Engineering Systems

In this study, we investigated the development of clinical disease and immune responses in the development of an experimental model of flea allergy dermatitis. Dogs were randomly divided into four treatment groups and were infested with fleas on two different feeding schedules (continuous and episodic). Group 1 consisted of four non-exposed dogs (negative controls) and Group 2 consisted of six dogs exposed to fleas continually. Groups 3 and 4 consisted of 14 dogs each that were exposed to fleas on an episodic schedule (two consecutive days every other week for 12 weeks). Group 4 also received intraperitoneal injections of a low dose of lectin (ricin) with immunomodulatory properties. The purpose of Group 4 was to investigate the effects of ricin on enhancing the development of clinical signs, flea antigen-specific IgE levels and altering the number of CD4þ and CD8þ T cell subsets in peripheral blood. Clinical signs developed in all flea exposed dogs, however, the dermatology lesion scores were less and shorter in duration for continuously exposed dogs compared to episodic exposed dogs, independent of ricin treatment. Lesion development was concentrated in the flea triangle and consisted principally of erythema, followed by alopecia, excoriation, papules, and crusts. CD4þ and CD8þ lymphocyte subsets or IgE levels were not altered by ricin treatment. Flea antigen-specific IgE values were highest in dogs exposed to fleas on a continuous basis compared to those episodically exposed. A greater percentage of clinical responder dogs with negative flea-specific IgE titers or negative intradermal test (IDT) were present in the episodic exposure groups than in the continuous exposure group. IgE titers corresponded slightly better with clinical responders than the IDT. The agreement between the IgE titers and IDT was good (weighted k ¼ 0:67). Histopathology of skin samples were consistent with a Type I hypersensitivity. In conclusion, we were able to develop a model of flea allergy dermatitis by experimentally exposing dogs to fleas on an episodic and continuous feeding schedule. In this study, continuously exposed dogs did not develop immunotolerance, and ricin did not enhance the development of FAD. #

In this paper, a structured approach for collecting lessons learned information obtained from system development projects is discussed. To aid in the analysis of lessons learned, the collection framework incorporates techniques associated with approximate reasoning. The primary goal of collecting lessons learned is to improve organizational and project performance. Therefore, this approach concentrates on focusing the collection of lessons learned on this goal. This approach ties the collection of lessons learned to the process model used by the organization. This model is typically based on capability models such as the Systems Engineering Capability Maturity Model (SE-CMM 1 ) and Software CMM (SW-CMM), as well as process standards such as ISO 9000 and the organizations standard engineering practices. This approach utilizes the organizations process framework to provide a structured, lessons learned program which can be used by projects during and at the completion of their execution.

In previous work we developed techniques for modeling software development processes quantitatively in terms of development cost, product quality, and project schedule using simulation. This work has predominately been applied to the software project management planning function. The goal of our current work is to develop a "forward looking" approach that integrates metrics with simulation models of the software development process in order to support the software project management controlling function. This "forward-looking" approach provides predictions of project performance and the impact of various management decisions. It can be used to assess the project's conformance to planned schedule and resource consumption. This paper reports on work with a leading software development firm to create an approach, that includes a flexible metrics repository and a discrete event simulation model.

In this paper, we present a "forward-looking" decision support framework that integrates timely metrics data with simulation models of the software development process in order to support the software project management control function. This forward-looking approach provides predictions of project performance and the impact of various management decisions. Tradeoffs among performance measures are accomplished using Outcome Based Control Limits (OBCLs) and are augmented using multi-criteria utility functions and financial measures of performance to evaluate various process alternatives. The decision support framework enables the program manager to take corrective action as necessary on a project with the simulation model providing insight on potential performance impacts of the proposed corrective actions. A real world example is presented.

This paper presents a systems engineering entrepreneurship approach to developing projects at a university that are complex, multi-disciplinary in nature, integration oriented, and that may span departments, colleges, and have long completion schedules. Fundamental systems engineering principles are used to manage cost, schedule, and performance aspects of projects as well as to manage and control project risk. Entrepreneurial principles are used as part of the cost-benefit analysis in project evaluation. As an illustrative example, we present a project to develop an adaptive optics and atmospheric turbulence compensation system for a 0.8 meter optical telescope. A system engineering approach is used to identify and document stakeholder requirements, establish a project baseline, and use a requirements driven methodology to manage and control the project throughout its system development life-cycle. This approach is most suitable for technically complex projects that require collaboration and integration of diverse activities and resources as is often the case for multi-disciplinary projects or activities in centers of excellence or multiuniversity research initiatives.

By implementing student teams and incorporating a systems engineering approach, we have developed a unique video game-based product that combines the entertaining aspects of a popular video game set in a magical world, with dynamic, adaptable, multi-disciplinary instructional material. The video game knowledge module (VGKM) system integrates the fun NeverWinter Nights TM role-playing video game environment with educational knowledge modules, an entertaining story line, and out-of-game activities to build and demonstrate a compelling, interactive, collaborative, multi-disciplinary learning tool. Different knowledge modules can be built by interested faculty, educators, or entrepreneurial development teams, emphasizing different subject matter and integrated to produce a tailored course of study, correct academic deficiencies, or assist with training and certification, qualification, or assessment programs. Knowledge modules can span disciplines, departments, and even colleges and universities. A built-in assessment and rewards and punishment capability is included in the VGKM. The knowledge modules have a variety of dynamic features such as multiple or single user capable, directed in-game interaction by an educator, remote site multi-user interaction over the internet, localized WLAN/LAN networks, and asynchronous learning. A significant aspect of VGKM is character persistence. This allows the student to transfer a character from module to module, and game to game while maintaining the characters attributes, "possessions", experience, accumulated items, abilities, and ingame treasures.

