Search

We will present the preliminary results of the CarbonSurvey project (Towards the Next Generation of Sensors for Surveying the Atmospheric Carbon Cycle). As outlined by the European Commission green report in the framework of the Operational Anthropogenic CO2 Emissions Monitoring and Verification Support (MVS) capacity, the existing ground-based networks currently do not meet all the operational requirements for the Copernicus CO2 MVS capacity due to the lack of in situ measurement data from urban areas and other emission hot spots. The main expected contribution of this project is to address this weakness through the development of a new generation of instruments to enable unprecedented CO2 monitoring capabilities, the biggest GHG contributor to human-caused global warming. The necessary scientific and technical contributions required to reach the main goal of the project involve two complementary developments: (i) firstly, a gas analyzer capable of obtaining the vertical profile (with resolution in altitude) of CO2 concentration. The system, which is based on the Laser Heterodyne Radiometry (LHR) technique, will operate from the Earth's surface analyzing the effect in the spectrum of the received sunlight of the atmospheric components to accurately find the distribution of the concentration of CO2 in the atmospheric column. Thus, this instrument will provide a characterization of the CO2 in the atmospheric volume located above the measurement site. Secondly, (ii) the project aims to develop Photacoustic Spectroscopy (PAS) cost-competitive photonic solution for in situ urban GHG measurements. As a main difference with today’s commercially available instrumentation, the system proposed, based on a compact photoacoustic sensing cell, will combine small-size, high sensitivity, and a straightforward field deployment capacity. This directly enables the possibility of providing an accurate, and potentially gap-free, map of the concentration of gases at ground level. These two sets of instruments provide indeed complementary information for a full reconstruction of the map of CO2 around the areas of interest. It is important to remark that both instrument designs will be equipped as well with an important additional feature: the ability to determine the isotopic fingerprint of CO2 in order to discern between natural and anthropogenic CO2 sources, such as fossil fuel combustion or biogenic respiration.The CarbonSurvey project is funded by the Spanish State Research Agency, it started December 2022 and last until November 2024. We will present the sensors design and the preliminary laboratory results. By the end of the project, we will have both sensor prototypes fully operational and calibrated at the Izaña Atmospheric center. Moreover, the sensing systems will be specifically designed for a straightforward in situ deployment in different areas of interest, providing full coverage of the most important blind spots existing today. This new generation of sensors could establish the necessary basis to guide decision-making policies in the green transition process ahead.

We present the design and characterization of a dual-frequency comb photo-acoustic spectroscopy set-up for ammonia detection in the near infrared. The system consists of an electro-optic dual comb generator and a cylinder resonator gas cell.

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 a sensor for Advance Driver Assistance Systems (ADAS) with unique properties in terms of measuring time and distance that are necessary to predict the pavement condition several meters ahead of a moving vehicle.

Dual comb spectroscopy (DCS) for combustion, industrial, and environmental studies provide transmission spectra of complex mixtures of gases with potentially unknown signatures. We evaluate Independent Component Analysis (ICA) as a processing tool for DCS signals.

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.

A photoacoustic gas system has been implemented for comparison of the performance of an electronic and an optical microphone as acoustic sensors. Results show a minimum detectable pressure of 79.5 μPa/Hz1/2 for the optical microphone.

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.

Optical frequency combs (OFCs) generated through the modulation of continuous-wave lasers offer higher power per component, simple comb spacing control, and lower complexity and cost than traditional OFCs. By these reasons, they are an interesting optical source for spectrometers. In this letter, a dual-comb architecture based on two OFCs generated from the same laser using optical phase modulators is presented. The system features a new placement for the spectroscopic sample that increases the versatility, robustness, and stability of the instrument without requiring any reconfiguration for carrying out different types of measurement. Besides this, a self-referenced detection setup based on digital phase sensitive multitone detection improves the SNR of the previous approaches offering more flexibility in its configuration and high data output rate. The proposed architecture is validated by measuring a ro-vibrational absorption feature of HCN at 1544.51 nm and characterizing the spectral response of a fiber-coupled Fabry–Perot filter.

