A metrological approach for multispectral photogrammetry

<p>This paper presents the design and development of a three-dimensional reference object for the metrological quality assessment of photogrammetry-based techniques, for application in the cultural heritage field. The reference object was 3D printed, with nominal manufacturing uncertainty of the order of 0.01 mm. The object was realized as a dodecahedron, and in each face, a different pictorial preparation was inserted. The preparations include several pigments, binders, and varnishes, to be representative of the materials and techniques used historically by artists.</p><p>Since the reference object’s shape, size and uncertainty are known, it is possible to use this object as a reference to evaluate the quality of a 3D model from the metric point of view. In particular, verification of dimensional precision and accuracy are performed using the standard deviation on measurements acquired on the reference object and the final 3D model. In addition, the object can be used as a reference for UV-induced Visible Luminescence (UVL) acquisition, being the materials employed UV-fluorescent. Results obtained with visible-reflected and UVL images are presented and discussed.</p>

The importance of sound velocity determination for bathymetric survey

Bathymetric surveys are carried out whenever there is a need to know the exact morphological trend of the seabed. For a correct operation of the echo sounder, which uses the principle of acoustic waves to scan the bottom and determine the depth, it is important to accurately determine the sound velocity in water, as it varies according to specific parameters (Density, Temperature, and Pressure). In this work, we want to analyse the role of sound velocity determination in bathymetric survey and its impact on the accuracy of depth measurement. The experiments are conducted on data set provided by “Istituto Idrografico della Marina Militare Italiana” (IIM), the official Hydrographic Office for Italy, and acquired in the Ligurian sea. In our case, the formulas of Chen &amp; Millero (UNESCO), Medwin, and Mackenzie were applied. The introduction of errors on chemical-physical parameters of the water column (Temperature, Pressure, Salinity, Depth) simulating inaccurate measurements, produces considerable impacts on sound velocity determination and subsequently a decrease of the depth value accuracy. The results remark the need to use precise probes and accurate procedures to obtain reliable depth data.

Doppler flow phantom failure detection by combining empirical mode decomposition and independent component analysis with short time Fourier transform

<p class="Abstract">Nowadays, objective protocols and criteria for the monitoring of phantoms failures are still lacking in literature, despite their technical limitations. In such a context, the present work aims at providing an improvement of a previously proposed method for the Doppler flow phantom failures detection. Such failures were classified as low frequency oscillations, high velocity pulses and velocity drifts. The novel objective method, named EMoDICA-STFT, is based on the combined application of the Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Short Time Fourier Transform (STFT) techniques on Pulsed Wave (PW) Doppler spectrograms. After a first series of simulations and the determination of adaptive thresholds, phantom failures were detected on real PW spectrograms through the EMoDICA-STFT method. Data were acquired from two flow phantom models set at five flow regimes, through a single ultrasound (US) diagnostic system equipped with a linear, a convex and a phased array probe, as well as with two configuration settings. Despite the promising outcomes, further studies should be carried out on a greater number of Doppler phantoms and US systems as well as including an in-depth investigation of the proposed method uncertainty.</p>

Characterisation of glue behaviour under thermal and mechanical stress conditions

New low-cost measuring devices require that the box housing and electronics have the cost aligned with the sensing system. Nowadays, metallic clips and/or glue are commonly used to fix the electronics to the box, thus providing the same motion of the structure to the sensing element. However, these systems may undergo daily or seasonal thermal cycles, and the combined effect of thermal and mechanical stress can determine significant uncertainties in the measurand evaluation. To study these effects, we prepared some parallel plates capacitors by using glue as a dielectric material. We used different types of fixing and sample assembly to separate the effects of glue softening on the capacitor active area and plates distance. Therefore, we assessed the sample modification by measuring the capacitance variation during controlled temperature cycles. We explored possible non-linear behaviour of the capacitance vs. temperature, and possible effects of thermal cycles on the glue geometry. Further work is still needed to properly assess the nature of this phenomenon and to study the effect of mechanical stress on the sample’s capacitance.

