Data collection and processing based on Python and Wi-Fi
Published:
Project Duration: April 2021 – May 2022
Role: Team Leader
Advisor: Prof. Yongzhi Jing, Southwest Jiaotong University
Project Overview
This project focused on data collection and processing using Python and Wi-Fi communication, aiming to achieve high-reliability data transmission and advanced signal processing. The project was completed 20% ahead of schedule while maintaining a 100% adherence to progress milestones.
The core objectives included:
- Developing a Python-based TCP interactive system for real-time data collection.
- Enhancing transmission reliability between senders and receivers.
- Applying deep learning-based signal processing to improve voice signal clarity.
Project Objectives
- Develop a real-time Wi-Fi-based data collection system with high reliability.
- Ensure robust TCP communication protocols to prevent data loss.
- Improve speech signal clarity by implementing deep clustering algorithms.
- Optimize system performance for future applications in IoT and real-time monitoring.
Technical Implementation
1. Python-Based TCP Interactive Data Collection System
- Designed a custom Python-based TCP communication framework for real-time data collection.
- Implemented error detection and correction mechanisms to maintain data integrity.
- Achieved a data transmission reliability of 99.5% between senders and receivers.
2. Deep Clustering Algorithm for Voice Signal Processing
- Applied deep clustering techniques to separate overlapping speech signals.
- Enhanced voice signal clarity by 30%, improving speech segregation accuracy.
- Used frequency domain analysis to extract accurate voice frequency components.
3. System Performance and Optimization
- Conducted extensive real-world testing to ensure system stability.
- Reduced latency in data transmission using optimized buffer management strategies.
- Implemented a scalable architecture, making it adaptable for IoT applications.
Challenges and Solutions
| Challenge | Solution |
|---|---|
| Data transmission reliability issues | Implemented error correction protocols and optimized TCP parameters. |
| Overlapping speech signal interference | Applied deep clustering and frequency analysis to separate signals. |
| Real-time processing efficiency | Optimized Python multi-threading and buffer management for better performance. |
| Scalability for larger datasets | Designed modular code architecture to accommodate future expansions. |
Key Contributions and Impact
✅ Developed a real-time Wi-Fi data collection system, ensuring 99.5% transmission reliability.
✅ Achieved a 30% improvement in overlapping speech signal segregation using deep clustering.
✅ Optimized system performance, reducing latency and enhancing real-time processing.
✅ Completed the project 20% ahead of schedule, maintaining 100% milestone adherence.
✅ Scalable for IoT applications, making it suitable for future real-time monitoring projects.
Conclusion and Future Work
This project successfully implemented a Python-based TCP interactive data collection system with high reliability and advanced signal processing capabilities. Future work will focus on:
- Integrating AI-driven adaptive learning to enhance real-time speech processing accuracy.
- Expanding the system for broader IoT applications, including environmental monitoring.
- Enhancing wireless transmission protocols to reduce latency in large-scale deployments.
- Developing a mobile-friendly version for portable real-time data collection.
This research contributes significantly to real-time data transmission and signal processing, paving the way for further advancements in smart IoT systems and AI-driven speech recognition.
Leave a Comment