This paper presents a novel integration technique for segmented 3D profiles measured by projected fringe profilometry. Fringe patterns are projected to the inspected surface. The projected patterns fix their positions relative to the tested object during two segmented measurements. Thus, finding two matched surface points becomes a problem of searching for two identical phases in the fused data sets. This novel integration technique can match images successfully and achieve pixel-to-pixel registration easily even in the presence of geometric deformation, illumination changes, and severe occlusions. It is superior to the other methods because of its: (1) High matching accuracy; (2) Improved robustness; (3) Reduced computational time; (4) Capability of compensating distortions of the optical system at every pixel location; (5) Suitable for images rotating or scaling; and (6) Suitable for any other projected fringe measurement method. We also propose a method to design and fabricate a 2-D fringe pattern which can be applied to the integration technique for segmented 3D profiles. Campered with using 1-D fringe patterns for image registration, using a 2-D fringe pattern saves the measurement time and further proveds more tolerence to hand the shadow and noise problems. Tests of the system performance have been carried out that the accuracy of the registration scheme is 5.96% of image pixel size. Therefore, this technique can be extensively used in modern high technology industry. Especially when it requires higher resolution close-up images or overcomes the issue of not every inspected object can be fully expressed just by a single full-field measurement, it is necessary to use this integration technique.

Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Jeff Shamma; Committee Member: Eric Feron; Committee Member: Magnus Egerstedt. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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With the purpose of classifying and modelling stress, different sensors, signal features, machine learning methods, and stress experiments have been compared. Two databases have been studied: the MIT driver stress database and a new experimental database, where three stress tasks have been performed for 9 subjects: the Trier Social Stress Test, the Socially Evaluated Cold Pressor Test and the d2 test, of which the latter is not classically used for generating stress. Support vector machine, naive Bayes, k-nearest neighbor and probabilistic neural network classification techniques were compared, with support vector machines achieving the highest performance in general (99.5 ±0.6 %$on the driver database and 91.4 ± 2.4 % on the experimental database). For both databases, relevant features include the mean of the heart rate and the mean of the galvanic skin response, together with the mean of the absolute derivative of the galvanic skin response signal. A new feature is also introduced with great performance in stress classification for the driver database. Continuous models for estimating stress levels have also been developed, based upon the perceived stress levels given by the subjects during the experiments, where support vector regression is more accurate than linear and variational Bayesian regression. / I syfte att klassificera och modellera stress har olika sensorer, signalegenskaper, maskininlärningsmetoder och stressexperiment jämförts. Två databaser har studerats: MIT:s förarstressdatabas och en ny databas baserad på egna experiment, där stressuppgifter har genomförts av nio försökspersoner: Trier Social Stress Test,  Socially Evaluated Cold Pressor Test och d2-testet, av vilka det sistnämnda inte normalt används för att generera stress. Support vector machine-, naive Bayes-, k-nearest neighbour- och probabilistic neural network-algoritmer har jämförts, av vilka support vector machine har uppnått den högsta prestandan i allmänhet (99.5 ± 0.6 % på förardatabasen, 91.4 ± 2.4 %  på experimenten). För båda databaserna har signalegenskaper såsom medelvärdet av hjärtrytmen och hudens ledningsförmåga, tillsammans med medelvärdet av beloppet av hudens ledningsförmågas derivata identifierats som relevanta. En ny signalegenskap har också introducerats, med hög prestanda i stressklassificering på förarstressdatabasen. En kontinuerlig modell har också utvecklats, baserad på den upplevda stressnivån angiven av försökspersonerna under experimenten, där support vector regression har uppnått bättre resultat än linjär regression och variational Bayesian regression.

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Research on multisensor data fusion aims at providing the enabling technology to combine information from several sources in order to form a unifi ed picture. The literature work on fusion of conventional data provided by non-human (hard) sensors is vast and well-established. In comparison to conventional fusion systems where input data are generated by calibrated electronic sensor systems with well-defi ned characteristics, research on soft data fusion considers combining human-based data expressed preferably in unconstrained natural language form. Fusion of soft and hard data is even more challenging, yet necessary in some applications, and has received little attention in the past. Due to being a rather new area of research, soft/hard data fusion is still in a edging stage with even its challenging problems yet to be adequately de fined and explored. This dissertation develops a framework to enable fusion of both soft and hard data with the Random Set (RS) theory as the underlying mathematical foundation. Random set theory is an emerging theory within the data fusion community that, due to its powerful representational and computational capabilities, is gaining more and more attention among the data fusion researchers. Motivated by the unique characteristics of the random set theory and the main challenge of soft/hard data fusion systems, i.e. the need for a unifying framework capable of processing both unconventional soft data and conventional hard data, this dissertation argues in favor of a random set theoretic approach as the first step towards realizing a soft/hard data fusion framework. Several challenging problems related to soft/hard fusion systems are addressed in the proposed framework. First, an extension of the well-known Kalman lter within random set theory, called Kalman evidential filter (KEF), is adopted as a common data processing framework for both soft and hard data. Second, a novel ontology (syntax+semantics) is developed to allow for modeling soft (human-generated) data assuming target tracking as the application. Third, as soft/hard data fusion is mostly aimed at large networks of information processing, a new approach is proposed to enable distributed estimation of soft, as well as hard data, addressing the scalability requirement of such fusion systems. Fourth, a method for modeling trust in the human agents is developed, which enables the fusion system to protect itself from erroneous/misleading soft data through discounting such data on-the-fly. Fifth, leveraging the recent developments in the RS theoretic data fusion literature a novel soft data association algorithm is developed and deployed to extend the proposed target tracking framework into multi-target tracking case. Finally, the multi-target tracking framework is complemented by introducing a distributed classi fication approach applicable to target classes described with soft human-generated data. In addition, this dissertation presents a novel data-centric taxonomy of data fusion methodologies. In particular, several categories of fusion algorithms have been identifi ed and discussed based on the data-related challenging aspect(s) addressed. It is intended to provide the reader with a generic and comprehensive view of the contemporary data fusion literature, which could also serve as a reference for data fusion practitioners by providing them with conducive design guidelines, in terms of algorithm choice, regarding the specifi c data-related challenges expected in a given application.

The two main challenges of the twenty-first century are the scarcity of energy sources and global warming; trigged by the emission of greenhouse gases. In this context, solar energy became increasingly relevant. Because it makes optimal use of the resources, minimizes environmental impacts, and is sustainable over time. However, before installing solar panels, it is convenient pre-assessing the amount of energy that a building can harvest. This study proposes a methodology to semi-automatically generate information a building scale; on a large area. This thesis integrates airborne Light Detection and Ranging (LiDAR) and WoldView-2 satellite data for modelling the solar energy potential of building rooftops in San Francisco, California. The methodology involved building detection solar potential analysis, and estimations at building scale. First, the outline of building rooftops is extracted using an object-based approach. Next, the solar modelling is carried out using the solar radiation analysis tool in ArcGIS, Spatial Analyst. Then, energy that could potentially be harvested by each building rooftop is estimated. The energy estimation is defined in economic and environmental terms.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 92-93).

Thesis (Ph.D.) -- McMaster University, 1997. / Includes bibliographical references Also available via World Wide Web.

Thesis (M.S.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 58-62).