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.

We describe a fiber laser interferometer that has been designed with the purpose of non-invasive continuous fetal heart rate monitoring. The measuring principle is based on the measurement of the arterial wave of a fetus from the arterial pulse wave of a pregnant patient, so the application requires the measurement of arterial pulse waves with very high resolution. The main characteristics of the specific application interferometer are: First, it is based on a common path topology in order to simplify the design of the non-contact optical probe to place on the body of the patient. Second, it uses a low power semiconductor laser at a safe operating wavelength of 1,55 μm. Third, a phase modulation with digital demodulation scheme is used in order to increase range and resolution simultaneously. The resulting interferometer provides up to 1 nm resolution and one millimeter range. Results showing arterial pulse waves measured on the wrist and a finger nail of a patient are presented.

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.

We report on an optical sensor for remote residual dry salt (NaCl) monitoring, with applications in road winter maintenance. The novelty of the sensing principle is the simultaneous measurement of the amplitude and decay time of the natural fluorescence of salt using synchronous detection. We present the characterisation of the luminous properties of salt that allow a sensor design with high sensitivity and stable measurements that match the requirements for the intended applications (concentrations below 20 g/m2). The proposed sensor system has a compact design and short response time, capable of being mounted on-board maintenance vehicles for real-time road condition assessment.

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.

The implementation of a vision system in which the processing core is based on an optoelectronic neural network design is described. The vision system captures an image using a complimentary metal-oxide semiconductor (CMOS) image sensor, and the optoelectronic neural processor performs the classification among a set of sample patterns. The neural-network hardware architecture is based on an optical broadcast of inputs to an electronic array of processing elements, one per class. The benefit of this optoelectronic architecture, and thus the vision application, is that it is potentially scalable to a very large number of processing elements operating in parallel and at very high speed.

The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect.

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.

In this paper we propose a method based on blind-source-separation of acoustic emissions of partial discharges in power equipment. The goal is to build an on-line monitoring system for automatic detection and localization of partial discharges and defects in the insulation. We have built a test bech to generate acoustic signals similar to those produced by partial discharges inside a propagating media and a data acquisition system with multiple acoustic detectors. We show that the INFOMAX algorithm used for blind-source-separation is able to decouple at least two different sources of partial discharges from the signal of two acoustic detector located at fixed positions.

On-line identification and classification of voltage and current disturbances in power systems are important tasks in the power quality monitoring and protection of power systems. Some power quality disturbances are non-stationary and transitory while other are steady-state variations that distort the voltage signal. One, two or more different power quality disturbances may appear at the same time. In this paper we propose a power quality monitoring system that employs Independent Component Analysis algorithm that is able to decouple multiple simultaneous power quality disturbances, and Support Vector Machines for identification the occurrence of a disturbance. We also show the first steps towards embedding the proposed system on an FPGA for online power quality monitoring.

In this paper we describe a fiber-based remote sensing device for the detection of liquid water and ice on the road surface suitable for on-board applications. The system is based on the different optical responses of water and ice to three near infrared wavelengths from low-cost semiconductor laser sources. The design of the sensor is divided in three main parts: The optical fiber-based illumination and collection optics, the optoelectronic system composed by the emitters/detector and the modulation/demodulation electronics and, finally, the data acquisition and digital processing system. The flexible optical design allows both the use of the sensor attached to a post by the road for static measurements, or to be incorporated into a road maintenance vehicle.

We present a successful design for a high-performance, low-resource-consuming hardware for Support Vector Classification and Support Vector Regression. The system has been implemented on a low cost FPGA device and exploits the advantages of parallel processing to compute the feed forward phase in support vector machines. In this paper we show that the same hardware can be used for classification problems and regression problems, and we show satisfactory results on an image recognition problem by SV multiclass classification and on a function estimation problem by SV regression.

