Investigation of an Optimum Detection Scheme for a Star-Field Mapping System

An investigation was made to determine the optimum detection scheme for a star-field mapping system that uses coded detection resulting from starlight shining through specially arranged multiple slits of a reticle. The computer solution of equations derived from a theoretical model showed that the greatest probability of detection for a given star and background intensity occurred with the use of a single transparent slit. However, use of multiple slits improved the system's ability to reject the detection of undesirable lower intensity stars, but only by decreasing the probability of detection for lower intensity stars to be mapped. Also, it was found that the coding arrangement affected the root-mean-square star-position error and that detection is possible with error in the system's detected spin rate, though at a reduced probability

Review of \u3ci\u3eEnergy Development and Wildlife Conservation in Western North America.\u3c/i\u3e Edited by David E. Naugle.

Current and pending energy developments are likely to alter nearly 100 million hectares of wildlife habitat. David Naugle has compiled the inaugural synthesis of energy development impacts on wildlife populations across western grassland, shrub land, and forested systems. Part 1 sets the stage, characterizing energy development in the West. In part 2 ( Biological Responses ), Johnson and St-Laurent (chapter 3) propose a unifying experimental framework to monitor and assess consequences of energy development, urging proactive rather than reactionary science, conducted as experiments rather than observations, better informing both science and management. ... [Naugle et al.] eloquently conclude that this book is a wake-up call to those who reject prioritizing landscapes for conservation and instead continue to work in highly degraded landscapes because they deny inevitable impacts of energy development. To save our western landscapes, prioritization, conservation, and protection of key wildlife resources will be necessary, with restoration and reclamation important but secondary components. The volume outlines novel ways to accomplish these lofty but attainable goals across the West

A Finite State Machine Approach to Cluster Identification Using the Hoshen-Kopelman Algorithm

The purpose of this study was to develop an efficient finite state machine implementation of the eponymous Hoshen-Kopelman cluster identification algorithm using the nearest-eight neighborhood rule suitable to applications such as computer modeling for landscape ecology. The implementation presented in this study was tested using both actual land cover maps, as well as randomly generated data similar to those in the original presentation of the Hoshen-Kopelman algorithm for percolation analysis. The finite state machine implementation clearly outperformed a straightforward adaptation of the original Hoshen-Kopelman algorithm on either data type. Research was also conducted to explore the finite state machine\u27s performance on a Palm mobile computing device, and while it was competitive, it did not exceed the performance of the straightforward Hoshen-Kopelman implementation. However, a discussion of why this was the case is provided along with a possible remedy for future hardware designs

Mixing with the radiofrequency single-electron transistor

By configuring a radio-frequency single-electron transistor as a mixer, we demonstrate a unique implementation of this device, that achieves good charge sensitivity with large bandwidth about a tunable center frequency. In our implementation we achieve a measurement bandwidth of 16 MHz, with a tunable center frequency from 0 to 1.2 GHz, demonstrated with the transistor operating at 300 mK. Ultimately this device is limited in center frequency by the RC time of the transistor's center island, which for our device is ~ 1.6 GHz, close to the measured value. The measurement bandwidth is determined by the quality factor of the readout tank circuit.Comment: Submitted to APL september 200

Patient attitudes towards analgesia and their openness to non-pharmacological methods such as acupuncture in the emergency department

Aims: To investigate patient attitudes to analgesia, opioids and non-pharmacological analgesia including acupuncture, in the ED. Methods: ED patients with pain were surveyed regarding: pain scores, satisfaction, addiction concern, non-pharmacological methods of pain relief, and acupuncture. Data were analysed using logistic regression. Results: Of 196 adult patients, 52.8% were ‘very satisfied’ with analgesia. Most patients (84.7%) would accept non-pharmacological methods including acupuncture (68.9%) and 78.6% were not concerned about addiction. Satisfaction was associated with male gender, and ‘adequate analgesia’ but not with opioids. Conclusion: Most patients were generally satisfied with ED analgesia and were open to non-pharmacologic analgesia including acupuncture

Large-Scale Integration of Nanoelectromechanical Systems for Gas Sensing Applications

We have developed arrays of nanomechanical systems (NEMS) by large-scale integration, comprising thousands of individual nanoresonators with densities of up to 6 million NEMS per square centimeter. The individual NEMS devices are electrically coupled using a combined series-parallel configuration that is extremely robust with respect to lithographical defects and mechanical or electrostatic-discharge damage. Given the large number of connected nanoresonators, the arrays are able to handle extremely high input powers (>1 W per array, corresponding to <1 mW per nanoresonator) without excessive heating or deterioration of resonance response. We demonstrate the utility of integrated NEMS arrays as high-performance chemical vapor sensors, detecting a part-per-billion concentration of a chemical warfare simulant within only a 2 s exposure period

Hierarchical Collective Agent Network (HCAN) for efficient 3 fusion and management of multiple networked sensors

Agent-based software systems and applications are constructed by integrating diverse sets of components that are intelligent, heterogeneous, distributed, and concurrent. This paper describes a multi-agent system to assure the operation efficiency and reliability in data fusion and management of a set of networked distributive sensors (NDS). We discuss the general concept and architecture of a Hierarchical Collective Agent Network (HCAN) and its functional components for learning and adaptive control of the NDS. Sophistication of a HCAN control environment and an anatomy of the agent modules for enabling intelligent data fusion and management are presented. An exemplar HCAN is configured to support dynamic data fusion and automated sensor management in a simulated distributive and collaborative military sensor network for Global Missile Defense (GMD) application