This paper presents a new power-line disturbance detection algorithm. The utilized recursive least square (RLS) prediction error lter extracts the power-line disturbance signal from recorded data, and the modi ed stop-and-go cell average constant false alarm rate (CA CFAR) detector makes a decision based on the squared output of the previous stage. The detection performance of the proposed algo...

We present a “twice universal” linear prediction algorithm over the unknown parameters and model orders, in which the sequentially accumulated square prediction error is as good as any linear predictor of order up to some M , for any individual sequence. The extra loss comprises of a parameter “redundancy” term proportional to (p/2)n-’ In(h), and a model order “redundancy” term proportional to ...

Abstract Various forms of artifacts can readily contaminate an electroencephalogram recorded using surface electrodes. A comparison several (EEG) de-noising methods is shown here. Five distinct noise are reduced three different strategies, and the results compared. These procedures Recursive Least Squares (RLS) adaptive algorithm, Mean (LMS) method, Fully Connected Neural Network (FCNN). The ti...

In most adaptive signal processing applications, system linearity is assumed and adaptive linear filters are thus used. The traditional class of supervised adaptive filters rely on error-correction learning for their adaptive capability. The kernel method is a powerful nonparametric modeling tool for pattern analysis and statistical signal processing. Through a nonlinear mapping, kernel methods...

Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, control performance DSVM-MPC influenced by accuracy parameters select speed optimal voltage vector. In order to identify accurately, predictive for PMSM discrete sp...

In this paper we concentrate on MPSK carrier frequency estmation based on random data modulation. We present a fast, open-loop frequency estimation and tracking techinque, which combines a feedforward estimator stucture and a recursive least square (RLS) predictor. It is suitable for the frequency estimation and large frequency acquisition and tracking required of burst mode satellite modems op...

The Data rates and spectrum efficiency of Wireless Mobile Communication have been significantly improved over the last decade or so. Recently, the advanced systems such as 3GPP LTE and terrestrial digital TV broadcasting have been sophisticatedly developed using OFDM and CDMA technology. In general, most mobile communication systems transmit bits of information in the radio space to receiver. T...

A new insight into vanadium redox flow batteries (VRFB) parameter estimation is presented. Driven by the electric vehicles proliferation, a hybrid fast-charging station with grid and renewable energy connection particularly considered. In this stationary application, VRFB operating as buffering module. This topology could contribute to reduce cost of charging station. However, make viable techn...

A new multichannel filtered-x recursive least square algorithm for active noise control systems is proposed. It is shown that the use of the filtered-x structure, instead of the commonly used modified filtered-x structure lead to a more efficient implementation and similar convergence performance and stability. The paper is also focused on examining the benefits of auxiliary normal equations so...

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...