Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion

In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over conventional frame-based cameras. Using principles inspired by the retina, its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it ideal for embedded systems on platforms such as drones and self-driving cars. However, event-based data sets are scarce and labels are even rarer for tasks such as object detection. We transferred discriminative knowledge from a state-of-the-art frame-based convolutional neural network (CNN) to the event-based modality via intermediate pseudo-labels, which are used as targets for supervised learning. We show, for the first time, event-based car detection under ego-motion in a real environment at 100 frames per second with a test average precision of 40.3% relative to our annotated ground truth. The event-based car detector handles motion blur and poor illumination conditions despite not explicitly trained to do so, and even complements frame-based CNN detectors, suggesting that it has learnt generalized visual representations

Infinite Excess Entropy Processes with Countable-State Generators

We present two examples of finite-alphabet, infinite excess entropy processes generated by invariant hidden Markov models (HMMs) with countable state sets. The first, simpler example is not ergodic, but the second is. It appears these are the first constructions of processes of this type. Previous examples of infinite excess entropy processes over finite alphabets admit only invariant HMM presentations with uncountable state sets.Comment: 13 pages, 3 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/ieepcsg.ht

The generation of noise by the fluctuations in gas temperature into a turbine

An actuator disc analysis is used to calculate the pressure fluctuations produced by the convection of temperature fluctuations (entropy waves) into one or more rows of blades. The perturbations in pressure and temperature must be small, but the mean flow deflection and acceleration are generally large. The calculations indicate that the small temperature fluctuations produced by combustion chambers are sufficient to produce large amounts of acoustic power. Although designed primarily to calculate the effect of entropy waves, the method is more general and is able to predict the pressure and vorticity waves generated by upstream or downstream going pressure waves or by vorticity waves impinging on blade rows