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We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance. We present our findings on videos from the Audio/Visual+Emotion Challenge (AV+EC2015).In our experiments, we analyze the effects of several hyperparameters on overall performance while also achieving superior performance to the baseline and other competing methods.

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A ne111 method for controlhg telerobots overtiast distances, where communication propagation delays exist. is presented. Such delays are potentially destabilizing, ond certainly degrade the hunzan teleoperat 01' Z int U it io n and performance. A ppl iciit ions include iizterizet-based robotac systems, as well as underwater and sp~ce-ba~srd systems. A carzon.icu1 state space forl i t dation is pwaented, taking iiito accotrnt t h trni.erurying i,on-det~riii,anist%c rzaturp of tlzz control and obsc,i.i:atron delays. A model of the delay cha,iucteristics fbr the comniurzication medium is also dei-iued.[rsing tlic state space fromework a general pwrpose supenrisory architecture is developed, allowing the pro-