Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving Threats

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil

Traction Characteristics of Cleated Athletic Shoes at Various Angles of Internal Rotation on Artificial Turf

As an alternative to natural grass playing fields, the installation of artificial turf surfaces has grown exponentially over the past several decades. Despite the growing popularity of artificial turf, little is known about the interaction between the player’s shoe and the turf surface. Previous research has sited the difficulty in maximizing performance (high traction), yet minimizing the risk of injury (low traction). Due to seemingly countless factors that affect the turf-shoe interaction, determining safe traction ranges for artificial turf is very difficult. Safe ranges between performance and risk of injury need to be found. The purpose of this study was to investigate whether traction characteristics vary based on a particular cleated athletic shoe on artificial turf at various angles of internal rotation during a linear translational motion. 4 U.S. Men’s size 12 cleated athletic shoes with a variety of stud styles from several different commonly used brands were tested on the artificial turf. Each cleated athletic shoe was set at various angles (0º, 30º, 60º, 90º) of internal rotation, and experienced linear translational motion while data was being collected. Significant differences were found within each cleated athletic shoe at various angles of internal rotation across all dependent variables (p=0.000). This could be attributed to a phenomenon termed the trench effect. There were no significant differences between cleated athletic shoes on artificial turf. Shoe-turf interactions are a very important consideration in athletics. This interaction is a determinant of the level of athletic performance and risk of injury. Shoe-turf interaction is a very stochastic process, and results should only be evaluated within the context of the test conditions

CHD associated with syndromic diagnoses: peri-operative risk factors and early outcomes

CHD is frequently associated with a genetic syndrome. These syndromes often present specific cardiovascular and non-cardiovascular co-morbidities that confer significant peri-operative risks affecting multiple organ systems. Although surgical outcomes have improved over time, these co-morbidities continue to contribute substantially to poor peri-operative mortality and morbidity outcomes. Peri-operative morbidity may have long-standing ramifications on neurodevelopment and overall health. Recognising the cardiovascular and non-cardiovascular risks associated with specific syndromic diagnoses will facilitate expectant management, early detection of clinical problems, and improved outcomes--for example, the development of syndrome-based protocols for peri-operative evaluation and prophylactic actions may improve outcomes for the more frequently encountered syndromes such as 22q11 deletion syndrome

The DEEP2 Galaxy Redshift Survey: Redshift Identification of Single-Line Emission Galaxies

We present two methods for determining spectroscopic redshifts of galaxies in the DEEP2 survey which display only one identifiable feature, an emission line, in the observed spectrum ("single-line galaxies"). First, we assume each single line is one of the four brightest lines accessible to DEEP2: Halpha, [OIII] 5007, Hbeta, or [OII] 3727. Then, we supplement spectral information with BRI photometry. The first method, parameter space proximity (PSP), calculates the distance of a single-line galaxy to galaxies of known redshift in (B-R), (R-I), R, observed wavelength parameter space. The second method is an artificial neural network (ANN). Prior information, such as allowable line widths and ratios, rules out one or more of the four lines for some galaxies in both methods. Based on analyses of evaluation sets, both methods are nearly perfect at identifying blended [OII] doublets. Of the lines identified as Halpha in the PSP and ANN methods, 91.4% and 94.2% respectively are accurate. Although the methods are not this accurate at discriminating between [OIII] and Hbeta, they can identify a single line as one of the two, and the ANN method in particular unambiguously identifies many [OIII] lines. From a sample of 640 single-line spectra, the methods determine the identities of 401 (62.7%) and 472 (73.8%) single lines, respectively, at accuracies similar to those found in the evaluation sets.Comment: 11 pages, 6 figures, accepted to Ap

Lip, a Human Gene Detected by Transfection of DNA From a Human Liposarcoma Encodes a Protein With Homology to Regulators of Small G Proteins

Purpose/Method. Transfection experiments have been used to identify activated oncogenes in a wide variety of tumour types. Here we describe the use of transfection experiments utilizing DNA from a human pleomorphic liposarcoma to identify a novel gene, designated lip which maps to chromosome 19

