Численный метод многоступенчатого дискретного преобразования Фурье цифрового сигнала

There are questions connected with work of automotive internal combustion engines quality control digitalization. The research goal is formalization of necessary and enough condition of automatic control of such engines’ vibration levels by discrete Fourier transformation digital methods without completing hardware-intensive multiplication operations. During research there are used methods of mathematic and hardware-intensive modeling of digital algorithms of discrete Fourier transformation work and differential filtration on programmed logical integral devices. There are principals of this engine vibrations’ level by digital methods of multi-stage discrete Fourier transformation, which does not require completing algorithmic multiplication operations. There are given formulas of such digital signals’ transformation of differential digital filters by only algebraic addition of timing counts of these signals. The research results have shown and confirmed a possibility of lowering hardware costs with hardware and software realization of digital algorithms of working automotive internal combustion engine vibrations on programmed logical integral devices.

The materials of the article consider the main methodological provisions of calculations and optimization of NPP power unit equipment parameters using mathematical modeling methods. For the effective implementation of tasks related to determining the optimal parameters and structures of NPP power unit equipment using mathematical modeling and well-developed multifactor optimization methods, it is necessary to fulfill a number of requirements at their productions. Practice shows that it is impractical to optimize with the help of a single mathematical (simulation) model the entire set of parameters characterizing a given power unit, because with such a formulation, the optimization tasks are often mutually incorrect due to a significant discrepancy in the accuracy of various source information, unequal influence of parameters on the target function, specific differences in the mathematical description of various units and elements of the power unit. In order to effectively optimize the parameters of NPP power units, it is necessary to create a system of interconnected mathematical models, which include: a group of detailed mathematical models of individual units and elements of power unit equipment; more generalized mathematical models for the main equipment of power units built on their basis; complete mathematical model of power units.

Auscultation, the technique of listening to heart sounds, remains a primary detection tool for diagnosing heart valve disorders. Other techniques, e.g. electrocardiography, ultrasound, etc, are accurate as well as informative; but expensive. With major advancement in the speed of computers, heart sound signals can be processed with ease by memory efficient digital signal processing and pattern recognition algorithms. This paper presents a digital signal processor (DSP) implementation of such a technique by using new threshold criteria. The proposed work can detect whether a heart sound recording belongs to a person suffering from valvular heart disease or not by giving ‘diseased’ or ‘not diseased’ decisions. The algorithm is tested for nine commonly occurring pathological problems and normal heart sound. The robustness of the algorithm is also checked against synthetically injected additive white Gaussian noise (AWGN) with different SNR levels. It is found to give an accuracy of 96.67% up to SNR values of 15 dB and 93.33% up to SNR values of 5 dB.

Introduction. Currently the most widely used digital signal processing methods are those using specialized digital computers, which utilize analog-to-digital conversion of not only the input signals but also the weighting coefficients of the digital filters. This conversion results in quantization noise in the input signals, which reduces the efficiency of the specialized computer. The operating algorithms of these specialized computers are determined by the discrete transfer functions of the digital filters. The transfer function of a specific digital filter is determined by a set of weighting coefficients, which, like the input signals, are quantized by level and loaded into the computer memory. When quantizing the weighting coefficients, errors arise, the magnitude of which is determined by the quantization step. Weighting coefficient quantization errors inevitably affect the frequency properties of digital filters. Objective. To evaluate the influence of the weighting coefficient quantization step on the frequency characteristics of digital low-pass and high-pass filters, band-pass and notch filters of various orders. Method. The study utilized the bilinear z-transformation of the frequency transfer functions of continuous analog filters. Results. Transfer functions in the z-plane of digital low-pass and high-pass filters, band-pass and notch filters of various orders were obtained. Distortions in the frequency transfer functions of digital filters were calculated as a function of the weighting coefficient quantization step. Conclusions. Quantization of weighting coefficients leads to inevitable distortions in the frequency transfer functions of digital filters, the extent of which is determined by the quantization step size. The influence of the quantization step on the resulting distortions in the frequency transfer functions of digital filters must be taken into account when designing digital computers, specifically when selecting the number of binary digits and the quantization step size of a specialized computer. The results of this work will be of great use to designers of digital signal processing devices.

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.

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.

To impose the reconfigurability and reusability of digital circuits for millimeterwave transmitter architectures, high-speed digital signal processing architectures are explored. The digital front-end of these next-generation transmitters can be implemented up to the maximum operating frequency to meet the requirements of the 5G NR FR2 frequency bands. This paper presents an efficient implementation of a reconfigurable digital signal processor (DSP) that contains programmable multistage multirate filters, operable up to 4 GHz, and a flexible generator for polar, outphasing, and multilevel outphasing modulation. The system achieves an excellent ACLR of 42 dB and EVM degradation of 1.61% with a 7-bit phase signal at a sampling frequency of 4 GHz for outphasing modulation. Digital synthesis of the circuit in a 22 nm FDSOI process results in a core area of 0.12 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and an estimated power consumption of 142 mW for a 200 MHz bandwidth 5G NR baseband signal.

Implementation of digital signal processing (DSP) algorithms in hardware, such as field programmable gate arrays (FPGAs), requires a large number of multiplications. In this paper, we introduce a novel multiplier structure that converts from 2's complement to canonical signed digit (CSD) representation in real time. The proposed algorithm increases the number of zero partial products (which can be simplified by shift operations) to approximately 66.7% compared with 50% for modified Booth's recoding. Also, the proposed hardware structure reduces the non-zero partial products to a minimum, and consequently the number of arithmetic operations in the carry-save structure is reduced. So, our proposed hardware decreases both the time required for multiplication and the power consumption of the multiplier. Furthermore, because the proposed structure uses real time CSD recoding, and does not require a fixed value for the multiplier input to be known a priori, the proposed multiplier can be used to implement digital filters with non-fixed filter coefficients, such as adaptive filters.

