Preliminary analysis on the noise characteristics of MWISP data

Noise is a significant part within a millimeter-wave molecular line datacube. Analyzing the noise improves our understanding of noise characteristics, and further contributes to scientific discoveries. We measure the noise level of a single datacube from MWISP and perform statistical analyses. We identified major factors which increase the noise level of a single datacube, including bad channels, edge effects, baseline distortion and line contamination. Cleaning algorithms are applied to remove or reduce these noise components. As a result, we obtained the cleaned datacube in which noise follows a positively skewed normal distribution. We further analyzed the noise structure distribution of a 3D mosaicked datacube in the range l = 40 ⋅ ° 7 to 43 ⋅ ° 3 and b = − 2 ⋅ ° 3 to 0 ⋅ ° 3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.

Variability in higher order structure of noise added to weighted networks

The complex behavior of many real-world systems depends on a network of both strong and weak edges. Distinguishing between true weak edges and low-weight edges caused by noise is a common problem in data analysis, and solutions tend to either remove noise or study noise in the absence of data. In this work, we instead study how noise and data coexist, by examining the structure of noisy, weak edges that have been synthetically added to model networks. We find that the structure of low-weight, noisy edges varies according to the topology of the model network to which it is added, that at least three qualitative classes of noise structure emerge, and that these noisy edges can be used to classify the model networks. Our results demonstrate that noise does not present as a monolithic nuisance, but rather as a nuanced, topology-dependent, and even useful entity in characterizing higher-order network interactions.

Real-Time Multirate Filtering of Digitized Torque Signals on Tiva Microcontroller using Fixed-Point Design with MATLAB

Correct bone screw torque is critical for positive patient outcomes after orthopaedic surgery. Models of the screwing process have been developed to allow a smart screwdriver to optimise the insertion torque. Experimental data is required to test these models, so a test-rig has been developed. Accurate torque measurement is a key part of the test-rig. An FIR filter was designed for this torque signal, implemented on the test-rig, and compared theoretically and experimentally to a mean filter and to no filtering. The FIR and mean filters both performed well, with the FIR achieving better theoretical results, and the mean filter achieving better experimental results. Better understanding of the noise structure and potential signal distortion would be required to improve the FIR filter or to conclusively compare it against the mean filter, however both perform sufficiently well for this application.

Assessing Statistical Performance of Time Series Interpolators

Real-world time series data often contain missing values due to human error, irregular sampling, or unforeseen equipment failure. The ability of a computational interpolation method to repair such data greatly depends on the characteristics of the time series itself, such as the number of periodic and polynomial trends and noise structure, as well as the particular configuration of the missing values themselves. The interpTools package presents a systematic framework for analyzing the statistical performance of a time series interpolator in light of such data features. Its utility and features are demonstrated through evaluation of a novel algorithm, the Hybrid Wiener Interpolator.

EXPERIMENTAL RESEARCH OF IMPACT NOISE STRUCTURE FLOORS WITH CHANGING THICKNESS OF MATERIALS AND SIZE FILLER

This paper considers experimental studies of the sound insulation structure of the floor to determine the effect of material thickness, polystyrene concrete and cement-sand screed, as well as the size of the aggregate (polystyrene granules) in polystyrene concrete on the sound insulation properties of floor construction, namely impact protection. It was determined that the thickness of the cement-sand screed in no way affects the improvement of impact noise. Due to the reduction of the size of the aggregate (expanded polystyrene granule) in polystyrene concrete, it was possible to reduce the thickness of the structure, which did not affect the insulation performance of impact noise.

Designing accurate emulators for scientific processes using calibration-driven deep models

Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

RESEARCH ON THE FRACTAL STATISTICAL CHARACTERISTICS AS POSSIBLE PROGNOSTIC PARAMETERS FOR EARTHQUAKES, GENERATED IN THE SEISMIC ZONE OF VRANCEA, IN THE PERIOD BETWEEN 01.08.2016 AND 30.12.2016

Using fractal analysis is an excellent alternative method for decode the seismic noise structure. Fractal analysis of microseismic noise could also be an appropriate method to detect earthquake indicators. The scientific goal is to detect standard signals, based on different earthquakes’ focal mechanisms, separating the "individual" behavior of the elements of the monitoring systems.The method for describing low-frequency microseismic noise from the network of seismic stations in a seismically active region of the Vrancea used. Seismic records of twenty-three broadband stations were analyzed, situated at distances of 20 to 500 km from the Vrancea earthquakes whit magnitudes Mw=5.7 and Mw=5.6 on September 23 and December 27, 2016, respectively. The daily assessment values of three multifractal parameters (characteristics of the multifractal singularity spectra of the waveform) from each station used for the description.The present paper is a continuation of previous work [Oynakov et al., 2019], where the effects of synchronization in the low-frequency microseismic field were found before the Vrancea earthquake with magnitude Mp=5.6 on October 28, 2019.The study shows that the noise coherence measure increased for stations, closer to the epicenter. However, the question of the source of this coherence remains open.

Identification of the noise structure of micromechanical inertial transducers of motion parameters

The article presents the results of the analysis of the noise structure of micromechanical transducers of motion parameters – micromechanical gyroscopes (MMG) and micromechanical accelerometers (MMA) of an experimental measuring unit of strapdown inertial position navigation systems. The unit is manufactured and developed at JSC «GYROOPTICS» (St. Petersburg). It consists of a LL–MMG triad with measuring ranges of ±400°/s and an axial-type MMA triad with measuring ranges of ± 50 g. Micromechanical gyroscopes and accelerometers manufactured using modern microelectronics technologies are among the most promising microsystem technology devices that are widely used as sensors of the primary information of strapdown inertial orientation and navigation systems. The accuracy of the functioning of the inertial orientation and navigation inertial systems is significantly affected by the noise structure of the output signals of the inertial motion parameters sensors. For this reason, the urgent task of identifying the noise of micromechanical gyroscopes and accelerometers. The noise structure of the angular rate and linear acceleration transducers of the tested SINS block was identified by the Allan variance method. The output signals of the transducers were recorded in normal climatic conditions, the sampling interval was 1.0 ms, and the recording duration was 90 minutes. The processing of the output signals of the transducers was carried out on the basis of special software using the AlaVar 5.2 program. It has been established that the predominant noise components of the transducers are the random walk of the output signal – white noise and the instability of the zero signal – flicker noise. No quantization noise was detected in the output signals of the transducers. The values of the noise characteristics in Allan variance of the output signals of the angular rate transducers and the linear acceleration of the test block are compared with the noise characteristics of the most advanced modules produced by foreign companies.