Functional requirements for the man-vehicle systems research facility

The NASA Ames Research Center proposed a man-vehicle systems research facility to support flight simulation studies which are needed for identifying and correcting the sources of human error associated with current and future air carrier operations. The organization of research facility is reviewed and functional requirements and related priorities for the facility are recommended based on a review of potentially critical operational scenarios. Requirements are included for the experimenter's simulation control and data acquisition functions, as well as for the visual field, motion, sound, computation, crew station, and intercommunications subsystems. The related issues of functional fidelity and level of simulation are addressed, and specific criteria for quantitative assessment of various aspects of fidelity are offered. Recommendations for facility integration, checkout, and staffing are included

UAV-assisted data dissemination based on network coding in vehicular networks

Efficient and emergency data dissemination service in vehicular networks (VN) is very important in some situations, such as earthquakes, maritime rescue, and serious traffic accidents. Data loss frequently occurs in the data transition due to the unreliability of the wireless channel and there are no enough available UAVs providing data dissemination service for the large disaster areas. UAV with an adjustable active antenna can be used in light of the situation. However, data dissemination assisted by UAV with the adjustable active antenna needs corresponding effective data dissemination framework. A UAV-assisted data dissemination method based on network coding is proposed. First, the graph theory to model the state of the data loss of the vehicles is used; the data dissemination problem is transformed as the maximum clique problem of the graph. With the coverage of the directional antenna being limited, a parallel method to find the maximum clique based on the region division is proposed. Lastly, the method\u27s effectiveness is demonstrated by the simulation; the results show that the solution proposed can accelerate the solving process of finding the maximum clique and reduce the number of UAV broadcasts. This manuscript designs a novel scheme for the UAV-assisted data dissemination in vehicular networks based on network coding. The graph theory is used to model the state of the data loss of the vehicles. With the coverage of the directional antenna being limited, then a parallel method is proposed to find the maximum clique of the graph based on the region division. The effectiveness of the method is demonstrated by the simulation

Improving Routing Efficiency, Fairness, Differentiated Servises And Throughput In Optical Networks

Wavelength division multiplexed (WDM) optical networks are rapidly becoming the technology of choice in next-generation Internet architectures. This dissertation addresses the important issues of improving four aspects of optical networks, namely, routing efficiency, fairness, differentiated quality of service (QoS) and throughput. A new approach for implementing efficient routing and wavelength assignment in WDM networks is proposed and evaluated. In this approach, the state of a multiple-fiber link is represented by a compact bitmap computed as the logical union of the bitmaps of the free wavelengths in the fibers of this link. A modified Dijkstra\u27s shortest path algorithm and a wavelength assignment algorithm are developed using fast logical operations on the bitmap representation. In optical burst switched (OBS) networks, the burst dropping probability increases as the number of hops in the lightpath of the burst increases. Two schemes are proposed and evaluated to alleviate this unfairness. The two schemes have simple logic, and alleviate the beat-down unfairness problem without negatively impacting the overall throughput of the system. Two similar schemes to provide differentiated services in OBS networks are introduced. A new scheme to improve the fairness of OBS networks based on burst preemption is presented. The scheme uses carefully designed constraints to avoid excessive wasted channel reservations, reduce cascaded useless preemptions, and maintain healthy throughput levels. A new scheme to improve the throughput of OBS networks based on burst preemption is presented. An analytical model is developed to compute the throughput of the network for the special case when the network has a ring topology and the preemption weight is based solely on burst size. The analytical model is quite accurate and gives results close to those obtained by simulation. Finally, a preemption-based scheme for the concurrent improvement of throughput and burst fairness in OBS networks is proposed and evaluated. The scheme uses a preemption weight consisting of two terms: the first term is a function of the size of the burst and the second term is the product of the hop count times the length of the lightpath of the burst

A Miniaturised Spectrometer Device for the Detection of Nitrogen Dioxide in an Urban Environment

