Adaptive Smoothing of Spectroscopic Data by a Linear Mean-Square Estimation

1984 ◽  
Vol 38 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Satoshi Kawata ◽  
Shigeo Minami
Keyword(s):  
Spectroscopic Data ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Smoothing Method ◽  
Window Function ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Smoothing ◽  
Local Statistics ◽  
Convolution Algorithm

An adaptive smoothing method based on a least mean-square estimation is developed for noise filtering of spectroscopic data. The algorithm of this method is nonrecursive and shift-varying with the local statistics of data. The mean and the variance of the observed spectrum at an individual sampled point are calculated point by point from its local mean and variance. By this method, in the resultant spectrum, the signal-to-noise ratio is maximized at any local section of the entire spectrum. Experimental results for the absorption spectrum of ammonia gas demonstrate that this method distorts less amount of signal components than the conventional smoothing method based on the polynomial curve-fitting and suppresses noise components satisfactorily. The computation time of this algorithm is rather shorter than that of the convolution algorithm with seven weighting coefficients. The a priori information for the estimation of the signal by this method are: the variance of noise, which can be attainable in the experiment; and the window function which gives the local statistics. The investigation of various types of window functions shows that the selection of the window function does not directly affect the performance of adaptive smoothing.

scholarly journals АНАЛИЗ И ПРОГНОЗИРОВАНИЕ ЭФФЕКТИВНОСТИ ФИЛЬТРАЦИИ ОДНОМЕРНЫХ СИГНАЛОВ НА ОСНОВЕ ДИСКРЕТНОГО КОСИНУСНОГО ПРЕОБРАЗОВАНИЯ

2019 ◽  
pp. 19-29
Author(s):  
Сергей Клавдиевич Абрамов ◽  
Виктория Валерьевна Абрамова ◽  
Сергей Станиславович Кривенко ◽  
Владимир Васильевич Лукин
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Statistical Parameter ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Standard Size ◽  
Filter Parameter ◽  
Filtering Efficiency ◽  
Threshold Setting

The article deals with the analysis of the efficiency and expedience of applying filtering based on the discrete cosine transform (DCT) for one-dimensional signals distorted by white Gaussian noise with a known or a priori estimated variance. It is shown that efficiency varies in wide limits depending upon the input ratio of signal-to-noise and degree of processed signal complexity. It is offered a method for predicting filtering efficiency according to the traditional quantitative criteria as the ratio of mean square error to the variance of additive noise and improvement of the signal-to-noise ratio. Forecasting is performed based on dependences obtained by regression analysis. These dependencies can be described by simple functions of several types parameters of which are determined as the result of least mean square fitting. It is shown that for sufficiently accurate prediction, only one statistical parameter calculated in the DCT domain can be preliminarily evaluated (before filtering), and this parameter can be calculated in a relatively small number of non-overlapping or partially overlapping blocks of standard size (for example, 32 samples). It is analyzed the variations of efficiency criteria variations for a set of realizations; it is studied factors that influence prediction accuracy. It is demonstrated that it is possible to carry out the forecasting of filtering efficiency for several possible values of the DCT-filter parameter used for threshold setting and, then, to recommend the best value for practical use. An example of using such an adaptation procedure for the filter parameter setting for processing the ECG signal that has not been used in the determination of regression dependences is given. As a result of adaptation, the efficiency of filtering can be essentially increased – benefit can reach 0.5-1 dB. An advantage of the proposed procedures of adaptation and prediction is their universality – they can be applied for different types of signals and different ratios of signal-to-noise.

scholarly journals Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

2018 ◽  
Vol 7 (2.17) ◽  
pp. 79
Author(s):  
Jyoshna Girika ◽  
Md Zia Ur Rahman
Keyword(s):  
Steady State ◽  
Speech Enhancement ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Union Rate ◽  
Step Size ◽  
Adaptive Noise

Removal of noise components of speech signals in mobile applications  is an important step to facilitate high resolution signals to the user. Throughout the communication method the speech signals are tainted by numerous non stationary noises. The Least Mean Square (LMS) technique is a fundamental adaptive technique usedbroadly in numerouspurposes as anoutcome of its plainness as well as toughness. In LMS technique, an importantfactor is the step size. It bewell-known that if the union rate of the LMS technique will be rapidif the step size is speedy, but the steady-state mean square error (MSE) will raise. On the rival, for the diminutive step size, the steady state MSE will be minute, but the union rate will be conscious. Thus, the step size offers anexchange among the convergence rate and the steady-state MSE of the LMS technique. Build the step size variable before fixed to recover the act of the LMS technique, explicitly, prefer large step size values at the time of the earlyunion of the LMS technique, and usetiny step size values when the structure is near up to its steady state, which results in Normalized LMS (NLMS) algorithms. In this practice the step size is not stable and changes along with the fault signal at that time. The Less mathematical difficulty of the adaptive filter is extremely attractive in speech enhancement purposes. This drop usually accessible by extract either the input data or evaluation fault.  The algorithms depend on an extract of fault or data are Sign Regressor (SR) Algorithms. We merge these sign version to various adaptive noise cancellers. SR Weight NLMS (SRWNLMS), SR Error NLMS (SRENLMS), SR Unbiased LMS (SRUBLMS) algorithms are individual introduced as a quality factor. These Adaptive noise cancellers are compared with esteem to Signal to Noise Ratio Improvement (SNRI). 

