Tabular algorithm for evaluating the discrete cross-correlation function of the flowmeter acoustic signals

Корреляционная обработка сигналов как частный случай использования цифровой обработки данных, получаемых с акустических датчиков, находит широкое применение в современных ультразвуковых расходомерах жидкости и газа. К ним можно отнести как непосредственно корреляционные меточные расходомеры, так и расходомеры преимущественно время-импульсного или время-пролетного типов, где корреляционная обработка акустических сигналов является дополнением к общему методу измерения объемного расхода жидкости и газа. Применение корреляционной обработки позволяет повысить разрешающую способность расходомера в целом и обеспечить выделение полезного сигнала на фоне присутствия шумов с высокой степенью достоверности. В статье описан способ вычисления дискретных корреляционных функций на основе обобщенного определения дискретной корреляционной функции через свертку дискретизированных сигналов с выходов датчиков потока. Суть данного метода сводится к вычислению набора значений кумулятивных произведений отсчетов зондирующих сигналов, взятых с разным шагом в зависимости от общего количества отсчетов сигналов и предполагаемого числа значений корреляционной функции. Полученный набор значений оформляется в виде двумерного массива или матрицы, однако для большего понимания его можно представить как таблицу. Результаты суммы отдельных элементов этой таблицы или матрицы, выбранных согласно установленному правилу, и будут являться конечными значениями взаимной корреляционной функции акустических сигналов. В рамках работы составлены непосредственно алгоритм вычисления дискретной корреляционной функции в соответствии с рассмотренным методом расчета корреляционной функции, приведены примеры вычисления программным способом взаимной и автокорреляционной функций акустических сигналов, приближенных по своим свойствам к сигналам реальных ультразвуковых расходомеров. Предложенный вариант расчета дискретных корреляционных функций может быть применен в энергоэффективных вычислительных модулях расходомеров, предназначенных для длительной эксплуатации от источника автономного питания, обладающих низкой производительностью. Correlation signal processing as a particular case of using a digital data processing obtained from acoustic sensors is widely used in modern ultrasonic liquid and gas flowmeters. These include both direct correlation flowmeters and predominantly a time-pulse or time-of-flight type’s flowmeters, where the correlation processing of acoustic signals is an addition to the general method for measuring the volumetric flow rate of liquid and gas. The use of correlation processing makes it possible to increase the resolution of the flowmeter as a whole and to ensure the useful signal extraction against the background of the noise presence with a high degree of reliability. The article describes a method for calculating discrete correlation functions based on the generalized definition of a discrete correlation function through the convolution of sampled signals from the flow sensors outputs. The essence of this method comes down to calculating a values set ​​of the cumulative products of the probing signal’s samples taken with different steps depending on the total number of signal samples and the assumed number of the correlation function samples. The resulting values sequence ​​is formatted as a two-dimensional array or matrix, but for better understanding it can be represented as a table. The results of the sum of the individual elements of this table or matrix, selected according to the established rule, will be the final values ​​of the cross-correlation function of acoustic signals. Within the framework, an algorithm for calculating the discrete correlation function is directly compiled in accordance with the considered method for calculating the correlation function, examples of software calculation of the cross- and autocorrelation functions of acoustic signals, which are close in their properties to the real signals of ultrasonic flowmeters, are given. The proposed option for calculating discrete correlation functions can be applied in energy-efficient computational modules of flowmeters designed for long-term operation from an autonomous power source with low performance.

Peculiarity of the use of pseudonoise signals in electrical prospecting equipment

The article provides a practical assessment of the effectiveness of the use of electrical prospecting equipment with pseudonoise sounding signals based on the results of field experiments with a new electrical prospecting measuring complex developed at the Scientific Station of the Russian Academy of Sciences. Two main sources of interference are considered, limiting the possibilities of effective use of pseudonoise signals in electrical prospecting equipment: “structural disturbances”, manifested in the process of correlation processing of recorded signals and interference, the source of which is an industrial power network with a frequency of 50 Hz. Methods of reducing the influence of the above noises on the sounding curve obtained during data processing are considered using specially developed algorithms for eliminating “structural disturbances” and suppressing residual noise and interference.

