{ The fast exact least-mean-square (LMS) algorithm is a computationally-eecient method for computing the outputs and updates for an adaptive LMS nite-impulse-response (FIR) lter. In this paper, we extend this method to several useful algorithms for feedforward active noise control: the ltered-X LMS, modiied ltered-X LMS, eecient modiied ltered-X LMS, periodic ltered-X LMS, and sequential ltered...

A fundamental relationship exists between the quality of an adaptive solution and the amount of data used in obtaining it. Quality is defined here in terms of “misadjustment,” the ratio of the excess mean square error (mse) in an adaptive solution to the min imum possible mse. The higher the misadjustment, the lower the quality is. The quality of the exact least squares solution is compared wit...

In multi-gigabit per second systems, due to speed limitation, decision feedback equalization with first post-cursor cancellation can cause additional pattern dependent jitters with conventional LMS adaptation algorithm. Combining LMS algorithm and edge equalization, effects of first tap feedback delay is relieved and more compromised timing and voltage margin is obtained. The result is verified...

In this paper, a Code-Jump (CJ) assisted Least Mean Square (LMS) calibration algorithm for high resolution successive approximation register (SAR) analog-to-digital converter (ADC) is proposed. This hybird does not require any specific input signal and has no impact on the ADC overall gain error. The revised LMS iteration used to improve accuracy of lowest bits (LSBs) capacitor in capacitive-di...

We present a method to deal with adaptive noise cancelling based on independent component analysis (ICA). Although popular least-mean-squares (LMS) algorithm removes noise components based on second-order correlation, the proposed ICA-based algorithm can utilize higher-order statistics. Additionally, extending to transform-domain adaptive filtering (TDAF) methods, normalized ICA-based algorithm...

In this paper ,a new idea to combine Adjusted Step Size Least Mean Square (ASSLMS) algorithm with standard LMS active tap detection technique is presented .Then the combined method is used for estimating unknown time-invariant (stationary) and time varying (nonstationary) Finite Impulse Response (FIR) channel for adaptive noise cancellation (ANC) application. The focus of this paper is to impro...

Least mean square-partial parallel interference cancelation (LMS-PPIC) is a partial interference cancelation using adaptive multistage structure in which the normalized least mean square (NLMS) adaptive algorithm is engaged to obtain the cancelation weights. The performance of the NLMS algorithm is mostly dependent to its step-size. A fixed and non-optimized step-size causes the propagation of ...