The present work describes some of the research effort on Active Noise Control (ANC) being jointly developed by the Catholic University of Minas Gerais (PUC-MINAS) and the Federal University of Minas Gerais (UFMG). Considerations about the implementation of Digital Signal Processing for noise control in ducts has been presented. The objective is to establish a study on Active Noise Control in ducts combining geometry and acoustic parameters modification together with adaptive digital filtering implementation. Both algorithm and digital signal processing details are also discussed. The main results for a typical application where real attenuation has been obtained are presented and considered according to their use in developing real applications. The authors also believe that the present text should provide an interesting overview for both designers and students concerned about Active Noise Control in ducts. (To be presented in Portuguese.)

Recently, optimal control on Buck converter for powering the latest computer central processor units (CPUs) has attracted more and more attention. In this paper, a novel digital control algorithm is presented to achieve the time-optimal response for dc-dc Buck converters without relying on any knowledge of converter design parameters such as output inductance, capacitance and even ESR value. This algorithm is based on the parabolic curve fitting analysis for deriving the algorithm formulas under the step load transients. Furthermore, this algorithm can be extended to adaptive voltage positioning (AVP) applications with simple modifications for a low ESR designed Buck converter. Also, it delivers a practical and cost-effective interface to AVP schemes due to the parameter-independent and current-sensorless detecting mechanism. Finally, simulations and experimental results of a 12 V-1.5 V Buck converter prototype are provided to validate the proposed schemes using digital signal processor (DSP) implementation.

Efflorescence is a common phenomenon of cement‐based materials, and the mathematical model can be used to simulate the process of efflorescence process calcium dissolution. It is inspired by the dissolution of cement‐based materials, and the Gerard model of calcium leaching is also used for reference. The mathematical model of efflorescence of cement‐based materials is established which based on the calcium ion solubility. It reveals the mathematical relationship of transformation process of Ca(OH)2 between solid phase and liquid phase and the calcium ion transport process in liquid phase. Also, the relationship between Ca(OH)2 content, the porosity, raw materials with physical and chemical properties of cement‐based materials and the key parameters on the mathematical efflorescence model are studied. The related physical and chemical parameters of cement‐based materials are taken into the mathematical model, and the liquid calcium ion distribution inside the material can be calculated after some time of efflorescence, which provides the basics of mathematical model for the prediction of efflorescence.

This paper proposes a practical query-by-singing/humming (QbSH) system that retrieves polyphonic music such as an MP3 and its commercial applications. The performance of music retrieval system is mainly affected by the server. This paper discusses developing the state-of-the-art server side software stack which has several managers and plug-in engines. It also describes implementation of digital signal processor (DSP) module for stand-alone embedded platforms. The paper shows three different models for its commercial applications like smart phone, laptop and karaoke. We evaluate the performance of the proposed system with polyphonic music datasets using users' humming datasets as the input query.

The role of software is becoming increasingly important in the implementation of digital signal processing (DSP) applications. As this trend intensifies, and the complexity of applications escalates, we are seeing an increased need for automated tools to aid in the development of DSP software. This paper reviews the state of the art in programming language and compiler technology for DSP software implementation. In particular, we review techniques for high-level block-diagram-based modeling of DSP applications; the translation of block-diagram specifications into efficient C programs using global target-independent optimization techniques; and the compilation of C programs into streamlined machine code for programmable DSP processors using architecture-specific and retargetable back-end optimizations. We also point out important directions for further investigation.

Achieving higher spectral efficiency is an efficient approach to address the bandwidth crisis of optical networks, but it faces significant technical challenges. High-spectral-efficiency non-orthogonal frequency-division multiplexing (NOFDM) has been proposed for meeting the requirement of optical networks' capacity upgrade. Compared to orthogonal frequency-division multiplexing, NOFDM can achieve higher spectral efficiency by compressing the subcarrier spacing narrower than the Nyquist orthogonal spacing, but this inevitably induces inter-carrier interference (ICI). ICI cancellation algorithms are crucial in NOFDM for correctly recovering the transmitted signal at the expense of extra processing power. Nowadays, processing power is no longer the obstacle to eliminate ICI due to the rapid development of application-specific integrated circuits for efficient implementation of digital signal processing. In this article, fast multiplexing and demultiplexing for NOFDM are implemented based on inverse fast Fourier transform and fast Fourier transform, respectively. Meanwhile, three representative ICI cancellation algorithms are discussed, and one of them is experimentally implemented. Furthermore, we propose non-orthogonal discrete multitone (NODMT) by integrating the advantages of NOFDM and DMT, which has the potential to achieve improved spectral efficiency for future optical networks.

The mitigation of transmission impairments in optical communication systems requires implementation of digital signal processing that can easily adapt to changing conditions and work on continuous streams of data. In this paper, the application of continual learning in deep neural networks for transmission impairments in long haul transmission systems is considered. This work investigates the novel use of continual learning to overcome the problem of catastrophic forgetting and remembrance in deep neural networks that can be used for dispersion and nonlinearity mitigation in long-haul transmission systems. We perform comparisons between state-of-the-art methods such as iCaRL, DGR, EWC, XdG and traditional DNNs implementations typically used in optical networks. We demonstrate improvements in training accuracy towards signal recovery, and increased flexibility for DNN equalization applications. The results show that the method can be successfully used for fiber lengths ranging from less than 1km to 250km and over with no need to retrain the network from scratch for each fibre length. The introduction of continual learning enables training DNNs on a continuous stream of data with no loss of prior knowledge.