Mixed Spectra for Stable Signals from Discrete Observations

This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.

Spatial uniformity tolerances for sound masking systems in open-plan offices

Electronic sound masking systems raise the ambient sound level in offices to a controlled minimum sound level in order to increase speech privacy and reduce distractions. Sound masking systems are calibrated to provide the most uniform sound field achievable, as a spatially non-uniform masking sound field could result in occupant perception and uneven speech privacy conditions. Tolerances for acceptable spatial uniformity vary between specifiers, and may be based on different evaluation methods using only a few discrete measurement points to represent an entire office space. However, the actual uniformity of a masking sound field across an office, and the parameters influencing it, has not been widely investigated. Thus, this study aims to investigate the masking sound uniformity in a typical open-plan office space using fine-grid measurements conforming to measurement method of ASTM E1573-18. Percentages of measured locations where the sound pressure levels were within specified tolerances (with increments of 0.5 dB) were calculated using the measured 1/3 octave band levels. The research also utilized geometric acoustical simulations to investigate how physical office parameters (number of loudspeakers, partition heights, ceiling absorption, and diffusion characteristics) affect the sound field uniformity of the sound masking system.

Measurement error model for the Norwegian common minke whale (Balaenoptera acutorostrata acutorostrata) surveys 2008-2013

A discrete measurement error model for radial distance and angle to detected objects in line transect surveys is considered. This approach directly quantifies the effect of measurement error on the estimated effective strip half-width. We apply the method to experimental data collected over the period 2008-2013 in North Atlantic both under the assumption of multiplicative and additive measurement errors. Our results indicate that the abundance estimates considering the measurement error are consistently larger than the abundance estimates without any measurement error correction.

Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network

Focusing on the issue of missing measurement data caused by complex and changeable working conditions during the operation of high-speed trains, in this paper, a framework for the reconstruction of missing measurement data based on a generative adversarial network is proposed. Suitable parameters were set for each frame. Discrete measurement data are taken as the input of the frame for preprocessing the data dimensionality. The convolutional neural network then learns the correlation between different characteristic values of each device in an unsupervised pattern and constrains and improves the reconstruction accuracy by taking advantage of the context similarity of authenticity. It was determined experimentally that when there are different extents of missing measurement data, the model described in the present paper can still maintain a high reconstruction accuracy. In addition, the reconstruction data also conform well to the distribution law of the measurement data.

Estimation of trajectory parameters of radio radiation sources in multi-position complexes by the maximum likelihood method

The paper presents a new approach to the secondary processing of radar information in multi-position passive complexes. The proposed approach is based on the use of maximum likelihood estimates of discrete measurement counts at finite time intervals, taking into account the motion of the radio emission source. The advantage of the proposed approach is the possibility to work on non-full measurements. The disadvantage is the delay in the output of the solution, as well as a decrease in the rate of output of the solution. The article presents an analysis of the effectiveness of the proposed approach, as well as the results of testing the developed algorithm on real measurements.

The Universality of the Kalman Filter

The universality of the Kalman filtering can be found in the control theory. The Kalman filter has found its applications in sophisticated autonomous systems and smart products, which are attributed to its realization in a single complex chip. In this chapter, considering the Kalman filter from the perspective of conditional characteristic function evolution and Itô calculus, three Kalman filtering theorems and their formal proof are developed. Most notably, this chapter reveals the following: (1) Kalman filtering equations are a consequence of the ‘evolution of conditional characteristic function' for the linear stochastic differential system coupled with the linear discrete measurement system. (2) The Kalman filtering is a consequence of the ‘stochastic evolution of conditional characteristic function' for the linear stochastic differential system coupled with the linear continuous measurement system. (3) The structure of the Kalman filter remains invariant under two popular stochastic interpretations, the Itô vs Stratonovich.

The Waring Distribution as a Low-Frequency Prediction Model: A Study of Organic Livestock Farms in Andalusia

Although the numbers are relatively small with respect to non-organic livestock, the importance of organic livestock farms lies in their sustainable coexistence with the natural environment and in the high-quality food products obtained. In this type of production, no artificial chemicals or genetically modified organisms are used, therefore there will be less impact on the environment and, in most cases, native breeds are employed. This paper describes a geostatistical study of organic livestock farms in Andalusia (southern Spain), conducted using information from the 2009 Agricultural Census, by classes of livestock. This region currently records the highest output in Spain for organic livestock farming. The number of farms was fitted according to the univariate generalized Waring distribution, which is presented as a means of analyzing this type of discrete measurement, using agricultural or livestock data. The Waring distribution is used when the frequency of occurrence of a phenomenon is very low and allows one to divide the variance. The most important outcome of this study is the finding that livestock data variability is mainly due to external factors such as the proneness component of the variance.

The maximum achievable level of error in measuring the mechanical drilling speed

The article is devoted to the problem of mechanical velocity measurement in deep wells. The standard error limit is set at 2,5 %. However, the existing methods of measurement of drilling parameters have large errors associated with the presence of friction forces in the well, temperature changes with depth, column layout, etc. The error is also introduced by the discrete measurement method, which manifests itself in smoothing of the useful signal and delay in the information about the speed value by half the averaging interval. The aim of the work is to determine the realistically achievable values of the mechanical drilling speed. For this, the reduced error was calculated according to the upper limit of measurement in accordance with GOST. The dependence of the error on the depth of the well and the drilling speed is graphically presented. The error estimation for different type of drilling and different column layout is given. The analysis of the obtained results showed that the set value of the error can be achieved not in the entire range of changes in the speed and depth of the well bottom.

Robust Filtering Options for Higher-Order Strain Fields Generated by Digital Image Correlation

Digital image correlation (DIC) systems have been used in many engineering fields to obtain surface full-field strain distribution. However, noise affects the accuracy and precision of the measurements due to many factors. The aim of this study was to find out how different filtering options; namely, simple mean filtering, Gaussian mean filtering and Gaussian low-pass filtering (LPF), reduce noise while maintaining the full-field information based on constant, linear and quadratic strain fields. Investigations are done in two steps. First, linear and quadratic strain fields with and without noise are simulated and projected to discrete measurement points which build up strain window sizes consisting of 6×5, 12×11, and 26×17 points. Optimal filter sizes are computed for each filter strategy, strain field type, and strain windows size, with minimal impairment of the signal information. Second, these filter sizes are used to filter full-field strain distributions of steel samples under tensile tests by using an ARAMIS DIC system to show their practical applicability. Results for the first part show that for a typical 12×11 strain window, simple mean filtering achieves an error reduction of 66–69%, Gaussian mean filtering of 72–75%, and Gaussian LPF of 66–69%. If optimized filters are used for DIC measurements on steel samples, the total strain error can be reduced from initial 240−300 μstrain to 100–150 μstrain. In conclusion, the noise-floor of DIC signals is considerable and the preferable filters were a simple mean with s*¯ = 2, a Gaussian mean with σ*¯ = 1.7, and a Gaussian LPF with D0*¯ = 2.5 in the examined cases.