soil conservation service (scs) adjusted the kostiakovs infiltration model by adding a constant coefficient, for improvement of estimation. the improved model has three constant parameters, which are difficult to calculate. so scs has taken the third constant parameter as equal to 0.65-0.7 cm to simplify the estimation. this parameter varies in different soil and often outranges the scs estimat...

The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that “kernelizes” the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thus, the KLMS can be interpreted as an approximate Bayesian filtering method. This allows us to systematicall...

This work presents a new variation of the commonly used Least Mean Squares Algorithm (LMS) for the identification of sparse signals with an a-priori known sparsity using a hard threshold operator in every iteration. It examines some useful properties of the algorithm and compares it with the traditional LMS and other sparsity aware variations of the same algorithm. It goes on to examine the app...