http://hdl.handle.net/1903/2756 2013-05-22T04:46:02Z http://hdl.handle.net/1903/13864 Title: ONTOLOGY-ENABLED TRACEABILITY MODELS FOR ENGINEERING SYSTEMS DESIGN AND MANAGEMENT Authors: Delgoshaei, Parastoo Abstract: This thesis describes new models and a system for satisfying requirements, and an architectural framework for linking discipline-specific dependencies through inter- action relationships at the ontology (or meta-model) level. In a departure from state-of-the-art traceability mechanisms, we ask the question: What design concept (or family of design concepts) should be applied to satisfy this requirement? Solu- tions to this question establish links between requirements and design concepts. The implementation of these concepts leads to the design itself. These ideas, and support for design-rule checking are prototyped through a series of progressively complicated applications, culminating in a case study for rail transit systems management. 2012-01-01T00:00:00Z http://hdl.handle.net/1903/13862 Title: An Optical Density Detection Platform with Integrated Microfluidics for In Situ Growth, Monitoring, and Treatment of Bacterial Biofilms Authors: Mosteller, Matthew Philip Abstract: Systems engineering strategies utilizing platform-based design methodologies are implemented to achieve the integration of biological and physical system components in a biomedical system. An application of this platform explored, in which an integrated microsystem is developed capable of the on-chip growth, monitoring, and treatment of bacterial biofilms for drug development and fundamental study applications. In this work, the developed systems engineering paradigm is utilized to develop a device system implementing linear array charge-coupled devices to enable real time, non-invasive, label-free monitoring of bacterial biofilms. A novel biofilm treatment method is demonstrated within the developed microsystem showing drastic increases in treatment efficacy by decreasing both bacterial biomass and cell viability within treated biofilms. Demonstration of this treatment at the microscale enables future applications of this method for the in vivo treatment of biofilm-associated infections. 2012-01-01T00:00:00Z http://hdl.handle.net/1903/13818 Title: Crowdsourced Monolingual Translation Authors: Hu, Chang Abstract: An enormous potential exists for solving certain classes of computational problems through rich collaboration among crowds of humans supported by computers. Solutions to these problems used to involve human professionals who are expensive to hire or difficult to find. Despite significant advances, fully automatic systems still have much room for improvement. Recent research has involved recruiting large crowds of skilled humans (``crowdsourcing''), but crowdsourcing solutions are still restricted by the availability of those skilled human participants. With translation, for example, professional translators incur high cost and are not always available; machine translation systems have been greatly improved recently, but still can only provide passable translation, and for only limited language pairs at that; crowdsourced translation is limited by the availability of bilingual humans. This dissertation describes crowdsourced monolingual translation, where monolingual translation is translation performed by monolingual people. Crowdsourced monolingual translation is a collaborative form of translation performed by two crowds of people who speak the source or the target language respectively, with machine translation as the mediating device. A general protocol to handle crowdsourced monolingual translation is introduced along with three systems that implement the protocol. The MonoTrans system initially established the feasibility of the protocol. Then, MonoTrans2 enabled lab experiments with a second implementation of the protocol. MonoTrans2 was also applied to a an emergency-response scenario in a developing country (Haiti). The MonoTrans Widgets system was deployed to a large crowd of casual web users with a third implementation of the protocol. These systems were studied in various settings, and were found to supply improvement in quality over both machine translation and monolingual post-editing. 2012-01-01T00:00:00Z http://hdl.handle.net/1903/13813 Title: FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS Authors: Guo, Huimin Abstract: Face recognition has been a long standing problem in computer vision. General face recognition is challenging because of large appearance variability due to factors including pose, ambient lighting, expression, size of the face, age, and distance from the camera, etc. There are very accurate techniques to perform face recognition in controlled environments, especially when large numbers of samples are available for each face (individual). However, face identification under uncontrolled( unconstrained) environments or with limited training data is still an unsolved problem. There are two face recognition tasks: face identification (who is who in a probe face set, given a gallery face set) and face verification (same or not, given two faces). In this work, we study both face identification and verification in unconstrained environments. Firstly, we propose a face verification framework that combines Partial Least Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a face with a large feature set combining shape, texture and color information. PLS regression is applied to perform multi-channel feature weighting on this large feature set. Finally the PLS regression is used to compute the similarity score of an image pair by One-Shot learning (using a fixed negative set). Secondly, we study face identification with image sets, where the gallery and probe are sets of face images of an individual. We model a face set by its covariance matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to Euclidean space. Then, discriminative learning is performed on the COV manifold: the learning aims to maximize the between-class COV distance and minimize the within-class COV distance. Sparse representation and dictionary learning have been widely used in face recognition, especially when large numbers of samples are available for each face (individual). Sparse coding is promising since it provides a more stable and discriminative face representation. In the last part of our work, we explore sparse coding and dictionary learning for face verification application. More specifically, in one approach, we apply sparse representations to face verification in two ways via a fix reference set as dictionary. In the other approach, we propose a dictionary learning framework with explicit pairwise constraints, which unifies the discriminative dictionary learning for pair matching (face verification) and classification (face recognition) problems. 2012-01-01T00:00:00Z