We present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both withinand between-channel deterministic information is considered. For each channel a functional that combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the rela...

In this paper Adaptive filter is designed and simulated using different algorithms for noise reduction in different signals. The developed filter has been analyzed using Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms for sinusoidal, chirp and saw-tooth signals. The performance of developed filter has been compared interms of Rate of Con...

This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We present sparsity-aware conventional and modified distributed CG algorithms using l1 and log-sum penalty functions. The proposed sparsity-aware diffusion distributed CG algorithms have an improved performance in terms of mean...

An adaptive minimum symbol error rate (MSER) linear multiuser detector (MUD) is proposed for direct sequence code division multiple access (DS-CDMA) systems employing multilevel pulseamplitude modulation ( -PAM) scheme. Based on a kernel density estimation for approximating the symbol error rate (SER) from training data, a least mean squares (LMS) style stochastic gradient algorithm called the ...

We propose a training symbol based channel estimation (TS-EST) algorithm that estimates the 2 × 2 Jones channel matrix. The estimated matrix entries are then used as the initial center taps of the 2 × 2 butterfly equalizer. Employing very few training symbols for TS-EST, ultrafast polarization tracking is achieved and tap update can be initially pursued using the decision-directed least mean sq...

This paper demonstrates the concept of an optical magnetometer for magnetocardiography. The magnetometer employs a standard Least-Mean-Squares (LMS) algorithm for heart magnetic field measurement within unshielded environment. Experimental results show that the algorithm can extract a weak heart signal from a much-stronger magnetic noise and detect the P, QRS, and T heart features and completel...

In a distributed network environment, the diffusion-least mean squares (LMS) algorithm gives faster convergence than the original LMS algorithm. It has also been observed that, the diffusion-LMS generally outperforms other distributed LMS algorithms like spatial LMS and incremental LMS. However, both the original LMS and diffusion-LMS are not applicable in non-linear environments where data may...

Adaptive filters that self-adjust their transfer functions according to optimizing algorithms are powerful adaptive systems with numerous applications in the fields of signal processing, communications, radar, sonar, seismology, navigation systems and biomedical engineering. An adaptive signal processing algorithm, e.g., the least mean squares (LMS) algorithm and the recursive least square (RLS...

This paper presents an adaptive digital resolution improvement method for extrapolating and recursive analog-to-digital converters (ADCs). The presented adaptively enhanced ADC (AE-ADC) digitally estimates the digital equivalent of the input signal by utilizing an adaptive digital filter (ADF). The least mean squares (LMS) algorithm also determines the coefficients of the ADF block. In this sch...

In this paper, we consider a minimum mean-squared error (MMSE) based receiver structure which combines multiuser detection (temporal filtering) and receiver beamforming (spatial filtering). Motivated by the high complexity of the optimum receiver, we propose rank constrained temporal-spatial filters which are simpler and near optimum. An iterative algorithm based on alternating minimization is ...