Redundant neural vision systems: competing for collision recognition roles

Ability to detect collisions is vital for future robots that interact with humans in complex visual environments. Lobula giant movement detectors (LGMD) and directional selective neurons (DSNs) are two types of identified neurons found in the visual pathways of insects such as locusts. Recent modelling studies showed that the LGMD or grouped DSNs could each be tuned for collision recognition. In both biological and artificial vision systems, however, which one should play the collision recognition role and the way the two types of specialized visual neurons could be functioning together are not clear. In this modeling study, we compared the competence of the LGMD and the DSNs, and also investigate the cooperation of the two neural vision systems for collision recognition via artificial evolution. We implemented three types of collision recognition neural subsystems – the LGMD, the DSNs and a hybrid system which combines the LGMD and the DSNs subsystems together, in each individual agent. A switch gene determines which of the three redundant neural subsystems plays the collision recognition role. We found that, in both robotics and driving environments, the LGMD was able to build up its ability for collision recognition quickly and robustly therefore reducing the chance of other types of neural networks to play the same role. The results suggest that the LGMD neural network could be the ideal model to be realized in hardware for collision recognition

Bioaugmentation for Improved Recovery of Anaerobic Digesters After Toxicant Exposure

Bioaugmentation was investigated as a method to decrease the recovery period of anaerobic digesters exposed to a transient toxic event. Two sets of laboratory-scale digesters (SRT = 10 days, OLR = 2 g COD/L-day), started with inoculum from a digester stabilizing synthetic municipal wastewater solids (MW) and synthetic industrial wastewater (WW), respectively, were transiently exposed to the model toxicant, oxygen. Bioaugmented digesters received 1.2 g VSS/L-day of an H2-utilizing culture for which the archaeal community was analyzed. Soon after oxygen exposure, the bioaugmented digesters produced 25–60% more methane than non-bioaugmented controls (p \u3c 0.05). One set of digesters produced lingering high propionate concentrations, and bioaugmentation resulted in significantly shorter recovery periods. The second set of digesters did not display lingering propionate, and bioaugmented digesters recovered at the same time as non-bioaugmented controls. The difference in the effect of bioaugmentation on recovery may be due to differences between microbial communities of the digester inocula originally employed. In conclusion, bioaugmentation with an H2-utilizing culture is a potential tool to decrease the recovery period, decrease propionate concentration, and increase biogas production of some anaerobic digesters after a toxic event. Digesters already containing rapidly adaptable microbial communities may not benefit from bioaugmentation, whereas other digesters with poorly adaptable microbial communities may benefit greatly

Re-engineering a nanodosimetry Monte Carlo code into Geant4: software design and first results

A set of physics models for nanodosimetry simulation is being re-engineered for use in Geant4-based simulations. This extension of Geant4 capabilities is part of a larger scale R&D project for multi-scale simulation involving adaptable, co-working condensed and discrete transport schemes. The project in progress reengineers the physics modeling capabilities associated with an existing FORTRAN track-structure code for nanodosimetry into a software design suitable to collaborate with an object oriented simulation kernel. The first experience and results of the ongoing re-engineering process are presented.Comment: 4 pages, 2 figures and images, to appear in proceedings of the Nuclear Science Symposium and Medical Imaging Conference 2009, Orland

Improving the Sensitivity of Advanced LIGO Using Noise Subtraction

This paper presents an adaptable, parallelizable method for subtracting linearly coupled noise from Advanced LIGO data. We explain the features developed to ensure that the process is robust enough to handle the variability present in Advanced LIGO data. In this work, we target subtraction of noise due to beam jitter, detector calibration lines, and mains power lines. We demonstrate noise subtraction over the entirety of the second observing run, resulting in increases in sensitivity comparable to those reported in previous targeted efforts. Over the course of the second observing run, we see a 30% increase in Advanced LIGO sensitivity to gravitational waves from a broad range of compact binary systems. We expect the use of this method to result in a higher rate of detected gravitational-wave signals in Advanced LIGO data.Comment: 15 pages, 6 figure

A balloon-borne high-resolution spectrometer for observations of gamma-ray emission from solar flares

The design, development, and balloon-flight verification of a payload for observations of gamma-ray emission from solar flares are reported. The payload incorporates a high-purity germanium semiconductor detector, standard NIM and CAMAC electronics modules, a thermally stabilized pressure housing, and regulated battery power supplies. The flight system is supported on the ground with interactive data-handling equipment comprised of similar electronics hardware. The modularity and flexibility of the payload, together with the resolution and stability obtained throughout a 30-hour flight, make it readily adaptable for high-sensitivity, long-duration balloon fight applications

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector