Cuttlefish Camouflage Quantification via Novel Neural Network Approaches and Hyperspectral Imaging

Rapid adaptive camouflage is a critical defense mechanism for cephalopods. The characterization of cephalopod camouflage has thus far been reserved almost exclusively to qualitative descriptions, and research on camouflage quantification remains in a nascent state. Qualitative characterizations do not capture the full multifactorial nature of camouflage, nor do they provide a comprehensive metric by which the degree and effectiveness of cephalopod camouflage from the perspective of a given predator can be quantitatively measured in a given scene. Here, I propose a “texture distance” metric that integrates lower and higher dimensional visual features to give a pixel-wise read out of the similarity between a cephalopod’s texture and its background texture. This metric is based on a previously developed algorithm that utilizes an artificial neural network to perform texture synthesis. Such a quantifying method would allow researchers to gain more insight into the amount of evolutionary pressure camouflage might exert on predator visual systems. The proposed metric is developed, validated, and used alongside other analyses to investigate the search strategies of human subjects in a camouflage detection task, especially with respect to the difference between human subjects who were successful or unsuccessful in the search task. Furthermore, hyperspectral images (HSI) of cephalopods under natural lighting conditions in the wild were used to measure chromatic and luminance discriminability in the visual color space of a subset of real natural fish predators. This HSI analysis suggests sophisticated color-matching across predator types, while also suggesting cephalopods are more detectable via changes in luminance. This result is compatible with the results of previous studies, but opens the door to exciting new research possibilities

Real time tracker based upon local hit correlation circuit for silicon strip sensors

For the planned high luminosity upgrade of the Large Hadron Collider (LHC), a significant performance improvement of the detectors is required, including new tracker and trigger systems that makes use of charged track information early on. In this note we explore the principle of real time track reconstruction integrated in the readout electronics. A prototype was built using the silicon strip sensor for the ATLAS phase-II upgrade. The real time tracker is not the baseline for ATLAS but is nevertheless of interest, as the upgraded trigger design has not yet been finalized. For this, a new readout scheme in parallel with conventional readout, called the Fast Cluster Finder (FCF), was included in the latest prototype of the ATLAS strip detector readout chip (ABC130). The FCF is capable of finding hits within 6 ns and transmitting the found hit information synchronously every 25 ns. Using the FCF together with external correlation logic makes it possible to look for pairs of hits consistent with tracks from the interaction point above a transverse momentum threshold. A correlator logic finds correlations between two closely spaced parallel sensors, a “doublet”, and can generate information used as input to a lowest level trigger decision. Such a correlator logic was developed as part of a demonstrator and was successfully tested in an electron beam. The results of this test beam experiment proved the concept of the real time track vector processor with FCF