Least Mean Square Measures of Voice Perturbation

1987 ◽  
Vol 30 (4) ◽  
pp. 529-538 ◽  
Author(s):  
Paul Milenkovic
Keyword(s):  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Processing Technique ◽  
Model Fit ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal Processing Technique ◽  
Normal Voice ◽  
Interpolation Technique ◽  
Low Levels

A signal processing technique is described for measuring the jitter, shimmer, and signal-to-noise ratio of sustained vowels. The measures are derived from the least mean square fit of a waveform model to the digitized speech waveform. The speech waveform is digitized at an 8.3 kHz sampling rate, and an interpolation technique is used to improve the temporal resolution of the model fit. The ability of these procedures to measure low levels of perturbation is evaluated both on synthetic speech waveforms and on the speech recorded from subjects with normal voice characteristics.

scholarly journals Identification of Input Nonlinear Control Autoregressive Systems Using Fractional Signal Processing Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Naveed Ishtiaq Chaudhary ◽  
Muhammad Asif Zahoor Raja ◽  
Junaid Ali Khan ◽  
Muhammad Saeed Aslam
Keyword(s):  
Signal Processing ◽  
Nonlinear Control ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Mean Square ◽  
Least Mean Square ◽  
Third Order ◽  
Processing Approach ◽  
Kernel Least Mean Square ◽  
True Values

A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that the proposed FLMS algorithm provides most accurate and convergent results than those of VLMS and KLMS under different scenarios and by taking the low-to-high signal-to-noise ratio.

Hardware Implementation of Kalman Least Mean Square-Based Adaptive Algorithm for Denoising Ambient Noises in Shallow Water Region

2014 ◽  
Vol 13 (03) ◽  
pp. 1450018
Author(s):  
S. Sakthivel Murugan ◽  
V. Natarajan ◽  
S. Prethivika
Keyword(s):  
Shallow Water ◽  
Ambient Noise ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Useful Signal ◽  
Custom Made ◽  
Source Signal

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noise ratio (SNR) and mean square error (MSE) for various wind speeds. An exhaustive set of data, collected using a custom made fixture containing two hydrophones, from shallow water regions in Bay of Bengal, have been used to verify the efficacy of this method. Based on the results obtained by simulation and Lab window simulator, hardware has been designed to denoise the useful signal. The defective source signal is passed through a Kalman filter based denoising hardware system. This system performs necessary operations to denoise the defective source signal and the final turnout is made free from ambient noise. The denoised signal is then stored in an external device for future use.

A Study of a various Acoustic Beamforming Techniques Using a Microphone Array

2015 ◽  
Vol 1 ◽  
pp. 7 ◽  
Author(s):  
Ahmed Abdalla ◽  
Suhad Mohammed ◽  
Abdelazeim Abdalla ◽  
Tang Bin ◽  
Mohammed Ramadan
Keyword(s):  
Signal To Noise Ratio ◽  
Microphone Array ◽  
Spatial Filtering ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Least Mean Squares ◽  
Lms Filter ◽  
Least Mean Square Algorithm ◽  
Simulation Results

In this paper, A study of numerous acoustic beamforming algorithms is carried out. Beamforming algorithms are techniques utilize to determine the Direction of arrival of (DOA) the speech signals while suppress out the corresponding noises and interferences. The simple delay and sum beamformer technique which use the constrained least mean squares (LMS) filter for spatial filtering is firstly investigated. Secondly, a constrained least mean square algorithm (also known as Frost Beamformer) is considered. The beamformer algorithms are simulated in MATLAB and therefore, the simulation results indicate that there a significant enhancement in the Signal-to-Noise-Ratio (SNR) for frost beamformer as compared to the simple delay and sum beamformer.

