As a BTech (Bachelor of Technology) student, you have the opportunity to showcase your skills and knowledge in a specific field of engineering or technology through a research-based project. These BTech projects are an essential part of your academic journey and provide you with hands-on experience in solving real-world problems.
At Takeoff Projects, we understand the importance of finding the perfect BTech project that aligns with your interests and career goals. That's why we have created a platform that offers a wide range of project ideas and topics for BTech students, covering various fields such as computer science, mechanical engineering, electrical engineering, and more. Each project idea includes a detailed project description, a list of required skills, and the project supervisor.
Project Code: TMMASP199
Project Title:Improving SNR, MSE and Denoising of EMG-Contaminated ECG Signals using EMDView DetailsProject Code: TMMASP198
Project Title:Implementation of Lightweight Shuffle net Based CNN for Classification of Arrhythmia for Wearables.View DetailsProject Code: TMMASP197
Project Title:Minimizing Error Rate in Lightweight and High Accurate RR Interval Compensation for Signals from Wearable ECG Sensors with Band Pass FilterView DetailsProject Code: TMMASP194
Project Title:A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analysis and Detection of Arrhythmia with BPMView DetailsProject Code: TMMASP193
Project Title:Implementation of Self-Organized Operational Neural Networks for R-Peak Detection in Holter ECGsView DetailsProject Code: TMMASP170
Project Title:Feature Extraction and Analysis of Speech Signal Based on Fractional Fourier TransformView DetailsProject Code: TMMASP169
Project Title:A Novel Approach to Speech Signal Segmentation Based on Time-Frequency AnalysisView DetailsProject Code: TMMASP168
Project Title:ECG Signal Extraction Method Based on Singular Value Selection and WaveletView DetailsProject Code: TMMASP166
Project Title:EEG Signal Processing and Emotion Recognition Using Convolutional Neural NetworkView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TMMASP199 | Improving SNR, MSE and Denoising of EMG-Contaminated ECG Signals using... | |
| 2 | TMMASP198 | Implementation of Lightweight Shuffle net Based CNN for Classification... | |
| 3 | TMMASP197 | Minimizing Error Rate in Lightweight and High Accurate RR Interval Com... | |
| 4 | TMMASP194 | A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analys... | |
| 5 | TMMASP193 | Implementation of Self-Organized Operational Neural Networks for R-Pea... | |
| 6 | TMMASP170 | Feature Extraction and Analysis of Speech Signal Based on Fractional ... | |
| 7 | TMMASP169 | A Novel Approach to Speech Signal Segmentation Based on Time-Frequenc... | |
| 8 | TMMASP168 | ECG Signal Extraction Method Based on Singular Value Selection and Wa... | |
| 9 | TMMASP167 | Feature Extraction in Speech Signal Recognition | |
| 10 | TMMASP166 | EEG Signal Processing and Emotion Recognition Using Convolutional Neur... |
Project Code: TMMASP199
Project Title:Improving SNR, MSE and Denoising of EMG-Contaminated ECG Signals using EMDView DetailsProject Code: TMMASP198
Project Title:Implementation of Lightweight Shuffle net Based CNN for Classification of Arrhythmia for Wearables.View DetailsProject Code: TMMASP197
Project Title:Minimizing Error Rate in Lightweight and High Accurate RR Interval Compensation for Signals from Wearable ECG Sensors with Band Pass FilterView DetailsProject Code: TMMASP194
Project Title:A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analysis and Detection of Arrhythmia with BPMView DetailsProject Code: TMMASP193
Project Title:Implementation of Self-Organized Operational Neural Networks for R-Peak Detection in Holter ECGsView DetailsProject Code: TMMASP170
Project Title:Feature Extraction and Analysis of Speech Signal Based on Fractional Fourier TransformView DetailsProject Code: TMMASP169
Project Title:A Novel Approach to Speech Signal Segmentation Based on Time-Frequency AnalysisView DetailsProject Code: TMMASP168
Project Title:ECG Signal Extraction Method Based on Singular Value Selection and WaveletView DetailsProject Code: TMMASP166
Project Title:EEG Signal Processing and Emotion Recognition Using Convolutional Neural NetworkView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TMMASP199 | Improving SNR, MSE and Denoising of EMG-Contaminated ECG Signals using... | |
| 2 | TMMASP198 | Implementation of Lightweight Shuffle net Based CNN for Classification... | |
| 3 | TMMASP197 | Minimizing Error Rate in Lightweight and High Accurate RR Interval Com... | |
| 4 | TMMASP194 | A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analys... | |
| 5 | TMMASP193 | Implementation of Self-Organized Operational Neural Networks for R-Pea... | |
| 6 | TMMASP170 | Feature Extraction and Analysis of Speech Signal Based on Fractional ... | |
| 7 | TMMASP169 | A Novel Approach to Speech Signal Segmentation Based on Time-Frequenc... | |
| 8 | TMMASP168 | ECG Signal Extraction Method Based on Singular Value Selection and Wa... | |
| 9 | TMMASP167 | Feature Extraction in Speech Signal Recognition | |
| 10 | TMMASP166 | EEG Signal Processing and Emotion Recognition Using Convolutional Neur... |
Project Code: TMMASP199
Project Title:Improving SNR, MSE and Denoising of EMG-Contaminated ECG Signals using EMDView DetailsProject Code: TMMASP198
Project Title:Implementation of Lightweight Shuffle net Based CNN for Classification of Arrhythmia for Wearables.View DetailsProject Code: TMMASP197
Project Title:Minimizing Error Rate in Lightweight and High Accurate RR Interval Compensation for Signals from Wearable ECG Sensors with Band Pass FilterView DetailsProject Code: TMMASP194
Project Title:A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analysis and Detection of Arrhythmia with BPMView DetailsProject Code: TMMASP193
Project Title:Implementation of Self-Organized Operational Neural Networks for R-Peak Detection in Holter ECGsView DetailsProject Code: TMMASP170
Project Title:Feature Extraction and Analysis of Speech Signal Based on Fractional Fourier TransformView DetailsProject Code: TMMASP169
Project Title:A Novel Approach to Speech Signal Segmentation Based on Time-Frequency AnalysisView DetailsProject Code: TMMASP168
Project Title:ECG Signal Extraction Method Based on Singular Value Selection and WaveletView DetailsProject Code: TMMASP166
Project Title:EEG Signal Processing and Emotion Recognition Using Convolutional Neural NetworkView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TMMASP199 | Improving SNR, MSE and Denoising of EMG-Contaminated ECG Signals using... | |
| 2 | TMMASP198 | Implementation of Lightweight Shuffle net Based CNN for Classification... | |
| 3 | TMMASP197 | Minimizing Error Rate in Lightweight and High Accurate RR Interval Com... | |
| 4 | TMMASP194 | A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analys... | |
| 5 | TMMASP193 | Implementation of Self-Organized Operational Neural Networks for R-Pea... | |
| 6 | TMMASP170 | Feature Extraction and Analysis of Speech Signal Based on Fractional ... | |
| 7 | TMMASP169 | A Novel Approach to Speech Signal Segmentation Based on Time-Frequenc... | |
| 8 | TMMASP168 | ECG Signal Extraction Method Based on Singular Value Selection and Wa... | |
| 9 | TMMASP167 | Feature Extraction in Speech Signal Recognition | |
| 10 | TMMASP166 | EEG Signal Processing and Emotion Recognition Using Convolutional Neur... |
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We take pride in promoting innovative and unconventional project ideas that challenge students to think outside the box and explore new and emerging technologies. Many students have found success by taking on BTech projects that address real-world problems and make a positive impact on society.
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
