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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.

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.

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.

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 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.