scholarly journals Artifact Elimination in EEG Signal using Block and Sign Based Normalized Least Mean Square Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 4340-4346
Keyword(s):  
Health Care ◽  
Signal To Noise Ratio ◽  
Complex Signal ◽  
Eeg Signal ◽  
Mean Square ◽  
Least Mean Square ◽  
Clinical Practices ◽  
Health Care Applications ◽  
Adaptive Noise ◽  
Remote Health

In this research the efficient and low computation complex signal acclimatizing techniques are projected for the improvement of Electroencephalogram (EEG) signal in remote health care applications. In clinical practices the EEG signal is extracted along with the artifacts and with some small constraints. Mainly in remote health care situations, we used low computational complexity filters which are striking. So, for the improvement of the EEG signal we introduced efficient and computation less Adaptive Noise Eliminators (ANE’s). These techniques simply utilize addition and shift operations, and also reach the required convergence speed among the other predictable techniques. The projected techniques are executed on real EEG signals which are stored and are compared with the effecting EEG arrangement. Our realizations visualize that the projected techniques offer the best concert over the previous techniques in terms of signal to noise ratio, mathematical complexity, convergence rate, Excess Mean Square error and Mis adjustment. This approach is accessible for the brain computer interface applications.

scholarly journals Removal of Structured Noise and Base Line Wander From ECG Signals via LMS Adaptive and Fixed Notch Filter

2018 ◽  
Vol 3 (8) ◽  
pp. 12
Author(s):  
Kawser Ahammed
Keyword(s):  
Signal To Noise Ratio ◽  
Notch Filter ◽  
Ecg Signal ◽  
Base Line ◽  
Mean Square ◽  
Least Mean Square ◽  
Performance Study ◽  
Comparative Performance ◽  
Simulation Results ◽  
Mean Square Difference

This research clearly demonstrates the comparative performance study of Least Mean Square (LMS) adaptive and fixed Notch filter in terms of simulation results and different performance parameters (mean square error, signal to noise ratio and percentage root mean square difference) for removing structured noise (50 Hz line interference and its harmonics) and baseline wandering from electrocardiogram (ECG) signal. The ECG samples collected from the PhysioNet ECG-ID database are corrupted by adding structured noise and base line wandering noise. The simulation results and numerical performance analysis of this research clearly show that LMS adaptive filter can remove noise efficiently from ECG signal than fixed notch filter

scholarly journals Comparative Study on Noise Reduction Effect of Fiber Optic Hydrophone Based on LMS and NLMS Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 301
Author(s):  
Zhihua Yu ◽  
Yunfei Cai ◽  
Daili Mo
Keyword(s):  
Adaptive Filtering ◽  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Harmonic Distortion ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Real Time Processing ◽  
Nlms Algorithm ◽  
Filtering Effect

Adaptive filtering has the advantages of real-time processing, small computational complexity, and good adaptability and robustness. It has been widely used in communication, navigation, signal processing, optical fiber sensing, and other fields. In this paper, by adding an interferometer with the same parameters as the signal interferometer as the reference channel, the sensing signal of the interferometric fiber-optic hydrophone is denoised by two adaptive filtering schemes based on the least mean square (LMS) algorithm and the normalized least mean square (NLMS) algorithm respectively. The results show that the LMS algorithm is superior to the NLMS algorithm in reducing total harmonic distortion, improving the signal-to-noise ratio and filtering effect.

scholarly journals CONTROL AND ESTIMATION OF BIOLOGICAL SIGNALS (ECG) USING ADAPTIVE SYSTEM

2014 ◽  
pp. 236-240
Author(s):  
SUSHANTA MAHANTY ◽  
ALOK RANJAN
Keyword(s):  
Signal To Noise Ratio ◽  
Computation Time ◽  
Noise Removal ◽  
Least Square ◽  
Ecg Signal ◽  
Least Mean Square ◽  
Performance Study ◽  
Recursive Least Square ◽  
Adaptive Filtration ◽  
Adaptive Noise

In this paper, we present a simple and efficient adaptive noise removal technique for de-noising the (ECG) signal. There are different techniques earlier used for de-noising the ECG signal ,adaptive filtration like least mean square (LMS), NLMS, BLMS , etc. In this paper we used recursive least square technique for adaptive filtration. The power line noises have been implemented according to their basic properties. After that, these noises have been mixed with ECG signal and nullify these noises using the LMS,NLMS and the RLS algorithms. Finally a performance study has been done between these algorithms based on their parameters and also discussed the effect of filter length and the corresponding signal to noise ratio. Results indicate that the noises cannot be handled by the LMS filtering whereas the RLS can handle these types of noises. Furthermore, most of the cases the RLS has achieved best effective noise cancellation performance although its computation time is slightly high. We are using the RLS Algorithm by matlab for simulation.

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