32 research outputs found

    Longevity improvement of optically activated, high gain GaAs photoconductive semiconductor switches

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    The longevity of high gain GaAs photoconductive semiconductor switches (PCSS) has been extended to over 100 million pulses at 23A, and over 100 pulses at 1kA. This is achieved by improving the ohmic contacts by doping the semi-insulating GaAs underneath the metal, and by achieving a more uniform distribution of contact wear across the entire switch by distributing the trigger light to form multiple filaments. This paper will compare various approaches to doping the contacts, including ion implantation, thermal diffusion, and epitaxial growth. The device characterization also includes examination of the filament behavior using open-shutter, infra-red imaging during high gain switching. These techniques provide information on the filament carrier densities as well as the influence that the different contact structures and trigger light distributions have on the distribution of the current in the devices. This information is guiding the continuing refinement of contact structures and geometries for further improvements in switch longevity

    Comparison of the Circadian Rhythms of Two Bee Pollinators, a Generalist and a Specialist, of Field Bindweed.

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    Annual Meeting of the Society-for-Integrative-and-Comparative-Biology (SICB) -- JAN 03-07, 2018 -- San Francisco, CA[No Abstract Available]Soc Integrat & Comparat Bio

    Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents

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    ABSTRACT Measurement and signal intelligence (MASINT) of the battlespace has created new requirements in information management, communication and interoperability as they effect surveillance and situational awareness. In many situations, stand-off remote sensing and hazard-interdiction techniques over realistic operational areas are often impractical and difficult to characterize. An alternative approach is to implement adaptive remote-sensing techniques with swarms of mobile agents employing collective behavior for optimization of mapping signatures and positional orientation (registration). We have expanded intelligent control theory using physics-based collective behavior models and genetic algorithms to produce a uniquely powerfid implementation of distributed ground-based measurement incorporating both local collective behavior, and inter-operative global optimization for sensor fusion and mission oversight. By using a layered hierarchical control architecture to orchestrate adaptive reconilguration of semiautonomous robotic agents, we can improve overall robustness and functionality in dynamic tactical environments without information bottlenecking. In our concept, each sensor is equipped with a miniaturized optical reflectance modulator which is interactively monitored as a remote transponder using a laser communication protocol from a remotemothership or operative. Robotdata-sharing at the groundlevelcanbe leveragedwithglobalevaluation criteria, including terrain overlays and remote imaging data. Information sharing and distributed intelligence opens up a new class of remote sensing applications in which small single-function autonomous observers at the local level can collectively optimize and measure large scale ground-level signatures. As the need for coverage and the number of agents grows to improve spatial resolution, cooperative behavior orchestrated by a global situational awareness umbrella will be an essential ingredient to offset increasing bandwidth requirements within the net. A system of this type is being developed which will be capable of sensitively detecting, tracking, and mapping spatial distributions of measurement signatures, which are nonstationary or obscured by clutter or interfering obstacles by virtue of adaptive reconfiguration. This methodology is being used to field an adaptive ground-penetrating impulse radar from a superposition of small radiating dipoles for detection of underground structures and to detecthemediate hazardous biological or chemical species in migrating plumes. This paper focuses on our recent work at Sandia National Laboratories toward engineering a physics-based swarm of mobile vehicles for distributed sensing applications. Our goal is to coordinate a sensor array that optimizes sensor coverage and multivariate signal analysis by implementing artificial intelligence and evolutionary computational techniques. These intelligent control systems integrate both globally operating decision-making systems and locally cooperative information-sharing modes using genetically-trained neural nehvorks. Once trained, neural networks have the ability to enhance real-time operational responses to dynamical environments, such as obstacle avoidance, responding to prevailing wind patterns, and overcoming other natural obscurants or interferences (jammers). The swarm realizes a collective set of sensor neurons with simple properties incorporating interactions based on basic community rules (potential fields) and complex interconnecting functions based on various neural network architectures, Therefore, the swarm is capable of redundant heterogeneous measurements which furnishes an additional degreeof robustnessand fault tolerancenot affordedby conventional systems,whileaccomplishing such cognitive tasks as generalization, error correction, pattern recognition, and sensor fision. The robotic platforms could be equipped with specialized sensor devices including transmitkeceive dipole antennas, chemical or biological "sniffers" in combination with recognition analysis tools, communication modulators, and laser diodes. Our group has been studying the collective behavior of an autonomous, multi-agent system applied to emerging threat applications. To accomplish such tasks, research in the fields of robotics, sensor technology, and swarms are being conducted within an integrated program. Mission scenarios under consideration include ground penetrating impulse radar (GPR) for detection of under-ground structures, airborne systems, and plume detectionh-emediation. We will describe our research in these areas and give a status report on our progress, including simulations and laboratory-based sensor experiments
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