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$20 USD /timme
Flagga för HONG KONG
clear water bay, hong kong
$20 USD /timme
Datum, tid och plats: 1:48 em
Gick med december 12, 2023
0 Rekommendationer

Murad A.

@mabdullahust

 5,0 (1 omdöme)
0,0
0,0
100%
100%
$20 USD /timme
Flagga för HONG KONG
clear water bay, hong kong
$20 USD /timme
100 %
Slutförda jobb
100 %
Inom budget
100 %
Inom tid
N/A
Återanställningsgrad

AI&ML|Python, C++, Verilog, PyTorch, Tensorflow

I'm an experienced researcher in machine learning, artificial intelligence, signal processing and designing decoders for 5G/6G. I can help you with designing and implementing machine learning algorithms for a wide range of applications (e.g., finance, biology and energy consumption). Knows Python, C++, CUDA C++, MATLAB, Linux, TensorFlow, PyTorch, and XGBoost. I also have experience working with Verilog and FPGAs. Here is the link to my GitHub page. [login to view URL]
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Portfolio

11628151
The purpose of this project is to fine-tune the Yolov8 model on the selected class from the OpenImageV7 dataset. I developed a notebook that has the following properties. 1) Automatically download the OpenImageV7 dataset of the selected class. 2) Preprocess the dataset and put it in the format that Yolov8 accepts. 3) Train and test the model. Here is the link to GitHub Notebook which can be run on Google Colab. https://github.com/Murad1997/yolov8-training/blob/main/Yolov8_training_notebook.ipynb
Fine-tuing of Yolov8 for the Segmentation on OpenImageV7
Fine-tuing of Yolov8 for the Segmentation on OpenImageV7
11618440
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset
11628151
The purpose of this project is to fine-tune the Yolov8 model on the selected class from the OpenImageV7 dataset. I developed a notebook that has the following properties. 1) Automatically download the OpenImageV7 dataset of the selected class. 2) Preprocess the dataset and put it in the format that Yolov8 accepts. 3) Train and test the model. Here is the link to GitHub Notebook which can be run on Google Colab. https://github.com/Murad1997/yolov8-training/blob/main/Yolov8_training_notebook.ipynb
Fine-tuing of Yolov8 for the Segmentation on OpenImageV7
Fine-tuing of Yolov8 for the Segmentation on OpenImageV7
11618440
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset
This project is related to the data analysis. Which I used the electric vehicles (EVs) dataset available through the Washington State Department of Licensing (DOL). The following are the key takeaways. 1) 88% memory reduction was achieved by efficiently storing the data without losing the information. This is crucial for big data projects. 2) Tesla has the highest number of registered EVs with the DOL Washington in both categories (i.e., Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). 3) Only considering the PHEVs Toyota has the highest number of registered vehicles. Here is the link to the notebook: https://github.com/Murad1997/DS-Portfolio/blob/main/Data_analysis_and_modeling.ipynb
Analysis of the Electric Vehicles (EVs) Dataset

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Flagga för Ali H.
@SpotContent
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Erfarenhet

Research Assistant

Signal Processing and Communications Lab
sep. 2019 - dec. 2023 (4 år, 3 månader)
I worked on many research projects ranging from computer vision to channel coding. Mainly, I worked on developing algorithms for display camera communication and polar codes and their variants. My results have been published in prestigious conferences and journals, and my team has been awarded a gold award in global AI competitions.

Research Assistant

Non-Destructive Testing Centre (NUST-PNEC), Pakistan
nov. 2016 - maj 2019 (2 år, 6 månader)
I worked on many research projects. Mainly, I was responsible for developing software using Python, C++, CUDA, and MATLAB for various hardware and implementing different AI, ML, signal, and image processing algorithms.

Utbildning

Master of Philosophy in Internet of Things (IoT)

The Hong Kong University of Science and Technology, Hong Kong 2019 - 2023
(4 år)

Bachelor's of Engineering in Electrical Engineering

National University of Science and Technology, Pakistan 2015 - 2019
(4 år)

Kvalifikationer

Deep Learning Specialization

Coursera
2018
In this certification, I gained comprehensive knowledge about various machine learning models and successfully implemented them from scratch in Python, utilizing the NumPy library. Additionally, I obtained proficiency in working with advanced deep learning networks, including convolutional neural networks, long-short term memory models, and multilayer perceptron models, using TensorFlow.

Publikationer

New Search for Optimized PAC Codes under SCL Decoding

IEEE
In this paper, we proposed a new way to construct polarization-adjusted convolutional (PAC) codes which achieve state-of-the-art performance with a practically affordable list size under successive cancellation list (SCL) decoding. Link to the paper: I have presented in the Globecom 2023, and the link is not available for now.

New Search for the Polarization-Adjusted Convolutional Codes with Respect to the AFER-Optimality

IEEE
In this paper, we leverage the high processing capabilities of the GPUs to develop an algorithm to optimize the polarization-adjusted convolutional (PAC) codes. Our results significantly outperform the previously best-known results. Link to the paper: https://ieeexplore.ieee.org/abstract/document/10206681

Some New Constructions of AFER-Optimal Binary Linear Block Codes

IEEE
In this paper, we propose some new constructions of the binary linear block codes that achieve the best value of the minimum distances and the optimal values of the corresponding error coefficients (i.e., the number of minimum-weight codewords). Link to the paper: https://ieeexplore.ieee.org/abstract/document/10206946

Wireless Sensor Network Based Structural Health Monitoring of Bridges

Journal of Testing and Evaluation
In this paper, we developed a network of sensors and deployed them in the field to get real-time data on the vibration of concrete bridges. Later on, advanced signal processing techniques (i.e., Fast-Fourier Transform and Hilbert Huang Transform) were applied to analyze the data. Link to the paper: https://www.astm.org/jte20180849.html

SHM of concrete bridge structures using wireless sensor networks

VDE
This paper presents a localized information processing approach for long-term automated online structural health monitoring (SHM) using a wireless sensor network (WSN). Feature-based Vibration examination is a powerful diagnostic tool for evaluating structural performance. Link to the paper: https://ieeexplore.ieee.org/abstract/document/8436105

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Toppkompetenser

C Programming 1
C++ Programming 1
Python
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Verilog / VHDL

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