Investigating the Tradespace between Increased Automation and Optimal Manning on Aircraft Carrier Decks

With increasing prevalence and capabilities of autonomous systems as part of complex heterogeneous manned-unmanned environments (HMUEs), an important consideration is the impact of the introduction of automation on the optimal assignment of human personnel. The US Navy has implemented optimal staffing techniques before in the 1990's and 2000's with a "minimal staffing" approach. The results were poor, leading to the degradation of Naval preparedness. Clearly, another approach to determining optimal staffing is necessary. To this end, the goal of this research is to develop human performance models for use in determining optimal manning of HMUEs. The human performance models are developed using an agent-based simulation of the aircraft carrier flight deck, a representative safety-critical HMUE. The Personnel Multi-Agent Safety and Control Simulation (PMASCS) simulates and analyzes the effects of introducing generalized maintenance crew skill sets and accelerated failure repair times on the overall performance and safety of the carrier flight deck. A behavioral model of four operator types (ordnance officers, chocks and chains, fueling officers, plane captains, and maintenance operators) is presented here along with an aircraft failure model. The main focus of this work is on the maintenance operators and aircraft failure modeling, since they have a direct impact on total launch time, a primary metric for carrier deck performance. With PMASCS I explore the effects of two variables on total launch time of 22 aircraft: 1) skill level of maintenance operators and 2) aircraft failure repair times while on the catapult (referred to as Phase 4 repair times). It is found that neither introducing a generic skill set to maintenance crews nor introducing a technology to accelerate Phase 4 aircraft repair times improves the average total launch time of 22 aircraft. An optimal manning level of 3 maintenance crews is found under all conditions, the point at which any additional maintenance crews does not reduce the total launch time. An additional discussion is included about how these results change if the operations are relieved of the bottleneck of installing the holdback bar at launch time.

Phase Locked Loop and Modulo Games, The Dogma Loops; and “Finding Ibrida”

This dissertation consists of two independent musical compositions and an article detailing the process of the design and assembly of an electric guitar with particular emphasis on the carefully curated suite of embedded effects.

The first piece, 'Phase Locked Loop and Modulo Games' is scored for electric guitar and a single echo of equal volume less than a beat away. One could think of the piece as a 15 minute canon at the unison at the dotted eighth note (or at times the quarter or triplet-quarter), however the compositional motivation is more about weaving a composite texture between the guitar and its echo that is, while in theory extremely contrapuntal, in actuality is simply a single [superhuman] melodic line.

The second piece, 'The Dogma Loops' picks up a few compositional threads left by ‘Phase Locked Loop’ and weaves them into an entirely new tapestry. 'Phase Locked Loop' is motivated by the creation of a complex musical composite that is for the most part electronically transparent. 'The Dogma Loops' questions that same notion of composite electronic complexity by essentially asking a question: "what are the inputs to an interactive electronic system that create the most complex outputs via the simplest musical means possible?"

'The Dogma Loops' is scored for Electric Guitar (doubling on Ukulele), Violin and Violoncello. All of the principal instruments require an electronic pickup (except the Uke). The work is in three sections played attacca; [Automation Games], [Point of Origin] and [Cloning Vectors].

The third and final component of the document is the article 'Finding Ibrida.' This article details the process of the design and assembly of an electric guitar with integrated effects, while also providing the deeper context (conceptual and technical) which motivated the efforts and informed the challenges to hybridize the various technologies (tubes, transistors, digital effects and a microcontroller subsystem). The project was motivated by a desire for rigorous technical and hands-on engagement with analog signal processing as applied to the electric guitar. ‘Finding Ibrida’ explores sound, some myths and lore of guitar tech and the history of electric guitar distortion and its culture of sonic exploration.

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.