There is an increasing interest in the operas of Georg Frideric Handel, both from a scholarly perspective, and that of the modern, professional opera company. Producers of Handelian opera have moved away from productions similar to those staged in Halle, Germany, in the 1920s, which featured vastly reduced recitative and stripped the da capo aria to a single statement of the 'A' section. Modern productions have restored Handel's musical text, and in addition have attempted to recreate the original dramatic conditions and ethos of the work. The problem faced by the Halle producers still exists, however. How does the modern producer satisfy the expectations of the modern audience, while remaining faithful to the intention of the composer and the original production. This paper will investigate a possible approach to staging Handelian opera, with specific reference to the 'Mad Scene' from Handel's opera Orlando. Included in this examination will be a discussion of eighteenth-century British staging practices. These elements will be considered in the light of stage design and scenic practices of the period. / Arts, Faculty of / Music, School of / Additional material: 1 videocassette (Koerner Library). / Graduate

The customary way to create jazz arrangements of the Western classical canon—informally called swingin’-the-classics—adapts the original composition to jazz conventions. Uri Caine (b.1956) has devised an alternative approach, most notably in his work with compositions by Gustav Mahler. He refracts Mahler’s compositions through an eclectic array of musical performance styles while also eschewing the use of traditional jazz structures in favor of stricter adherence to formal ideas in the original score than is usual in a jazz arrangement. These elements and the manner in which Caine incorporates them in his Mahler arrangements closely parallel the practices of a translator who chooses to create a “foreignizing” literary translation. The 19th-century philosopher and translation theorist Friedrich Schleiermacher explained that in a foreignizing translation “the translator leaves the writer alone as much as possible and moves the reader toward the writer.” Foreignizing translations accentuate the otherness of the original work, approximating the foreign text’s form and syntax in the receiving language and using an uncommon, heterogeneous vocabulary. The resulting translations, which challenge readers with their frequent defiance of the conventions of the receiving linguistic culture, create literal, exaggerated readings that better convey authors’ characteristic use of their own languages for a new audience. My study of Caine’s music—which includes a survey of previously unavailable manuscripts and an exploration of selected arrangements using an analytical method designed to address the qualities in music that parallel foreignizing translation-contextualizes Caine’s modifications to Mahler’s compositions to generate intertextual readings that simultaneously highlight the ways that Mahler was innovative within his own tradition.

This work focuses on evaluating the parameters of the human operator as the driver of the vehicle simulator. In the first part, the thesis focuses on the examination of human operator parameters evaluation. In the second part of the thesis is a proposal of various scenarios focused on a specific area of measurement. At the end, the work focuses on the processing and evaluation of measured data obtained from the drivers tested.

Is there a significant difference in the design of deep networks for the tasks of classifying object-centric images and scenery images? How to design networks that extract the most representative features for scene recognition? To answer these questions, we design studies to examine the scales and richness of image features for scenery image recognition. Three methods are proposed that integrate the scale factor to the deep networks and reveal the fundamental network design strategies. In our first attempt to integrate scale factors into the deep network, we proposed a method that aggregates both the context and multi-scale object information of scene images by constructing a multi-scale pyramid. In our design, integration of object-centric multi-scale networks achieved a performance boost of 9.8%; integration of object- and scene-centric models obtained an accuracy improvement of 5.9% compared with single scene-centric models. We also exploit bringing the attention scheme to the deep network and proposed a Scale Attentive Network (SANet). The SANet streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the inter-dependency among scales, and further assists feature re-calibration as well as the aggregation process. The proposed network achieved a Top-1 accuracy increase by 1.83% on Place365 standard dataset with only 0.12% additional parameters and 0.24% additional GFLOPs using ResNet-50 as the backbone. We further bring the scale factor implicitly into network backbone design by proposing a Deep-Narrow Network and Dilated Pooling module. The Deep-narrow architecture increased the depth of the network as well as decreased the width of the network, which uses a variety of receptive fields by stacking more layers. We further proposed a Dilated Pooling module which expanded the pooling scope and made use of multi-scale features in the pooling operation. By embedding the Dilated Pooling into Deep-Narrow Network, we obtained a Top-1 accuracy boost of 0.40% using less than half of the GFLOPs and parameters compared to benchmark ResNet-50.

