A 3D-model consists out of triangles, and in many cases, the amount of triangles are unnecessarily large for the application of the model. If the camera is far away from a model, why should all triangles be there when in reality it would make sense to only show the contour of the model? Mesh decimation is often used to solve this problem, and its goal is to minimize the amount of triangles while still keep the visual representation intact. Having the decimation algorithm being structure aware, i.e. having the algorithm aware of where the important parts of the model are, such as corners, is of great benefit when doing extreme simplification. The algorithm can then decimate large, almost planar parts, to only a few triangles while keeping the important features detailed. This thesis aims to describe the development of a structure aware decimation algorithm for the company Spotscale, a company specialized in creating 3D-models of drone footage.

Degree awarded (2004): DScCS, Computer Science, George Washington University / A common practice to save power and energy in embedded systems is to "put to sleep" or disable parts of the hardware. The memory system consumes a significant portion of the energy budget of the overall system, so it is a natural target for energy optimization techniques. The principle of software locality makes the memory subsystem an even better choice, since all memory blocks but the ones immediately required can be disabled at any given time. This opportunity is the motivation for developing energy optimization techniques to dynamically and selectively control the power state of the different parts of the memory system. This dissertation develops a set of algorithms and techniques that can be organized into a hardware/software co-development tool to help designers apply the selective powering of memory blocks to minimize energy consumption. In data driven embedded systems, most of the data memory is used either by global static variables or by dynamic variables. Although techniques already exist for energy-aware allocation of global static arrays under certain constraints, very little work has focused on dynamic variables, which are actually more important to event driven/data driven embedded systems than their static counterparts. This dissertation addresses this gap, and extends and consolidates previous allocation techniques in a unique framework. A formal model for memory energy optimization for dynamic and global static variables and efficient algorithms for energy aware allocation of variables to memory are presented. Dependencies between generic code and data are uncovered, and this information is exploited to fine-tune a system. A framework is presented for retrieving this profile information which is then used to design energy aware allocation algorithms for dynamic variables, including heuristics for segmentation and control of the memory heap. By working at the assembly code level, these techniques can be integrated into any compiler regardless of the source language. The proposed techniques were implemented and tested against data intensive benchmarks, and experimental results indicate significant savings of up to 50% in the memory system energy consumption. / Advisory Committee: Professor Bhagirath Narahari, Professor Hyoeong-Ah Choi (Chair), Professor Rahul Simha, Professor Shmuel Rotenstreich, Professor Can E. Korman, Dr. Yul Williams

Thesis (Ph.D.) - University of Glasgow, 2008. / Ph.D. thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Computing Science, University of Glasgow, 2008. Includes bibliographical references. Print version also available.

As ubiquitous computing (ubicomp) technologies reach maturity, smart phones and context-based services are gaining mainstream popularity. A smart phone accompanies its user throughout (nearly) all aspects of his life, becoming an indispensable assistant the busy user relies on to help navigate his life, using map applications to navigate the physical world, email and instant messaging applications to keep in touch, media player applications to be entertained, etc. As a smart phone is capable of sensing the physical and virtual context of the user with an array of “hard” sensors (e.g., GPS, accelerometer) and “soft” sensors (e.g., email, social network, calendar),it is well-equipped to tailor the assistance it provides to the user. Over the life of a smart phone, it is entrusted with an enormous amount of personal information, everything from context-information sensed by the phone to contact lists to call-logs to passwords. Based on this rich set of information it is possible to model the behavior of the user, and use the models to detect anomalies (i.e., significant variations) in the user’s behavior. Anomaly detection capabilities enable a variety of application domains such as device theft detection, improved authentication mechanisms, impersonation, prevention, physical emergency detection, remote elder-care monitoring, and other proactive services. There has been extensive prior research on anomaly detection in various application domain areas (e.g., fraud detection, intrusion detection). Yet these approaches cannot be used in ubicomp environments as 1) they are very application-specific and not versatile enough to learn complex day to day behavior of users, 2) they work with a very small number of information sources with a relatively uniform stream of information (unlike sensor data from mobile devices), and 3) most approaches require labeled or semi-labeled data about anomalies (in ubicomp environments, it is very costly to create labeled datasets). Existing work in the field of anomaly detection in ubicomp environments is quite sparse. Most of the existing work focuses on using a single sensor information stream (GPS in most cases) to detect anomalies in the user’s behavior. However there exists a somewhat richer vein of prior work in modeling user behavior with the goal of behavior prediction; this is again limited mostly to a single sensor stream or single type of prediction (mostly location). This dissertation presents the notion of modeling mobile user behavior as a collection of models each capturing an aspect of the user’s behavior such as indoor mobility, typing patterns, calling patterns. A novel mechanism is developed for combining these models (i.e.,CobLE), which operate on asynchronous information sources from the mobile device, taking into consider how well each model is estimated to perform in the current context. These ideas are concretely implemented in an extensible framework, McFAD. Evaluations carried out using real-world datasets on this framework in contrast to prior work show that the framework for detecting anomalous behavior, 1) vastly reduces the training data requirement, 2) increases coverage, and 3) dramatically increases performance.

