In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and...

This paper compares performance of finite impulse response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square (LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation results, in terms of steady-state mean-square estimation error (MSE) and average bit-error rate (BER) metrics, are found for the frequency-selective Ray...

Least median of squares (LMS) regression is a robust method to t equations to observed data (typically in a linear model). This paper describes an approximation algorithm for LMS regression. The algorithm generates a regression solution with median residual no more than twice the optimal median residual. Random sampling is used to provide a simple O(n log 2 n) expected time algorithm in the two...

This paper develops low-complexity adaptive receivers for space-time block-coded (STBC) transmissions over frequency-selective fading channels. The receivers are useful for equalization purposes for single user transmissions and for joint equalization and interference cancellation for multiuser transmissions. The receivers exploit the rich code structure of STBC codes in order to deliver recurs...

Filtering data in real-time requires dedicated hardware to meet demanding time requirements. Programmable Digital Signal Processor (PDSP) systems are replaced by FPGA systems due to their parallel architecture, wide bandwidth, and flexible nature. For speech processing, here we present the applicability of a FPGA system. The adaptive filtering algorithms is used to estimate the signals statisti...

In this paper a new class of simplified low-cost analog artificial neural networks with on chip adaptive learning algorithms are proposed for solving linear systems of algebraic equations in real time. The proposed learning algorithms for linear least squares (LS), total least squares (TLS) and data least squares (DLS) problems can be considered as modifications and extensions of well known alg...

The linear least mean squares (LMS) algorithm has been recently extended to a reproducing kernel Hilbert space, resulting in an adaptive filter built from a weighted sum of kernel functions evaluated at each incoming data sample. With time, the size of the filter as well as the computation and memory requirements increase. In this paper, we shall propose a new efficient methodology for constrai...