The goal of this paper is to introduce the joined LMS and LMF adaptive algorithm (LMFSF) for data echo cancellation. The idea is to use an error estimate as smooth as possible, and after comparison with the selected thresholds to decide whether for the next step we apply LMS or LMF. In order to avoid oscillations from LMS to LMF and back, we use two hysteresis loops. It will be shown through si...

Learning Management Systems (LMSes) handle all aspects of the learning process, a crucial part of educational technology. This study elicits and models the functional and non-functional requirements of two academic and one industrial LMS. The overall purpose is to provide a general requirements model, grounded on existing systems and collected evidence, to aid future LMS development. Goal model...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

Uterine leiomyosarcoma (Ut-LMS) develops more often in myometrium of the uterine body than in the uterine cervix. The development of gynecologic tumors is often correlated with female hormone secretion; however, the development of Ut-LMS is not substantially correlated with hormonal conditions, and the risk factor(s) are not yet known. Importantly, a diagnostic-biomarker, which distinguishes ma...

Hepatocyte growth factor activator inhibitors (HAI-1 and HAI-2) are Kunitz-type serine protease inhibitors that have a broad inhibitory spectrum against serine proteases. This study examined the role of HAI-1 and HAI-2 in uterine leiomyosarcoma (LMS) patients, and in vitro. HAI-1 and HAI-2 was examined in uterine normal smooth muscle, leiomyoma and LMS specimens using immunohistochemistry. We i...

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing -norm penalty...

K. Martin M. A. Quigley S. Rogers This article describes how Aventis Pharmaceuticals, in partnership with IBM, is implementing a learning management system (LMS) globally both in the Research and Development organization and in clinical groups within the Commercial Operations organization. It also discusses how Aventis is applying an innovative change management approach that meets challenges t...

The chain of applying adaptivity to Learning Management Systems (LMS) is still deficient; there is a gap between authoring adaptive materials and delivering them in LMS. In this paper, we extend My Online Teacher (MOT), an adaptive authoring system, by adding compatibility with IMS Question & Test Interoperability (QTI) and IMS Content Packaging (CP). Thus, the authors can utilize the authored ...

Learning management systems (LMS) are very successful in e-education but provide only little adaptivity. Adapting courses to the needs of individual learners such as to their learning styles improves the learning progress of learners. However, a requirement for providing adaptivity is to know the needs of learners. In this paper, we describe an approach that enables LMS to identify the learning...

The least-mean-square (LMS) estimator is a nonlinear estimator with information dependencies spanning the entire set of data fed into it. The traditional analysis techniques used to model this estimator obscure these dependencies; to simplify the analysis they restrict the estimator to the finite set of data sufficient to span the length of its filter. Thus the finite Wiener filter is often con...