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Design of Least Mean Square Algorithm for Adaptive Noise Canceller
This paper describes the concept of adaptive noise cancelling, an alternative method of estimating signals corrupted by additive noise or interference. The method uses a “primary” input containing the corrupted Signal and a “reference” input containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (LMS) algorithm. Computer simulations with uncorrelated Gaussian noise and signals confirm the results of the analysis and demonstrate the effectiveness of the least mean squares (LMS) algorithms.This Adaptive Noise Canceller is then useful for enhancing the S/N ratio of data collected from sensors (or sensor arrays) working in noisy environment, or dealing with potentially weak signals.
Uploaded by iserp on 04/21/2011
Digital publication details: 5 pages.

Kalman Filter Estimation Applied to a Satellite Attitude Control System
Kalman filters are commonly used for state estimation. In this case the Kalman filter will be used for error state estimation. The error states will be used as input to the attitude control algorithm.
Uploaded by spconguy on 02/28/2011
Digital publication details: 10 pages.

Estimation of Pedestrian Distribution in Indoor Environments using Multiple Pedestrian Tracking
We propose a two-tier data analysis approach for estimating distribution of pedestrian locations in an indoor space using multiple pedestrian detection and tracking. Multiple pedestrian detection uses laser measurement for sensing pedestrians in a heavily occluded environment which is usually the case with most indoor environments. . We adapt a particle filter based multiple pedestrian tracker to address the constraints of a limited number of sensors, heavy occlusion and real-time execution
Uploaded by muhammademad on 05/13/2009
Digital publication details: 6 pages.

Filtering Electrocardiographic Signals usingfiltered- X LMS algorithm
In this paper, a simple and efficient filtered- X Least Mean Square (FXLMS) algorithm is used for the removal of different kinds of noises from the ECG signal. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle artifacts and motion artifacts. Finally different adaptive structures are implemented to remove artifacts from ECG signals and tested on real signals obtained from MITBIH data base.
Uploaded by ideseditor on 01/20/2013
Digital publication details: 5 pages.

Performance Evaluation of channel estimation in TD-SCDMA system
In previous B. Steiner channel estimation algorithm adopted in TD-SCDMA system, but there are some errors in estimated channel due to noise. In this paper, we evaluate the performance of Time Division-Synchronous Code Division Multiple Access (TD-SCDMA) systems with channel estimation. In channel estimation we implement the channel estimation algorithm to equalize the channel and reduce the BER in the received signal. The Channel estimation is carried out at the receiver. In TD-SCDMA, the uplink and downlink transmissions use the same frequency band for duplex transmission by using synchronized time intervals. The simulation result shows that the system performance is effectively increased.
Uploaded by on 03/05/2013
Digital publication details: 4 pages.

Simulation of Adaptive Noise Canceller for an ECGsignal Analysis
In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we have presented an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation application. We simulate the adaptive filter in MATLAB with anoisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and stability.
Uploaded by ideseditor on 01/13/2013
Digital publication details: 4 pages.

Real-Time Active Noise Cancellation with Simulinkand Data Acquisition Toolbox
This paper presents the feasibility of implementing single channel negative feedback Active Noise Cancellation technique using adaptive filters in Real-time environment[1]. In order to establish the suitability and credibility of LMS Algorithm for adaptive filtering in real world scenario, its efficiency was tested beyond system based ideal simulations. Within the MATLAB® software environment two different methods were used to perform Real-time ANC namely Simulink® and Data Acquisition ToolboxTM. Human voice is used as test signal. For processing and performing adaptive filtering, Block LMS Filter was utilised in Simulink and Error Normalised Step Size algorithm was used in between input and output of Signals by DAQ (Data Acquisition) toolbox interface.
Uploaded by ideseditor on 01/08/2013
Digital publication details: 5 pages.

Development of Robust Adaptive Inversemodels using Bacterial Foraging Optimization
Adaptive inverse models find applications in communication and magnetic channel equalization, recovery of digital data and adaptive linearization of sensor characteristics. In presence of outliers in the training signal, the model accuracy is severely reduced. In this paper three robust inverse models are developed by recursively minimizing robust norms using BFO based learning rule. The performance of these models is assesses through simulation study and is compared with those obtained by standard squared norm based models. It is in general, observed that the Wilcoxon norm based model provides best performance. Moreover the squared error based model is observed to perform the worst.
Uploaded by ideseditor on 01/17/2013
Digital publication details: 6 pages.

An Introduction to the Kalman Filter
description of kalman filter from online
Uploaded by onghenggneesayshi on 11/18/2011
Digital publication details: 81 pages.

An Accurate, Agile and Stable Traffic Rate EstimationTechnique for TCP Traffic
Traffic rate estimation is an integral part of many high speed network services and components. Algorithms suchas traffic conditioning, scheduling and admission control aredependent on accurate rate estimation. Several rateestimation techniques have been proposed however, theinherent bursty nature of Internet traffic, especially TCPtraffic, does not allow for easy rate estimation. Short termchanges may obscure output results or a change in traffic ratemay not always be detected. Thus estimators may not alwayspossess ideal characteristics of agility, stability and accuracy.As agility and stability are inter-dependent a single rateestimator cannot always be configured to be both agile andstable. In this paper a rate estimation technique is proposedthat uses two rate estimation techniques to configure an agileestimator in measuring the actual changes of traffic in a timelymanner as well as a stable one in ignoring short term variationsof traffic.
Uploaded by ideseditor on 03/05/2013
Digital publication details: 8 pages.
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