The digital Fourier transform (DFT) and the adaptive least mean square (LMS) algorithm have existed for some time. This paper establishes a connection between them. The result is the “LMS spectrum analyzer,” a new means for the calculation of the DFT. The method uses a set of N periodic complex phasors whose frequencies are equally spaced from dc to the sampling frequency. The phasors are weigh...

In this brief, we develop an least mean square (LMS) algorithm that converge in a statistical sense to the global minimum of the mean square error (MSE) objective function. This is accomplished by estimating the gradient as a smoothed version of the MSE. The smoothed MSE objective begins as a convex functional in the mean. The amount of dispersion or smoothing is reduced, such that over time it...

This review paper presents the analysis about features of Learning Management System (LMS) and ways to include parental participation into LMS. A list 20 popular LMs given. The main LMS discussed. importance including parents Leaning Systems also

Establishing functional relationships between multi-domain protein sequences is a non-trivial task. Traditionally, delineating functional assignment and relationships of proteins requires domain assignments as a prerequisite. This process is sensitive to alignment quality and domain definitions. In multi-domain proteins due to multiple reasons, the quality of alignments is poor. We report the c...

Partial updating of LMS filter coefficients is an effective method for reducing the computational load and the power consumption in adaptive filter implementations. The Sequential Partial Update LMS algorithm is one popular algorithm in this category. In [5] a first order stability analysis of this algorithm was performed on wide sense stationary signals under the restrictive assumption of smal...

Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for LMS. We performed expression profiling on 99 cases of LMS with 3'end RNA sequencing (3SEQ) and demonstrated the existence of 3 molecular subtypes in this cohort. We consequently showed that these molecular subtypes are re...

Sparse adaptive filtering has gained much attention due to its wide applicability in the field of signal processing. Among the main algorithm families, sparse norm constraint adaptive filters develop rapidly in recent years. However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity o...

In order to improve the performance of Least Mean Square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on l0 norm – a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a z...

This paper describes a method for implementing immersive audio rendering filters for single or multiple listeners and loudspeakers. In particular, the paper is focused on the case of single or two listeners with different loudspeaker arrays to determine the weighting vectors for the necessary FIR and IIR filters using the LMS (least-mean-squares) adaptive inverse algorithm. It describes transfo...

This paper investigates the image-based characterization of the land mobile satellite (LMS) channel. For a comprehensive multi-satellite model, a traditional measurementbased approach is not feasible due to the lack of available satellites at all possible orbital positions and the drawbacks of the measurement system when a high gain tracking antenna is involved (e.g. Ka/Ku band). To overcome th...