AEGIS: Extinction and Star Formation Tracers from Line Emission

Strong nebular emission lines are a sensitive probe of star formation and extinction in galaxies, and the [O II] line detects star forming populations out to z>1. However, star formation rates from emission lines depend on calibration of extinction and the [O II]/H-alpha line ratio, and separating star formation from AGN emission. We use calibrated line luminosities from the DEEP2 survey and Palomar K magnitudes to show that the behavior of emission line ratios depends on galaxy magnitude and color. For galaxies on the blue side of the color bimodality, the vast majority show emission signatures of star formation, and there are strong correlations of extinction and [O II]/H-alpha with restframe H magnitude. The conversion of [O II] to extinction-corrected H-alpha and thus to star formation rate has a significant slope with M_H, 0.23 dex/mag. Red galaxies with emission lines have a much higher scatter in their line ratios, and more than half show AGN signatures. We use 24 micron fluxes from Spitzer/MIPS to demonstrate the differing populations probed by nebular emission and by mid-IR luminosity. Although extinction is correlated with luminosity, 98% of IR-luminous galaxies at z~1 are still detected in the [O II] line. Mid-IR detected galaxies are mostly bright and intermediate color, while fainter, bluer galaxies with high [O II] luminosity are rarely detected at 24 microns.Comment: 4 pages, 3 figures. Accepted for publication in ApJ Letters AEGIS special editio

The DEEP2 Galaxy Redshift Survey: Color and Luminosity Dependence of Galaxy Clustering at z~1

We present measurements of the color and luminosity dependence of galaxy clustering at z~1 in the DEEP2 Galaxy Redshift Survey. Using volume-limited subsamples in bins of both color and luminosity, we find that: 1) The clustering dependence is much stronger with color than with luminosity and is as strong with color at z~1 as is found locally. We find no dependence of the clustering amplitude on color for galaxies on the red sequence, but a significant dependence on color for galaxies within the blue cloud. 2) For galaxies in the range L/L*~0.7-2, a stronger large-scale luminosity dependence is seen for all galaxies than for red and blue galaxies separately. The small-scale clustering amplitude depends significantly on luminosity for blue galaxies, with brighter samples having a stronger rise on scales r_p<0.5 Mpc/h. 3) Redder galaxies exhibit stronger small-scale redshift-space distortions ("fingers of god"), and both red and blue populations show large-scale distortions in xi(r_p,pi) due to coherent infall. 4) While the clustering length, r_0, increases smoothly with galaxy color (in narrow bins), its power-law exponent, gamma, exhibits a sharp jump from the blue cloud to the red sequence. The intermediate color `green' galaxy population likely includes transitional galaxies moving from the blue cloud to the red sequence; on large scales green galaxies are as clustered as red galaxies but show infall kinematics and a small-scale correlation slope akin to the blue galaxy population. 5) We compare our results to a semi-analytic galaxy formation model applied to the Millenium Run simulation. Differences between the data and the model suggest that in the model star formation is shut down too efficiently in satellite galaxies.Comment: 28 pages, 17 figures, emulateapj format, accepted to ApJ, updated to match published versio

The DEEP2 Galaxy Redshift Survey: Mean Ages and Metallicities of Red Field Galaxies at z ~ 0.9 from Stacked Keck/DEIMOS Spectra

As part of the DEEP2 galaxy redshift survey, we analyze absorption line strengths in stacked Keck/DEIMOS spectra of red field galaxies with weak to no emission lines, at redshifts 0.7 <= z <= 1. Comparison with models of stellar population synthesis shows that red galaxies at z ~ 0.9 have mean luminosity-weighted ages of the order of only 1 Gyr and at least solar metallicities. This result cannot be reconciled with a scenario where all stars evolved passively after forming at very high z. Rather, a significant fraction of stars can be no more than 1 Gyr old, which means that star formation continued to at least z ~ 1.2. Furthermore, a comparison of these distant galaxies with a local SDSS sample, using stellar populations synthesis models, shows that the drop in the equivalent width of Hdelta from z ~ 0.9 to 0.1 is less than predicted by passively evolving models. This admits of two interpretations: either each individual galaxy experiences continuing low-level star formation, or the red-sequence galaxy population from z ~ 0.9 to 0.1 is continually being added to by new galaxies with younger stars.Comment: A few typos were corrected and numbers in Table 1 were revise