Application of the Complex Moments for Selection of an Optimal Sensor

In the first time we apply the statistics of the complex moments for selection of an optimal pressure sensor (from the available set of sensors) based on their statistical/correlation characteristics. The complex moments contain additional source of information and, therefore, they can realize the comparison of random sequences registered for almost identical devices or gadgets. The proposed general algorithm allows to calculate 12 key correlation parameters in the significance space. These correlation parameters allow to realize the desired comparison. New algorithm is rather general and can be applied for a set of other data if they are presented in the form of rectangle matrices. Each matrix contains N data points and M columns that are connected with repetitious cycle of measurements. In addition, we want to underline that the value of correlations evaluated with the help of Pearson correlation coefficient (PCC) has a relative character. One can introduce also external correlations based on the statistics of the fractional/complex moments that form a complete picture of correlations. To the PCC value of internal correlations one can add at least 7 additional external correlators evaluated in the space of fractional and complex moments in order to realize the justified choice. We do suppose that the proposed algorithm (containing an additional source of information in the complex space) can find a wide application in treatment of different data, where it is necessary to select the “best sensors/chips” based on their measured data, presented usually in the form of random rectangle matrices.

Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

Exploring the distribution of statistical feature parameters for natural sound textures

Sounds like “running water” and “buzzing bees” are classes of sounds which are a collective result of many similar acoustic events and are known as “sound textures”. A recent psychoacoustic study using sound textures has reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the ‘cochlear envelope’. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures’ marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 64% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 88% of the variance in the 496 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.

Likelihood-based estimation of substructure content from single-wavelength anomalous diffraction (SAD) intensity data

SAD phasing can be challenging when the signal-to-noise ratio is low. In such cases, having an accurate estimate of the substructure content can determine whether or not the substructure of anomalous scatterer positions can successfully be determined. Here, a likelihood-based target function is proposed to accurately estimate the strength of the anomalous scattering contribution directly from the measured intensities, determining a complex correlation parameter relating the Bijvoet mates as a function of resolution. This gives a novel measure of the intrinsic anomalous signal. The SAD likelihood target function also accounts for correlated errors in the measurement of intensities from Bijvoet mates, which can arise from the effects of radiation damage. When the anomalous signal is assumed to come primarily from a substructure comprising one anomalous scatterer with a known value of f′′ and when the protein composition of the crystal is estimated correctly, the refined complex correlation parameters can be interpreted in terms of the atomic content of the primary anomalous scatterer before the substructure is known. The maximum-likelihood estimation of substructure content was tested on a curated database of 357 SAD cases with useful anomalous signal. The prior estimates of substructure content are highly correlated to the content determined by phasing calculations, with a correlation coefficient (on a log–log basis) of 0.72.

Local Information as an Essential Factor for Quantum Entanglement

Quantum entanglement is not only a fundamental concept in quantum mechanics but also a special resource for many important quantum information processing tasks. An intuitive way to understand quantum entanglement is to analyze its geometric parameters which include local parameters and correlation parameters. The correlation parameters have been extensively studied while the role of local parameters have not been drawn attention. In this paper, we investigate the question how local parameters of a two-qubit system affect quantum entanglement in both quantitative and qualitative perspective. Firstly, we find that the concurrence, a measure of quantum entanglement, of a general two-qubit state is bounded by the norms of local vectors and correlations matrix. Then, we derive a sufficient condition for a two-qubit being separable in perspective of local parameters. Finally, we find that different local parameters could make a state with fixed correlation matrix separable, entangled or even more qualitatively entangled than the one with vanished local parameters.

Resistivity Data Modeling for Subsurface Volcanostratigraphy Construction of Cibadak Sub-Watershed, Bogor, West Java, Indonesia.

