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This paper presents an implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms (GAs). The optimization process entails the selection of component values in a switching regulator, in order to meet the static and dynamic requirements. Although the proposed method inherits characteristics of evolutionary computations that involve randomness, recombination, and survival of the fittest, it does not perform a whole-circuit optimization. Thus, intensive computations that are usually found in stochastic optimization techniques can be avoided. Similar to many design approaches for power electronics circuits, a regulator is decoupled into two components, namely the power conversion stage (PCS) and the feedback network (FN). The PCS is optimized with the required static characteristics, whilst the FN is optimized with the required static and dynamic behaviors of the whole system. Systematic optimization procedures will be described and the technique is illustrated with the design of a buck regulator with overcurrent protection. The predicted results are compared with the published results available in the literature and are verified with experimental measurements.

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Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject's eye movements. A gaze deviation (GaDe) image is then proposed to characterize the subject's eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN) that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image's features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.