The nature and complexity of the security threats faced by our society in recent years have made it clear that a smart pervasive surveillance system constitutes the most effective cure, as it presents a conducive framework for seamless interaction between preventative capabilities and investigative protocols. Applications such as wild-life preserve monitoring, natural disaster warnings, and facility surveillance tend to be characterized by large and remote geographic areas, requiring large numbers of unattended sensor nodes to cover the volume-of-interest. Such large unattended sensor networks add new challenges as well as complicate the system management problem. Such challenges can be in the form of distributed operation with collaborative decision making, adaptive performance, and energy-aware strategies, to name a few. To meet the challenges of these mission-critical applications, the sensor system must exhibit capabilities such as heterogeneous and self-organized behaviour, data and information fusion, and collaborative resources control and management. Sensor Management (SM) refers to the process that plans and controls the use of the sensor nodes in a manner that synergistically maximizes the success rate of the whole system in achieving the goals of its mission in assessing the situation in a timely, reliable, and accurate fashion. Managing heterogeneous sensors involves making decisions and compromises regarding alternate sensing strategies under time and resource availability constraints. As a result, the performance of the collective sensors dictates the performance of the entire system. Consequently, there is a need for an intelligent Sensor Management Framework (SMF) to drive the system performance. SMF provides a control system to manage and coordinate the use of sensing resources in a manner that maximizes the system success rate in achieving its goals. An SMF must handle an overwhelming amount of information collected, and adapt to the highly dynamic environments, in addition to network and system limitations. This thesis proposes a resource-aware and intelligent SMF for managing pervasive sensor systems in surveillance context. The proposed SMF considerably improves the process of information acquisition by coordinating the sensing resources in order to gather the most reliable data from a dynamic scene while operating under energy constraints. The proposed SMF addresses both the operation of the coordination paradigm, as well as, the local and collaborative decision making strategies. A conceptual analysis of the SM problem in a layered structure is discussed to introduce an open and flexible design framework based on the service-oriented architecture to provide a modular, reusable, and extendable framework for the proposed SMF solution. A novel sensor management architecture, called Extended Hybrid Architecture for Sensor Management (E-HASM), is proposed. E-HASM combines the operation of the holonic, federated, and market-based architectures in a complementary manner. Moreover, a team-theoretic formulation of Belief-Desire-Intention (BDI), that represent the E-HASM components, is proposed as a mechanism for effective energy-aware decision making to address the local sensor utility. Also, intelligent schemes that provide adaptive sensor operation to the changes in environment dynamics and sensor energy levels are designed to include adaptive sleep, active sensing, dynamic sensing range, adaptive multimodality, and constrained communication. Furthermore, surveillance systems usually operate under uncertainty in stochastic environment. Therefore, this research formulates the collaborative decision-making entities as Partially Observable Markov Decision Processes (POMDP). To increase the tracking quality and the level of the information reliability, cooperation between the sensors is adopted, which adds an extra dimension in the design of the proposed SMFs. The propose SMF is implemented using the Jadex platform and is compared to the popular centralized architecture. The results illustrate the operation of the proposed SMF outperforms in terms of tracking quality, detection rate, energy consumption, network lifetime, and scalability.

In this work, an existing generator for layout of Static Random Access Memory (SRAM) is improved. The tool is completed with a block decoder, which was missing when the thesis started. A feature of generating schematic files is also added. The schematics are important to get a better overview, to test parts properly, and enable Layout versus Schematics (LVS) checks. The main focus of this thesis work is to implement and evaluate a new SRAM cell, called Zero Aware Asymmetric SRAM cell. This cell saves major power when zeros are stored. Furthermore the pull-up circuit is modified to be less power consuming. Other parts are also modified to fit the new memory cell. Several minor flaws are corrected in the layout generator. It does still not produce a complete memory without manual interventions, but it does at least create all parts with one command. Several tests, including Design Rule Checks (DRC) and LVS checks, are carried out both on minor and larger parts. Development of documentation that makes it easier for users and developers to use and understand the tool is initiated.