In Mt. Salak, there are six volcanic facies divided by eruption time seen from geomorphology data analysis and to identified the subsurface layer DC Resistivity method is applied. Beside resistivity, geostatistical parameters also influence the result model interpretation, so for obtain best model correlation parameters such as tilting, surfacing, variogram, grid method, and logarithmic distribution is applied. Using 18 points of acquisition data subsurface model is produce and then section model made to describe vertical resistivity distribution then correlated with facies lithology model. Based on that, produce three facies resistivity type namely: 0 – 100 Ohm.m (Low Resistivity Value) Interpreted as pyroclastic material composed as tuff and breccia that lies under lava. 100 – 300 Ohm.m (Medium Resistivity Value) Interpreted as breccia lithology type. Harder that pyroclastic material due to by this product is avalanches of lava. And >300 Ohm.m (High Resistivity Value) Interpreted as lava lithology that lies at high elevation and the hardest lithology in this area. From the model, pyroclastic layer that is modeled found at low elevation and based on the direction it described as oldest facies layer, but at the bottom of this layer lies high resistivity value that unknown product. It can be Mt. Pangrango product due to at low elevation predicted as combine area product from product of Mt. Salak and Pangrango. High resistivity value show lava lithology and lava facies located in high elevation and medium resistivity describe breccia lithology as avalanche product of lava (youngest pyroclastic facies) and found at 500 – 100 meters msl.

Comparative study on the design methods for fly ash-flexible pavement

Advancement in the design of pavement structures in the recent decade has brought about the use of finite element modelling (FEM) tools. Numerical simulation of flexible pavement through these models are yielding positive results and enhancing pavement design year after year. Various factors contribute to this success; yet, material characterization model in FEM is a major/critical factor. However, in using FEM, there are various material characterization input methods which are; input through laboratory testing; secondly, through correlation and lastly a backward calculation from deflection measurements. Overall, input methods are more realistic and give a better understanding of the mechanical behaviour of the material, nevertheless quite difficult to obtain. Although, the use of fly-ash stabilizer in pavement structure is not new yet its use has not been fully implemented in FEM design. As a result, a comparative study is considered based on input and correlation parameters on fly ash stabilized flexible pavement using Abaqus. Furthermore, the results show that the material input method provides better results and gives some amount of certainty on the design life of the pavement.

Low-cost electromyography: validity against a commercial system depends on exercise type and intensity

The aim of this study was to assess the validity of a custom-made low cost (LC) and a commercial surface EMG apparatus in controlled experimental conditions and different exercise types: maximal voluntary contractions (MVC) at 105, 90, 75, 60, 45 and 30° knee angle and explosive fix-end contractions of the knee extensors (75°) at an isometric dynamometer. sEMG of vastus lateralis was recorded from the same electrodes simultaneously, then analyzed in the same way; sEMG were finally expressed in percentage of those collected at 75°MVC. LC underestimated the sEMG signal at the more extended knee angles (30-60°), significant difference was observed only at 30°. In the explosive contractions no differences between devices were observed in average and peak sEMG, as well as in the time to peak and the activation time. Bland-Altman tests and correlation parameters indicate the LC device is not sensible enough to detect the time to peak and the peak values of the sEMG signal properly. Results suggest low-cost systems might be a valid alternative to commercial ones, but attention must be paid when analyzing rapid events.

Multidimensional Psychological Evaluation of Air Conditioner Sounds and Prediction via Correlation Parameters

Air conditioners are regarded as a major source of noise in built environments. Although noise control technology has reduced the sound produced by air conditioners to a comparatively low level, some people may still feel that certain aspects of the sound quality lead to discomfort. Indeed, both the sound level and the sound quality of an air conditioner can affect user’s acoustic comfort. The aim of this study was to determine the factors that significantly influence the subjective response to the sound of air conditioners. We assessed the A-weighted equivalent continuous sound pressure level (LAeq) and factors extracted from the autocorrelation function (ACF) and interaural cross-correlation (IACF). Subjective loudness, sharpness, and annoyance were evaluated using a paired comparison method. Multiple regression analyses were performed using a linear combination of LAeq, the ACF factors, IACF factors, and assessment of their standard deviations. The multiple regression analyses indicated that LAeq, the delay time of the first maximum peak, the width of the first decay of the ACF, and the magnitude and width of the IACF could predict subjective responses to air conditioner sounds.