Crosscorrelation image processing for surface shape reconstruction using fiducial markers

Optical methods for deformations diagnostic and surface shape measurement are widely used in scientific research and industry. Most of these methods are based on triangulating a set of two-dimensional points in the images appropriate to the same three-dimensional points of the object in space. Various algorithms to search such points are applied. The possibility of using cross-correlation processing of digital images to search these points is considered in the work. Algorithms based on the correlation function calculation are widely employed in such a popular flow diagnostic method as PIV. The cameras of a stereo system for surface shape measurement can be widely spaced, and the tilt angles relative to the surface can differ significantly. This leads to the fact that the images taken from the cameras cannot be directly processed by the correlation function because it is not invariant to rotation. To solve this problem, fiducial markers are used to find an initial estimate of displacement of the images relative to each other. This approach makes it possible to successfully apply correlation processing for stereo system images with a large stereo base.

EVALUATING THE COMPLEXITY OF IMPLEMENTING DATA PROCESSING ON IOT-DEVICES

A method for implementing data processing in the Internet of Things systems, based on the end device, is considered. While existing approaches are based on the Cloud or Edge paradigm, processing on the end device of the IoT system allows you to reduce the amount of data transmitted at the initial stage. Correlation processing is an effective way to detect signals, however, practical implementations with a long pulse response duration are not suitable for low-power devices. The paper compares a number of implementations with an estimate of the number of computational operations, as well as an improved approach that reduces not only the number of operations, but also the processing delay. In addition, the implementation study is carried out when implementing on the basis of field programmable gate arrays (FPGA). The directions related to the research of signal processing directly on intermediate devices and, especially, on end devices (on-sensor processing) are represented to a lesser extent. This fact is due to the fundamental limitations of the end devices and systems of the Internet of Things, as well as the contradictory requirements. First of all, the devices should be as cheap as possible, autonomous, compact and at the same time have low power consumption. These requirements limit the performance of end devices. The network, in turn, must provide the required quality of service (QoS) and the speed and reliability of data transmission. The implementation of data processing on end devices in IoT systems is of great scientific and practical interest. This article will consider an approach based on correlation processing (consistent filtering). The traditional approach with large orders of filters on low-power, from a computational point of view, devices is redundant and not always feasible.

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

ABSTRACT Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

Low-PAPR Waveforms with Shaped Spectrum for Enhanced Low Probability of Intercept Noise Radars

Noise radars employ random waveforms in their transmission as compared to traditional radars. Considered as enhanced Low Probability of Intercept (LPI) radars, they are resilient to interference and jamming and less vulnerable to adversarial exploitation than conventional radars. At its simplest, using a random waveform such as bandpass Gaussian noise as a probing signal provides limited radar performance. After a concise review of a particular noise radar architecture and related correlation processing, this paper justifies the rationale for having synthetic (tailored) noise waveforms and proposes the Combined Spectral Shaping and Peak-to-Average Power Reduction (COSPAR) algorithm, which can be utilized for synthesizing noise-like sequences with a Taylor-shaped spectrum under correlation sidelobe level constraints and assigned Peak-to-Average-Power-Ratio (PAPR). Additionally, the Spectral Kurtosis measure is proposed to evaluate the LPI property of waveforms, and experimental results from field trials are reported.

Monopulse radar tracking by range and angular coordinates with a broadband sounding signal.

A block diagram of the receiving part of a monopulse radar tracking by range and angular coordinates with a broadband sounding signal is presented. The features of correlation processing of a broadband linear-frequency-modulated signal with full frequency demodulation and algorithms for estimating coordinates are considered. A comparative analysis of the results of calculating errors in measuring the coordinates of air targets in short-range radars using narrow-band and broadband signals and different viewing angles is carried out.

GPU-Based Parallel Implementation of VLBI Correlator for Deep Space Exploration System

Very Long Baseline Interferometry (VLBI) solution can yield accurate information of angular position, and has been successfully used in the field of deep space exploration, such as astrophysics, imaging, detector positioning, and so on. The increase in VLBI data volume puts higher demands on efficient processing. Essentially, the main step of VLBI is the correlation processing, through which the angular position can be calculated. Since the VLBI correlation processing is both computation-intensive and data-intensive, the CPU cluster is usually employed in practical application to perform complex distributed computation. In this paper, we propose a parallel implementation of VLBI correlator based on graphics processing unit (GPU) to realize a more efficient and economical angular position calculation of deep space target. On the basis of massively GPU parallel computing, the coalesced access strategy and the parallel pipeline strategy are introduced to further accelerate the VLBI correlator. Experimental results show that the optimized GPU-based VLBI method can meet the real-time processing requirements of the received data stream. Compared with the sequential method, the proposed approach can reach a 224.1× calculation speedup, and a 36.8× application speedup. Compared with the multi-CPUs method, it can achieve 28.6× calculation speedup and 4.7× application speedup.