Prajesh Biswas
- Login: biswaprajesh
- Registered on: 12/25/2025
- Last sign in: 12/25/2025
Issues
| open | closed | Total | |
|---|---|---|---|
| Assigned issues | 4 | 0 | 4 |
| Reported issues | 4 | 0 | 4 |
Projects
| Project | Roles | Registered on |
|---|---|---|
| IoT Music Recommender | Manager | 12/25/2025 |
Activity
12/25/2025
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08:55 AM IoT Music Recommender Bug #4508 (Resolved): GSR Sensor Signal Noise Interference
- Commit: r102 - Added 10-point moving average filter to sensor_input.cpp. Accuracy improved by 15%.
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07:26 AM IoT Music Recommender Bug #4508 (Resolved): GSR Sensor Signal Noise Interference
- The skin conductance (GSR) readings are showing erratic spikes when the ESP32 WiFi module is transmitting data. Resolution needed: Implement a 10-point moving average software filter or add a decoupling capacitor to the sensor power line.
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08:51 AM IoT Music Recommender Feature #4509 (Resolved): Spotify Web API OAuth2 Authentication
- Commit: r105 - Implemented PKCE flow for OAuth2 authentication; verified secure token exchange and refresh logic. Resolved 401 Unauthorized error during playlist trigger tests.
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08:31 AM IoT Music Recommender Feature #4509 (Closed): Spotify Web API OAuth2 Authentication
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07:27 AM IoT Music Recommender Feature #4509 (Resolved): Spotify Web API OAuth2 Authentication
- Setup the secure login flow using Spotify's Developer API. The system must be able to request "user-modify-playback-state" permissions to trigger the recommended playlists automatically based on the detected emotion.
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07:28 AM IoT Music Recommender Support #4510 (New): Real-time Emotion Dashboard UI
- Create a web-based dashboard using Flask/React to visualize the incoming bio-signals and the resulting emotion classification. This will serve as the primary interface for User Acceptance Testing (UAT).
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07:25 AM IoT Music Recommender Feature #4507 (New): Implement HRV Calculation Algorithm
- Develop a Python/C++ script to extract Heart Rate Variability (HRV) from raw pulse sensor peaks. This feature is critical for the ML model to distinguish between "High Stress" and "Active Excitement."