Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding.

Digital media is ubiquitous and produced in ever-growing quantities. This necessitates a constant evolution of compression techniques, especially for video, in order to maintain efficient storage and transmission. In this work, we aim at exploiting non-local redundancies in video data that remain difficult to erase for conventional video codecs We design convolutional neural networks with a particular emphasis on low memory and computational footprint.

Detection of acute periodontal pain from physiological signals.

Objective: To investigate the feasibility of the detection of brief orofacial pain sensations from easily recordable physiological signals by means of machine learning techniques.

Approach: A total of 47 subjects underwent periodontal probing and indicated each instance of pain perception by means of a push button. Simultaneously, physiological signals were recorded and, subsequently, autonomic indices were computed.