The Role of Emotional and Facial Expression in Synthesised Sign Language Avatars

This thesis explores the role that underlying emotional facial expressions might have in regards to understandability in sign language avatars. Focusing specifically on Irish Sign Language (ISL), we examine the Deaf community’s requirement for a visual-gestural language as well as some linguistic attributes of ISL which we consider fundamental to this research. Unlike spoken language, visual-gestural languages such as ISL have no standard written representation. Given this, we compare current methods of written representation for signed languages as we consider: which, if any, is the most suitable transcription method for the medical receptionist dialogue corpus. A growing body of work is emerging from the field of sign language avatar synthesis. These works are now at a point where they can benefit greatly from introducing methods currently used in the field of humanoid animation and, more specifically, the application of morphs to represent facial expression. The hypothesis underpinning this research is: augmenting an existing avatar (eSIGN) with various combinations of the 7 widely accepted universal emotions identified by Ekman (1999) to deliver underlying facial expressions, will make that avatar more human-like. This research accepts as true that this is a factor in improving usability and understandability for ISL users. Using human evaluation methods (Huenerfauth, et al., 2008) the research compares an augmented set of avatar utterances against a baseline set with regards to 2 key areas: comprehension and naturalness of facial configuration. We outline our approach to the evaluation including our choice of ISL participants, interview environment, and evaluation methodology. Remarkably, the results of this manual evaluation show that there was very little difference between the comprehension scores of the baseline avatars and those augmented withEFEs. However, after comparing the comprehension results for the synthetic human avatar “Anna” against the caricature type avatar “Luna”, the synthetic human avatar Anna was the clear winner. The qualitative feedback allowed us an insight into why comprehension scores were not higher in each avatar and we feel that this feedback will be invaluable to the research community in the future development of sign language avatars. Other questions asked in the evaluation focused on sign language avatar technology in a more general manner. Significantly, participant feedback in regard to these questions indicates a rise in the level of literacy amongst Deaf adults as a result of mobile technology

Integrierte Konzepte tiefer Neuronaler Netze zur monokularen Informationsgewinnung im Autonomen Fahrzeug

Tiefe Neuronale Netze im Allgemeinen und Convolutional Neural Networks (CNNs) im Speziellen konnten in den letzten Jahren in den Bereichen Maschinelles Sehen und Bildverarbeitung in verschiedensten Domänen große Erfolge erzielen. Herausforderungen bestehen dabei vor allem in der Anpassung der Methoden an domänenspezifische Tasks und die Reduktion des im Normalfall recht hohen Ressourcenbedarfs, insbesondere für mobile Anwendungen wie das Autonome Fahren. Dort werden CNNs zur Bildverarbeitung meist für die Gewinnung relevanter Informationen über die Umgebung aus monokularen Kameras verwendet. Hierzu werden auf einem Kamerabild verschiedene Algorithmen zur Lösung unterschiedlicher Tasks ausgeführt. Im Rahmen dieser Arbeit wird mit dem MultiNet-Ansatz ein Konzept zur integrierten Bearbeitung verschiedener Tasks in einem gemeinsamen CNN-Modell vorgestellt. Der Ressourcenbedarf kann so im Vergleich zu einer getrennten Ausführung separater Modelle bei gleichbleibender Qualität der Ergebnisse deutlich reduziert werden. Zusätzlich wird ein Verfahren vorgestellt, welches bei ebenfalls gleichbleibender Ergebnisqualität durch eine Kombination der angepassten Methoden Pruning und Knowledge Distillation den Ressourcenverbrauch eines CNN-Modells signifikant reduzieren kann. Für die Domäne des Autonomen Fahrens werden CNN-Architekturen zur allgemeinen Objektdetektion an die Anforderungen der domänenspezifischen Detektion von Objekten im Fahrzeugumfeld angepasst. Hierbei findet eine getrennte Betrachtung von statischen und dynamischen Verkehrsobjekten statt. Zur Lösung der Herausforderungen bei der Detektion von statischen Verkehrsobjekten wie Ampeln oder Verkehrsschildern wird ein Ansatz zur hierarchischen Detektion gleichartiger Objekte vorgestellt. Für die Detektion von dynamischen Verkehrsobjekten wie anderen Verkehrsteilnehmern wird ein Ansatz zur direkten 3D Detektion von Objekten in einem Kamerabild eingeführt. Derartige Ansätze offenbaren durch ihre Komplexität allerdings eine Herausforderung beim Training vieler CNN-Modelle. Der Fortschritt und die Konvergenz des Trainingsprozesses werden in wesentlichen Teilen durch die Zusammensetzung der verwendeten Trainingsdaten bedingt. Um den Trainingsprozess mit den jeweils verfügbaren Trainingsdaten bestmöglich durchführen zu können, wird eine dynamische Fehlerfunktion vorgestellt, welche die einzelnen Datenpunkte angepasst an deren aktuelle Schwierigkeit im Trainingsprozess automatisch gewichtet. Eine Evaluation auf öffentlich verfügbaren Datensätzen wird für alle im Rahmen dieser Arbeit vorgestellten Konzepte durchgeführt

