Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Athlete Monitoring in Canadian Football

INTRODUCTION: Sports performance optimization relies heavily on the balance between increasing training load (TL) and appropriate recovery. In high performance settings, the crucial role that athlete monitoring plays in this intricate balancing act is widely recognized. PURPOSE: Due to the violent and unique demands of Canadian football, minimal research and few practical monitoring tools are available for coaches and practitioners. The thesis aim therefore, is to provide a body of research that begins to address athlete monitoring challenges in Canadian football. CHAPTER III: Study 1 was designed to validate the Session-Ratings of Perceived Exertion (sRPE) method of quantifying internal TL in football players. Statistically significant correlations for all individual players between sRPE and two criterion heart rate-based measures were found. Results confirm that sRPE is a highly practical and valid tool for Canadian football application. CHAPTER IV: Despite frequent use in other sports, the high injury occurrence in football often prevents consistent neuromuscular fatigue (NMF) monitoring using a maximal countermovement jump (CMJ). Further, little direct evidence exists supporting the relationship between athlete CMJ performance and NMF. Study 2 addressed these issues by assessing the acute-fatiguing effects of a game simulation (G-Sim) on postural sway (PS), CMJ performance and lab-based NMF measures in football players. Congruency between all measures post G-Sim suggests that submaximal PS monitoring has the potential to supplement CMJ in NMF tracking of football players hampered by minor injuries. CHAPTER V: Recognizing that acute-fatiguing effects may misrepresent fatigue across extended training periods, study 3 applied previous methodology (study 1 & 2) to evaluate PS as a valid NMF indicator over a competitive 11-week season. Significant associations between both CMJ and PS performance with weekly Global TL fluctuations provided evidence of NMF assessments valid across a football season. There was no evidence of differences in NMF status between starters and non-starters of the weekly game. CONCLUSION: Thesis findings confirm the validity and practicality of sRPE and the NMF monitoring tools of CMJ and PS across a competitive football season. This initial work provides a spring-board for future research as it has broadened our knowledge of athlete responses to- and monitoring in- Canadian football

Crystal Cartography: Mapping Nanostructure with Scanning Electron Diffraction

Nanostructure describes the network of defective and distorted atomic structure existing on the nanoscale within materials. This nanostructure bridges the gap between idealised crys- talline structure and real materials, playing a deterministic role in tailoring physico-chemical properties, as well as providing a basis for mechanistic understanding of complex processes such as mechanical deformation and phase transformation. Characterising nanostructure, to develop understanding of materials, requires experimental techniques capable of probing the structure with spatial resolution on the order of nanometres and across regions of interest up to micrometres. Recent developments in electron microscopy, enabling the acquisition of numerous diffraction patterns in a spatially resolved manner, combined with modern computational power, provides a route to meet this need as developed in this work. Scanning electron diffraction (SED) involves the acquisition of a two-dimensional elec- tron diffraction pattern at each probe position in a two-dimensional scan of a specimen. An information rich 4-dimensional (4D-SED) dataset is obtained that can be analysed extensively post-facto using a wide-range of computational methods. The acquisition of such 4D-SED data from the specimen at numerous orientations may also enable the reconstruction of nanostructure in three-dimensions via tomographic methods. In this work, methods for the acquisition and analysis of 4D-SED data are developed and applied to reveal nanostructure in two and three-dimensions. These methods are applied to various prototypical characterisation challenges in materials science, particularly: strain mapping in two and three dimensions, revealing inter-phase crystallographic relationships, mapping grains in two-dimensional materials, and probing nanostructure in polyethylene

Tensor displays: compressive light field synthesis using multilayer displays with directional backlighting

We introduce tensor displays: a family of compressive light field displays comprising all architectures employing a stack of time-multiplexed, light-attenuating layers illuminated by uniform or directional backlighting (i.e., any low-resolution light field emitter). We show that the light field emitted by an N-layer, M-frame tensor display can be represented by an Nth-order, rank-M tensor. Using this representation we introduce a unified optimization framework, based on nonnegative tensor factorization (NTF), encompassing all tensor display architectures. This framework is the first to allow joint multilayer, multiframe light field decompositions, significantly reducing artifacts observed with prior multilayer-only and multiframe-only decompositions; it is also the first optimization method for designs combining multiple layers with directional backlighting. We verify the benefits and limitations of tensor displays by constructing a prototype using modified LCD panels and a custom integral imaging backlight. Our efficient, GPU-based NTF implementation enables interactive applications. Through simulations and experiments we show that tensor displays reveal practical architectures with greater depths of field, wider fields of view, and thinner form factors, compared to prior automultiscopic displays.United States. Defense Advanced Research Projects Agency (DARPA SCENICC program)National Science Foundation (U.S.) (NSF Grant IIS-1116452)United States. Defense Advanced Research Projects Agency (DARPA MOSAIC program)United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award)Alfred P. Sloan Foundation (Fellowship

