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| Kompresyjne próbkowanie× | Projektowanie filtrów FIR× | |
|---|---|---|
| Dziedzina | Przetwarzanie sygnałów | Przetwarzanie sygnałów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2006 | 1987 |
| Twórca≠ | Emmanuel Candès, Justin Romberg, and Terence Tao | Thomas W. Parks and C. Sidney Burrus |
| Typ≠ | Sparse signal recovery | Finite Impulse Response filter design |
| Źródło pierwotne≠ | Candes, E. J., Romberg, J., & Tao, T. (2006). Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete and Inaccurate Measurements. IEEE Transactions on Information Theory, 52(2), 489–509. DOI ↗ | Parks, T. W., & Burrus, C. S. (1987). Digital Filter Design. John Wiley & Sons. link ↗ |
| Inne nazwy≠ | Compressed Sensing, CS, Sparse Recovery, Sub-Nyquist Sampling | FIR Design, Finite impulse response, Non-recursive filter design |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | Compressive Sensing (CS) is a signal acquisition and reconstruction technique that exploits signal sparsity to recover high-resolution signals from far fewer samples than required by the Nyquist sampling theorem. Developed by Emmanuel Candès, Justin Romberg, and Terence Tao in 2006, compressive sensing challenges the traditional sampling paradigm by showing that signals with sparse representations can be reconstructed from sub-Nyquist random measurements using nonlinear optimization. | Finite Impulse Response (FIR) filters are digital filters with an impulse response that settles to zero in finite time, making them fundamentally stable and easy to analyze. Unlike their IIR counterparts, FIR filters are inherently stable, can have exactly linear phase response, and are widely used in applications from audio processing to telecommunications where phase distortion must be minimized. |
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