Development of personalized medicine is determined by the synergy of scientists from several fields of medicine, mathematics, computer science and instrumentation. Approaches based on modern methods of measuring, signal processing and machine learning complement the main methods for studying biological processes, make it possible to identify the mechanisms of the disease and personalize the treatment strategy. The article is devoted to the study of models and methods that characterize the processes of cerebral blood flow autoregulation for methodological support of measuring systems in the field of digital personalized medicine. Analysis of systemic arterial pressure and blood flow velocity in the arteries of the base of the brain signals, which characterize the cerebral blood flow autoregulation, makes it possible to determine the nature of the violation of cerebral autoregulation processes in patients. The article proposes to use fractal methods for signal analysis based on the calculation of the Hölder multifractal spectrum and the correlation dimension of signals. The advantage of fractal methods is that they can be applied to signals without a characteristic scale that are scale invariant.

A simple model for data obtained by measurement is described. The data model satisfies requirements for evaluation and reporting of measurement uncertainty given in the Guide to the expression of uncertainty in measurement (GUM). The model supports what is called internally consistent and transferable calculations, which are qualities favoured by the GUM. In this way, more rigorous GUM-compliant digital data processing can be supported than the common reporting format of a single value for measurement uncertainty presently allows.

Autonomous control systems rely on input from sensors, so it is crucial that the sensor input is validated to ensure that it is ‘right’ and that the measurements are traceable to the International System of Units. The measurement and control of temperature is widespread, and its reliable measurement is key to maximising product quality, optimising efficiency, reducing waste and minimizing emissions such as CO2 and other harmful pollutants. Degradation of temperature sensors in harsh environments such as high temperature, contamination, vibration and ionising radiation causes a progressive loss of accuracy that is not apparent. Here we describe some new developments to overcome the problem of ‘calibration drift’, including self-validating thermocouples and embedded phase-change cells which self-calibrate in situ by means of a built-in temperature reference and practical primary thermometers such as the Johnson noise thermometer which measure temperature directly and do not suffer from calibration drift. All these developments will provide measurement assurance which is an essential part of digitalisation to ensure that sensor output is always ‘right’, as well as providing essential ‘points of truth’ in a sensor network. Some progress in digitalisation of calibrations to make them available to end-users via a website and/or an Application Programming Interface is also described.

We present a framework for easy annotating, archiving, retrieving and searching measurement data from a large-scale data archival system. Our tool extends and simplifies the interaction with the database and is implemented in popular scientific applications used for data analysis, namely MATLAB and python. This allows scientists to execute complex interactions with the database for data curation and retrieval tasks in a few simple lines of accessible templated code. Scientists can now ensure their measurement data is well curated and FAIR (findable, accessible, interoperable and reusable) compliant without requiring specific data skills or knowledge. Our tools allow users to perform SQL type queries on the data from simple templated scripts allowing data retrieval from long term storage systems.

Bathymetric data acquired by a single beam echo sounder, as well as those derived by a navigational chart, require interpolation procedure to pass from cloud point dataset to continuous tridimensional representation. Among different algorithms available in GIS software, Kriging interpolators are very powerful tools to process bathymetric data. This paper aims to analyze the accuracy levels that can be reached using Kriging. Bathymetric information included in two Electronic Navigational Charts (ENCs) of the Bay of Pozzuoli (nominal scale 1:30.000) is used for digital 3D model of this area. Interpolation processes are performed in GIS environment (software: ArcGIS 10.3.1, including the extension Geostatistical Analyst, by ESRI); the achieved results are analyzed by varying the choice of the mathematic function for semi variogram. The experiments carried out in this study demonstrate how the careful choice of the semi variogram model can help to increase the accuracy of the interpolation process.