The voice communication between humans and machines is the idea people have been thinking about a long time. For high level of the voice communication with the control system it is important to ensure good quality of the speech signal processing with additive noise in real environments. This paper describes a proposed method for optimal adjustment parameters of the adaptive filter with an LMS a...

Uterine leiomyosarcomas (LMS) are difficult to distinguish from benign leiomyomas without surgery. In this study we performed transcervical needle biopsy on 475 patients, 8 LMS patients and 467 patients with non-sarcomas (non-LMS) in a high-risk group for LMS, and evaluated whether examinations performed with Ki-67 and CD34 immunohistochemical analyses in addition to the standard hematoxylin-eo...

We have shown that duct modeling using the generalized RBF neural network (DM RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM RBF), at first the duct is identified using a generalized RBF network, after that N stage of time delay of the input signal to th...

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...

The aim of the present study was to evaluate, among older persons, the association between respiratory impairment and hospitalisation for chronic obstructive pulmonary disease (COPD), based on spirometric Z-scores, i.e. the LMS (lambda, mu, sigma) method, and a competing risk approach. Using data on 3,563 white participants aged 65-80 yrs (from the Cardiovascular Health Study) we evaluated the ...

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...

Learning Management Systems (LMS) are now being widely used in education. Their main function is to systematically manage and deliver learning materials. There is a need for LMS that can deliver personalised material suited to the learner's learning requirements and their learning style. A method and associated techniques have been developed that addresses these issues by use of a multi-dimensi...

Metastasectomy represents the standard treatment for improving survival in patients with lung metastases (LMs) from bone (BS) or soft-tissue sarcoma (STS). Recently, radiofrequency ablation (RFA) of the LMs has been proved to be a useful option which can promise the similar effect to metastasectomy. The aim of this study was to determine prognostic factors, including tumor volume doubling time ...

Least mean square (LMS)-based adaptive algorithms have attracted much attention due to their low computational complexity and reliable recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods have been proposed based on different sparse penalties, such as l1-norm LMS or zeroattracting LMS (ZA-LMS), reweighted ZA-LMS, and lp-norm LMS. However, th...

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...