For maximal market penetration, popular mobile applications are typically supported on all major platforms, including Android and iOS. Despite the vast differences in the look-and-feel of major mobile platforms, applications running on these platforms in essence provide the same core functionality. As an application is maintained and evolved, programmers need to replicate the resulting changes on all the supported platforms, a tedious and error-prone programming process. Commercial automated source-to-source translation tools prove inadequate due to the structural and idiomatic differences in how functionalities are expressed across major platforms. In this thesis, we present a new approach---Native-2-Native---that automatically synthesizes code for a mobile application to make use of native resources on one platform, based on the equivalent program transformations performed on another platform. First, the programmer modifies a mobile application's Android version to make use of some native resource, with a plugin capturing code changes. Based on the changes, the system then parameterizes a web search query over popular programming resources (e.g., Google Code, StackOverflow, etc.), to discover equivalent iOS code blocks with the closest similarity to the programmer-written Android code. The discovered iOS code block is then presented to the programmer as an automatically synthesized Swift source file to further fine-tune and subsequently integrate in the mobile application's iOS version. Our evaluation, enhancing mobile applications to make use of common native resources, shows that the presented approach can correctly synthesize more than 86% of Swift code for the subject applications' iOS versions. / Master of Science

No description available.

No description available.

Computing is entering an era of hundreds to thousands of processing elements per chip, yet no known parallelism form scales to that degree. To address this problem, we investigate the foundation of a computer architecture where processing elements and memory are contextually partitioned based upon facets of a user's life. Such Contextual Partitioning (CP), the situational handling of inputs, employs a method for allocating resources, novel from approaches used in today's architectures. Instead of focusing components on mutually exclusive parts of a task, as in Thread Level Parallelism, CP assigns different physical components to different versions of the same task, defining versions by contextual distinctions in device usage. Thus, application data is processed differently based on the situation of the user. Further, partitions may be user specific, leading to personalized architectures. Our focus is mobile devices, which are, or can be, personalized to one owner. Our investigation is centered on leveraging CP for accurate and real-time speech recognition on mobile devices, scalable to large vocabularies, a highly desired application for future user interfaces. By contextually partitioning a vocabulary, training partitions as separate acoustic models with SPHINX, we demonstrate a maximum error reduction of 61% compared to a unified approach. CP also allows for systems robust to changes in vocabulary, requiring up to 97% less training when updating old vocabulary entries with new words, and incurring fewer errors from the replacement. Finally, CP has the potential to scale nearly linearly with increasing core counts, offering architectures effective with future processor designs. / Master of Science

Zugl.: Saarbrücken, Univ., Diss., 2006

Thesis (doctoral) - Univ., Saarbrücken, 2006. / Includes bibliographical references and index.

