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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.

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.