About
Motivated and skilled Embedded Engineer with over 1 year of industry experience in firmware development, SystemC modelling, hardware-software integration, and AI accelerator design. Seeking a challenging role in embedded systems development to apply deep technical knowledge and problem-solving skills in real-time systems.
Software Developer
- Website: mudassir.web.app
- Phone: +91 7022627210
- Email: [email protected]
- City: Bangalore,Karnataka India
Skills
Resume
Explore my detailed resume to see my professional experience, projects, and technical skills. Click the button below to view or download my CV.
Projects
Here are some of my academic and personal projects, categorized based on their domain – Embedded Systems, AI/ML, and Other Innovative Solutions.
AI-Driven Adaptive Ventilator
Designed an adaptive ventilator with a built-in oxygen concentrator using embedded systems and real-time control.
Contactless Attendance System
Built a posture recognition-based attendance system for classrooms using computer vision and IoT.
Heart Attack Prediction
Developed a system to predict heart attacks using facial expression and posture analysis with ML models.
AI-Based Plant Disease Detection
Implemented an image classification model to detect plant diseases and recommend treatment solutions.
ProInterns.in
Founded and managed an online internship platform, handled SEO, website development, and certificate generation tool.
📚 Research Works
🚀 Accelerating AI Workloads on Embedded Linux
Designed a TL-Verilog-based systolic array and convolution accelerator, integrated with Linux-VP. Achieved real-time task offloading and significant speedup in matrix and digit recognition workloads compared to CPU-only execution.
🫁 Advanced Lung Cancer Segmentation & Classification
Compared UNet vs UNet++ on lung CT images using image enhancement and evaluated with Dice, Jaccard, and Bland-Altman plots. Used VGG models for classification with precision, recall, and F1 metrics.
🧠 Brain Tumor Classification & Segmentation
Evaluated Xception and MobileNet for classification; MLUNet and DeepLab for segmentation on brain MRIs. Compared models using accuracy and segmentation precision.
❤️ Heart Attack Prediction
Built a CNN-based system using facial/posture recognition and heart rate sensor for real-time heart attack prediction with SMS-based emergency alerts.
🧬 UNet vs UNet++ for Brain MRI Segmentation
Applied UNet and UNet++ for brain MRI segmentation. Preprocessed data and evaluated performance using Dice score, sensitivity, specificity, and VGG-based classification.
🧘 Smart Yoga Pose Prediction & Correction
Combined MediaPipe with VGG16/VGG19 for real-time yoga pose classification and correction with audio-visual feedback.
🩺 PCOS Classification Using Machine Learning
Applied RNN, FNN, CNN, and LSTM to classify PCOS using clinical data. Evaluated models on accuracy, precision, recall, and F1 score to aid in diagnosis and treatment planning.