<p dir="ltr">Mobile vision is becoming the norm, transforming our daily lives. It powers numerous applications, enabling seamless interactions between the digital and physical worlds, such as augmented reality, real-time object detection, and many others. The popularity of mobile vision has spurred advancements from both computer vision (CV) and mobile edge computing (MEC) communities. The former focuses on improving analytics accuracy through the use of proper deep neural networks (DNNs), while the latter addresses the resource limitations of mobile environments by coordinating tasks between mobile and edge devices, determining which data to transmit and process to enable real-time performance. </p><p dir="ltr"> Despite recent advancements, existing approaches typically integrate the functionalities of the two camps at a basic task level. They rely on a uniform on-device processing scheme that streams the same type of data and uses the same DNN model for identical CV tasks, regardless of the analytical complexity of the current input, input size, or latency requirements. This lack of adaptability to dynamic contexts limits their ability to achieve optimal efficiency in scenarios involving diverse source data, varying computational resources, and differing application requirements. </p><p dir="ltr">Our approach seeks to move beyond task-level adaptation by emphasizing customized optimizations tailored to dynamic use scenarios. This involves three key adaptive strategies: dynamically compressing source data based on contextual information, selecting the appropriate computing model (e.g., DNN or sub-DNN) for the vision task, and establishing a feedback mechanism for context-aware runtime tuning. Additionally, for scenarios involving movable cameras, the feedback mechanism guides the data capture process to further enhance performance. These innovations are explored across three use cases categorized by the capture device: one stationary camera, one moving camera, and cross-camera analytics. </p><p dir="ltr">My dissertation begins with a stationary camera scenario, where we improve efficiency by adapting to the use context on both the device and edge sides. On the device side, we explore a broader compression space and implement adaptive compression based on data context. Specifically, we leverage changes in confidence scores as feedback to guide on-device compression, progressively reducing data volume while preserving the accuracy of visual analytics. On the edge side, instead of training a specialized DNN for each deployment scenario, we adaptively select the best-fit sub-network for the given context. A shallow sub-network is used to “test the waters”, accelerating the search for a deep sub-network that maximizes analytical accuracy while meeting latency requirements.</p><p dir="ltr"> Next, we explore scenarios involving a moving camera, such as those mounted on drones. These introduce new challenges, including increased data encoding demands due to camera movement and degraded analytics performance (e.g., tracking) caused by changing perspectives. To address these issues, we leverage drone-specific domain knowledge to optimize compression for object detection by applying global motion compensation and assigning different resolutions at a tile-granularity level based on the far-near effect. Furthermore, we tackle the more complex task of object tracking and following, where the analytics results directly influence the drone’s navigation. To enable effective target following with minimal processing overhead, we design an adaptive frame rate tracking mechanism that dynamically adjusts based on changing contexts.</p><p dir="ltr"> Last but not least, we extend the work to cross-camera analytics, focusing on coordination between one stationary ground-based camera and one moving aerial camera. The primary challenge lies in addressing significant misalignments (e.g., scale, rotation, and lighting variations) between the two perspectives. To overcome these issues, we propose a multi-exit matching mechanism that prioritizes local feature matching while incorporating global features and additional cues, such as color and location, to refine matches as needed. This approach ensures accurate identification of the same target across viewpoints while minimizing computational overhead by dynamically adapting to the complexity of the matching task. </p><p dir="ltr">While the current work primarily addresses ideal conditions, assuming favorable weather, optimal lighting, and reliable network performance, it establishes a solid foundation for future innovations in adaptive video processing under more challenging conditions. Future efforts will focus on enhancing robustness against adversarial factors, such as sensing data drift and transmission losses. Additionally, we plan to explore multi-camera coordination and multimodal data integration, leveraging the growing potential of large language models to further advance this field.</p>

