FaceAUG: A Cross-Platform Application for Real-Time Face Augmentation in Web Browser

Sun, Tianyue Ruby

doi:10.1109/aivr50618.2020.00058

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…In contemporary high energy physics detectors, pixelized detectors, either silicon detectors or Low-Gain Avalanche Detectors (LGADs) [1][2][3] are widely used for tracking, timing or calorimeters. As the number of detector elements or pixels continues to increase towards the future, much lower power consumption of the front-end circuits is desired.…”

Section: Introductionmentioning

confidence: 99%

The CMOS Pseudo-Thyristor: a zero-static current discriminator circuit

Wen,

Fu,

Gong

et al. 2024

J. Inst.

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A very low power discriminator circuit for pixelized detectors, called the Pseudo-Thyristor is described in this document. It is a positive feedback topology using regular PMOS and NMOS field-effect transistors (FET's) with zero static current. When a small charge is injected into the circuit, it flips rapidly due to the positive feedback and outputs a logic transition for further digitization. Simulation shows that in a 65 nm process, it is possible to achieve a detecting threshold below 5 fC while maintain the average power consumption below 10 micro-Watts when the hit occupancy is <10% for 40 MHz operation.

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Section: Introductionmentioning

confidence: 99%

The CMOS Pseudo-Thyristor: a zero-static current discriminator circuit

Wen,

Fu,

Gong

et al. 2024

J. Inst.

View full text Add to dashboard Cite

show abstract

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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Implementation of smart social distancing for COVID-19 based on deep learning algorithm

Haq

Jan

2022

Multimed Tools Appl

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The first step to reducing the effect of viral disease is to prevent the spread which could be achieved by implementing social distancing (reducing the number of close physical interactions between peoples). Almost every viral disease whose means of communication is air, and enters through mouth or nose, definitely will affect our vocal organs which cause changes in features of our voice and could be traceable using feature analysis of voice using deep learning. The detection of an affected person using deep neural networks and tracking him would help us in the implementation of the social distancing rule in an area where it is needed. The aim of this paper is to study different solutions which help in enabling, encouraging, and even enforcing social distancing. In this paper, we implemented and analyzed scenarios on the basis of COVID-19 patient detection using cough and tracking him using smart cameras, or emerging wireless technologies with deep learning techniques for prediction and preventing the spread of disease. Thus these techniques are easy to be implemented in the initial stage of any pandemic as well and will help us in the implementation of smart social distancing (apply whenever needed).

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FaceAUG: A Cross-Platform Application for Real-Time Face Augmentation in Web Browser

Cited by 3 publications

References 4 publications

The CMOS Pseudo-Thyristor: a zero-static current discriminator circuit

The CMOS Pseudo-Thyristor: a zero-static current discriminator circuit

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

Implementation of smart social distancing for COVID-19 based on deep learning algorithm

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