Nonlinear Systems for Distances and Displacements Measurement

1999 ◽  
Author(s):  
Almantas Mozuras ◽  
Asta Kontvainaite
Keyword(s):  
Physical System ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Measurement Range ◽  
Experimental Investigations ◽  
Signal To Noise ◽  
Measuring Process ◽  
Time Intervals ◽  
Nonlinear Distortions ◽  
Typical Time

Abstract In conventional methods, a physical system is considered more suitable for measurement purposes the greater its linearity is. However, purely linear converting systems are not available. The use of the linear features in the measurement process causes the drawbacks: systematic error due to nonlinear distortions, low signal-to-noise ratio, low measurement range, necessity to evaluate a great number of a priori parameters in order to obtain an absolute result, and low thermal stability because every a priori parameter itself has a temperature dependence. To exclude these drawbacks a method has been developed using nonlinearities in the base of displacements measuring process. The method is implemented using electretic, electrostatic, and photoelectric transducers. The contactless transducer is placed parallel to the surface of the object which displacements are measured. The transducer is driven to harmonic oscillations. Typical time intervals of the coded signal are measured. The object displacements are determined according to the changes of the typical time intervals. The method itself has no errors because approximations have been not made while deriving the relations. The source of errors is inaccurate registration of the start and end of the typical time intervals. The measurement is possible only if the physical system is nonlinear. The results of experimental investigations confirm the theoretical conclusions. The method allows one to increase measurement range significantly (for example, measurement range of the conventional capacitance meters is ∼10−2mm and in the proposed method measurement range using capacitance converter is about 1 mm).

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 An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

Reproducibility and Variability of Speech Muscle Activity in Athetoid Dysarthria of Cerebral Palsy

1984 ◽  
Vol 27 (4) ◽  
pp. 502-517 ◽  
Author(s):  
Peter D. Neilson ◽  
Nicolas J. O'Dwyer
Keyword(s):  
Cerebral Palsy ◽  
Muscle Activity ◽  
Signal To Noise Ratio ◽  
Test Sentence ◽  
Emg Activity ◽  
Variable Component ◽  
Signal To Noise ◽  
Voluntary Activity ◽  
Time Intervals ◽  
Involuntary Movements

Athetoid dysarthria is thought to result from involuntary movements which are variable and irregular in nature. In this study, electromyographic (EMG) activity recorded from six speech muscles was quantified during repetitions of a test sentence by normal and athetoid adult subjects. In the athetoid subjects the articulation of the test sentence was disrupted intermittently by involuntary activity which usually occurred in the time intervals between the syllables in the test sentence, rather than during articulation of the syllables themselves, The EMG activity associated with each syllable in the test sentence was partitioned into reproducible and variable components. The ratio of the reproducible component to the variable component—the signal-to-noise ratio—did not differ Significantly between the two subject groups. In the athetoid subjects, however, the reproducible component of the EMG activity was grossly abnormal. We concluded that this abnormal voluntary activity, rather than variable involuntary activity, was the primary cause of athetoid dysarthria.

scholarly journals A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fei Zhang ◽  
Zijing Zhang ◽  
Luxi Yang ◽  
Xinyu Zhang
Keyword(s):  
Prior Information ◽  
Likelihood Function ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Estimation Accuracy ◽  
Indoor Environments ◽  
Signal To Noise ◽  
Probability Hypothesis Density ◽  
Localization And Mapping ◽  
Noise Ratio

The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.

AN AUDITORY BRAIN-COMPUTER INTERFACE WITH ACCURACY PREDICTION

2012 ◽  
Vol 22 (03) ◽  
pp. 1250009 ◽  
Author(s):  
M. A. LOPEZ-GORDO ◽  
F. PELAYO ◽  
A. PRIETO ◽  
E. FERNANDEZ
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Signal To Noise ◽  
Computer Interfaces ◽  
Dichotic Listening Task ◽  
Shift Keying ◽  
Trial Basis ◽  
Noise Ratio ◽  
Listening Task ◽  
Binary Phase Shift Keying

Fully auditory Brain-computer interfaces based on the dichotic listening task (DL-BCIs) are suited for users unable to do any muscular movement, which includes gazing, exploration or coordination of their eyes looking for inputs in form of feedback, stimulation or visual support. However, one of their disadvantages, in contrast with the visual BCIs, is their lower performance that makes them not adequate in applications that require a high accuracy. To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy.

scholarly journals Automatic Modulation Recognition for MFSK Using Modified Covariance Method

2015 ◽  
Vol 5 (3) ◽  
pp. 429
Author(s):  
Hanan M.Hamee ◽  
Jafer Wadi
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Modulation Index ◽  
Modulation Classification ◽  
A Priori Information ◽  
Modulation Recognition ◽  
Digital Signals ◽  
Signal To Noise ◽  
Automatic Modulation Recognition ◽  
Priori Information

This paper presents modulation classification method capable of classifying<br />MFSK digital signals without a priori information using modified covariance<br />method. This method using for calculation features for FSK modulation<br />should have a good properties of sensitive with FSK modulation index and<br />insensitive with signal to noise ratio SNR variation. The numerical<br />simulations and investigation of the performance by the support vectors<br />machine one against all (SVM-OAA) as a classifier for classifying 6 digitally<br />modulated signals which gives probability of correction classification up to<br />85.85 at SNR=-15dB.

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