Thesis (MComm)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: The consumerisation of mobile technology is driving the mobile revolution and enterprises are forced to incorporate mobile solutions into their business processes in order to remain competitive. While there are many benefits relating to the investment in and use of mobile technology, significant risks are also being introduced into the business. The fast pace of technological innovation and the rate of adoption of mobile technology by employees has, however, created an environment where enterprises are deploying mobile solutions on an ad hoc basis. Enterprises are only addressing the risks as they are occurring and resulting in losses. The key contributing factor to this lack of governance and management is the fact that those charged with governance do not understand the underlying mobile technology components. The purpose of this research is to improve the understanding of the underlying components of mobile technology. The research further proposes to use this understanding to identify the significant risks related to mobile technology and to formulate appropriate internal controls to address these risks. The findings of the research identified the following underlying components of mobile technology: mobile devices; mobile infrastructure, data delivery mechanisms and enabling technologies; and mobile applications. Based on an understanding of the components and subcategories of mobile technology, a control framework was used to identify the significant risks related to each component and subcategory. The significant risks identified included both risks to the users (including interoperability, user experience, connectivity and IT support) as well as risks to the enterprise’s strategies (including continuity, security, cost and data ownership). The research concludes by formulating internal controls that the enterprise can implement to mitigate the significant risks. This resulted in two matrixes that serve as quick-reference guides to enterprises in the identification of significant risks at an enterprise specific mobile technology component level, as well as the relevant internal controls to consider. The matrixes also assist enterprises in determining the best mobile solutions to deploy in their business, given their strategies, risk evaluation and control environment. / AFRIKAANSE OPSOMMING: Die mobiele revolusie word deur die verbruiker van mobiele tegnologie aangedryf en, ten einde kompeterend te bly, word ondernemings gedwing om mobiele tegnologie in hul besigheidsprosesse te implementeer. Terwyl daar baie voordele verbonde is aan die investering in en gebruik van mobiele tegnologie, word die besigheid egter ook blootgestel aan wesenlike risiko’s. Die vinnige tempo waarteen mobiele tegnologie ontwikkel en deur werknemers aangeneem word, het egter ʼn omgewing geskep waarin ondernemings mobiele tegnologie op ʼn ad hoc basis ontplooi. Besighede spreek eers die risiko’s aan nadat dit reeds voorgekom het en verliese as gevolg gehad het. Die hoof bydraende faktor tot die tekort aan beheer en bestuur van mobiele tegnologie is die feit dat diegene verantwoordelik vir beheer, nie onderliggend mobiele tegnologie komponente verstaan nie. Die doel van hierdie navorsing is om die begrip van die onderliggende komponente van mobiele tegnologie te verbeter. Die navorsing poog verder om die wesenlike risiko’s verbonde aan mobiele tegnologie te identifiseer en om toepaslike interne beheermaatreëls te formuleer wat die risiko’s sal aanspreek. Die bevindinge van die navorsing het die volgende onderliggende komponente van mobiele tegnologie geïdentifiseer: mobiele toestelle; mobiele infrastruktuur, data afleweringsmeganismes, en bemagtigende tegnologieë; en mobiele toepassings. Gebaseer op ʼn begrip van die komponente en subkategorieë van mobiele tegnologie, is ʼn kontrole raamwerk gebruik om die wesenlike risiko’s verbonde aan elke komponent en subkategorie van die tegnologie, te identifiseer. Die wesenlike risiko’s sluit beide risiko’s vir die gebruiker (insluitend kontinuïteit, gebruikerservaring, konnektiwiteit en IT ondersteuning) sowel as risiko’s vir die onderneming se strategieë (insluitend kontinuïteit, sekuriteit, koste en data eienaarskap) in. Die navorsing sluit af met die formulering van die beheermaatreëls wat geïmplementeer kan word om die wesenlike risiko’s aan te spreek. Dit het gelei tot twee tabelle wat as vinnige verwysingsraamwerke deur ondernemings gebruik kan word in die identifisering van wesenlike risiko’s op ʼn onderneming-spesifieke tegnologie komponentvlak asook die oorweging van relevante interne beheermaatreëls. Die tabelle help ondernemings ook om die beste mobiele tegnologie vir hul besigheid te implementeer, gebaseer op hul strategie, risiko evaluering en beheeromgewing.

Aus der Einleitung: "Auf der Hannovermesse 2011 wurde zum ersten Mal der Begriff "Industrie 4.0" der Öffentlichkeit bekannt gemacht. Die Akademie der Technikwissenschaften hat in einer Arbeitsgruppe diese Grundidee der vierten Revolution der Industrieproduktion weiterbearbeitet und 2013 in einem Abschlussbericht mit dem Titel „Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0“ veröffentlicht (BmBF, 2013). Die Grundidee besteht darin, wandlungsfähige und effiziente Fabriken unter Nutzung moderner Informationstechnologie zu entwickeln. Basistechnologien für die Umsetzung der intelligenten Fabriken sind: — Cyber-Physical Systems (CPS) — Internet of Things (IoT) und Internet of Services (IoS) — Big Data Analytics and Prediction — Social Media — Mobile Computing Der Abschlussbericht fokussiert den Wertschöpfungsschritt der Produktion, während die Fragen der Produktentwicklung weitgehend unberücksichtigt geblieben sind. Die intelligente Fabrik zur Herstellung intelligenter Produkte setzt aber auch die Weiterentwicklung der Produktentwicklungsmethoden voraus. Auch hier gibt es einen großen Handlungsbedarf, der sehr stark mit den Methoden des „Modellbasierten Systems-Engineering“ einhergeht. ..."

Zugl.: Zürich, Techn. Hochsch., Diss.