In sheared coherent beam interferometric imaging, an estimate of the average reflectivity profile of the object can be computed from measurements of point to point phase differences in the far field interference pattern and a suitable phase reconstruction technique. The phase difference information is encoded in the irradiance of three identical, shifted and superimposed speckled laser beam patterns. A minimum variance phase reconstruction technique is presented to estimate the phase of the field in the measurement plane from the phase differences and evaluate its performance. Prior knowledge of the phase covariance is used in the minimum variance reconstructor. Analytic calculations and computer simulations are used to evaluate phase reconstruction errors as a function of object coherence area and spatial sample spacing in the measurement plane. The performance of the minimum variance reconstructor is compared to two least squares reconstructor implementations. Theoretical performance comparisons are made between the minimum variance reconstructor and a new implementation of the least squares formalism. The new least squares reconstructor uses the same error metric as the minimum variance reconstructor but does not use any statistical information in estimating the measurement plane phase function. Comparisons of the minimum variance reconstructor with a conventional implementation of the least squares formalism are also made. The performance of the minimum variance reconstructor is demonstrated for objects which are optically smooth as well as optically rough. A small random double point source object is used to demonstrate the near diffraction limited resolution of the minimum variance wavefront reconstructor. Phase and image reconstructions are demonstrated for extended objects.

Phase differences in the far field of a coherently illuminated object are used to estimate the twodimensional phase in the measurement plane of an imaging system. A previously derived phasecorrelation function is used in a minimum-variance phase-estimation algorithm to map phase-difference measurements optimally to estimates of the phase on a grid of points in the measurement plane. Theoretical and computer-simulation comparisons between the minimum-variance phase estimator and conventional least-squares estimators are made. The minimum-variance phase estimator produces a lower aperture-averaged mean-square phase error for all values of a sampling parameter ␤.

Accurate density estimation methodologies play an integral role in a variety of scientific disciplines, with applications including simulation models, decision support tools, and exploratory data analysis. In the past, histograms and kernel density estimators have been the predominant tools of choice, primarily due to their ease of use and mathematical simplicity. More recently, the use of wavelets for density estimation has gained in popularity due to their ability to approximate a large class of functions, including those with localized, abrupt variations. However, a well-known attribute of wavelet bases is that they can not be simultaneously symmetric, orthogonal, and compactly supported. Multiwavelets-a more general, vector-valued, construction of wavelets-overcome this disadvantage, making them natural choices for estimating density functions, many of which exhibit local symmetries around features such as a mode. We extend the methodology of wavelet density estimation to use multiwavelet bases and illustrate several empirical results where multiwavelet estimators outperform their wavelet counterparts at coarser resolution levels.

This paper proposes a new affine registration algorithm for matching two point sets in IR 2 or IR 3 . The input point sets are represented as probability density functions, using either Gaussian mixture models or discrete density models, and the problem of registering the point sets is treated as aligning the two distributions. Since polynomials transform as symmetric tensors under an affine transformation, the distributions' moments, which are the expected values of polynomials, also transform accordingly. Therefore, instead of solving the harder problem of aligning the two distributions directly, we solve the softer problem of matching the distributions' moments. By formulating a least-squares problem for matching moments of the two distributions up to degree three, the resulting cost function is a polynomial that can be efficiently optimized using techniques originated from algebraic geometry: the global minimum of this polynomial can be determined by solving a system of polynomial equations. The algorithm is robust in the presence of noises and outliers, and we validate the proposed algorithm on a variety of point sets with varying degrees of deformation and noise.

The modus operandi for machine learning is to represent data as feature vectors and then proceed with training algorithms that seek to optimally partition the feature space S ⊂ R n into labeled regions. This holds true even when the original data are functional in nature, i.e. curves or surfaces that are inherently varying over a continuum such as time or space. Functional data are often reduced to summary statistics, locally-sensitive characteristics, and global signatures with the objective of building comprehensive feature vectors that uniquely characterize each function. The present work directly addresses representational issues of functional data for supervised learning. We propose a novel classification by discriminative interpolation (CDI) framework wherein functional data in the same class are adaptively reconstructed to be more similar to each other, while simultaneously repelling nearest neighbor functional data in other classes. Akin to other recent nearest-neighbor metric learning paradigms like stochastic k -neighborhood selection and large margin nearest neighbors, our technique uses class-specific representations which gerrymander similar functional data in an appropriate parameter space. Experimental validation on several time series datasets establish the proposed discriminative interpolation framework as competitive or better in comparison to recent state-of-the-art techniques which continue to rely on the standard feature vector representation.