An Online Integrated Fingerprint Image System

Huang, Ching-Yu; Liu, Limin; Chen, Yui-Liang

doi:10.18178/ijmlc.2019.9.1.764

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…GUI is a communication tools which is easier to link both of user and system and can creating interface path between them [21]. It should be developed so it can use for multi-purpose like inputs and also utilize to display the result [22]. In order the GUI is able to work properly in a given task, it needs to have a few common functions.…”

Section: Triangulation Methodsmentioning

confidence: 99%

Graphical User Interface (GUI) for Local Positioning System Based on Labview

Hasan¹,

Hussein²,

Saad³

et al. 2019

IJMLC

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Local position system (LPS) becomes more and more diversified and increasingly important in large numbers of applications and contexts such as healthcare, targeted, monitoring, tracking and security. In LPS, three anchors at least with known position are employed with one tag for localization. In this paper, LPS which contains three anchors and one tag is used to get positioning. Tag position represents user location. It estimates by using UWB technology which sending signal based on triangulation method with assist of Labview. By using Labview coding the position of each anchors can be read successful after connection all anchors and tag with PC and router. The positioning of tag in (x, y) direction, successfully update immediately when tag moving which is shown in graph. Therefore, the Graphical User Interface (GUI) for LPS based on Labview is achieved to determine the user positioning with positioning error less than 17cm. We believe this paper would catalyze further investigation by the researcher which deal with both of LPS field and Labview. Index Terms-Local positioning system, anchors, tag, UWB, graphical user interface, Labview. 2 , 2 represent position of the receiver 2,(both are known position ). (x, y) is unknown position of source position.

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Section: Triangulation Methodsmentioning

confidence: 99%

Graphical User Interface (GUI) for Local Positioning System Based on Labview

Hasan¹,

Hussein²,

Saad³

et al. 2019

IJMLC

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“…An online Integrated Fingerprint Image system was created with web-based tools that can view, edit, apply pattern recognition, analyze, and interact with pictures in real-time [ 54 ]. The system can extract characteristics and discriminate between normal and abnormal regions of the image, while allowing for comments.…”

Section: Front-end Deep Learning Web Appsmentioning

confidence: 99%

“…Using the results of picture recognition in Sketcher, 54 a convolutional neural network model that recognizes drawings was implemented in [36], to offer human-like complimenting feedback.…”

Section: Art Creationmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

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An Online Integrated Fingerprint Image System

Cited by 2 publications

References 8 publications

Graphical User Interface (GUI) for Local Positioning System Based on Labview

Graphical User Interface (GUI) for Local Positioning System Based on Labview

Front-end deep learning web apps development and deployment: a review

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