Electronic_Transmission_Detector_Presentation_with_Visuals.pptx

It is a project presentation. So, i need to make it attractive , more visual effects and suitable template for this

1. Electronic Transmission Detector for Exam Halls Using SDR and Multimodal Sensing Your Name / Team Name Institution / Course

2. Introduction - Competitive exams (NEET, JEE) demand fairness - Students may misuse hidden wireless devices - Manual monitoring is not enough - Goal: Detect & stop unauthorized signals in real time

3. Problem Statement - Small devices are hard to spot visually - Large exam halls → hundreds of students - Devices use cellular, Wi-Fi, Bluetooth, ISM bands - Existing systems miss low-power/burst signals - Need: Automatic, reliable, real-time detection

4. Proposed Solution - Monitor RF bands (cellular, Wi-Fi, Bluetooth) - Multiple SDR receivers to locate sources - Machine learning to classify signals - Extra sensors: microphones, cameras, IR - Dashboard: real-time alerts, heatmaps, actions

5. Technologies Used - SDR (RTL-SDR / Airspy) → spectrum monitoring - Antennas: omnidirectional + directional - Processing: Raspberry Pi / Intel NUC - Software: Python, GNU Radio, SoapySDR - Machine Learning: scikit-learn, TensorFlow Lite - Dashboard: Flask/React - Optional: microphones, cameras, IR sensors

6. Components Hardware: - SDR dongles (RTL-SDR, Airspy) - Antennas (omni + directional) - Raspberry Pi 4 - GPSDO (time sync) - Microphone array / Cameras / IR sensors Software: - Raspberry Pi OS / Ubuntu - GNU Radio, Python (NumPy, SciPy) - ML libraries (Scikit-learn, TensorFlow Lite) - Flask/React (dashboard)

7. Cost Estimation - Basic Prototype: ₹26,000 – ₹28,000 - Enhanced Setup: ₹40,000 – ₹60,000 (Includes SDRs, antennas, Raspberry Pi, sensors, etc.)

8. Conclusion - Ensures fair and transparent exams - Detects & locates cheating devices in real time - Reduces manual effort & false positives - Improves trust in competitive examinations

9. Thank You Q & A

10. SDR Setup Antenna → SDR Receiver (RTL-SDR / Airspy) → Raspberry Pi / NUC → Signal Processing (GNU Radio, Python ML) → Dashboard (Alerts & Heatmaps)

11. Multimodal Sensing in Exam Hall • SDR Receivers: Detect RF transmissions • Cameras: Seat-level monitoring • Microphones: Detect ultrasonic/voice relays • IR/Proximity Sensors: Detect hidden devices → Combined data improves accuracy & reduces false alarms