scholarly journals Design and Analysis of Air Quality Monitoring System

2021 ◽  
pp. 100-105
Author(s):  
R. Guruprasath ◽  
S. Sabeenamarry ◽  
P. Sathya ◽  
V. Vinitha ◽  
J. Suganthi
Keyword(s):  
Estimation Error ◽  
Lms Algorithm ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Least Mean Square ◽  
A Posteriori ◽  
Adaptive Noise Cancellation ◽  
The Difference ◽  
Adaptive Noise ◽  
Posteriori Estimation

In the adaptive noise cancellation (ANC) challenge, a novel least-mean-square (LMS) algorithm for filtering speech sounds has been created. It is focused on minimising the difference weight vector's squared Euclidean norm under a stability restriction specified over the a posteriori estimation error. The Lagrangian methodology was employed for this reason in order to propose a nonlinear adaptation rule described in terms of the product of differential inputs and errors, which is a generalisation of the normalised (N)LMS algorithm. The proposed approach improves monitoring ability in this sense, as shown by studies using the AURORA 2 and 3 speech databases. They include a thorough output assessment as well as a thorough comparison to regular LMS algorithms with nearly the same computational load, such as the NLMS and other recently published LMS algorithms including the updated (M)-NLMS, the error nonlinearity (EN)-LMS, or the normalised data nonlinearity (NDN)-LMS adaptation.

Implementation of Adaptive Noise Canceller System for Audio-Related Applications

2013 ◽  
Vol 3 (4) ◽  
pp. 51-67
Author(s):  
Swati S. Godbole ◽  
Sanjay B. Pokle
Keyword(s):  
Adaptive Filtering ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Noise Component ◽  
Wide Range ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Filtering Techniques

This paper describes the performance of Adaptive Noise Cancellation system. Basic concept of adaptive noise canceller is to process signals from two input sources and to reduce the level of undesired noise with adaptive filtering techniques. Adaptive filtering techniques play vital role in wide range of applications. An implementation of adaptive noise cancellation system is used to remove undesired noise from a received signal for various audio related applications that has been developed and implemented by MATLAB. The dual channel adaptive noise cancellation system uses an adaptive filter with least mean square algorithm to cancel noise component from primary signal picked up by primary sensor. Various parameters such as convergence behavior, tracking ability of the algorithm, signal to noise ratio, mean square error etc. of ANC system are studied, analyzed for various applications of adaptive noise cancellation and the same are discussed in this paper.

Study on Anti-Noise Materials with Multi-Level Filter Based on the Adaptive Noise Cancellation System

2014 ◽  
Vol 886 ◽  
pp. 390-393
Author(s):  
Jing Mo ◽  
Wei He ◽  
Dan Su ◽  
Jing Wei Wu
Keyword(s):  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Filter Performance ◽  
Multi Level ◽  
Adaptive Noise ◽  
Filtering Effect

It presents the Multi-level filters idea of the adaptive noise cancellation system based on the fact that the adaptive noise cancellation system cant filter out noise signal completely. According to the linear combination and the variable step-size LMS algorithm, it analyzes the effects of the two level filters. Theory analyzing and simulation results prove that the multi-level filter can get a better the filtering effect than the one-filter, which improves the filter performance in terms of the fast convergence speed, tracking speed and the low maladjustment error. And the anti-noise materials with multi-level filter based on the adaptive noise cancellation system has the good de-noising ability of noisy signals.

Detection of Weak Signals Based on RBF Neural Network Filtering

2011 ◽  
Vol 211-212 ◽  
pp. 846-849
Author(s):  
Jian Jun Li
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Adaptive Neural Network ◽  
Self Study ◽  
Characteristic Signal ◽  
Adaptive Noise ◽  
Band Signal ◽  
Detection Of Weak Signals

The algorithm is presented in this paper based on the character about RBF adaptive neural network filtering needn’t previous information of input single and noise and has better ability of nonlinear mapping and self-study. The adaptive noise cancellation system is designed. The system can improve LMS algorithm slow convergence speed and extraction of narrow band signal faults and has small amount of calculation and real-time good characteristic. The effect is better at Using this system in the field of life characteristic signal detection identification. Results show that the system has the high feasibility and validity.

scholarly journals Some features of numerical diagnostics of instantaneous blow-up of the solution by the example of solving the equation of slow diffusion

2021 ◽  
pp. 77-86
Author(s):  
И.В. Пригорный ◽  
А.А. Панин ◽  
Д.В. Лукьяненко
Keyword(s):  
Blow Up ◽  
Initial Boundary ◽  
Initial Boundary Value Problem ◽  
A Posteriori ◽  
Order Of Accuracy ◽  
Ill Posed ◽  
The Difference ◽  
Initial Boundary Value ◽  
Richardson Extrapolation Method ◽  
Posteriori Estimation

В работе демонстрируется, как метод апостериорной оценки порядка точности разностной схемы по Ричардсону позволяет сделать вывод о некорректности постановки (в смысле отсутствия решения) решаемой численно начально-краевой задачи для уравнения в частных производных. Это актуально в ситуации, когда аналитическое доказательство некорректности постановки ещё не получено или принципиально невозможно. The paper demonstrates how the method of a posteriori estimation of the order of accuracy for the difference scheme according to the Richardson extrapolation method allows one to conclude that the formulation of the numerically solved initial-boundary value problem for a partial differential equation is ill-posed (in the sense of the absence of a solution). This is important in a situation when the ill-posedness of the formulation is not analytically proved yet or cannot be proved in principle.

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1942-1945
Author(s):  
Jie Zhang ◽  
Shi Qi Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Noise Cancellation ◽  
Biological Species ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Swarm Optimization ◽  
Adaptive Noise

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

The Design of Adaptive Noise Cancellation Filter Based on DSP Chip

2014 ◽  
Vol 971-973 ◽  
pp. 1786-1790
Author(s):  
Xiu Min Wang ◽  
Ting Ting Li ◽  
Liang Shan
Keyword(s):  
Digital Filters ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Speech Communication ◽  
Adaptive Noise Cancellation ◽  
Change Characteristics ◽  
The Common ◽  
Adaptive Noise

The speech signal usually could not be extracted correctly from the digital speech communication system with strong interference. As for this kind of system, the common fixed coefficient digital filters (FIR, IIR) are unable to achieve the best effect of filtering. Whereas the adaptive filter could extract the available signals properly by adjusting the filter coefficient automatically without knowing the change characteristics of the noise signal. In this paper, we designed an adaptive noise cancellation filter based on LMS algorithm on the DSP chip and verification of the filter was done on the TMS320C5509 platform. The results show that the adaptive noise cancellation designed in this paper could extract the available signals properly and improve the quality of the speech communication.

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