As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detected signals with various degrees of automation. This paper investigates the application of autocorrelation function (ACF) method to decompose EMG signals to their frequency components. It was found that using the proposed method gives a quite good frequency resolution as compared to that resulting from using short time fast Fourier transform (STFFT); thus more MU’s can be distinguished.
Signal denoising is directly related to sample estimation of received signals, either by estimating the equation parameters for the target reflections or the surrounding noise and clutter accompanying the data of interest. Radar signals recorded using analogue or digital devices are not immune to noise. Random or white noise with no coherency is mainly produced in the form of random electrons, and caused by heat, environment, and stray circuitry loses. These factors influence the output signal voltage, thus creating detectable noise. Differential Evolution (DE) is an effectual, competent, and robust optimisation method used to solve different problems in the engineering and scientific domains, such as in signal processing. This paper looks
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تقييم قابلية اداء متطلبات العمل للعاملين في الصناعة باستخدام طريقة القصور الذاتي
In this work, the effect of different particle size on the nonlinear optical properties of silver nanoparticles in de-ionized water was studied. The experimental observation of the far field diffraction patterns by CCD camera in two and three dimensions. The maximum change of nonlinear refractive index and the relative phase shift were calculated. The self-defocusing technique was used with a continuous-wave radiation from DPSS Blue laser .The wavelength is 473 nm with an output power of 270 mW. All the Ag colloids samples containing the sizes 15, 30, 50, and 70 nm of silver nanoparticles used in the study were chemically prepared. It was found that the nonlinear refractive index is a particle size dependent and of the order of 10-7 cm2/
... Show MoreThis research shows the problem of the economic development of underdeveloped countries in an unconventional way, as these papers explain the problems of the economic development. This research not only reviews the problems, but it illustrates them in a philosophical way, basis of the data of modernity, this mean it is a process of connecting between the absence of the modernity values and the failure of development in underdeveloped countries. The Search follows the descriptive approach to get to the goal of search by four main axes. The first axis includes clarifying modernity and its principles, the second axis includes clarifying the economic development , the third axis includes the features of the mod
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Political speeches are represented in different shapes as political forum, events or as inaugural speech. This research critically analyzes the inaugural Speech of the President Donald Trump which was delivered on 20th ,January, 2017 from the site<www.cnn.com> retrieved on 10th ,May,2017. The objectives of the study are: First: classifying and discussing well known micro structures (linguistic feature) of the speech, and second: classifying the macro structures i.e. the delivered political inaugural speech in which he includes social structures. To reach to the objectives of the study, the researcher will adopt Norman Fairclough’s three dimensional Analytical Model(
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In this paper, compared eight methods for generating the initial value and the impact of these methods to estimate the parameter of a autoregressive model, as was the use of three of the most popular methods to estimate the model and the most commonly used by researchers MLL method, Barg method and the least squares method and that using the method of simulation model first order autoregressive through the design of a number of simulation experiments and the different sizes of the samples.
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Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (EEG and EMG signals) is proposed in this paper and are integrated with machine learning techniques to recognize the upper-limb motions of subjects. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrate that usin
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Abstract
The government spending in Iraq and witnessed the changes and developments, especially after 2003, which outweighed consumer spending at the expense of capital expenditure and increased support and diversity of trends towards improving pension conditions for member
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The aim of this paper is to estimate a nonlinear regression function of the Export of the crude oil Saudi (in Million Barrels) as a function of the number of discovered fields.
Through studying the behavior of the data we show that its behavior was not followed a linear pattern or can put it in a known form so far there was no possibility to see a general trend resulting from such exports.
We use different nonlinear estimators to estimate a regression function, Local linear estimator, Semi-parametric as well as an artificial neural network estimator (ANN).
The results proved that the (ANN) estimator is the best nonlinear estimator am
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