Alhussain Almarhabi
Ph.D. Candidate | Deep/Machine Learning | Data Engineer | Electronic and communication engineer | Signal processing
My objective is to obtain a position that supports my academic aspirations. With the aim to acquire analytical abilities and the technical skills necessary for the innovations and applications in the field. I am focused on Signal processing and Data engineering.

I believe that others success is my success. I am fast-learner and hardworking.
Doctor of Philosophy - PhD
Electrical Engineering
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Stevens Institute of Technology
https://www.linkedin.com/school/stevens-institute-of-technology/
Master's degree
M.Sc. in Electrical Engineering, focusing on signal processing and data engineering
Electrical Engineering 
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Stevens Institute of Technology
https://www.linkedin.com/school/stevens-institute-of-technology/
Certificate
Data Engineering
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Stevens Institute of Technology
https://www.linkedin.com/school/stevens-institute-of-technology/
king Abdulaziz university / electronic and communication engineer
Radio spectrum awareness is an important topic to overcome many challenges, such as spectrum utilization and sharing appearing with the development of technologies in wireless communications. Some practical tasks of radio spectrum awareness are related to signal detection and identification for improving the system’s reliability, efficiency, and security. Eye diagrams are essential for measurement tools used by engineers to simulate, evaluate and debug systems. Eye diagrams reflect many vital parameters for signal integrity degradation, such as timing jitter, crosstalk, and inter-symbol interference. Therefore, using an eye diagram containing a valuable feature from the system could be helpful for spectrum awareness tasks. In this paper, we use deep learning to study and identify classes within quadrature amplitude modulation using eye diagrams and explored related impacts to enable radio spectrum awareness. Our results show that deep learning neural networks capable of classifying quadrature amplitude modulation types with eye diagrams at presenting timing jitter and varying signal-to-noise ratios.
QAM Signal Classification and Timing Jitter Identification Based on Eye Diagrams and Deep Learning
Alhussain Almarhabi, Hatim Alhazmi, Abdullah Samarkandi, Mofadal Alymani, Mohsen H Alhazmi, Yu-Dong Yao
Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels in quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.
Classification of QPSK Signals with Different Phase Noise Levels Using Deep Learning
Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Mofadal Alymani, Mohsen Alhazmi, Zikang Sheng, and Yu-Dong Yao
design a controlled cooling system for photovoltaic solar panel (PV) that will be used to collect power as maximum as possible. The main problem in cooling system project is increasing of temperature of photovoltaic panel and this increasing in temperature (thermal degree) of photovoltaic panel cause damage in the solar panel. Our objectives are to design a photovoltaic system with controlled cooling system, to increase the output of electrical energy, to reduce a heat of photovoltaic to less than 47 ˚C and to increase the efficiency of the system .
a controlled cooling system of a photovoltaic panel
• Utilized a nilearn Python package and unsupervised ReNA algorithm to investigate for hiding feature in MRI scans
• Fast detection of abnormalities using ReNA algorithm
• Investigate the location of detected abnormalities
Fast Detection of Brains with Abnormalities Using Recursive Agglomerative Clustering (ReNA)
https://aalmarhabi.github.io/mri-rena/
• Develop (self-created) datasets for the languages recognition device.
• Designed simple neural networks to recognize languages.
• Designed a Visual Geometry Group to recognize languages.
• Implement three different languages to be recognized by the models.
Different Languages Font Recognition (DLFR) via Raspberry Pi
https://github.com/code-Eng/DLFR
• Carried out convex optimization problem to minimize ripple in FIR filter.
• Utilized Matlab and CVX package to develop a code to solve the optimization problem.
• Designed Matlab GUI to generate FIR filter based on client specifications.
Finite Impulse Response Design via Convex Optimization
• Used Yahoo music database to implement the recommendations system.
• Developed different methods and model for the system.
• Refined system accuracy using ensemble molding.
Implement Music Recommendation System
CPE695 Applied Machine Learning & CPE646 Pattern Recognition and classification
• Implemented a classifier using University of Wisconsin breast cancer dataset.
• Applied K nearest neighbors and support vector machine algorithm as the main classifier.
• Improved KNN classifier with 96% accuracy and SVMs with 97% accuracy.
Breast Cancer Diagnostic: Benign or Malignant