This paper presents the development of a system for realizing interactive exhibitions in the context of a science and technology center. The core functionality of the system is provided by a positioning subsystem comprised of a fixed infrastructure of transmitters and a sensor worn by a user. The operating principle of the positioning system is based on inductive coupling of resonators. Information about the position of the user is transferred to an information system for processing and displaying. Possible use cases include interactive games, information retrieval interfaces and educational scenarios.
In this paper, the authors discuss the issues associated to measurements executed in the domains of software engineering and metrology. Similarities and differences are highlighted also with the aim of discussing the level of knowledge in both domains. A measurement process model is proposed and used as a guiding scheme throughout this paper. It is shown that both domains could benefit from the definition of a rigorously identified common measurement framework
Estimating the state of charge of batteries is a critical task for every battery-powered device.In this work, we propose a machine learning approach based on electrochemical impedance spectroscopy and convolutional neural networks.A case study based on Samsung ICR18650-26J lithium-Ion batteries is also presented and discussed in detail.A classification accuracy of 80% and top-2 classification accuracy of 95% were achieved on a test battery not used for model training.
The conformance test to which electronic devices are subjected after the manufacturing process, indicates if the device complies with an a priori given requirement set. On the basis of the test result, the component is considered to be working or not–working. However, because of the measurement uncertainty introduced by the testing bench assessment and by the chosen estimation algorithm, the manufacturer could include in the production process a component which does not respect the given requirements or could reject a working–device, thus affecting both testing and productivity costs. In this paper, it is considered the problem of the estimation of spectral parameters of analog–to–digital converters (ADCs). In particular, the risks to which both manufacturers and consumers of ADCs are subjected, are explicitly evaluated.
In the paper, the authors consider the performance of histogram-based ADC testing under the assumption of input-equivalent wideband noise, which models either noise sources inside the device or unwanted disturbances corrupting the stimulus signal employed for carrying out the test. Theoretical relationships are presented which allow the design of the test parameters needed to meet a given test accuracy. Moreover, it is shown, that the histogram test is effective in providing information on the deterministic behavior of the tested device and that it can be made insensitive to the effects of input-equivalent noise. Finally, the obtained results are employed to determine the test performance in estimating the device effective number of bits, and simulations results are provided which validate the theoretical derivations.
A robotic arm moving its end-effector along user-defined trajectories is used to calibrate a Magnetic Positioning System (MPS). The principle of operation of the positioning system is reviewed. Position and attitude of an active coil are estimated measuring the induced voltage on a set of fixed coils, with known position and versor. The Unscented Kalman Filter (UKF) is used to smooth the measured trajectories. In order to estimate the precision up to which ground-truth trajectories are traced by the robotic arm, a calibration procedure of the robot is illustrated. The active coil is fixed on the end-effector of the robot, and ground-truth trajectories are then tracked in order to calibrate the whole MPS plus UKF system. A wide ground-truth trajectory is used to obtain a fine estimate of positions and versors of the fixed coils. Using the parameters thus obtained, several trajectories are tracked. The UKF is tuned in order to minimize the error affecting the estimated position and attitude of the active coil at each point of any trajectory.
Efficient energy management in battery-powered devices requires reliable estimation of the battery state of charge. We developed a data-driven state-of-charge estimation method based on machine learning and electrochemical impedance spectroscopy. Several states-of-charge models were trained and tested using an original measurement dataset from a set of commercial Samsung ICR18650-26 J lithium-Ion batteries. The implications of the curse of dimensionality for this task have been analyzed, and the effectiveness of different feature reduction techniques to avoid classification model overfitting was investigated.
This paper considers least square (LS) based estimation of the amplitude and square amplitude of a quantized sine wave, done by considering random initial record phase. Using amplitude- and frequency-domain modeling techniques, it is shown that the estimator is inconsistent, biased, and has a variance that may be underestimated if the simple model of quantization is applied. The effects of both sine wave offset values and additive Gaussian noise are considered. General estimator properties are derived, without making simplifying assumptions on the role of the quantization process, to allow assessment of measurement uncertainty, when this LS procedure is used.
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