Hardware implementations of computer-generated holography: a review

Computer-generated holography (CGH) is a technique to generate holographic interference patterns. One of the major issues related to computer hologram generation is the massive computational power required. Hardware accelerators are used to accelerate this process. Previous publications targeting hardware platforms lack performance comparisons between different architectures and do not provide enough information for the evaluation of the suitability of recent hardware platforms for CGH algorithms. We aim to address these limitations and present a comprehensive review of CGH-related hardware implementations

Touch Screen Operated Data Warehouse Application

This thesis was about real time tracking system. Although there have been many existing real-time tracking systems, they are not specific enough for the demands of this particular logistics tracking system. It is possible to integrate between these systems but it is not cost effective for the purpose of the project. The idea of this project is to create an interactive multi-touch based data ware-house application which is used to trace packages and projects from all over the world using GPS and GPRS technology. Simply, this project is used to answer the question “where is my package”. It is combined by two tracking systems: AIS system and GPS/GPRS system to increase reliability and efficiency. Through research into GPS/GPRS and AIS communications, the project describes the relevant characteristics of possible methods which are used in tracking sys-tems, the integration of those systems and some possible solutions for battery problem which have occured in most tracking systems because of high power consumption in the GPS/GPRS devices.Opinnäytetyö tarkastelee reaaliaikaista seurantajärjestelmää. Vaikka on ollut paljon nykyisiä reaaliaikaisen seurannan järjestelmiä, ne eivät ole riittävän täsmällisiä, jotta vaatimukset tässä logistiikan seurantajärjestelmä. On mahdollista yhdistää näiden järjestelmien välillä, mutta se ei ole kustannustehokasta hankkeen kannalta. Tämän projektin tarkoituksena on luoda vuorovaikutteisia multi-touch- ominaisuuteen tietoja toteutus-sovellus, jota käytetään jäljittämään paketteja ja hankkeita eri puolilta maailmaa GPS ja GPRS-tekniikalla. Yksinkertaisesti, tämä projekti on otettu käyttöön, jotta se vastaisi kysymykseen "missä on minun paketti". Siinä on yhdistetty kaksi seurantajärjestelmät: AIS-järjestelmä ja GPS / GPRS-järjestelmä, mikä parantaa luotettavuutta ja tehokkuutta. Tutkimalla GPS / GPRS-ja AIS viestintää, hankkeessa kuvataan keskeiset ominaisuudet mahdollisia menetelmiä, joita käytetään seurannan jär-jestelmät, integrointi näihin järjestelmiin ja joitakin mahdollisia ratkaisuja akun ongelma, joka on ollut olemassa useimmissa seurantajärjestelmissä johtuen suuresta tehonkulutuksesta GPS / GPRS-laitteissa

Cheating and Virtual Crime in Massively Multiplayer Online Games

Massively Multiplayer Online Games (MMOG) have become extremely popular since the birth of the Internet, with many millions of players playing games such as Poker and World of Warcraft. However, they do not seem to be well understood, and academic research into them has been limited. This project explains the nature of MMOG, and the relationship between MMOG and information security. This project discusses the problem of cheating in MMOG and it explains what cheating is, how it occurs, and how information security can be used to prevent it. The nature of virtual economies in MMOG is discussed, and the virtual crimes that have affected MMOG along with preventative measures are examined

A system to predict the S&P 500 using a bio-inspired algorithm

The goal of this research was to develop an algorithmic system capable of predicting the directional trend of the S&P 500 financial index. The approach I have taken was inspired by the biology of the human retina. Extensive research has been published attempting to predict different financial markets using historical data, testing on an in-sample and trend basis with many employing sophisticated mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, I moved to an out-of-sample strategy and am able to predict tomorrow’s (t+1) directional trend of the S&P 500 at 55.1%. The key elements that underpin my bio-inspired out-of-sample system are: Identification of 51 financial market data (FMD) inputs, including other indices, currency pairs, swap rates, that affect the 500 component companies of the S&P 500. The use of an extensive historical data set, comprising the actual daily closing prices of the chosen 51 FMD inputs and S&P 500. The ability to compute this large data set in a time frame of less than 24 hours. The data set was fed into a linear regression algorithm to determine the predicted value of tomorrow’s (t+1) S&P 500 closing price. This process was initially carried out in MatLab which proved the concept of my approach, but (3) above was not met. In order to successfully meet the requirement of handling such a large data set to complete the prediction target on time, I decided to adopt a novel graphics processing unit (GPU) based computational architecture. Through extensive optimisation of my GPU engine, I was able to achieve a sufficient speed up of 150x to meet (3). In achieving my optimum directional trend of 55.1%, an extensive range of tests exploring a number of trade offs were carried out using an 8 year data set. The results I have obtained will form the basis of a commercial investment fund. It should be noted that my algorithm uses financial data of the past 60-days, and as such would not be able to predict rapid market changes such as a stock market crash

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia