The electrocardiogram (ECG) is the graphical representation of heart’s functionality. It is an important tool used for the diagnosis of cardiac abnormalities. ECG signals are usually weak and susceptible to external noise and interference. Adaptive filter is a good tool to reduce the influence of ambient noise/interference on the ECG signals. Adaptive filter uses Least mean squares (LMS) algori...

We have presented an adaptive algorithm similar to both the signerror and NLMS adaptive algorithms that is capable of reducing the geomagnetic noise present in magnetic anomaly detection systems. Through both theory and application of this algorithm to actual MAD flight data, we have proven this algorithm’s insensitivity to transients in the desired response, with a corresponding improvement in...

In this paper, adaptive filtering is adopted for active automotive engine vibration isolation where both transient and stationary engine internal excitations as well as structure flexibility are considered. The adaptive filtering problem is formulated using a linear regression model representation. This allows for an application of a general family of state-of-the-art recursive parameter estima...

Typical approaches to acoustic echo cancellation (AEC) in mobile telephones employ adaptive linear algorithms, such as the normalized least mean squares (NLMS) algorithm. Smaller, cheaper components on these devices introduce nonlinearities into the echo path, which adversely affect performance of linear AEC systems and necessitate means of nonlinear compensation. Memoryless nonlinear blocks th...

The program ”Filtering and Identification Tool” designed for MATLAB provides an easy to handle tool for linear system identification of continuous time domain systems. But also discrete time models may be identified. The only property the process model must fulfill is that it has to be linear in the parameters. A graphical user interface guides the research engineer through all steps. As most o...

This paper proposes a novel adaptive filtering algorithm which converges faster than the Krylov proportionate normalized least mean square (KPNLMS) algorithm. KPNLMS is known to exhibit faster convergence than the standard NLMS algorithm for all unknown systems. Our algorithm is named Krylov proportionate normalized least mean fourth (KPNLMF) and it deals with mean fourth minimization of the er...

In order to achieve fast convergence and less computation for adaptive filters, a joint method combining a whitening process and the NLMS algorithm is a hopeful approach. However, updating the filter coefficients is not synchronized with the reflection coefficient updating resulting in unstable behavior. We analyzed effects of this, and proposed the “Synchronized Learning Algorithm” to solve th...

One of the main objectives within the adaptive signal processing is the noise suppression, i.e. the detection of information bearing signal in noise. Adaptive filter application ANC (Adaptive Noise Cancellation) using adaptive Filter algorithm is an important basis for signal processing. The device automatically filters out the components of the signal which are not related to the orignal signa...