Implementation of the Trigonometric LMS Algorithm using Original Cordic Rotation

The LMS algorithm is one of the most successful adaptive filtering algorithms. It uses the instantaneous value of the square of the error signal as an estimate of the mean-square error (MSE). The LMS algorithm changes (adapts) the filter tap weights so that the error signal is minimized in the mean square sense. In Trigonometric LMS (TLMS) and Hyperbolic LMS (HLMS), two new versions of LMS algorithms, same formulations are performed as in the LMS algorithm with the exception that filter tap weights are now expressed using trigonometric and hyperbolic formulations, in cases for TLMS and HLMS respectively. Hence appears the CORDIC algorithm as it can efficiently perform trigonometric, hyperbolic, linear and logarithmic functions. While hardware-efficient algorithms often exist, the dominance of the software systems has kept those algorithms out of the spotlight. Among these hardware- efficient algorithms, CORDIC is an iterative solution for trigonometric and other transcendental functions. Former researches worked on CORDIC algorithm to observe the convergence behavior of Trigonometric LMS (TLMS) algorithm and obtained a satisfactory result in the context of convergence performance of TLMS algorithm. But revious researches directly used the CORDIC block output in their simulation ignoring the internal step-by-step rotations of the CORDIC processor. This gives rise to a need for verification of the convergence performance of the TLMS algorithm to investigate if it actually performs satisfactorily if implemented with step-by-step CORDIC rotation. This research work has done this job. It focuses on the internal operations of the CORDIC hardware, implements the Trigonometric LMS (TLMS) and Hyperbolic LMS (HLMS) algorithms using actual CORDIC rotations. The obtained simulation results are highly satisfactory and also it shows that convergence behavior of HLMS is much better than TLMS.Comment: 12 pages, 5 figures, 1 table. Published in IJCNC; http://airccse.org/journal/cnc/0710ijcnc08.pdf, http://airccse.org/journal/ijc2010.htm

Adaptive LMS filters for cellular CDMA overlay situations

This paper extends and complements previous research we have performed on the performance of nonadaptive narrowband suppression filters when used in cellular code-division multiple-access (CDMA) overlay situations. An adaptive least mean square (LMS) filter is applied to a cellular CDMA overlay in order to reject narrowband interference. An accurate expression for the steady-state tap-weight covariance matrix is derived for the real LMS algorithm for arbitrary statistics of the overlaid interference. Numerical results illustrate that when the ratio of the narrowband interference bandwidth to the spread spectrum bandwidth is small, the LMS filter is very effective in rejecting the narrowband interference. Furthermore, it is seen that the performance of the LMS filter in a CDMA overlay environment is not significantly worse than the performance of an ideal Wiener filter, assuming the LMS filter has had sufficient time to converge.published_or_final_versio

Extension of Impulse Detectors to Spatial Dimension and their Utilization as Switch in the LMS L-SD Filter

In this paper, one kind of adaptive LMS filters based on order statistics is used for two-dimensional filtration of noisy greyscale images degraded by mixed noise. The signal-dependent adaptive LMS L-filter (L-SD) consists of two normalized constrained adaptive LMS L-filters, because they have better convergence properties than simple LMS algorithm. Moreover, first filter suppresses the noise in homogeneous regions and second filter preserves the high components of filtered image. Some versions of spatial order statistic detectors were developed from the impulse detectors and were employed as switch between output these filters

Introducing Adaptive filters Based on Shadow Concept for Speech Processing

This paper presents the new approach to introducing adaptive Filter with LMS Algorithm based on Shadow concept. Which is useful for the cancellation of the noise component overlap with Speech signal in the same frequency range, but fixed LMS algorithm produces minimum convergence rate and fixed steady state error. So we presents design, implementation and performance  of adaptive FIR filter, based on  Shadow concept, which produces minimum mean square error compare to fixed LMS, and we also obtains denoised Speech signal at output, and also we propose to calculate SNR values of  Adaptive Filter with LMS algorithm with and without Shadow concept

Active control of vibrant actuators with adaptive adjustment of the reference

An improved adaptive filter for eliminating periodic vibrations of unknown frequency in rotary machinery is presented. The proposed canceller is based on a usual bank of digital adaptive notch filters, each filter tuned in the cancellation of one harmonic. The amplitude and phase of each harmonic is adaptively adjusted by an LMS-based algorithm. Moreover, the central frequency of each notch filter is also adaptively adjusted (fine tuning). The resulting algorithm, tested in an industrial application, shows effectiveness in cancelling unknown periodic disturbances, reducing environmental noise and maintenance problems.Peer ReviewedPostprint (published version