CALIBRATION OF SPECTRAL IRRADIANCE SOURCES USING A FIBER-COUPLED SYSTEM

The standards and Calibration Laboratory has recently developed the calibration service for the enclosed-type irradiance light source in the spectral range from 300 nm to 850 nm. The calibration is based on the source-based method measured by a fiber-coupled system. In this paper, the calibration setup, measurement model and the associated uncertainty are presented. The expanded measurement uncertainty is estimated to be less than 3,3 % over the measured spectral range which can support the needs from the testing and certification industry.

Highly Dense FBG Temperature Sensor Assisted with Deep Learning Algorithms

In this paper, we demonstrate the application of deep neural networks (DNNs) for processing the reflectance spectrum from a fiberoptic temperature sensor composed of densely inscribed fiber Bragg gratings (FBG). Such sensors are commonly avoided in practice since close arrangement of short FBGs results in distortion of the spectrum caused by mutual interference between gratings. In our work the temperature sensor contained 50 FBGs with the length of 0.95 mm, edge-to-edge distance of 0.05 mm and arranged in the 1500–1600 nm spectral range. Instead of solving the direct peak detection problem for distorted signal, we applied DNNs to predict temperature distribution from entire reflectance spectrum registered by the sensor. We propose an experimental calibration setup where the dense FBG sensor is located close to an array of sparse FBG sensors. The goal of DNNs is to predict the positions of the reflectance peaks of the reference sparse FBG sensors from the reflectance spectrum of the dense FBG sensor. We show that a convolution neural network is able to predict the positions of FBG reflectance peaks of sparse sensors with mean absolute error of 7.8 pm that is slightly higher than the hardware reused interrogator equal to 5 pm. We believe that dense FBG sensors assisted with DNNs have a high potential to increase spatial resolution and also extend the length of a fiber optical sensors.

Optimal Uncalibrated RSS Indoor Positioning and Optimal Reference Node Placement Using Cramér-Rao Lower Bound

In this paper we propose a global positioning algorithm of multiple assets based on Received Signal Strength (RSS) measurements that takes into account the gain uncertainties of each hardware transceiver involved in the system, as well as the uncertainties on the Log-Distance Path Loss (LDPL) parameters. Such a statistical model is established and its Maximum Likelihood Estimator (MLE) is given with the analytic expression of the Cramér-Rao Lower Bound (CRLB). Typical values of those uncertainties are given considering whether calibration is done in production, in situ, or if hardware is used uncalibrated, in order to know what is the expected accuracy in function of the calibration setup. Results are tested by numerical simulations and confronted to real measurements in different room configurations, showing that the theoretical bound can be reached by the proposed MLE algorithm.

Setup for the dynamic calibration of bridge amplifiers from DC up to 10 kHz

<p class="Abstract">Measurements of mechanical quantities are often carried out with transducers with a bridge output. The output signals are conditioned using bridge amplifiers. If dynamically changing quantities are going to be measured traceably, the bridge amplifier must be calibrated dynamically.</p>This paper describes a dynamic bridge amplifier calibration setup based on the new PTB dynamic bridge standard. The calibration is carried out by the synchronous sampling of the bridge amplifier output voltage and a reference signal provided by the calibrated dynamic bridge standard. The dynamic bridge standard enables calibrations in a frequency range from DC (static calibration) up to 10 kHz. An overview of the different measurement uncertainty contributions is given, and the first measurement results show good agreement with a previously established measurement setup.