Recent studies show that emotion is a mechanism for fast decision-making in human and other animals. Mathematical models have been developed for describing emotion in mammals. These models, similar to other bioinspired models, must be implemented in embedded platforms for industrial and real applications. In this paper, brain emotional learning based intelligent controller, which is based on mammalian middle brain, is designed and implemented on field-programmable gate arrays, and this emotional controller is applied for controlling of laboratorial overhead traveling crane in model-free and embedded manner. The main features of this controller are leaning capability, providing a model-free control algorithm, robustness and the ability to respond swiftly. By designing appropriate stress signals, a designer can implement a proper trade among control objectives.

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In this paper, we propose an adaptive routing algorithm based on fuzzy logic in which each link cost is dynamically determined based on the current network condition. Using this algorithm, the traffic is distributed through the nodes that are less congested or have a spare capacity. The technique using a fuzzy controller takes advantage of two factors that are the number of empty spaces in the buffer of each neighbor and the waiting time for the previous packet. The output of the fuzzy controller is the link cost so that in each router, the link with the lowest cost is chosen as the optimal route. To evaluate the proposed routing method, we have used two multimedia applications and a random traffic profile. The experimental results show that the proposed routing scheme improves the performance up to 30% with a negligible hardware overhead.

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