Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., L1-norm LMS or zero-attracting LMS (sparse LMS or ZA-LMS), reweighted zero attracting LMS (RZA-LMS) and Lp-norm LMS (LP-LMS), have been proposed b...

Abstract Introduction: The present study was conducted to investigate the association between the serum vitamin D levels and severity of disease in chronic rhino sinusitis (CRS) patients. Materials and Methods: This prospective cross-sectional study was conducted on a total of 93 patients suffering from chronic rhino sinusitis with nasal polyposis (CRS w NP). Serum level...

This paper presents new adaptive filtering techniques used in speech enhancement system. Adaptive filtering schemes are subjected to different trade-offs regarding their steady-state misadjustment, speed of convergence, and tracking performance. Fractional Least-Mean-Square (FLMS) is a new adaptive algorithm which has better performance than the conventional LMS algorithm. Normalization of LMS ...

background and objectives: uterine smooth muscle tumors are the most common human neoplasm .they are divided clinically as benign and malignant but there is  another group of  lesions which is difficult to place in these two categories ,so-called smooth  muscle tumors of uncertain malignant potential (stump) and differentiation  of these tumors on the basis of h&e staining is impossible . t...

This pilot study examined the effects of a teacher-taught, locomotor skill (LMS)-based physical activity (PA) program on the LMS and PA levels of minority preschooler-aged children. Eight low-socioeconomic status preschool classrooms were randomized into LMS-PA (LMS-oriented lesson plans) or control group (supervised free playtime). Interventions were delivered for 30 min/day, five days/week fo...

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...

This work is part of a first phase of the project “DOMAIN SPECIFIC MODELING FOR THE LEARNING OBJECTS BUILD PLATFORM-INDEPENDENT " and seeks to make a comparison between Open Source LMS to get a first approximation of common modules them, and then start building the ontology compatible with all LMS studied, for that reason this paper is organized as follows: 1.Select Tools to work. 2. Contextual...

This paper provides comparative analysis of ten types of e-Learning Management Systems (LMS) established on the market. We recognize four LMS types: (1) proprietary LMS, (2) mainly proprietary and partly standard based LMS, (3) mainly standard based LMS and partly proprietary LMS and (4) open architecture LMS. The analysis shows that “standard/proprietary” systems lead e-Learning market at pres...

In this paper, we propose two novel p-norm penalty least mean square (lp-LMS) algorithms as supplements of the conventional lp-LMS algorithm established for sparse adaptive filtering recently. A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error, w...