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This paper introduces techniques for Deep Learning in conjunction with spiked random neural networks that closely resemble the stochastic behaviour of biological neurons in mammalian brains. The paper introduces clusters of such random neural networks and obtains the characteristics of their collective behaviour. Combining this model with previous work on extreme learning machines, we develop multilayer...
Emergency navigation algorithms for evacuees in confined spaces typically treat all evacuees in a homogeneous manner, using a common metric to select the best exit paths. In this paper, we present a quality of service (QoS) driven routing algorithm to cater to the needs of different types of evacuees based on age, mobility, and level of resistance to fatigue and hazard. Spatial information regarding...
This paper proposes a neural network solution to the classical signal processing problem of detection of a synchronous recurrent transient signal in noise. If a signal exists, it is assumed to be one of M known signals which may sometimes occur (probabilistically) in successive intervals. Several neural network configurations are applied to this problem and compared with each other and with the optimum...
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