Hire the Best Digital Signal Processing Specialists

I'm a Telecommunications Engineer and expert in programming (MATLAB, VHDL, Verilog, and Python) and Signal Processing. My skills are included but not limited to: MATLAB Programming, Embedded Design Systems and Embedded Programming (focused on Yocto Linux Customization) FPGA and Digital Design, Python Programming, Signal Processing and Data Analysis, EEG and Bio-Signal Processing, Artificial Intelligence, 5G and Beyond 5G Communication Systems. Compressive Sensing and Sparse Coding, and Reinforcement Learning are also my recent fields of research. I will design your GUI software by PyQt5 and .Net C# tools.

I’m a Professional Engineer of California in Electronics, Communications, and Controls, experienced in RF, embedded systems, and automated test development for hardware-focused teams. I am educated on the West Coast in Electrical Engineering with an emphasis on wireless technologies and am working toward a Graduate degree in Signal and Image Processing. Whether you need help with prototyping, validation, troubleshooting, or building reliable engineering workflows, I can help. - Experience with RF systems, embedded hardware, Python tools, and lab/test automation - Support from concept development through integration, validation, and production handoff - Strong communication, documentation, and cross-functional collaboration

I have spent the last 13 years becoming an expert in the field of acoustics and vibration. Coupling that with an MBA, I can help solve complex problems while being cost-conscious. I worked in industry for five years as a noise and vibration (NVH) consultant, so expanding my services to Upwork is a natural next step. I have worked in the machine learning, automotive, aerospace, military, consumer goods industries. I use AI in as many tasks as possible to deliver impressive results at impressive speed. I'd love to have a complimentary chat to discuss how I could help solve your problem. Additional Proficiencies: Data acquisition Signal processing Machine Learning City Noise Ordinances Speech Transmission Classification Testing Access to: MATLAB Python LabVIEW Test Lab COMSOL Solidworks

Worked with major industry leaders and proved as a creative visionary responsible for building powerful, innovative solutions through application of DSP knowledge and Embedded System expertise. • Conceived and productized SigmaStudio for SHARC - graphical programming support for SHARC processors • Architected, executed and productized 14 fixed-point Audio CODECs, 2 Image CODECs. • Designed more than 30 audio and image post processing, control system and communication algorithms. • Developed and supported software algorithm and system components for digital Radio. • Developed, integrated and participated in field trials of 4 major Automotive Amplifiers - Audio subsystem. • Proved and inventor and technologist responsible for managing high-energy development teams. • Awarded 2 US patents and 2 innovations disclosure in wide area of technologies. • Developed 12 Audio/Image CODECS, 10 Audio based systems, several audio post processing algorithms.

I received a PhD degree in applied mathematics and signal processing from Télécom SudParis, France, in December 2017. I'm currently an Associate Professor at École Nationale Supérieure des Télécommunications et des TIC (ENSTTIC) in Algeria. My research interests focus on signal processing for communications and radio monitoring. I have extensive experience using MATLAB/Simulink, Software Defined Radio (SDR) platforms, and Vector Signal Analysis software VSA 89600.

I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.