Concurrent programming method for digital signal processing

The field of advanced digital signal processing methods is one of the fastest developing scientific and technical disciplines, and is important in the field of Shunt Active Power Filter control methods. Shunt active power filters are highly desirable to minimize losses due to the increase in the number of nonlinear loads (deformed power). Currently, there is rapid development in new adaptive, non-adaptive, and especially hybrid methods of digital signal processing. Nowadays, modern methods of digital signal processing maintain a key role in research and industrial applications. Many of the best practices that have been used to control shunt active power in industrial practice for decades are now being surpassed in favor of new progressive approaches. This systematic research review classifies the importance of using advanced signal processing methods in the field of shunt active power filter control methods and summarizes the extant harmonic extraction methods, from the conventional approach to new progressive methods using genetic algorithms, artificial intelligence, and machine learning. Synchronization techniques are described and compared as well.

A new, simple Digital Signal Processing (DSP) method for investigating dynamic behavior of Power Systems is considered, which combines Integrating Zero Crossing (IZC) and Quasisynchronous Sample Interpolation (QSI). It offers accurate (about 0.001%) and fast (30-40 ms) tracking, estimating and predicting the actual power-line frequency and its deviation, as well as spectral harmonic components of the actual power line voltages and currents with high accuracies (0.02%-0.05%), due to almost complete elimination of aliasing and leakage effects. The results of a computer simulation of this method and of many real-life experiments are given and explained. The proposed method (IZC, QSI) has been compared to other DSP methods used for Power System investigation and applied in Underfrequency Relay Protection (URP); Load Shedding and Restoration (LSR); Acquisition, Monitoring, and Control (AMC); Diagnostics and Quality Analysis (DQA); and in other industrial applications.

Methods of digital signal processing in control and monitoring systems are relevant today and largely replace the classic analog methods. This article discusses the main methods of digital signal processing and synthesis of appropriate devices, primarily digital filters.

In this work, a board based on a Spartan-3 field-programmable gate array was designed as a hardware prototyping platform for development of a multichannel digital gamma spectrometer. The device is compatible with various detectors like high-purity germanium, NaI, and CsI detectors. Before implementing the hardware, the method for digital signal processing for gamma spectroscopy was developed. The aim of this paper is to introduce a robust tool suitable for optimizing the design of complicated systems. The characteristics of this method, such as its ability to implement adaptive shaping, are investigated. The optimum conditions for digital filtering were determined using MATLAB/Simulink. This scheme can be useful for commercial production. Simulation was used to examine each processing unit in the whole signal processing procedure including cusplike shaping. The proposed method can be used as a computer design tool for optimizing digital multichannel analyzers or digital nuclear spectrometers.

Non-invasive measuring of blood perfusion of tissue is important for establishing the influence of vascular diseases on microcirculation. A signal processing method for laser-Doppler flowmetry is presented in this article. The method is based on the digital filtering. A couple of filters are suggested. One of them is a digital passband filter, second - a digital filter transforming spectrum P(f) of the input signal to fP(f) spectrum. Performance of the suggested method is investigated in comparison with behaviour of the PERIMED instrument as well as with applications based on DFT.

Problem statement. Currently, the main efforts in the development of the general theory of extrapolation and its practical applications are aimed at predicting processes, moments of time and fields. At the same time, as a result of increased processing quality requirements, it is necessary to take into account the effect of various kinds of interference and noise. Therefore, most of the existing approaches to solving extrapolation problems rely to some extent on analytical methods. At the same time, the formulation of tasks mainly concerns processes and sequences, the changes of which are largely determined by random components. Due to the fact that the mathematical description of objects and the environment (or their probabilistic characteristics) is often unknown in advance, and it is not possible to determine them, an adaptive approach is typical for solving such problems. With an adaptive approach, some suboptimal solution is sought, obtained, for example, by using maximum likelihood or minimum average risk criteria. It is from this point of view that the problems of filtering, interpolation and extrapolation are often considered. The main focus is on the application of the basic principles of the theory of systems and adaptive Kalman filtering. It is assumed that the Gaussian approximation, a linear model of the system, as well as linear processing of input sequences are used. The optimal joint assessment of the parameters of the state model leads to a nonlinear filter structure, the implementation of which is associated with high requirements for performance and computer memory. Therefore, preference is usually given to suboptimal methods, on the basis of which simplified algorithms are formed that do not require large amounts of computer memory. Goal – development of adaptive algorithms for the functioning of multidimensional linear and nonlinear extrapolators implemented in the time and private domain by digital signal processing (DSP) methods, as well as analysis of the quality characteristics of their functioning and application features. Algorithms for the functioning of multidimensional extrapolators are proposed using examples of the development of adaptive algorithms using the method of linear prediction "forward" and "backward" and their ombinations. Algorithms for the operation of nonlinear "forward" extrapolators based on Kolmogorov-Gabor series in the time and frequency domains have been developed. Adaptive algorithms of nonlinear extrapolators based on the use of partitioning methods for calculating convolution are proposed. It is shown that algorithms for the functioning of nonlinear extrapolators based on the use of partitioning methods for calculating convolution are more efficient than linear shift-invariant systems and at the same time require less memory. The features of creating adaptive filtering, interpolation and extrapolation algorithms using neurocomputers built on the basis of existing microchips of domestic and foreign production are considered.

