Bio-inspired vision sensors are particularly appropriate candidates for navigation of vehicles or mobile robots due to their computational simplicity, allowing compact hardware implementations with low power dissipation. The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector.
Our Monte Carlo model shows that the incorporation of multiple transit regions into a single Gunn device is a feasible means of increasing the output power of the device. From our simulations, the power attainable from these multiple-transit-region Gunn diodes increases linearly with the square of the number of transit regions, while the efficiency remains approximately the same. We have found that the coherent transfer of domains occurs in all the investigated devices (up to eight transit regions). There seems to be no obvious upper limit to the number of transit regions that can be incorporated into a single device (in the absence of thermal limitations).
We demonstrate heterodyne mixing of a 94 GHz millimetre wave photonic signal, supplied by a Gunn diode oscillator, with coherent acoustic waves of frequency ~100 GHz, generated by pulsed laser excitation of a semiconductor surface. The mixing takes place in a millimetre wave Schottky diode, and the intermediate frequency electrical signal is in the 1–12 GHz range. The mixing process preserves all the spectral content in the acoustic signal that falls within the intermediate frequency bandwidth. Therefore this technique may find application in high-frequency acoustic spectroscopy measurements, exploiting the nanometre wavelength of sub-THz sound. The result also points the way to exploiting acoustoelectric effects in photonic devices working at sub-THz and THz frequencies, which could provide functionalities at these frequencies, e.g. acoustic wave filtering, that are currently in widespread use at lower (GHz) frequencies.
Abstract-The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement.
The vector components of the winning node Wk with minimum distance Di; is then updated as follows where TJ is the learning rate. The topological ordering property is imposed by also updating weight vectors of nodes in the neighbourhood of the winning node. This can be achieved by the following learning rulewhere N j is a neighbourhood function (defining the region around Wk ) based on the topological displacement of neighbouring neuron from the winning neuron. The size of N j decreases as training progresses.In the vast majority of implementations, the SOM input data and neurons are represented by real numbers, making it difficult to implement on a hardware architecture like the Field Programmable Gate Array (FPGA). However, in many applications the data is either presented as a binary string, or may be conveniently recoded as such (a "binary signature"). For example, in image processing applications a bank of Haar filters produces a long binary signature. In this paper we present a new learning algorithm which takes binary inputs and maintains tri-state weights (neuron) in the SOM. We also present the FPGA implementation of this binary Self Organizing Map (bSOM). The bSOM is designed for efficient hardware implementation, having both greatly reduced circuit size compared to a real-valued SOM, and exceptionally fast execution and training times.In section II, we review previous implementations of SOM on hardware architectures. The novel bSOM algorithm is then presented in III, followed by its FPGA implementation in section IV. Section V, presents the experimental results in software and hardware, and we conclude in section VI.During training, the "nearest" neuron prototype vector to the input vector is identified -this is called the "winning" neuron -using a distance metric, D. The Euclidean distance is most frequently used as the metric.For a given network with M neurons and N-dimensional input vector x, the distance for neuron with weight vector Wj (j < M) is given by
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