General Nathan F. Twining distinguished himself in leading the American Fifteenth Air Force during the last full year of World War II in the European Theatre. Drawing on the leadership qualities he had already shown in combat in the Pacific Theatre, he was the only USAAF leader who commanded three separate air forces during World War II. His command of the Fifteenth Air Force gave him his biggest, longest lasting, and most challenging experience of the war, which would be the foundation for the reputation that eventually would win him appointment to the nation's highest military post as Chairman of the Joint Chiefs of Staff during the Cold War.

In this article, I introduce a novel airborne network mobility model, called the Smooth Turn Mobility Model, that captures the correlation of acceleration for airborne vehicles across time and spatial coordinates. E?ective routing in airborne networks (ANs) relies on suitable mobility models that capture the random movement pattern of airborne vehicles. As airborne vehicles cannot make sharp turns as easily as ground vehicles do, the widely used mobility models for Mobile Ad Hoc Networks such as Random Waypoint and Random Direction models fail. Our model is realistic in capturing the tendency of airborne vehicles toward making straight trajectory and smooth turns with large radius, and whereas is simple enough for tractable connectivity analysis and routing design.

Managing large-scale dynamical systems (e.g., transportation systems, complex information systems, and power networks, etc.) in real-time is very challenging considering their complicated system dynamics, intricate network interactions, large scale, and especially the existence of various uncertainties. To address this issue, intelligent techniques which can quickly design decision-making strategies that are robust to uncertainties are needed. This dissertation aims to conquer these challenges by exploring a data-driven decision-making framework, which leverages big-data techniques and scalable uncertainty evaluation approaches to quickly solve optimal control problems. In particular, following techniques have been developed along this direction: 1) system modeling approaches to simplify the system analysis and design procedures for multiple applications; 2) effective simulation and analytical based approaches to efficiently evaluate system performance and design control strategies under uncertainty; and 3) big-data techniques that allow some computations of control strategies to be completed offline. These techniques and tools for analysis, design and control contribute to a wide range of applications including air traffic flow management, complex information systems, and airborne networks.

Non-rigid point set registration of significantly uneven deformations is a challenging problem for many applications such as pose estimation, three-dimensional object reconstruction, human movement tracking. In this dissertation, we present a novel probabilistic non-rigid registration method to align point sets with significantly uneven deformations by enforcing constraints from corresponding key points and preserving local neighborhood structures. The registration method is treated as a density estimation problem. Incorporating correspondence among key points regulates the optimization process for large, uneven deformations. In addition, by leveraging neighborhood embedding using Stochastic Neighbor Embedding (SNE) as well as an alternative means based on Locally Linear Embedding (LLE), our method penalizes the incoherent transformation and hence preserves the local structure of point sets. Also, our method detects key points in the point sets based on geodesic distance. Correspondences are established using a new cluster-based, region-aware feature descriptor. This feature descriptor encodes the association of a cluster to the left-right (symmetry) or upper-lower regions of the point sets. We conducted comparison studies using public point sets and our Human point sets. Our experimental results demonstrate that our proposed method successfully reduced the registration error by at least 42.2% in contrast to the state-of-the-art method. Especially, our method …

Significant research efforts have been devoted to large-scale dynamical systems, with the aim of understanding their complicated behaviors and managing their responses in real-time. One pivotal technological obstacle in this process is the existence of uncertainty. Although many of these large-scale dynamical systems function well in the design stage, they may easily fail when operating in realistic environment, where environmental uncertainties modulate system dynamics and complicate real-time predication and management tasks. This dissertation aims to develop systematic methodologies to evaluate the performance of large-scale dynamical systems under uncertainty, as a step toward real-time decision support. Two uncertainty evaluation approaches are pursued: the analytical approach and the effective simulation approach. The analytical approach abstracts the dynamics of original stochastic systems, and develops tractable analysis (e.g., jump-linear analysis) for the approximated systems. Despite the potential bias introduced in the approximation process, the analytical approach provides rich insights valuable for evaluating and managing the performance of large-scale dynamical systems under uncertainty. When a system’s complexity and scale are beyond tractable analysis, the effective simulation approach becomes very useful. The effective simulation approach aims to use a few smartly selected simulations to quickly evaluate a complex system’s statistical performance. This approach was originally developed …

The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable …

In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a downfall if the GPS stream is not collected or processed correctly. Inaccurate or incomplete or even improperly interpreted historical data can lead to the inability to develop accurately performing prediction algorithms. As GPS chipsets become the standard in the ever increasing number of mobile devices, the opportunity for the collection of GPS data increases remarkably. the goal of this study is to build a comprehensive system that addresses the following challenges: (1) collection of GPS data streams in a manner such that the data is highly usable and has a reduction in errors; (2) processing and reduction of the collected data in order to prepare it and …