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In this paper, we consider an online least square regression problem where the objective function is composed of a quadratic loss function and an L1 regularization on model parameter. For each training sample, we propose to approximate the L1 regularization by a convex function. This results in an overall convex approximation to the original objective function. We apply an efficient accelerated stochastic...
In this paper, a novel stochastic approximation technique is presented as a low-complexity alternative to conventional least squares-based digital predistortion model extraction solutions. The proposed technique is based on the simultaneous perturbation stochastic approximation (SPSA) algorithm. It avoids the hardware-intensive matrix operations associated with least squares by using an iterative...
This paper presents a powerful performance and convergence speed of Variable Step-Size Transform Domain Incremental/Diffusion Least Mean Square (VSS-TD-I/D-LMS). It modifies and extends several already existing algorithms of VSS-LMS and VSS-TD-LMS to wireless sensor adaptive networks. The effect of transform domain along with power normalization plays a rule in reduce eigenvalue spread of input autocorrelation...
This paper presents a new structure for the variable step-size transform domain NLMS (VSS-TDNLMS) adaptive filter. The pipelining of VSS-TDNLMS is achieved by introducing an amount of delay into the feedback loops. The transform function is realized efficiently by classical recursive implementation. To reduce the hardware complexity, we use multiplier-less implementation to realize the constant coefficient...
A novel least mean squares (LMS) method that exploits sparsity level information for sparse channel estimation is presented and studied in this paper. This method utilizes the channel sparsity level information by incorporating a penalty term into the cost function and has better performance than the compared methods which do not take into account the sparsity level information. The convergence analysis...
In this paper, a novel joint recursive least square (RLS) and least mean square (LMS) adaptive equalization algorithm for indoor, short range wireless communications under staircase environment is proposed. In this algorithm, the RLS is initially used to realize the fast convergence. Then, the LMS is adapted to rapidly optimize the receiving signals. Once the imbalance of the equalizer or severe channel...
In this paper, a p-norm-like constraint is utilized to develop a sparse least mean fourth algorithm for sparse channel estimation. By incorporating the p-norm-like constraint into the cost function of conventional least mean fourth (LMF) algorithm, a p-norm-like constraint least mean fourth (PNC-LMF) algorithm is achieved to exploit the sparsity property of the broadband sparse wireless communication...
We demonstrate experimentally adaptive recursive-least-square frequency-domain equalization (RLS-FDE) for mode-division-multiplexing. The RLS-FDE algorithm improves convergence speed as transmission distance increases. For MDM transmission over a 1,000-km few-mode fiber, the convergence speed was increased by 17.5 times in comparison with LMS-FDE.
The wave concept iterative procedure is very efficient numerical method for the electromagnetic modeling in high frequency domain. But, the numerical complexity of the iterative method in modeling complexes structures and a large number of iterations to converge to the optimal result is a very interesting problem to study. For this reason, In this paper we will implement a different adaptive algorithms...
Electrocardiogram (ECG) can help to diagnose range of diseases including heart arrhythmias, heart enlargement, heart inflammation (pericarditis or myocarditis) and coronary heart disease. ECG consists of noise which is non stationary that affects the reliability of ECG waveform. In this paper an adaptive filter for denoising ECG signal based on Least Mean Squares (LMS), Normalized Least Mean Square...
The priority of current era in noise cancellation field aims at blocking the low frequency noise since most real life noises operate below 1 KHz. The noise which creates obstruction in everyday communication needs to be dealt in an effective way. Acoustic Noise Cancellation (ANC) is hence regarded as most sought after solution. ANC has created its own niche in this field where a wide range of industrial...
In recent times noise cancellation is a vital issue, as it is responsible for reducing undesired disturbances in the process of communication. Active Noise Cancellation (ANC) is the most effective technique to cancel noise. ANC has been an active area of research and various adaptive methodologies have been employed to achieve a better ANC scheme. In the ANC technique, the aim is to minimize the noise...
Active Noise Control (ANC) has been gaining an increasing interest in recent years. Much attention has been devoted to design of efficient control algorithms, enabling noise reduction at a high level, with computational load acceptable by currently available electronics. Among different approaches to noise control, employment of vibrating plates as secondary sources or as active barriers is particularly...
Adaptive channel equalisation is a signal processing technique to mitigate inter-symbol interference (ISI) in a time dispersive channel. To this end, the use of least mean squares (LMS) algorithm and its variants is widespread since they minimise the minimum mean squared error (MMSE) criteria by online stochastic gradient algorithms and they asymptotically tend to the optimal Weiner solution for linearly...
Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this paper, a group sparse regularized least-mean-square (LMS) algorithm is proposed to cope with the identification problems for multiple/multi-channel systems. In particular, the coefficients of impulse response function for each system are assumed to be sparse. Then, the...
The active power harmonic filtering is performed by injecting equal-but-opposite of the distortion into the power line. The harmonic on-line tracking is an essential part of the filtering process. In this paper, the linear adaptive neurons (ADALINE), a version of ANN (Artificial Neural Network), is used to perform adaptive on-line tracking of the power system harmonics. The ADALINE can not only accurately...
In this paper a new simplified adaptive filter algorithm is introduced which is based on the hybrid operation of variable step-size and fixed step-size least mean square adaptive algorithm. In this proposed algorithm the variable step-size is used in the first stage, the algorithm adopts the fixed step size least mean square (LMS) whenever an acceptable mean square error threshold is reached that...
Adaptive Beamforming plays a key role in interference mitigation and target tracking. The Least Means Squares (LMS) algorithm has been successfully employed to accomplish this task given a reference signal. However, under severe reception conditions, LMS does not converge and locates the antenna beam on a wrong direction. Genetic Algorithms (GA) has shown to perform very well in global optimization...
The idea of finding low-rank solutions to matrix or tensor optimization tasks by greedy rank-one methods has been showing itself repeatedly in the literature. The simplest method, and often a central building block in accelerated methods, consists in performing updates along low-rank approximations of the negative gradient. This is convenient as it does increase the rank in a prescribed manner per...
This paper presents a practical implementation of all digital calibration algorithm for the gain and timing mismatches in undersampling Time-Interleaved Analog-to-Digital Converter (TI-ADC). A new Least Mean Square (LMS) based detection scheme is proposed to increase convergence speed as well as to enhance the estimate accuracy. Monte Carlo simulations for a four-channel undersampling 60 dB SNR TI-ADC...
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