Context-aware systems can dynamically adapt to user situations to provide smarter services. In general, context refers to the information that can be used to characterize these situations, and context models are deployed to specify contextual information described in context-aware systems. However, even though user context is highly dynamic, existing context models either focus on modeling static views of context or lack appropriate design abstractions to deal with dynamic aspects and interactions involving contextual elements such location, time, user roles, social relationships, and changing preferences. Moreover, virtual environments have not been modelled by most of the existing context models even though online interaction is very common and popular. This thesis presents a dynamic user-centric context model that can be used to model the aspects of context-aware systems that are subject to frequent change. Four case studies are proposed to illustrate the applicability of the approach taken by this thesis, and they are in the domains of mobile e-healthcare, mobile commerce, mobile tourism, and mobile augmented reality gaming. Benefits of the proposed model include avoiding the development of context-aware systems from scratch, enabling future use of model-driven approaches, and reducing implementation effort.

Homes that can collaborate with their residents rather than simply provide shelter are becoming a reality. These homes such as Georgia Techs Aware Home and MITs house_n can potentially provide support to their residents. Because aging adults may be faced with increasing mental and/or physical limitation(s) they may stand to benefit, in particular, from supports provided by these homes if they utilize the technologies they offer. However, the advanced technology in these aware homes often makes use of sensing devices that capture some kind of image-based information. Image-based information capture has previously been shown to elicit privacy concerns among users, and even lead to disuse of the system. The purpose of this study was to explore the privacy concerns that older adults had about a home equipped with visual sensing devices. Using a scenario-based structured interview approach I investigated how the type of images the home captures as well as the physical and mental health of the residents of the home affected privacy concerns as well as perceived benefits. In addition, responses to non-scenario-based open ended structured interview questions were used to gain an understanding of the characteristics of the influential variables. Results suggest that although most older adults express some concerns about using a visual sensing device in their home, the potential benefits of having such a device in specific circumstances outweigh their concerns. These findings have implications in privacy and technology acceptance theory as well as for designers of home based visual monitoring systems.

Reflection is one of the most important factors that affects learning. A good learning strategy is supposed to improve a learner's reflection level. In addition, the past studies has proved u-learning can enhance learning performance, motivation and efficiency of learners. This study integrates these into learning activity of distinguishing vegetation, let learner reflect in u-learning environment. Almost of past researches promoted learners¡¦ reflection was by text-based. However, the limit of learning activity's character and mobile device¡¦s screen, the design of text-based promotes is confined. Therefore, this study proposes a video-based reflection prompts strategies in high media richness, and the Context-Aware Reflection Prompt System (CRPS) can detect learner¡¦s location by QR Code to provide appropriate guidance, facilitating learner to observe and reflect. Further to explore effects of reflection level and satisfaction between video-based and text-based reflection prompt strategies. This study recruited 70 college students to participate in this experiment, and divide into two groups of video-based and text- based reflection prompts. The results showed that the reflection level of the video-based reflection prompt group is significantly improved than of the text-based reflection prompt group; however, there was no differences found on the satisfaction. In addition, this study further investigated the learners¡¦ opinions and perspectives toward a video-based and a text-based reflection prompt strategy, and interviewed some learners to obtain the potential factors which may affect satisfaction.

With the development of artificial intelligence , the application of recommender systems has been extended to fields such as e-commerce shopping cart analysis or video recommendation system. These systems provide user a recommended resource set based on their habits or behavior patterns to help users saving searching cost. However, these techniques have not been successfully adopted to help users search functions on smart-phones more efficiency. This research is designated to build the context-aware system, which can generate the list of operations predicting which function user might use under certain contexts through continuously learning users operation patterns and related device perceived scenario. The system utilize event-condition-action patterns to describe user frequent behaviors, and the research will focus on developing innovative Action-Condition-Fit algorithm to figure the similarity between action pattern sets and real-time scenario. Proposed system and algorithm will then be built on Google App Engine and Android device to empirically validate its performance through field test.

Broadcasting is a communication model for a node to emit the packets via wireless channels to its neighbor nodes. In mobile ad hoc networks (MANETs), it is commonly implemented through flooding to find routes, send alarm signals and page a particular host. Conventionally, ad hoc routing protocols, such as AODV, use blind flooding extensively for on-demand route discovery, which could result in a high number of redundant retransmissions, leading to serious contention and collisions referred to as the broadcast storm problem. A gossip-based approach, in which each node forwards a message with some probability, has been proposed in past years to alleviate this problem. The approach combines gossiping with AODV (denoted as AODV+G) and exhibits a significant performance improvement in simulations. In this paper, we make a mathematical inference from observing the behavior of the gossip-based approach, and improve the gossip-based approach by employing the remaining energy of nodes in the gossip mechanism (denoted as AODV+GE) to extend the lifetime of the entire network and improve the packet delivery ratio. Through mathematical inference and simulations we show that AODV+GE outperforms AODV+G in terms of the lifetime of the whole network, average node energy consumption, and packet delivery ratio.

To manage the ever-increasing volume of documents, organizations and individuals typically organize documents into categories (or category hierarchies) to facilitate their document management and support subsequent document retrieval and access. Document clustering is an intentional act that should reflect individuals¡¦ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user¡¦s categorization context defined by or relevant to the target task under consideration. However, existing document clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate personalized or context-aware document clustering. In response, we design, implement and empirically evaluate three document clustering techniques capable of facilitating personalized or contextual document clustering. First, we extend an existing document clustering technique (specifically, the partial-clustering-based personalized document-clustering (PEC) approach) and propose the Collaborative Filtering¡Vbased personalized document-Clustering (CFC) technique to overcome the problem of small-sized partial clustering encountered by the PEC technique. Particularly, the CFC technique expands the size of a user¡¦s partial clustering based on the partial clusterings of other users with similar categorization preferences. Second, to support contextual document clustering, we design and implement a Context-Aware document-Clustering (CAC) technique by taking into consideration a user¡¦s categorization preference (i.e., a set of anchoring terms) relevant to the context of a target task and a statistical-based thesaurus constructed from the World Wide Web (WWW) via a search engine. Third, in response to the problem of small-sized set of anchoring terms which can greatly degrade the effectiveness of the CAC technique, we extend CAC and propose a Collaborative Filtering-based Context-Aware document Clustering (CF-CAC) technique. Our empirical evaluation results suggest that our proposed CFC, CAC, and CF-CAC techniques better support the need of personalized and contextual document clustering than do their benchmark techniques.

Context-aware computing is based on using knowledge about the current context. Interpretation of current context to an understandable knowledge is carried out by reasoning over context and in some cases by matching the current context with the desired context. In this thesis we concentrated on context matching issue in context-aware computing domain. Context matching can be done in various ways like it is done in other matching processes. Our matching approach is best matching in order to generate granular similarity results and not to be limited to Boolean values. We decided to use Ontology as the encoded domain knowledge for our matching method. Context matching method is related to the method that we represent context. We selected conceptual graphs to represent the context. We proposed a generic algorithm for context matching based on the ontological information that benefit from the conceptual graph theory and its advantages.

Recently, distributed detection has been intensively studied. The prevailing model for distributed detection (DD) is a system involving both distributed local sensors and a fusion center. In a DD system, multiple sensors work collaboratively to distinguish between two or more hypotheses, e.g., the presence or absence of a target. In this thesis, the classical DD problem is reexamined in the context of wireless sensor network applications. For minimize the error probability at the fusion center, we consider the conventional method that designs the optimal binary local sensor decision rule in a channel-aware system, i.e., it integrates the transmission channel characteristics for find the optimal binary local sensor decision threshold to minimize the error probability at the fusion center. And there have different optimal local sensor decision thresholds for different channel state information. Because of optimal multi-bit (soft) local sensor decision is more practical than optimal binary local sensor decision. Allowing for multi-bit local sensor output, we also consider another conventional method that designs the optimal multi-bit (soft) local sensor decision rule in a channel-aware system. However, to design the optimal local sensor decision rule, both of two conventional methods are easily trapped into local optimal thresholds, which are depended on the pre-selected initialization values. To overcome this difficulty, we consider several modified Simulated Annealing (SA) algorithms. Based on these modified SA algorithms and two conventional methods, we propose two novel SA algorithms for implementing the optimal local sensor decision rule. Computer simulation results show that the employments of two novel SA algorithms can avoid trapping into local optimal thresholds in both optimal binary local sensor decision problem and optimal multi-bit local sensor decision problem. And two novel SA algorithms offer superior performance with lower search points compared to conventional SA algorithm.