Immersive analytics for oncology patient cohorts

This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness

Data-driven machine translation for sign languages

This thesis explores the application of data-driven machine translation (MT) to sign languages (SLs). The provision of an SL MT system can facilitate communication between Deaf and hearing people by translating information into the native and preferred language of the individual. We begin with an introduction to SLs, focussing on Irish Sign Language - the native language of the Deaf in Ireland. We describe their linguistics and mechanics including similarities and differences with spoken languages. Given the lack of a formalised written form of these languages, an outline of annotation formats is discussed as well as the issue of data collection. We summarise previous approaches to SL MT, highlighting the pros and cons of each approach. Initial experiments in the novel area of example-based MT for SLs are discussed and an overview of the problems that arise when automatically translating these manual-visual languages is given. Following this we detail our data-driven approach, examining the MT system used and modifications made for the treatment of SLs and their annotation. Through sets of automatically evaluated experiments in both language directions, we consider the merits of data-driven MT for SLs and outline the mainstream evaluation metrics used. To complete the translation into SLs, we discuss the addition and manual evaluation of a signing avatar for real SL output

Selected proceedings of the 50th Linguistic Symposium on Romance Languages

Synopsis: The present volume presents a selection of the revised and peer-reviewed proceedings articles of the 50th Linguistic Symposium on Romance Languages (LSRL 50) which was hosted virtually by the faculty and students from the University of Texas at Austin. With contributions from rising and senior scholars from Europe and the Americas, the volume demonstrates the breadth of research in contemporary Romance linguistics with articles that apply corpus-based and laboratory methods, as well as theory, to explore the structure, use, and development of the Romance languages. The articles cover a wide range of fields including morphosyntax, semantics, language variation and change, sociophonetics, historical linguistics, language acquisition, and computational linguistics. In an introductory article, the editors document the sudden transition of LSRL 50 to a virtual format and acknowledge those who helped them to ensure the continuity of this annual scholarly meeting

Designing a Patient-Centered Clinical Workflow to Assess Cyberbully Experiences of Youths in the U.S. Healthcare System

Cyberbullying or online harassment is often defined as when someone repeatedly and intentionally harasses, mistreats, or makes fun of others aiming to scare, anger or shame them using electronic devices [296]. Youths experiencing cyberbullying report higher levels of anxiety and depression, mental distress, suicide thoughts, and substance abuse than their non-bullied peers [360, 605, 261, 354]. Even though bullying is associated with significant health problems, to date, very little youth anti-bullying efforts are initiated and directed in clinical settings. There is presently no standardized procedure or workflow across health systems for systematically assessing cyberbullying or other equally dangerous online activities among vulnerable groups like children or adolescents [599]. Therefore, I developed a series of research projects to link digital indicators of cyberbullying or online harassment to clinical practices by advocating design considerations for a patient-centered clinical assessment and workflow that addresses patients’ needs and expectations to ensure quality care. Through this dissertation, I aim to answer these high-level research questions:RQ1. How does the presence of severe online harassment on online platforms contribute to negative experiences and risky behaviors within vulnerable populations? RQ2. How efficient is the current mechanism of screening these risky online negative experiences and behaviors, specifically related to cyberbully, within at-risk populations like adolescent in clinical settings? RQ3. How might evidence of activities and negative harassing experiences on online platforms best be integrated into electronic health records during clinical treatment? I first explore how harassment is presented within different social media platforms from diverse contexts and cultural norms (study 1,2, and 3); next, by analyzing actual patient data, I address current limitations in the screening process in clinical settings that fail to efficiently address core aspect of cyberbullying and their consequences within adolescent patients (study 4 and 5); finally, connecting all my findings, I recommend specific design guidelines for a refined screening tool and structured processes for implementation and integration of the screened data into patients’ electronic health records (EHRs) for better patient assessment and treatment outcomes around cyberbully within adolescent patients (study 6)

Computational Modeling and Analysis of Multi-timbral Musical Instrument Mixtures

In the audio domain, the disciplines of signal processing, machine learning, psychoacoustics, information theory and library science have merged into the field of Music Information Retrieval (Music-IR). Music-IR researchers attempt to extract high level information from music like pitch, meter, genre, rhythm and timbre directly from audio signals as well as semantic meta-data over a wide variety of sources. This information is then used to organize and process data for large scale retrieval and novel interfaces. For creating musical content, access to hardware and software tools for producing music has become commonplace in the digital landscape. While the means to produce music have become widely available, significant time must be invested to attain professional results. Mixing multi-channel audio requires techniques and training far beyond the knowledge of the average music software user. As a result, there is significant growth and development in intelligent signal processing for audio, an emergent field combining audio signal processing and machine learning for producing music. This work focuses on methods for modeling and analyzing multi-timbral musical instrument mixtures and performing automated processing techniques to improve audio quality based on quantitative and qualitative measures. The main contributions of the work involve training models to predict mixing parameters for multi-channel audio sources and developing new methods to model the component interactions of individual timbres to an overall mixture. Linear dynamical systems (LDS) are shown to be capable of learning the relative contributions of individual instruments to re-create a commercial recording based on acoustic features extracted directly from audio. Variations in the model topology are explored to make it applicable to a more diverse range of input sources and improve performance. An exploration of relevant features for modeling timbre and identifying instruments is performed. Using various basis decomposition techniques, audio examples are reconstructed and analyzed in a perceptual listening test to evaluate their ability to capture salient aspects of timbre. These tests show that a 2-D decomposition is able to capture much more perceptually relevant information with regard to the temporal evolution of the frequency spectrum of a set of audio examples. The results indicate that joint modeling of frequencies and their evolution is essential for capturing higher level concepts in audio that we desire to leverage in automated systems.Ph.D., Electrical Engineering -- Drexel University, 201