The objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant’s everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error.

In this paper the capacity of non-uniform sampling rate conversion techniques, involving different interpolation methods, aimed at wow defect reduction, is examined. Involved are: linear interpolation, four polynomial-based interpolation methods and the windowed sincbased method. The examined polynomial methods are: Lagrange interpolation, polynomial fitting with additional noise reduction, Hermitan and Spline. The performance of an artificially distorted audio signal, restored using non-uniform resampling, is evaluated on the basis of standard audio defect measurement criteria and compared for all of the aforementioned interpolation methods. The chosen defect descriptors are: total harmonic distortion, total harmonic distortion plus noise and signal to noise ratio.

Dual quaternions and dual quaternion interpolation are powerful mathematical tools for the spatial analysis of rigid body motions. In this paper, after a review of some basic results and formulas, it will be presented an attempt to use these tools for the the kinematic modeling of human joints. In particular, the kinematic parameters extracted from experimentally acquired data are compared with those theoretically computed from dual quaternions rigid body motion interpolation.

Persistent organic pollutants (POPs) originating from agrochemical industries have become an urgent environmental problem worldwide. Ordinary kriging, as an optimal geostatistical interpolation technique, has been proved to be sufficiently robust for estimating values with finite sampled data in most of the cases. In this study, ordinary kriging interpolation integrate with 3D visualization methods is applied to characterize the monochlorobenzene contaminated soil for an agrochemical industrial site located in Jiangsu province. Based on 944 soil samples collected by Geoprobe 540MT and monitored by SGS environmental monitoring services, 3D visualization in terms of the spatial distribution of pollutants in potentially contaminated soil, the extent and severity of the pollution levels in different layers, high concentration levels and isolines of monochlorobenzene concentrations in this area are provided. From the obtained results, more information taking into account the spatial heterogeneity of soil area will be helpful for decision makers to develop and implement the soil remediation strategy in the future.

The article shows the methodology and calculation procedures based on Lagrange polynomial interpolation which were used to determine standard performance characteristics of the Polish production engine, type ANDORIA 4CTi90-1BE6. They allow to simplify the experimental research by maintaining a minimum number of measurement points and estimating the remaining data in an analytical way. The methods presented are convenient when it comes to the practical side because they eliminate the need for exploration of mathematical equations describing the various curves, which can be cumbersome and time consuming in the case of nonautomated accounts. The results of analysis were applied to actual experimental results, indicating sufficient accuracy of the resulting approximations. As a result, procedures may be used in bench testing of a similar profile, especially with repeated cycles of the experiment, such as optimization of operating parameters of combustion engines.

Sample-time errors can greatly degrade the dynamic range of a time-interleaved sampling system. In this paper, a novel correction technique employing a cubic spline interpolation is proposed for inter-channel sample-time error compensation. The cubic spline interpolation compensation filter is developed in the form of a finite-impulse response (FIR) filter structure. The correction method of the interpolation compensation filter coefficients is deduced. A 4GS/s two-channel, time-interleaved ADC prototype system has been implemented to evaluate the performance of the technique. The experimental results showed that the correction technique is effective to attenuate the spurious spurs and improve the dynamic performance of the system.