Signal Processing projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Signal Processing projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Signal Processing Projects
-
Comparative Analysis of Machine Learning Algorithms With Advanced Feature Extraction for ECG Signal Classification
The project aims to classify ECG heartbeat signals to help detect heart conditions early. It focuses on using machine learning methods to analyze ECG data efficiently and accurately. The MIT-BIH arrhythmia dataset is used, grouped into five main beat types. Different classifiers are tested, and Random Forest achieves the best performance. The work highlights automated ECG analysis as a tool for clinical and remote monitoring applications. -
A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System
This project created a new CPR training system that listens to the sounds made during chest compressions. The system uses these sounds to check how deep, fast, and well the compressions are done. It uses a smartphone and special sounds from a training device to give accurate feedback. This helps people practice CPR at home without expensive equipment. -
A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network
This project focuses on finding the direction from which signals come in a three-dimensional space. It uses a special type of artificial intelligence called a deep convolutional neural network to analyze data from a small antenna array. The system can accurately predict angles from any direction, even when multiple signals arrive at the same time. The goal is to make signal detection faster, precise, and reliable in different conditions. -
Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation
This project studies slow and deep breathing exercises to improve relaxation and heart function. It compares different breathing styles and measures how they affect the heart using simple signals. The study also uses computer programs to identify breathing patterns automatically. The results show which breathing method works best immediately and which one has longer-lasting benefits. -
From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People
This project studies how to monitor sleep using a small, comfortable ear sensor instead of traditional scalp electrodes. Researchers used existing large EEG datasets to train a computer model and then improved it by fine-tuning with ear-EEG data. The approach works well for older adults and matches expert sleep stage classifications closely. This method could help track sleep remotely and non-invasively, especially in elderly patients. -
Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring
This project focuses on using wearable device signals to detect heart problems. It studies PPG data, which can be measured continuously and easily by consumer devices. The goal is to find unusual patterns in PPG that indicate when an ECG test should be taken. The proposed method uses a deep learning model to accurately predict these heart abnormalities. -
A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography
This project focuses on improving heart monitoring using a wearable device that measures chest vibrations. Walking and movement often create noise that makes the signals unclear. The researchers developed a new method to remove this noise and get accurate heart rate readings. This makes wearable heart monitoring more reliable for everyday use and clinical care. -
Pulse2AI An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications
This project created Pulse2AI, a system that cleans and prepares wearable health signals for machine learning. It takes raw data from devices like heart rate or blood pressure monitors and makes it ready for analysis. Using Pulse2AI improved accuracy in estimating blood pressure and breathing rate. The framework works with different types of signals and can help in many remote health monitoring tasks. -
Digital Sensing Systems for Electromyography
This project develops a new system to record muscle signals from the forearm. It makes collecting data easier and reduces wiring and power problems. The system can check signal quality while monitoring muscles in real time. It works as well as current medical devices and can help improve future AI-based prosthetics and computer interfaces. -
Low Complexity Overloaded MIMO Non-Linear Detector with Iterative LLR Estimation
This project develops a new method to detect signals in overloaded MIMO systems more efficiently. It uses special estimation techniques to improve accuracy while reducing computation. Simulations show it performs better than traditional methods and is simpler to implement. This makes wireless communication faster and more reliable.
Did you like this research project?
To get this research project Guidelines, Training and Code…
How We Help You with Signal Processing Projects
At UniPhD, we provide complete guidance and support for Signal Processing projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
Our experts have extensive experience guiding students in computer science, electronics, and electrical domains, ensuring successful completion of academic and research projects.
Signal Processing Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Signal Processing research and thesis development. We offer fast-track dissertation writing services to help you complete your Signal Processing thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Signal Processing Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Signal Processing final year project.
Signal Processing Research Support for PhD Scholars
UniPhD offers advanced Signal Processing research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.