Acoustic Modelling for Under-Resourced Languages

Automatic speech recognition systems have so far been developed only for very few languages out of the 4,000-7,000 existing ones. In this thesis we examine methods to rapidly create acoustic models in new, possibly under-resourced languages, in a time and cost effective manner. For this we examine the use of multilingual models, the application of articulatory features across languages, and the automatic discovery of word-like units in unwritten languages

Design and Optimization of Mobile Cloud Computing Systems with Networked Virtual Platforms

A Mobile Cloud Computing (MCC) system is a cloud-based system that is accessed by the users through their own mobile devices. MCC systems are emerging as the product of two technology trends: 1) the migration of personal computing from desktop to mobile devices and 2) the growing integration of large-scale computing environments into cloud systems. Designers are developing a variety of new mobile cloud computing systems. Each of these systems is developed with different goals and under the influence of different design constraints, such as high network latency or limited energy supply. The current MCC systems rely heavily on Computation Offloading, which however incurs new problems such as scalability of the cloud, privacy concerns due to storing personal information on the cloud, and high energy consumption on the cloud data centers. In this dissertation, I address these problems by exploring different options in the distribution of computation across different computing nodes in MCC systems. My thesis is that "the use of design and simulation tools optimized for design space exploration of the MCC systems is the key to optimize the distribution of computation in MCC." For a quantitative analysis of mobile cloud computing systems through design space exploration, I have developed netShip, the first generation of an innovative design and simulation tool, that offers large scalability and heterogeneity support. With this tool system designers and software programmers can efficiently develop, optimize, and validate large-scale, heterogeneous MCC systems. I have enhanced netShip to support the development of ever-evolving MCC applications with a variety of emerging needs including the fast simulation of new devices, e.g., Internet-of-Things devices, and accelerators, e.g., mobile GPUs. Leveraging netShip, I developed three new MCC systems where I applied three variations of a new computation distributing technique, called Reverse Offloading. By more actively leveraging the computational power on mobile devices, the MCC systems can reduce the total execution times, the burden of concentrated computations on the cloud, and the privacy concerns about storing personal information available in the cloud. This approach also creates opportunities for new services by utilizing the information available on the mobile device instead of accessing the cloud. Throughout my research I have enabled the design optimization of mobile applications and cloud-computing platforms. In particular, my design tool for MCC systems becomes a vehicle to optimize not only the performance but also the energy dissipation, an aspect of critical importance for any computing system

In Car Audio

This chapter presents implementations of advanced in Car Audio Applications. The system is composed by three main different applications regarding the In Car listening and communication experience. Starting from a high level description of the algorithms, several implementations on different levels of hardware abstraction are presented, along with empirical results on both the design process undergone and the performance results achieved

Epibenthic and mobile species colonisation of a geotextile artificial surf reef on the south coast of England

With increasing coastal infrastructure and use of novel materials there is a need to investigate the colonisation of assemblages associated with new structures, how these differ to natural and other artificial habitats and their potential impact on regional biodiversity. The colonisation of Europe’s first artificial surf reef (ASR) was investigated at Boscombe on the south coast of England (2009–2014) and compared with assemblages on existing natural and artificial habitats. The ASR consists of geotextile bags filled with sand located 220m offshore on a sandy sea bed at a depth of 0-5m. Successional changes in epibiota were recorded annually on differently orientated surfaces and depths using SCUBA diving and photography. Mobile faunal assemblages were sampled using Baited Remote Underwater Video (BRUV). Distinct stages in colonisation were observed, commencing with bryozoans and green algae which were replaced by red algae, hydroids and ascidians, however there were significant differences in assemblage structure with depth and orientation. The reef is being utilised by migratory, spawning and juvenile life-history stages of fish and invertebrates. The number of non-native species was larger than on natural reefs and other artificial habitats and some occupied a significant proportion of the structure. The accumulation of 180 benthic and mobile taxa, recorded to date, appears to have arisen from a locally rich and mixed pool of native and non-native species. Provided no negative invasive impacts are detected on nearby protected reefs the creation of novel yet diverse habitats may be considered a beneficial outcome

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research

System Design for Intelligent Web Services

The devices and software systems we interact with on a daily basis are more intelligent than ever. The computing required to deliver these experiences for end-users is hosted in Warehouse Scale Computers (WSC) where intelligent web services are employed to process user images, speech, and text. These intelligent web services are emerging as one of the fastest growing class of web services. Given the expectation of users moving forward is an experience that uses intelligent web services, the demand for this type of processing is only going to increase. However, today’s cloud infrastructures, tuned for traditional workloads such as Web Search and social networks, are not adequately equipped to sustain this increase in demand. This dissertation shows that applications that use intelligent web service processing on the path of a single query require orders of magnitude more computational resources than traditional Web Search. Intelligent web services use large pretrained machine learning models to process image, speech, and text based inputs and generate a prediction. As this dissertation investigates, we find that hosting intelligent web services in today’s infrastructures exposes three critical problems: 1) current infrastructures are computationally inadequate to host this new class of services, 2) system designers are unaware of the bottlenecks exposed by these services and the implications on future designs, 3) the rapid algorithmic churn of these intelligent services deprecates current designs at an even faster rate. This dissertation investigates and addresses each of these problems. After building a representative workload to show the computational resources required by an application composed of three intelligent web services, this dissertation first argues that hardware acceleration is required on the path of a query to sustain demand moving forward. We show that GPU- and FPGA-accelerated servers can improve the query latency on average by 10x and 16x. Leveraging the latency reduction, GPU- and FPGA-accelerated servers reduce the Total Cost of Ownership (TCO) by 2.6x and 1.4x, respectively. Second, we focus on Deep Neural Networks (DNN), a state-of-the- art algorithm for intelligent web services and design a DNN-as-a-Service infrastructure enabling application-agnostic acceleration and single-point of optimization. We identify compute bottlenecks that inform the design of a Graphics Processing Unit (GPU) based system; addressing the compute bottlenecks translates to a throughput improvement of 133x across seven DNN based applications. GPU-enabled datacenters show a TCO improvement over CPU-only designs by 4-20x. Finally, we design a runtime system based on a GPU equipped server that improves current systems accounting for recent advances in intelligent web service algorithms. Specifically, we identify asynchronous processing key for accelerating dynamically configured in- telligent services. We achieve on average 7.6x throughput improvements over an optimized CPU baseline and 2.8x over the current GPU system. By thoroughly addressing these problems, we produce designs for WSCs that are equipped to handle the future demand for intelligent web services. The investigations in this thesis address significant computational bottlenecks and lead to system designs that are more efficient and cost-effective for this new class of web services.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137055/1/jahausw_1.pd

PDA Interface for Humanoid Robots

To fulfill a need for natural, user-friendly means of interacting and reprogramming toy and humanoid robots, a growing trend of robotics research investigates the integration of methods for gesture recognition and natural speech processing. Unfortunately, efficient methods for speech and vision processing remain computationally expensive and, thus, cannot be easily exploited on cost- and size-limited platforms. Personal Digital Assistants (PDAs) are ideal low-cost platforms to provide simple speech and vision-based communication for a robot. This paper investigates the use of Personal Digital Assistant (PDA) interfaces to provide multi-modal means of interacting with humanoid robots. We present PDA applications in which the robot can track and imitate the user's arm and head motions, and can learn a simple vocabulary to label objects and actions by associating the user's verbal utterance with the user's gestures. The PDA applications are tested on two humanoid platforms: a mini doll-shaped robot, Robota, used as an educational toy with children, and DB, a full body 30 degrees of freedom humanoid robot