegetation, Built-Up Area, Deep Water, and Shallow Water. The measurement of the area of vegetation from 2019 and 2020 using Otsu’s thresholding showed environmental changes. Meanwhile, environmental improvements occurred in seven areas in the vegetation area category, with a 31.86% rise from 2019 to 2020. The increase in the area of green areas in the Komodo National Park all because tourist restriction and there is no climate fluctuations during the time of study.
Dielectric materials are polar materials for energy storage applications such as capacitors, transformer, and other electrical devices. The great dielectric properties generally depend on easily switchable polarization and higher-order structure in a material. Filler composite in the flexible dielectric polymer is then considered to rearrange polymer chain. However, the filler becomes agglomeration easily at high loading content in polymer, resulting in high energy loss and low electrical breakdown. This work presents the treated Polypyrrole (PPy) filler by 3-Aminopropyltriethoxysilane for avoiding agglomeration in PVDF-HFP thin film. These 30 μm PVDF-HFP film thickness is fabricated by tape casting method with N, N-dimethylformamide (DMF) solvent. The distributions of PPy filler on PVDF-HFP are observed by SEM image. Dielectric constant, dielectric loss, and conductivity are analyzed. As a result, the maximum silane content was found on 1 wt% for 1 wt% PPy/PVDF-HFP to maximized dielectric constant and reduce dielectric loss and conductivity. The conductive of PPy filler was lowered by covering with electrical insulating silane, resulting in decreased dielectric loss and conductivity. Then, polymer chain with silane bonding easily polarized under the electric field, resulting in an intensification of dielectric constant around 2.5 times compared with non-silane. Afterward, this dielectric constant clearly decreased when it reached to exceeded silane content as 5-20 wt%. Treated PPy with the suitable silane content in PVDF-HFP performs good dielectric properties for advanced energy storage in this work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.