In this paper we present a hardware architecture for a Support Vector Machine intended for vision applications to be implemented in a FPGA device. The architecture computes the contribution of each support vector in parallel without performing multiplications by using a CORDIC algorithm and a hardware-friendly kernel function. Additionally input images are not preprocessed for feature extraction as each image is treated as a point in a high dimensional space.

Support vector machines (SVM) present very interesting features in the field of image processing, but the intensive calculation needed complicates its use in real-time applications. We present an architecture for a SVM which simplifies most of the calculus using an optoelectronic matrix-vector multiplier (OMVM).

In this paper we present the scheme for a control circuit used in an image processing system which is to be implemented in a neural network which has a high level of connectivity and reconfiguration of neurons for integration and trigger based on the Address-Event Representation. This scheme will be employed as a pre-processing stage for a vision system which employs as its core processing an Optical Broadcast Neural Network (OBNN). [Optical Engineering letters 42 (9), 2488(2003)]. The proposed vision system allows the possibility to introduce patterns from any acquisition system of images, for posterior processing.

In this paper we investigate a hardware Pulse Couple Neural Network (PCNN) to be used as the pre-processing stage for a vision system which uses as processing core the Optical Broadcast Neural Network (OBNN) Processor [Optical Engineering Letters 42 (9), 2488 (2003)]. The temporal patterns are to remain constant independently of the position of the spatial pattern in the input image and its orientation. The objective is to obtain synchronous temporal patterns, with fixed pulse rates, from a determined spatial pattern.

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.

In this paper we describe the implementation of a vision system based on an optoelectronic neural network architecture which is based on an optical broadcast interconnection scheme. The architecture of the neural network processor has been designed to exploit the computational characteristics of electronics and the communication characteristics of optics, thus it is based on an optical broadcast of input signals to a dense array of processing elements. In the proposed vision system, a CMOS sensor capture the image of an object, the output of the camera is introduced to the optoelectronic processor which compares the input image with a set of reference patterns, the optoelectronic processor provides the reference pattern that best match with the input image. The processing core of the system is an optoelectronic architecture that has been configured as a Hamming neural network.

We describe the hardware implementation of a Hamming classifier using an optoelectronic architecture. It is composed of two layers, the first layer is an optoelectronic matrix-vector multiplier based on the optical broadcast architecture; it is a novel architecture composed of a set of electronic neurons that receive the input sequentially by means of an optical broadcast interconnection. The second layer is an electronic winner take all circuit. The main characteristic of the system is that it is readily scalable in speed and size to large numbers of pixel neurons. We will describe the optoelectronic architecture, the hardware implementation of a prototype and evaluation of its performance characteristics.

This paper reports the recent steps to the attainment of a compact high-speed optoelectronic neuroprocessor based on an optical broadcast architecture that is used as the processing core of a vision system. The optical broadcast architecture is composed of a set of electronic processing elements that work in parallel and whose input is introduced by means of an optical sequential broadcast interconnection. Because of the special characteristics of the architecture, that exploits electronics for computing and optics for communicating, it is readily scalable in number of neurons and speed, thus improving the performance of the vision system. This paper focuses on the improvement of the optoelectronic system and electronic neuron design to increase operation speed with respect to previous designs.

This paper reports the recent steps to the attainment of a compact high-speed optoelectronic neuroprocessor based on an optical broadcast architecture. The optical broadcast architecture is composed of a set of electronic processing elements that work in parallel and whose input is introduced by means of an optical sequential broadcast interconnection. Because of the special characteristics of the architecture, that exploits electronics for computing and optics for communicating, it is readily scalable in number of neurons and speed. This paper focuses on the improvement of the optoelectronic system and electronic neuron design to increase operation speed with respect to previous prototype.