Monitoring of air pollutants, such as Nitrogen Dioxide (NO2), that are toxic or environmentally damaging is a key metric for environmental protection agencies worldwide. There is a constant need to develop new technologies and methodologies that provide real-time, low cost pollution measurements over a broad range of sampling sites, particularly in urban and industrial areas. Typically, detection of pollutants in urban environments is performed using a variety of techniques, many of which are expensive, require complex setups and are in fixed locations. The novel system presented in this thesis is designed for portable, low cost and in-situ detection of pollutants such as NO2 using Differential Optical Absorption Spectroscopy (DOAS). The basis for the system is the new generation of miniature fibre optic spectrometers that provide measurement specifications close to those of more traditional high cost DOAS instruments which are used to cross calibrate the initial data. The system can also be calibrated against other NO2 sampling techniques such as chemiluminescence (CL) monitors. Based on laboratory calibration, the final measurement accuracy of the system in the field was less than ± 0.5 ppb over a calibration dynamic range of 0-50 ppb under typical daylight conditions. This level of accuracy was achieved with repeated measurements, n=10, of custom made calibration cells containing known concentrations of NO2. Laboratory experiments using a controlled light source were designed to examine the correlation between absorption and differential absorption using the NO2 calibration cells with an algorithm based on the Beer-Lambert Law. These laboratory calibration tests verified that differential absorption determined using the novel system can be used to quantify NO2 concentrations both in the laboratory and in the open atmosphere of a surrounding urban area. Field tests, using ambient sunlight as a source, were then conducted and compared to concurrent CL measurements. Good correlations have been confirmed between the novel-DOAS data and the established NO2 quantification methods. Results prove that low cost spectrometer systems can be used to identify and quantify gaseous pollutants in real-time using ambient sunlight

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 197, September 1979

This bibliography lists 193 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1979

COMPLEX ADAPTIVE SYSTEMS AND THE THRESHOLD EFFECT: TOWARDS A GENERAL TOOL FOR STUDYING DYNAMIC PHENOMENA ACROSS DIVERSE DOMAINS

Most interesting phenomena in natural and social systems include transitions and oscillations among their various phases. A new phase begins when the system reaches a threshold that marks a qualitative change in system characteristics. These threshold effects are found all around us. In economics, this could be movement from a bull market to a bear market; in sociology, it could be the spread of political dissent, culminating in rebellion; in biology, the immune response to infection or disease as the body moves from sickness to health. Complex Adaptive Systems (CAS) has proven to be a powerful framework for exploring these and other related phenomena. Our hypothesis is that by modeling differing complex systems we can use the known causes and mechanisms in one domain to gain insight into the controlling properties of similar effects in another domain. To that end, we have created a general CAS model; one that is flexible enough so that it can be individually tailored and mapped to phenomena in various domains, yet retains sufficient commonality across applications to facilitate a deeper, cross-disciplinary understanding of these phenomena. In this work, we focus on the threshold effect. We show that the general model successfully replicates key features of a CAS. And we demonstrate its general applicability by adapting the model to three domains: cancer cells and the immune response; political dissent in a polity; and a marine ecosystem

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 133)

This special bibliography lists 276 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in September 1974

Robust and stochastic approaches to network capacity design under demand uncertainty

This thesis considers the network capacity design problem with demand uncertainty using the stochastic, robust and distributionally robust stochastic optimization approaches (DRSO). Network modeling in itself has found wide areas of application in most fields of human endeavor. The network would normally consist of source (origin) and sink (destination) nodes connected by arcs that allow for flows of an entity from the origin to the destination nodes. In this thesis, a special type of the minimum cost flow problem is addressed, the multi-commodity network flow problem. Commodities are the flow types that are transported on a shared network. Offered demands are, for the most part, unknown or uncertain, hence a model that immune against this uncertainty becomes the focus as well as the practicability of such models in the industry. This problem falls under the two-stage optimization framework where a decision is delayed in time to adjust for the first decision earlier made. The first stage decision is called the "here and now", while the second stage traffic re-adjustment is the "wait and see" decision. In the literature, the decision-maker is often believed to know the shape of the uncertainty, hence we address this by considering a data-driven uncertainty set. The research also addressed the non-linearity of cost function despite the abundance of literature assuming linearity and models proposed for this. This thesis consist of four main chapters excluding the "Introduction" chapter and the "Approaches to Optimization under Uncertainty" chapter where the methodologies are reviewed. The first of these four, Chapter 3, proposes the two models for the Robust Network Capacity Expansion Problem (RNCEP) with cost non-linearity. These two are the RNCEP with fixed-charge cost and RNCEP with piecewise-linear cost. The next chapter, Chapter 4, compares the RNCEP models under two types of uncertainties in order to address the issue of usefulness in a real world setting. The resulting two robust models are also comapared with the stochastic optimization model with distribution mean. Chapter 5 re-examines the earlier problem using machine learning approaches to generate the two uncertainty sets while the last of these chapters, Chapter 6, investigates DRSO model to network capacity planning and proposes an efficient solution technique