scholarly journals A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation

2013 ◽  
Vol 06 (09) ◽  
pp. 869-876 ◽  
Author(s):  
Noman Qaid Al-Naggar
Keyword(s):  
Noise Cancellation ◽  
New Method ◽  
Mean Square ◽  
Least Mean Square ◽  
Lung Sounds ◽  
Adaptive Noise Cancellation ◽  
Adaptive Noise

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.

scholarly journals Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Marlin Ramadhan Baidillah ◽  
Zengfeng Gao ◽  
Al-Amin Saichul Iman ◽  
Masahiro Takei
Keyword(s):  
Electrical Impedance ◽  
Random Noise ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Impedance Tomography ◽  
Adaptive Noise

Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

scholarly journals Performance of Adaptive Noise Cancellation with Normalized Last-Mean-Square Based on the Signal-to-Noise Ratio of Lung and Heart Sound Separation

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Noman Q. Al-Naggar ◽  
Mohammed H. Al-Udyni
Keyword(s):  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Mean Square ◽  
Adaptive Noise Cancellation ◽  
Signal To Noise ◽  
Step Size ◽  
Nlms Algorithm ◽  
Noise Ratio ◽  
Adaptive Noise

The adaptive algorithm satisfies the present needs on technology for diagnosis biosignals as lung sound signals (LSSs) and accurate techniques for the separation of heart sound signals (HSSs) and other background noise from LSS. This study investigates an improved adaptive noise cancellation (ANC) based on normalized last-mean-square (NLMS) algorithm. The parameters of ANC-NLMS algorithm are the filter length Lj parameter, which is determined in 2n sequence of 2, 4, 8, 16, … , 2048, and the step size (μn), which is automatically randomly identified using variable μn (VSS) optimization. Initially, the algorithm is subjected experimentally to identify the optimal μn range that works with 11 Lj values as a specific case. This case is used to study the improved performance of the proposed method based on the signal-to-noise ratio and mean square error. Moreover, the performance is evaluated four times for four μn values, each of which with all Lj to obtain the output SNRout matrix (4 × 11). The improvement level is estimated and compared with the SNRin prior to the application of the proposed algorithm and after SNRouts. The proposed method achieves high-performance ANC-NLMS algorithm by optimizing VSS when it is close to zero at determining Lj, at which the algorithm shows the capability to separate HSS from LSS. Furthermore, the SNRout of normal LSS starts to improve at Lj of 64 and Lj limit of 1024. The SNRout of abnormal LSS starts from a Lj value of 512 to more than 2048 for all determined μn. Results revealed that the SNRout of the abnormal LSS is small (negative value), whereas that in the normal LSS is large (reaches a positive value). Finally, the designed ANC-NLMS algorithm can separate HSS from LSS. This algorithm can also achieve a good performance by optimizing VSS at the determined 11 Lj values. Additionally, the steps of the proposed method and the obtained SNRout may be used to classify LSS by using a computer.

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