In this paper we propose of a novel hardware electronic-optoelectronic neural network processor for vision applications. The architecture of the proposed neuroprocessor is based on a hybrid optic and optoelectronic implementation of the system. Some neural operations, like interconnection weight storage and assignment, are done in the electronic domain while the interconnection between processing elements is done optically. By this way we exploit the communication strength of optics and the computational strength of electronics in an optical fashion. The main characteristics of the architecture are that it is fully interconnected, the interconnections are fully programmable, it avoids optical alignment problems, and it is readily scalable to large numbers of pixel neurons. We will describe the architecture, the hardware implementation of a first prototype and its functionality for pattern recognition applications. The neural network models we have implemented on our neuroprocessor have been a basic logic functions operator, a Hopfield network and the matching scores layer of a Hamming network.

In this paper we present an optoelectronic hardware implementation of a neural network system based on optoelectronic devices and electronic techniques. The system is composed of basic cells with optoelectronic artificial neurons; every basic cells exploits the communication strengths of optics to broadcast the input to all neurons and the computational strengths of electronics to assign the interconnections weights. Description of the architecture of the basic cell, the first implementation of a prototype based on the proposed system and examples of configuration of the neural system are described.

We present experimental and theoretical results obtained from the study of the effects of optical feedback in low-cost Fabry-Perot laser diodes due to the presence of an external cavity created by an external reflective or diffusive vibrating target. Experimental results show that a change in the length of the external cavity produces the well-known amplitude modulation of the optical output power and, depending on the amount of optical feedback, a subperiodicity appears in the amplitude modulation of the output power. The experiments show that the subperiodicity appears independently of the length of the external cavity and is due to mode hopping between different longitudinal laser modes. Numerical analysis focused on the effects observed support that the mode hop occurs between modes whose round-trip phase delay along the external cavity is out of phase, thus producing a subperiodicity of the total amplitude modulation.

We describe the concept of a vision system based on an optoelectronic hardware neural processor. The proposed system is composed of a pulse coupled neural network (PCNN) preprocessor stage that converts an input image into a temporal pulsed pattern. These pulses are inputs to the optical broadcast neural network (OBNN) processor, which classifies the input pattern between a set of reference patterns based on a pattern matching strategy. The PCNN is to provide immunity to the scale, rotation, and translation of objects in the image. The OBNN provides high parallelism and a high speed hardware neural processor.

A novel, compact optoelectronic hardware neural network ar- chitecture based on an optical broadcast scheme is proposed and dem- onstrated. The basic cell in the system is composed of electronic neu- rons that share the same time-distributed, optical broadcast input. The system combines the computational strengths of electronics and the communication strengths of optics, and employs a modular reconfig- urable architecture that is potentially scalable to a very large number of neurons while maintaining compactness. These characteristics are real- ized in an architecture that combines the integration of the processing elements in complementary metal-oxide semiconductor (CMOS) tech- nology with the construction of efficient optical interconnection elements with focusing properties based on multiplexed volume holograms. © 2005 Society of Photo-Optical Instrumentation Engineers. (DOI: 10.1117/1.1902994) Subject terms: optical neural networks; complementary metal-oxide semiconduc- tor sensor arrays; electronic processing; optical interconnects; volume holograms.

We present the first implementation, results, and performance analysis of a vision system whose processing core is a prototype hardware neural network based on an optical broadcast architecture. The system captures an im- age by a CMOS image sensor, compares it with a set of sample patterns (classes), and provides an output that indi- cates the class which the input image corresponds to. Due to the optoelectronic neural processor characteristics, the num- ber of classes can be enlarged without penalty on the opera- tion speed of the system. © 2004 Society of Photo-Optical Instru

A new optoelectronic hardware architecture of a neural network processor is proposed. A basic cell in the system is composed of electronic neurons that share the same time-distributed input; the input is introduced by means of an optical broadcast. The system combines the computational strength of electronics and the communica- tion strength of optics in an optimal manner and it is poten- tially scalable to a very large number of neurons. A descrip- tion of the system is given and the behavior of a first prototype is shown. © 2003 Society of Photo-Optical Instrumentation