GoCity is designed to provide city visitors with up-to-date and context-aware information while they are exploring a city using Android mobile phones. This thesis not only introduces the design and analysis of GoCity, but also discusses four problems in leveraging three concepts—context-awareness, self-adaptation, and usability—in current mobile application design. First, few contexts other than location and time have been used in actual mobile applications. Second, there is no clear classification of context information for mobile application design. Third, mobile application designers lack systematic mechanisms to address sensing and monitoring requirements under changing context situations. This is crucial for effective self-adaptation. Fourth, most mobile applications have low usability due to poor user interface (UI) design. The model proposed in this thesis addresses these issues by (i) supporting diverse context dimensions, (ii) monitoring context changes continuously and tailoring the application behavior according to these changes, and (iii) improving UI design using selected usability methods. In addition, this thesis proposes two classifications of context information for mobile applications: source-based classification—personal context, mobile device context, and environmental context; and property-based classification—static context and dynamic context. The combination of these two classifications helps determine the observed context and its polling rate—the rate at which the context is collected—effectively. A distinctive feature of GoCity is that it supports two interaction modes—static mode and dynamic mode. In static mode, the application generates results only after the user sends the request to it. In other words, it does not actively generate results for users. In contrast, in the dynamic mode, the application continuously updates results even if the user does not send any request to it. The notion of an autonomic element (AE) is used for the dynamic mode to make GoCity self-adaptive. The polling rates on different contexts are also handled differently in the dynamic mode because of the differences among context properties. In addition, GoCity is composed of, but not limited to, four sub-applications. Each sub-application employs a variety of context information and can be implemented as an independent mobile application. Regarding usability, GoCity focuses on providing a simple and clear user interface as well as supporting user expectations for personalization. An experiment which involves a person visiting the city of Victoria was conducted to evaluate GoCity. In this evaluation, three determining factors of usability were employed to qualitatively and quantitatively assess GoCity. In addition, the static mode and dynamic mode were evaluated separately. / Graduate

This thesis presents the design and implementation of I-Shop, a context-aware, shopping smartphone application designed to provide shoppers with relevant advertisements for product and services available in close proximity. We argue that current context-aware mobile applications exhibit significant limitations in the following domains: (1) use of context, (2) invasion of privacy, (3) spam management, and (4) platform dependency. The proposed context model attempts to tackle these shortcomings by exploiting available contextual information from social media networks such as Facebook. Our goal is to use a user’s personal information, such as their native language and personal interests, to direct the most relevant advertisements to them. To alleviate any privacy issues, a user’s personal information is never sent out to any back-end services and only apply the filters locally. In addition, unlike most other predictive approaches that track the user’s location history, we follow a reactive approach which triggers only when the user is close to a shopping area. When a user arrives to a particular shopping area, the application asks whether she wishes to view any advertisements of local products and services. Upon approval, the application retrieves deals on products including services sorted by domain from databases, such as Groupon and our custom extended deals database. Finally, the application filters the retrieved data according to personal interests and then displays the results. As a proof of concept, we designed and implemented the I-Shop prototype application. We built I-Shop as a hybrid application using IBM’s state-of-the-art Worklight infrastructure. This approach lets developers optimize their time and effort; enabling a “write once, deploy everywhere” development model that not only reduces development costs but also increases application performance by providing a combination of native and web capabilities. In addition, I-Shop also leverages several features offered by the IBM Worklight infrastructure including cross-platform support, direct update, internalization, and integration of third-party libraries and toolkits. / Graduate

The thesis will explore the idea of feeling Japanese spirituality of Shinto through a contemporary work of art, the animated film My Neighbour Totoro (1988). The idea of a felt spirituality revolves around Shinto’s notion of kami, divine entities whose existence becomes manifest through one’s feeling and perception to awe-inspiring things of the natural world and the aesthetic notion of aware, an immediate felt emotional response that coincides as the response/reaction when coming into contact with awe-inspiring things. This thesis conceives aware to be the meeting point in which the human and kami world converge, a Shinto concept known as shinjin-g itsu, or the meeting of the human spirit with kami. This thesis will uncover themes of Shinto spirituality through a close reading of the functionality of specific components of the film: music, setting, characters, character interactions, and symbolism. Themes such as nature, community, symbolism and the role of aesthetics within the film will be discussed to showcase the idea of a spiritual encounter. It is a spiritual encounter/meeting that is facilitated through the aesthetics and components of the film which elicits a response of aware from the viewer. / Graduate / 0322 / 0332 / 0900 / scarbul@gmail.com

Mobile phones are becoming a primary platform for information access. More and more people use their mobile devices as one of their major communication access tools. Commuters are increasingly carrying their mobile devices with them almost everywhere. Mobile devices fit perfectly into an ideal environment for realizing ubiquitous computing. A major aspect of ubiquitous computing is context-aware applications where the applications collect information about the environment that the user is in and use this information to achieve their goals or improve performance. The location of the device is one of the most important pieces of context information. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes, and greatly improves the performance of other applications, for example it can be associated with automobile navigation devices. A feature available to mobile applications in the Android platform makes it possible to determine a device's location without any additional hardware or sensor mechanisms, by simply using the native capability of the built-in wireless network card. Since the release of Android systems, there have been numerous applications developed to introduce new ways of tracking locations. Recently, there have been many papers on location estimation leveraging ubiquitous wireless networks. In this thesis, we develop an Android application to collect useful Wi-Fi information without registering a location listener with a network-based provider, such as Wi-Fi connections or data connections. Therefore it provides a passive, privacy-preserving, non-intrusive and power-saving way of achieving location awareness to Android mobile users. Accurate estimation of the location information can bring a more contextual experience to mobile users. We save the passively collected data of the IDs of Wi-Fi access points and the received signal strengths to a database in order to help us structure the data and analyse it. We employ some heuristics to infer the location information from the data. Our work presents a location tracking technique mainly based on Basic Service Set identification (BSSID) and/or Received Signal Strength Indicator (RSSI) using Wi-Fi information. It falls into one of the most active fields in mobile application development --location-based or location-aware applications.

Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 135-151) Also available in print.

Thesis--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references (p.63-67).