Naive Bayes texture classification applied to whisker data from a moving robot

Many rodents use their whiskers to distinguish objects by surface texture. To examine possible mechanisms for this discrimination, data from an artificial whisker attached to a moving robot was used to test texture classification algorithms. This data was examined previously using a template-based classifier of the whisker vibration power spectrum [1]. Motivated by a proposal about the neural computations underlying sensory decision making [2], we classified the raw whisker signal using the related ‘naive Bayes’ method. The integration time window is important, with roughly 100ms of data required for good decisions and 500ms for the best decisions. For stereotyped motion, the classifier achieved hit rates of about 80% using a single (horizontal or vertical) stream of vibration data and 90% using both streams. Similar hit rates were achieved on natural data, apart from a single case in which the performance was only about 55%. Therefore this application of naive Bayes represents a biologically motivated algorithm that can perform well in a real-world robot task

Two-photon fluorescence imaging of GaN micro-LED arrays

This paper presents a simple yet powerful two-photon fluorescence imagining technique for minimally invasive evaluation of the gallium nitride based-structure deep within a Micro-LED array. By exciting the GaN-based heterostructure via two-photon absorption, the resulting fluorescence can be used to generate optical sections of the active medium within the sample

Two-photon fluorescence imaging of gallium nitride micro-LED arrays

We present a simple yet powerful two-photon fluorescence imaging technique for minimally invasive evaluation of the gallium nitride-based structure deep within a micro-LED array. By exciting the GaN-based heterostructure via two-photon absorption, the resulting fluorescence can be used to generate optical sections of the active medium within the sample.link_to_subscribed_fulltex