Hwang W. Profile | Freelancer

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Matlab and Mathematica

Mobile App Development

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$20 USD / hour

beijing,

$20 USD / hour

It's currently 11:16 PM here

Joined June 24, 2020

1 Recommendation

Hwang W.

@GaoJim2020

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$20 USD / hour

beijing,

$20 USD / hour

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embedding PCB Layout C programming android

I have experience in embedding device development and Android app development for 9+ years. And I have also experience developing in fingerprint device. I am rich experienced in writing firmware and combining embedding devices with mobile devices and very interested. Skill: Microcontroller, PCB Layout, Android I am not looking for money or one project but I want to increase my business relations with you for a long time. As can see I am a new freelancer. Thus, hiring process will create extra credit on my future. I think the biggest is credit. I am confident that everyone who wants my service will be 100% satisfied.

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Portfolio

9582167

Android app and MCU firmware for LED lighting control

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

The rapid development of the mobile internet and the internet of things (IoT) leads to challenging requirements for the 5th generation(5G) of the wireless communication systems.
I modeled and simulated a deep learning added multi-user detection in uplink non-orthogonal multiple access (NOMA) via MATLAB.

Multi-user detection for NOMA IoT Networks in 5G

The rapid development of the mobile internet and the internet of things (IoT) leads to challenging requirements for the 5th generation(5G) of the wireless communication systems.
I modeled and simulated a deep learning added multi-user detection in uplink non-orthogonal multiple access (NOMA) via MATLAB.

Multi-user detection for NOMA IoT Networks in 5G

Stock price prediction has spurred the interest of researchers over the years to develop better predictive models. The most used techniques fall into two broad categories, namely statistical and machine learning techniques.
Particle filter is a Monte Carlo technique for solution of the state estimation problem.
Particle filter is used to model non-gaussian time series. Stock market data is one of typical non-gaussian time series.
The key of particle filter is to represent the required posterior density function by a set of random samples(particles) and its associated weights.
I developed suitable posterior density function for stock market time series in MATLAB and Python programming.
This program is very useful to predict the price of various stock markets.

Prediction of Stock Market using Particle Filter Algorithm

Stock price prediction has spurred the interest of researchers over the years to develop better predictive models. The most used techniques fall into two broad categories, namely statistical and machine learning techniques.
Particle filter is a Monte Carlo technique for solution of the state estimation problem.
Particle filter is used to model non-gaussian time series. Stock market data is one of typical non-gaussian time series.
The key of particle filter is to represent the required posterior density function by a set of random samples(particles) and its associated weights.
I developed suitable posterior density function for stock market time series in MATLAB and Python programming.
This program is very useful to predict the price of various stock markets.

Prediction of Stock Market using Particle Filter Algorithm

Stock price prediction has spurred the interest of researchers over the years to develop better predictive models. The most used techniques fall into two broad categories, namely statistical and machine learning techniques.
Particle filter is a Monte Carlo technique for solution of the state estimation problem.
Particle filter is used to model non-gaussian time series. Stock market data is one of typical non-gaussian time series.
The key of particle filter is to represent the required posterior density function by a set of random samples(particles) and its associated weights.
I developed suitable posterior density function for stock market time series in MATLAB and Python programming.
This program is very useful to predict the price of various stock markets.

Prediction of Stock Market using Particle Filter Algorithm

9582072

Android for lane and zebra-crossing detection via camera

9582167

Android app and MCU firmware for LED lighting control

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

I made 3D modelling for real object in solidworks programming.

Solidworks Project

The rapid development of the mobile internet and the internet of things (IoT) leads to challenging requirements for the 5th generation(5G) of the wireless communication systems.
I modeled and simulated a deep learning added multi-user detection in uplink non-orthogonal multiple access (NOMA) via MATLAB.

Multi-user detection for NOMA IoT Networks in 5G

The rapid development of the mobile internet and the internet of things (IoT) leads to challenging requirements for the 5th generation(5G) of the wireless communication systems.
I modeled and simulated a deep learning added multi-user detection in uplink non-orthogonal multiple access (NOMA) via MATLAB.

Multi-user detection for NOMA IoT Networks in 5G

Stock price prediction has spurred the interest of researchers over the years to develop better predictive models. The most used techniques fall into two broad categories, namely statistical and machine learning techniques.
Particle filter is a Monte Carlo technique for solution of the state estimation problem.
Particle filter is used to model non-gaussian time series. Stock market data is one of typical non-gaussian time series.
The key of particle filter is to represent the required posterior density function by a set of random samples(particles) and its associated weights.
I developed suitable posterior density function for stock market time series in MATLAB and Python programming.
This program is very useful to predict the price of various stock markets.

Prediction of Stock Market using Particle Filter Algorithm

Stock price prediction has spurred the interest of researchers over the years to develop better predictive models. The most used techniques fall into two broad categories, namely statistical and machine learning techniques.
Particle filter is a Monte Carlo technique for solution of the state estimation problem.
Particle filter is used to model non-gaussian time series. Stock market data is one of typical non-gaussian time series.
The key of particle filter is to represent the required posterior density function by a set of random samples(particles) and its associated weights.
I developed suitable posterior density function for stock market time series in MATLAB and Python programming.
This program is very useful to predict the price of various stock markets.

Prediction of Stock Market using Particle Filter Algorithm

Stock price prediction has spurred the interest of researchers over the years to develop better predictive models. The most used techniques fall into two broad categories, namely statistical and machine learning techniques.
Particle filter is a Monte Carlo technique for solution of the state estimation problem.
Particle filter is used to model non-gaussian time series. Stock market data is one of typical non-gaussian time series.
The key of particle filter is to represent the required posterior density function by a set of random samples(particles) and its associated weights.
I developed suitable posterior density function for stock market time series in MATLAB and Python programming.
This program is very useful to predict the price of various stock markets.

Prediction of Stock Market using Particle Filter Algorithm

9582072

Android for lane and zebra-crossing detection via camera

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Mobile App Development

Matlab and Mathematica

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