On the Advantages of the LMS Spectrum
نویسندگان
چکیده
| Based on the least mean squares (LMS) algorithm , the LMS spectrum analyzer can be used to re-cursively calculate the discrete Fourier transform (DFT) of a sliding window of data. In this paper, we compare the LMS spectrum analyzer with the straightforward non-adaptive implementation of the recursive DFT. In particular , we demonstrate the robustness of the LMS spectrum analyzer to the propagation of round-oo errors, a property that is not shared by other recursive DFT algorithms.
منابع مشابه
Evaluation of the learning management system using students’ perceptions
Background: Learning Management System (LMS) is a web-based system designed to support teaching and learning at an institution. The capabilities of any LMS are required to be evaluated to detect the room for improvement. This study aimed at discovering the students’ perceptions of the functions of the LMS at Iran University of Medical Sciences (IUMS). Methods: This qualitative s...
متن کاملFrequency Estimation of Unbalanced Three-Phase Power System using a New LMS Algorithm
This paper presents a simple and easy implementable Least Mean Square (LMS) type approach for frequency estimation of three phase power system in an unbalanced condition. The proposed LMS type algorithm is based on a second order recursion for the complex voltage derived from Clarke's transformation which is proved in the paper. The proposed algorithm is real adaptive filter with real parameter...
متن کاملEffectiveness of different types of learning materials used by students in courses of basic medical sciences
Introduction. Learning materials (LMs), are submitted to students in different types, from class notes to referring students to different references, which can have different effectiveness. Therefore, evaluation of effectiveness of commonly used types can help the university faculties in selection of more appropriate LMs for students. Methods. 1. The data regarding the types of LMs used in di...
متن کاملAn Analytical Model for Predicting the Convergence Behavior of the Least Mean Mixed-Norm (LMMN) Algorithm
The Least Mean Mixed-Norm (LMMN) algorithm is a stochastic gradient-based algorithm whose objective is to minimum a combination of the cost functions of the Least Mean Square (LMS) and Least Mean Fourth (LMF) algorithms. This algorithm has inherited many properties and advantages of the LMS and LMF algorithms and mitigated their weaknesses in some ways. The main issue of the LMMN algorithm is t...
متن کاملOptimization Capabilities of LMS and SMI Algorithm for Smart Antenna Systems (RESEARCH NOTE)
In the present paper convergence characteristics of Sample matrix Inversion (SMI) and Least Mean Square (LMS) adaptive beam-forming algorithms (ABFA) are compared for a Smart Antenna System (SAS) in a multipath environment. SAS are employed at base stations for radiating narrow beams at the desired mobile users. The ABFA are incorporated in the digital signal processors for adjusting the weight...
متن کامل