We have designed and simulated an adaptive line enhancer system for conferencing. This system is based upon a leastmean-square (LMS) and recursive adaptive algorithm (RLS) Performance of ALE is compared for LMS&RLS algorithms.

A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LM...

Though the Leaky Least-Mean-Square (LLMS) algorithm mitigates the drifting problem of the LMS algorithm, its performance is similar to that of the LMS algorithm in terms of convergence rate. In this paper, we propose a new LLMS algorithm that has a better performance than the LLMS algorithm in terms of the convergence rate and at the same time solves the drifting problem in the LMS algorithm. T...

AbslractThis paper studies the effect of array calibration errors on the performance of various direction 6nding @F) based signal copy algorithms. Unlike blind copy me€hods, this class of algorithms requires an estimate of the directions of arrival (DOA’s) of the signals in order to compute the copy weight vectors. Under the assumption that the observation time is sufficiently long, the followi...

Digital active noise control (ANC) for headphones usually has to predict the noise because of the latency of AD-conversion. In adaptive feedback ANC, the prediction is based on the noise that entered the headphone. This noise is low pass filtered due to the physical barrier of the ear cups. In this paper, this low pass characteristic is exploited to define a prediction filter which does not req...

Abstract — We show in this paper that many different least-squares problems which have applications in signal processing may be seen as special cases of a more general vector space minimization problem called the Minimum Norm problem. We show that special cases of the Minimum Norm problem include: least squares fitting of a finite set of points to a linear equation and to a quadratic equation; ...

This paper studies the eeect of array calibration errors on the performance of various DF (direction nding) based signal copy algorithms. Unlike blind copy methods, this class of algorithms requires an estimate of the directions of arrival (DOAs) of the signals in order to compute the copy weight vectors. Under the assumption that the observation time is suuciently long, the following algorithm...

In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scen...

Cold orbital forging (COF) as an advanced incremental metal-forming technology has been widely used in processing vehicle parts. During the COF process, vibration on machine injures service life of and quality forged part. The study control is therefore necessary. In this study, dynamic model established, performances some key positions are obtained using Matlab&Simulink software. Subsequen...

In this paper we present a general formalism for the establishment of the family of selective partial update affine projection algorithms (SPU-APA). The SPU-APA, the SPU regularized APA (SPU-R-APA), the SPU partial rank algorithm (SPU-PRA), the SPU binormalized data reusing least mean squares (SPU-BNDR-LMS), and the SPU normalized LMS with orthogonal correction factors (SPU-NLMS-OCF) algorithms...