Every day robots are becoming more common in the society. Consequently, they must have certain basic skills in order to interact with humans and the environment. One of these skills is the capacity to understand the places where they are able to move. Computer vision is one of the ways commonly used for achieving this purpose. Current technologies in this field offer outstanding solutions applied to improve data quality every day, therefore producing more accurate results in the analysis of an environment. With this in mind, the main goal of this research is to develop and validate an efficient object-based scene understanding method that will be able to help solve problems related to scene identification for mobile robotics. We seek to analyze state-of-the-art methods for finding the most suitable one for our goals, as well as to select the kind of data most convenient for dealing with this issue. Another primary goal of the research is to determine the most suitable data input for analyzing scenes in order to find an accurate representation for the scenes by meaning of semantic labels or point cloud features descriptors. As a secondary goal we will show the benefits of using semantic descriptors generated with pre-trained models for mapping and scene classification problems, as well as the use of deep learning models in conjunction with 3D features description procedures to build a 3D object classification model that is directly related with the representation goal of this work. The research described in this thesis was motivated by the need for a robust system capable of understanding the locations where a robot usually interacts. In the same way, the advent of better computational resources has allowed to implement some already defined techniques that demand high computational capacity and that offer a possible solution for dealing with scene understanding issues. One of these techniques are Convolutional Neural Networks (CNNs). These networks have the capacity of classifying an image based on their visual appearance. Then, they generate a list of lexical labels and the probability for each label, representing the likelihood of the present of an object in the scene. Labels are derived from the training sets that the networks learned to recognize. Therefore, we could use this list of labels and probabilities as an efficient representation of the environment and then assign a semantic category to the regions where a mobile robot is able to navigate, and at the same time construct a semantic or topological map based on this semantic representation of the place. After analyzing the state-of-the-art in Scene Understanding, we identified a set of approaches in order to develop a robust scene understanding procedure. Among these approaches we identified an almost unexplored gap in the topic of understanding scenes based on objects present in them. Consequently, we propose to perform an experimental study in this approach aimed at finding a way of fully describing a scene considering the objects lying in place. As the Scene Understanding task involves object detection and annotation, one of the first steps is to determine the kind of data to use as input data in our proposal. With this in mind, our proposal considers to evaluate the use of 3D data. This kind of data suffers from the presence of noise, therefore, we propose to use the Growing Neural Gas (GNG) algorithm to reduce noise effect in the object recognition procedure. GNGs have the capacity to grow and adapt their topology to represent 2D information, producing a smaller representation with a slight noise influence from the input data. Applied to 3D data, the GNG presents a good approach able to tackle with noise. However, using 3D data poses a set of problems such as the lack of a 3D object dataset with enough models to generalize methods and adapt them to real situations, as well as the fact that processing three-dimensional data is computationally expensive and requires a huge storage space. These problems led us to explore new approaches for developing object recognition tasks. Therefore, considering the outstanding results obtained by the CNNs in the latest ImageNet challenge, we propose to carry out an evaluation of the former as an object detection system. These networks were initially proposed in the 90s and are nowadays easily implementable due to hardware improvements in the recent years. CNNs have shown satisfying results when they tested in problems such as: detection of objects, pedestrians, traffic signals, sound waves classification, and for medical image processing, among others. Moreover, an aggregate value of CNNs is the semantic description capabilities produced by the categories/labels that the network is able to identify and that could be translated as a semantic explanation of the input image. Consequently, we propose using the evaluation of these semantic labels as a scene descriptor for building a supervised scene classification model. Having said that, we also propose using semantic descriptors to generate topological maps and test the description capabilities of lexical labels. In addition, semantic descriptors could be suitable for unsupervised places or environment labeling, so we propose using them to deal with this kind of problem in order to achieve a robust scene labeling method. Finally, for tackling the object recognition problem we propose to develop an experimental study for unsupervised object labeling. This will be applied to the objects present in a point cloud and labeled using a lexical labeling tool. Then, objects will be used as the training instances of a classifier mixing their 3D features with label assigned by the external tool.

The ability to reconstruct 3D scenes of environments is of great interest in a number of fields such as autonomous driving, surveillance, and virtual reality. However, traditional methods often rely on multiple cameras or sensor-based depth measurements to accurately reconstruct 3D scenes. In this thesis we propose an alternative, deep learning-based approach to 3D scene reconstruction for objects of interest, using nothing but single RGB images. We evaluate our approach using the Deep Object Pose Estimation (DOPE) neural network for object detection and pose estimation, and the NVIDIA Deep learning Dataset Synthesizer for synthetic data generation. Using two unique objects, our results indicate that it is possible to reconstruct 3D scenes from single RGB images within a few centimeters of error margin.

Perception is a key feature in how any creature or autonomous system relates to its environment. While there are many types of perception, this thesis focuses on the improvement of the visual robotics perception systems. By implementing a broadband passive sensing system in conjunction with current perception algorithms, this thesis explores scene reconstruction and world modeling. The process involves two main steps. The first is stereo correspondence using block matching algorithms with filtering to improve the quality of this matching process. The disparity maps are then transformed into 3D point clouds. These point clouds are filtered again before the registration process is done. The registration uses a SAC-IA matching technique to align the point clouds with minimum error.  The registered final cloud is then filtered again to smooth and down sample the large amount of data. This process was implemented through software architecture that utilizes Qt, OpenCV, and Point Cloud Library. It was tested using a variety of experiments on each of the components of the process.  It shows promise for being able to replace or augment existing UGV perception systems in the future. / Master of Science

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Bloodstain Pattern Analysis is a rapidly growing area of research in the forensic science field. It is not uncommon for blood to be present on surfaces such as clothing, furniture, carpet, and more, during the commission of a crime. . Research of how blood interacts with different porous surfaces, such as textiles and fabrics, is relatively unexplored in the field of forensic science. Prior to a bloodshed event in which blood may be deposited onto clothing, the fabric may have been laundered in a variety of ways. In this research, swatches of a 100% nylon, water resistant fabric were subjected to seventeen different laundering and soiling processes. The laundering products used included Tide® Liquid Laundry Detergent, Downy® Fabric Softener, Downy® Unstoppables In-Wash Scent Boosters, Bounce® Dryer Sheets, Clorox® Zero Splash Bleach Packs, and OxiClean™ Max Force Laundry Stain Remover. Soiling included wearing swatches of fabric and leaving them in direct sunlight. Whole human blood was inverted, vortexed, then transferred using a disposable transfer pipette. The sample was held 36 in./3 ft. above each sample at a 90-degree angle, using an apparatus made from a flat edge and a protractor to ensure consistency. Blood drops were deposited onto each swatch of fabric, photographed, and microscopically examined. The drip stains were measured and characteristics of the blood, fabric, and the interaction of the two were recorded. Results showed some trends, such as an increased breakdown of fabric structure when bleach was used, and an increase in wicking when treated with scent boosters. Overall, the results were varied in all comparisons.