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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.

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.

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).

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.

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 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