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Novel H.265 Video Traffic Prediction Models Using Artificial Neural Networks, Collin Daly
Novel H.265 Video Traffic Prediction Models Using Artificial Neural Networks, Collin Daly
Honors College Theses
In this work, we propose the use of non-linear, autoregressive neural network models for predicting video frame sizes. This model utilizes H.265 encoded video traces as inputs and the predicted future frame sizes as outputs. This model is developed to predict ultra-high definition video frame encoded with H.265 within IP networks. The video I, P, and B frames are predicted separately to improve model prediction accuracy. This approach is verified in MATLAB using various H.265 video traces. The results indicate that the proposed models were able to predict the video traffic fairly accurately.
Machine Learning Through Mimicry And Association, Nickolas S. Holcomb
Machine Learning Through Mimicry And Association, Nickolas S. Holcomb
Honors College Theses
Adaptability is a key missing features that has impeded the growth of assistive robotics. In the traditional model, all actions must be explicitly coded by a skilled programmer familiar with the hardware. This project explores a method of teaching a webcam equipped arm type robot new primitive movement using visual demonstrations.