Salīdzināt metodes

	Blīvās uztveršanas metodes	Adaptīvais LMS filtrs
Nozare	Signālu apstrāde	Signālu apstrāde
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2006	1960
Autors	Emmanuel Candès, Justin Romberg, and Terence Tao	Bernard Widrow and Marcian E. Hoff
Tips	Sparse signal recovery	Gradient descent adaptive filtering
Pirmavots	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.	Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE Wescon Convention Record, 4, 96–104.
Citi nosaukumi	Compressed Sensing, CS, Sparse Recovery, Sub-Nyquist Sampling	LMS Filter, Adaptive LMS Algorithm, Gradient Descent Filtering
Saistītās	4	4
Kopsavilkums	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.	The Least Mean Squares (LMS) filter is an adaptive signal processing algorithm that continuously updates filter coefficients to minimize the squared error between the filter output and a desired signal. Introduced by Bernard Widrow and Marcian Hoff in 1960, the LMS algorithm is one of the most widely used adaptive filtering techniques due to its simplicity, low computational cost, and ability to track time-varying signals.