| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Introduction.mp4 | 35.5 MB | ||
| 2 - MUSIC for DoA Theory | |||
| 3 - Fixed point Modeling | |||
| 10. Jacobi Rotations Model.mp4 | 141.2 MB | ||
| 10. cordic py.py | 3.1 KB | ||
| 10. covariance py.py | 4.6 KB | ||
| 10. jacobi fpga py.py | 10.6 KB | ||
| 10. jacoby py.py | 6 KB | ||
| 11. CORDIC Model.mp4 | 89.3 MB | ||
| 11. cordic fpga py.py | 9.1 KB | ||
| 11. cordic py.py | 3.1 KB | ||
| 12. MUSIC Denominator Model.mp4 | 115.7 MB | ||
| 12. spectrum fpga py.py | 6.2 KB | ||
| 12. spectrum py.py | 5.4 KB | ||
| 13. MUSIC Algorithm Model.mp4 | 69.9 MB | ||
| 13. cordic py.py | 3.1 KB | ||
| 13. covariance py.py | 4.6 KB | ||
| 13. jacoby py.py | 6 KB | ||
| 13. music fpga py.py | 6 KB | ||
| 13. spectrum py.py | 5.4 KB | ||
| 4 - Additional Resources | |||
| 14. Bonus Lecture.mp4 | 26.4 MB | ||
| 14. Extended Practical Workshops and Implementation Resources.url | 0 B | ||
| 9. Covariance Matrix Model.mp4 | 94.4 MB | ||
| 9. covariance fpga py.py | 6.1 KB | ||
| 9. covariance py.py | 4.6 KB | ||
| 3. Phased Arrays Foundation.mp4 | 23.7 MB | ||
| 3. music sim 0 py.py | 4.3 KB | ||
| 4. MUSIC Algorithm Overview.mp4 | 41.8 MB | ||
| 4. live covariance py.py | 4.1 KB | ||
| 4. live music py.py | 6.1 KB | ||
| 5. Covariance Matrix.mp4 | 41.2 MB | ||
| 5. live unitary py.py | 5.1 KB | ||
| 6. Jacobi Eigenvalue Algorithm.mp4 | 48.6 MB | ||
| 7. CORDIC Algorithm.mp4 | 61.5 MB | ||
| 7. live cordic py.py | 10.3 KB | ||
| 8. MUSIC Pseudospectrum.mp4 | 14 MB | ||
| 2. Environment Setup & Workflow.mp4 | 5.7 MB | ||
| 2. requirements txt.txt | 204.8 B |
Multiple Signal Classification (MUSIC) for DoA
https://WebToolTip.com
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 12m | Size: 810 MB
Understand the MUSIC algorithm for DoA through phased arrays, covariance analysis, and fixed-point modeling
What you'll learn
Understand the MUSIC algorithm and its processing stages for direction-of-arrival estimation
Model MUSIC processing blocks using fixed-point arithmetic and analyze quantization effects
Evaluate precision–performance trade-offs in fixed-point signal processing implementations
Validate fixed-point models against floating-point reference implementations
Prepare fixed-point models for later FPGA and HLS-based implementation
Requirements
Basic knowledge of digital signal processing and linear algebra is recommended. Familiarity with Python is required.
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