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A pavement condition sensor aims to estimate the pavement surface grip, and therefore the sensor must detect the presence of water, snow, slush or the like on the surface of the pavement. The leading technology for such a sensor is near infrared diffuse reflectance spectroscopy using a wavelength range around a water absorption peak. In this paper we present a pavement condition sensor based on said technique that uses an electro-optic dual optical frequency comb generator as optical source. The proposed sensor provides unique properties in terms of measuring integration time that are necessary to advance the pavement condition several meters ahead of a moving vehicle. This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (grant TEC2017-86271-R) and by the Madrid Government (Comunidad de Madrid-Spain) under personnel Grant PEJ-2019-AI/IND-13093 and under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M26), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

We propose an optical thickness measurement device based on electro-optic dual-optical frequency combs (dual-OFCs). Optical frequency combs (OFCs) are light sources that provide an optical signal consisting of many equidistant monochromatic tones. In this paper, we present an agile dual-OFC architecture with adjustable frequency separation of the comb modes and total frequency span, allowing high-resolution measurements of the thickness of transparent thick samples. This architecture is based on a single continuous-wave laser diode and external electro-optic devices to implement the dual-comb sensor, allowing easy control of the optical spectrum of the interrogation source (dual-OFC). As it is characteristic of dual-OFC systems, the optical transmittance function of the sample (etalon) is directly translated to the radiofrequency domain, where detection, demodulation, and processing of signals are performed. The shift in the complexity of implementation from the optical to the electronic domain yields many advantages, as acquisition and signal processing are made independently on the optical characteristics of the sample (thickness). This work was supported by the Spanish Ministry of Economy and Competitiveness under the RTC-2015-4205-4 Salicar and TEC-2014-52147-R Mossi grants.

A fast and reliable photoacoustic (PA) sensor for trace gas detection is reported. The sensor is based on a 3D-printed resonant cell in combination with a continuous wave mode-hop-free external cavity quantum cascade laser to rapidly acquire gas absorption data in the midinfrared range. The cell is designed so as to minimize the window PA background at a selected acoustic resonance. The goal is a resonant PA cell capable of detecting the traces of gases using wavelength modulation of the laser source and second harmonic detection. The versatility and enhancement of the limit of detection at sub-ppm levels are investigated by monitoring specific lines of hydrogen sulfide (H2S). The noise-equivalent absorption normalized to laser-beam power and detection bandwidth is 1.07×10-8 W cm-1 Hz-1/2 for H2S targeting the absorption line at 1247.2 cm−1. These properties make the sensor suitable for various practical sensors for water quality applications.

Partial discharges (PD) detection is a widely extended technique for the diagnosis of electrical equipment. Ultra-high frequency (UHF) detection techniques appear as the best choice if the goal is to detect PD online and to locate devices with insulation problems in substations and overhead lines. The location of PD is based on the determination of the difference of the time of arrival of electromagnetic pulses radiated by a source of PD to an array of antennas distributed around the monitored area. However, when measuring electromagnetic pulses radiated by PD activity many interfering signals, such as those coming from television (TV), global positioning system (GPS), wireless communication signals and others coming from electrical equipment distort the waveform detected by the sensors. Under these circumstances, the application of traditional techniques to estimate the time differences may fail. In this paper, the use Blind Source Separation (BSS) techniques applied to pairs of UHF sensors is proposed to extract the information of the difference of the time of arrival of the electromagnetic pulses radiated by a source of PD. The paper is focused on the application of the algorithm and the description of an experimental setup for controlled generation and detection of PD to verify the performance of the proposed technique.

The goal of an automatic monitoring system of partial discharges (PDs), based on acoustic emission (AE) detection, is the identification of the type of source of PD and its localization. In the event that multiple deterioration processes are present in the electrical equipment, more than one PD source may be active and their AE signals may overlap on the sensors. This overlapping effect modifies the temporal and frequency characteristics of the measured signals compared to the characteristics of the signals from a single PD source and thus, automatic classification becomes very difficult. In this paper we have proposed applying blind signal separation (BSS) techniques to recover the signals from each source, therefore separating each temporal and frequency characteristic. We have tested the proposed algorithm: firstly using synthetic mixed signals from two types of PD sources and secondly using real signals from a test bench specifically designed to control the position, time and amplitude of the AEs.

2017 IEEE Sensors In this paper we present the design and the first results of a prototype road condition sensor based on diffuse reflectance spectroscopy in the near infrared using semiconductor lasers. The objective of the sensor is to alert drivers or autonomous driving assistance systems of a road condition, such the presence of water or mud or ice, that will reduce the surface grip a few meters ahead the vehicle. This work was supported by the Spanish Ministry of Economy and Competitiveness under the RTC-2015-4205-4 “SALICAR” grant.

A sensitive optical microphone for photoacoustic spectroscopy based on the common path topology of a fibre laser Doppler vibrometer (FLDV) using phase-generated carrier demodulation and a slim diaphragm as an acoustic wave transducer was demonstrated. A resonant gas cell was adapted to enhance gas-detection performance and simultaneously provide efficient cancellation of the window background acoustic signal. Ammonia (NH3) was selected as the target gas. The absorption line was experimentally identified using a distributed feedback laser diode emitting at 1530 nm. The linearity and sensitivity of the gas sensor were measured using wavelength modulation spectroscopy with second harmonic detection. A Teflon diaphragm was used to implement the optical microphone, along with the FLDV, showing a minimum detectable pressure of 79.5 &mu, Pa/Hz1/2. The noise-equivalent absorption sensitivity for NH3 detection at the absorption line at 1531.7 nm was 1.85 &times, 10&minus, 8 W cm&minus, 1 Hz&minus, 1/2, and the limit of detection was 785 ppbv.

In this paper a new laser range finder approach is presented. It is based on a fan of beams of different optical frequencies that are projected to the target. The detected back-reflected beams contain frequency-encoded information about its reflection angles, which are used for the calculation of the range by means of triangulation methods. The fan of beams is generated from a fieldable electro-optic dual optical frequency comb generator and a diffractive optical system, without any moving parts. A proof-of-concept prototype of a range finder based on this approach was implemented and experimentally tested. An analysis of main sources of errors is also presented.

et al., In this study the first results on evaluation and assessment of grafted bioengineered skin substitutes using an optical Diffuse Reflectance Spectroscopy (DRS) system with a remote optical probe are shown. The proposed system is able to detect early vascularization of skin substitutes expressing the Vascular Endothelial Growth Factor (VEGF) protein compared to normal grafts, even though devitalized skin is used to protect the grafts. Given the particularities of the biological problem, data analysis is performed using two Blind Signal Separation (BSS) methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). These preliminary results are the first step towards point-of-care diagnostics for skin implants early assessment.

We describe the implementation of a vision system based on a hardware neural processor. The architecture of the neural network processor has been designed to exploit the computational characteristics of electronics and the communication characteristics of optics in an optimal manner, thus it is based on an optical broadcast of input signals to a dense array of processing elements. The vision system has been built by use of a prototype implementation of a neural network processor with discrete optic and optoelectronic devices. It has been adapted to work as a Hamming classifier of the images taken with a 128 x 128 complementary metal-oxide semiconductor image sensor. Its results, performance characteristics of the image classification system, and an analysis of its scalability in size and speed, with the improvement of the optoelectronic neural processor, are presented.