Development and characterisation of a self-powered measurement buoy prototype by means of piezoelectric energy harvester for monitoring activities in a marine environment

<p class="Abstract">In the interest of our society, for example in Smart City but also in other specific backgrounds, environmental monitoring is an essential activity to measure the quality of different ecosystems. In fact, the need to obtain accurate and extended measurements in space and time has considerably become relevant. In very large environments, such as marine ones, technological solutions are required for the use of smart, automatic, and self-powered devices in order to reduce human maintenance service. This work presents a simple and innovative layout for a small self-powered floating buoy, with the aim of measuring and transmitting the detected data for visualization, storage and/or elaboration. The power supply was obtained using a cantilever harvester, based on piezoelectric patches, converting the motion of ripple waves. Such type of waves is characterized by frequencies between 1.50 Hz and 2.50 Hz with oscillation between 5.0 ° and 7.0 °. Specifically, a dedicated experimental setup was created to simulate the motion of ripple waves and to evaluate the suitability of the proposed design and the performance of the used harvester. Furthermore, a dynamic analytical model for the harvester has been defined and the uncertainty correlated to the harvested power has been evaluated. Finally, the harvested voltage and power have shown how the presented buoy behaves like a frequency transformer. Hence, although the used cantilever harvester does not work in its resonant frequency, the harvested electricity undergoes a significant increase.</p><p class="Abstract"><span lang="EN-US"><br /></span></p>

Application of wearable EEG sensors for indoor thermal comfort measurements

Multidomain comfort theories have been demonstrated to interpret human thermal comfort in buildings by employing human-centered physiological measurements coupled with environmental sensing techniques. Thermal comfort has been correlated with brain activity through electroencephalographic (EEG) measurements. However, the application of low-cost wearable EEG sensors for measuring thermal comfort has not been thoroughly investigated. Wearable EEG devices provide several advantages in terms of reduced intrusiveness and application in real-life contexts. However, they are prone to measurement uncertainties. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different indoor temperatures. Twenty-three participants were enrolled, and the EEG signals were recorded at three ambient temperatures: cold (16 °C), neutral (24 °C), and warm (31 °C). Then, the analysis of brain Power Spectral Densities (PSDs) was performed, to investigate features correlated with thermal sensations. Statistically significant differences of several EEG features, measured on both frontal and temporal electrodes, were found between the three thermal conditions. Results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to thermal comfort measurement, but only after a dedicated signal processing to remove the uncertainty due to artifacts.

An adaptive learning algorithm for spectrum sensing based on direction of arrival estimation in cognitive radio systems

<p class="Abstract">In cognitive radio systems, estimating primary user direction of arrival (DOA) measurement is one of the key issues. In order to increase the probability detection multiple sensor antennas are used and they are analysed by using subspace-based technique. In this work, we considered wideband spectrum with sub channels and here each sub channel facilitated with a sensor for the estimation of DOA. In practical spectrum sensing process interference component also encounters in the sensing process. To avoid this interference level at output of receiver, we used an adaptive learning algorithm known as Normalised Least Absolute Mean Deviation (NLAMD) algorithm. Further to achieve better performance a bias compensator function is applied in weight coefficient updating process. Using this hybrid realization, the vacant spectrum can be sensed based on DOA estimation and number of vacant locations in each channel can be identified using maximum likelihood approach. In order to test at the diversified conditions different threshold parameters 0.1, 0.5, 1 are analysed.</p>

Skin potential response for stress recognition in simulated urban driving

In this paper, we address the problem of possible stress conditions arising in car drivers, thus affecting their driving performance. We apply various Machine Learning (ML) algorithms to analyse the stress of subjects while driving in an urban area in two different situations: one with cars, pedestrians and traffic along the course, and the other characterized by the complete absence of any of these possible stress-inducing factors. To evaluate the presence of a stress condition we use two Skin Potential Response (SPR) signals, recorded from each hand of the test subjects, and process them through a Motion Artifact (MA) removal algorithm which reduces the artifacts that might be introduced by the hand movements. We then compute some statistical features starting from the cleaned SPR signal. A binary classification ML algorithm is then fed with these features, giving as an output a label that indicates if a time interval belongs to a stress condition or not. Tests are carried out in a laboratory at the University of Udine, where a car driving simulator with a motorized motion platform has been prearranged. We show that the use of one single SPR signal, along with the application of ML algorithms, enables the detection of possible stress conditions while the subjects are driving, in the traffic and no traffic situations. As expected, we observe that the test individuals are less stressed in the situation without traffic, confirming the effectiveness of the proposed slightly invasive system for detection of stress in drivers.