The issues related to the study of the possibilities of mathematical and software modeling of digital algorithms for discrete Fourier transform without algorithmic multiplication operations are considered. The relevance of the study is due to the need to reduce the computational complexity of digital signal processing algorithms. The purpose of the study is to formalize a numerical method for the multi–stage discrete Fourier transform of a digital complex signal. The methods of mathematical and software modeling of numerical methods of digital signal processing were used in the study. The results of the study showed and confirmed the possibility of modeling a numerical method of multi-stage discrete Fourier transform of a digital complex signal based on numerical methods of difference digital filtering with integer difference coefficients of various orders of difference and a numerical method of reducing direct calculations of complex functions to perform simple operations of addition and shift. The concept of a multi-stage discrete Fourier transform and the concept of this transformation are defined and described. Its mathematical model is proposed and the numerical method of such transformation is formalized. It is noted that it allows to reduce the computational complexity of the hardware and software implementation of digital signal processing through the use of numerical methods of differential digital filtering, directed enumeration and comparative analysis of acceptable sets of numerical values of integer coefficients, which provides the possibility of reducing the error of the numerical method of multi-stage discrete Fourier transform. To reduce the computational complexity of his algorithms for isolating the harmonic components of a digital complex signal, the principles of dividing its frequency spectrum into narrow-band spectral components, frequency shifting of these components and their low-frequency difference digital filtering without performing arithmetic multiplication operations are applied.

The article considers an effective algorithm for group delay correction in the channels of multichannel information and measurement systems. The description of the method is carried out for the case of harmonics measuring for current and voltage in electrical networks. The algorithm is based on the linear interpolation method using a non-recursive digital filter. The filter coefficients are a discretized windowed sinc. After the description of the method, the results of effective band modeling of the interpolating filter are presented. In conclusion, recommendations are given to reduce the amount of processor memory in the implementation of the algorithm.

Randomization is nonlinear procedure for deliberate introduction of randomness or noise into an information sequence. Depending on the rule according to which the randomization is fulfilled, it can be casual or pseudo-random. In this paper we present the theoretical basis of the randomization procedure, and also methods of digital signal processing based on the randomization.

A correct definition of mode parameters is of great importance for examination of power quality indices under non-sinusoidal modes, these parameters being effective values of current, voltage, active and reactive power. A wavelet transform is a later method of digital signal processing. This method enables information about signal to be saved both in amplitude-frequency and time space. The technique of defining effective current values of non-sinusoidal transient mode is presented. This technique assumes the preliminary frequency decomposition of the examining signal using the packet wavelet transform. Modernization of existing mathematical models of instantaneous, active and reactive power calculation as well as distortion power under non-sinusoidal modes based on the packet wavelet transform is carried out. The comparison of simulation modeling results shows qualitative coincidence with analytical mode calculation.

In this work, we describe different configurations of optical fiber interferometers (types Michelson and Mach-Zehnder) used to measure velocities during dynamical material's behaviour studies. We detail the algorithms of processing developed and optimized to improve the performance of these interferometers especially in terms of time and frequency resolutions. Three methods of analysis of interferometric signals were studied. For Michelson interferometers, the time-frequency analysis of signals by Short-Time Fourier Transform (STFT) is compared to a time-frequency analysis by Continuous Wavelet Transform (CWT). The results have shown that the CWT was more suitable than the STFT for signals with low signal-to-noise, and low velocity and high acceleration areas. For Mach-Zehnder interferometers, the measurement is carried out by analyzing the phase shift between three interferometric signals (Triature processing). These three methods of digital signal processing were evaluated, their measurement uncertainties estimated, and their restrictions or operational limitations specified from experimental results performed on a pulsed power machine.

The development of electric power systems has raised the requirements of relay protection — sensitivity, speed, and reliability of functioning—for all types of short circuits (SC). The raised requirements can be satisfied by using more complete damage information extracted during the traveling-wave stage of the transient process accompanying SC. The experience gained in working out directional traveling-wave relay protection is generalized and questions concerning the use of traveling-wave principles for developing smart relay protection networks with complex topology and devices making it possible to control power flows in ac lines are examined. An algorithm is proposed for tuning out external SC and determining the damaged phase by using modern methods of digital signal processing.

In 24-hour Non-invasive Blood Pressure (NIBP) measurement, most of the automatic devices use oscillometric method. Accurate measurement of BP at finger using an Oscillometric Waveform (OMW) remains a challenge, particularly when the waveform is collected in inflation, because of the bad signal-to-noise ratio. In the present work, we aimed at measuring Ambulatory Blood Pressure by extracting OMW signal at finger with a new method of linear inflation control and digital signal processing. With the PID control of the pump and real time signal processing, we implemented the blood pressure measurement system and new algorithm. For the validation of our suggested method, we compare the effect of our method and traditional method. Finally, the method is validated in a pilot study against Welch Allyn ProBP 3400 BP monitors.

A software-based digital signal processing (DSP) method using the data system processor has been developed to demodulate the photoacoustic responses of a sample to the fundamental phase modulation frequency and its harmonic frequencies (up to the ninth harmonic) in step-scan FT-IR photoacoustic measurements, without the use of any additional hardware. The DSP algorithm and its sampling depth multiplexing advantages are compared to conventional hardware demodulation. Comparison of results from the DSP method to those from hardware demodulators are shown at both the phase modulation frequency and the harmonics, and application of the DSP method to step-scan photoacoustic measurements with phase modulation is discussed as it applies to obtaining depth profile information in heterogeneous materials.

Five digital signal processing methods have been developed to process output signals from sensor arrays to improve the sensor characteristics. A comparative study of eight force sensor arrays using a 200 element test set showed that by using complicated signal processing methods such as artificial neural networks, overall error can be reduced by 6 to 83 percent.