The electrocardiographic (ECG) signal is a transthoracic manifestation of the electrical activity of the heart and is widely used in clinical applications. This chapter describes an unbiased linear adaptive filter (ULAF) to attenuate high-frequency random noise present in ECG signals. The ULAF does not contain a bias in its summation unit and the filter coefficients are normalized. During the a...

The cell impedance analysis technique is a label-free, non-invasive method, which simplifies sample preparation and allows applications requiring unmodified cell retrieval. However, traditional impedance measurement methods suffer from various problems (speed, bandwidth, accuracy) for extracting the cellular impedance information. This thesis proposes an improved system for extracting precise c...

A new non-linear Recursive Least Squares (RLS) algorithm is presented in the context of pattern classification problems. The algorithm incorporates the non-linearity of the filter’s output in the updating rules of the classical RLS algorithm. The proposed method yields lower stationary error levels when compared to the standard LMS and RLS algorithms in a classical application of pattern classi...

This paper proposes an adaptive algorithm, which can be pipelined, as an extension of the delayed least mean square (DLMS) adaptive algorithm. The proposed algorithm provides a capability to achieve high throughput with less degradation of the convergence characteristic than the DLMS algorithm. An architecture for pipelined implementation of the proposed algorithm is considered, and based on th...

In this paper an adaptation technique using both genetic algorithm (GA) and least mean square algorithm (LMS) is proposed. The key control parameters tap-length and tap-weight are updated using the GA and the LMS algorithm respectively according to the value of mean square error (MSE). The proposed algorithm results optimal solutions very fast by reducing initialization and derivative problems....

This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search–based least–mean–squares(LMS)/recursive least squares(RLS) link selection algorithms and sparsity–inspired LMS/RLS link selection algorithms that can exploit the topology of networks with poor–quality link...

The present system of speech enhancement is developing by adaptive filtering approach in digital filters. The adaptive filter utilizes the least mean square algorithm for noise removal, but in practical application of LMS algorithm, a key parameter is the step size. As it is known, if the step size is large, the convergence rate of LMS algorithm will be rapid, but the steady-state mean square e...

The paper presents a stochastic optimization algorithm for computing of least median of squares regression (LMS) introduced by (Rousseeuw and Leroy 1986). As the exact solution is hard to obtain a random approximation is proposed, which is much cheaper in time and easy to program. A MATLAB program is included.

Previous work on the adaptive lattice filters for narrowband interference rejection in a direct sequence (DS) spread spectrum communication system has centered around the use of one-sided prediction only. For two-sided interpolation, that is known to achieve better interference rejection than its one-sided counterpart, only the least-mean-squared (LMS) algorithms have been considered thus far i...