Indoor Positioning System (IPS) focuses on locating objects inside Buildings. Till date, GPS has helped us obtain accurate locations outdoors. These locations have helped us in many ways like navigating to a destination point, tracking people etc. Indoor Positioning System aims at navigating and tracking objects inside buildings. [1] IndoorAtlas is a technology that works on the theory of Indoor Positioning System. Book-Hunt is an Android mobile application which majorly makes use of IndoorAtlas therefore making use of the technique of indoor tracking. This Android mobile application is designed for Libraries. It is designed specifically for John M. Pfau Library, CSUSB, to help the students locate a book in the Library. When a student selects a book, a marker is pointed towards the book and also on the student’s current location. This application aims at saving time for student searching a particular book in the Library. Book- Hunt makes use of three tools Android Studio, Google Maps and IndoorAtlas

For autonomous navigation, robots and vehicles must have accurate estimates of their current state (i.e. location and orientation) within an inertial coordinate frame. If a map is given a priori, the process of determining this state is known as localization. When operating in the outdoors, localization is often assumed to be a solved problem when GPS measurements are available. However, in urban canyons and other areas where GPS accuracy is decreased, additional techniques with other sensors and filtering are required. This thesis aims to provide one such technique based on monocular vision. First, the system requires a map be generated, which consists of a set of geo-referenced video images. This map is generated offline before autonomous navigation is required. When an autonomous vehicle is later deployed, it will be equipped with an on-board camera. As the vehicle moves and obtains images, it will be able to compare its current images with images from the pre-generated map. To conduct this comparison, a method known as image correlation, developed at Johns Hopkins University by Rob Thompson, Daniel Gianola and Christopher Eberl, is used. The output from this comparison is used within a particle filter to provide an estimate of vehicle location. Experimentation demonstrates the particle filter's ability to successfully localize the vehicle within a small map that consists of a short section of road. Notably, no initial assumption of vehicle location within this map is required.

Modern data centers must provide performance assurance for complex system software such as web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. In recent years, more and more data centers start to adopt server virtualization strategies for resource sharing to reduce hardware and operating costs by consolidating applications previously running on multiple physical servers onto a single physical server. In this dissertation, several power efficient algorithms are proposed to effectively reduce server power consumption while achieving the required application-level performance for virtualized servers. First, at the server level this dissertation proposes two control solutions based on dynamic voltage and frequency scaling (DVFS) technology and request batching technology. The two solutions share a performance balancing technique that maintains performance balancing among all virtual machines so that they can have approximately the same performance level relative to their allowed peak values. Then, when the workload intensity is light, we adopt the request batching technology by using a controller to determine the time length for periodically batching incoming requests and putting the processor into sleep mode. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase the processor frequency for performance guarantees. Second, at the datacenter level, this dissertation proposes a performance-controlled power optimization solution for virtualized server clusters with multi-tier applications. The solution utilizes both DVFS and server consolidation strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the cluster level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Finally, this dissertation proposes a VM scheduling algorithm that exploits core performance heterogeneity to optimize the overall system energy efficiency. The four algorithms at the three different levels are demonstrated with empirical results on hardware testbeds and trace-driven simulations and compared against state-of-the-art baselines.

In recent years, multi-core processors have come to dominate the field in desktop and high performance computing. Graphics processors traditionally used in CAD, video games, and other 3-d applications, have become more programmable and are now suitable for general purpose computing. This thesis explores multi-core processors and GPU performance and limitations in two computational chemistry applications: a memory bound component of ab-initio modeling and a computationally bound Monte Carlo simulation. For the applications presented in this thesis, exploiting multiple processors is done using a variety of tools and languages including OpenMP and MKL. Brook+ and the Compute Abstraction Layer streaming environments are used to accelerate applications on AMD GPUs. This thesis gives qualitative assertions about these languages and tools regarding ease of use and optimization in addition to quantitative analyses of performance. GPUs can yield modest performance improvements with little effort in some applications and even larger speedups with simple optimizations.

In recent years, multi-core processors have come to dominate the field in desktop and high performance computing. Graphics processors traditionally used in CAD, video games, and other 3-d applications, have become more programmable and are now suitable for general purpose computing. This thesis explores multi-core processors and GPU performance and limitations in two computational chemistry applications: a memory bound component of ab-initio modeling and a computationally bound Monte Carlo simulation. For the applications presented in this thesis, exploiting multiple processors is done using a variety of tools and languages including OpenMP and MKL. Brook+ and the Compute Abstraction Layer streaming environments are used to accelerate applications on AMD GPUs. This thesis gives qualitative assertions about these languages and tools regarding ease of use and optimization in addition to quantitative analyses of performance. GPUs can yield modest performance improvements with little effort in some applications and even larger speedups with simple optimizations.

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Autism Spectrum Disorder makes it difficult to for a child communicate, have social interactions and go through daily life. Visual cues are often used to help a child associate an image with an event. With technology becoming more and more advanced, we now have a way to remind a child of an event with wearable technology, such as a watch. This new technology can help a child directly with the Visual Scheduling Application and various other applications. These applications allow children and their families to be easily able to keep track of the events on their schedule and notify them when an event occurs. With the Autism Management Platform and related website, a parent can easily create events to help a child throughout the day. The child can associate an image with events, allowing for a clearer understanding of what to do when an event occurs. Wearable technology has become a new way to interact with the user in a very unobtrusive manner. With this new technology, we can help associate a visual event to a child’s schedule and interrupt when needed to help make the child’s life easier on a daily basis.

Execution time estimation plays an important role in computer system design. It is particularly critical in real-time system design, where to meet a deadline can be as important as to ensure the logical correctness of a program. To accurately estimate the execution time of a program can be extremely challenging, since the execution time of a program varies with inputs, the underlying computer architectures, and run-time dynamics, among other factors. The problem becomes even more challenging as computing systems moving from single core to multi-core platforms, with more hardware resources shared by multiple processing cores. The goal of this research is to investigate the relationship between the execution time of a program and the underlying architecture features (e.g. cache size, associativity, memory latency), as well as its run-time characteristics (e.g. cache miss ratios), and based on which, to estimate its execution time on a multi-core platform based on a regression approach. We developed our test platform based on GEM5, an open-source multi-core cycle-accurate simulation tool set. Our experimental results show clearly the strong relationship of the program execution time to architecture features and run-time characteristics. Moreover, we developed different execution time estimation algorithms using the regression approach for different programs with different software characteristics to improve the estimation accuracy.

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With the rapid growth in consumer electronics, people expect thin, smart and powerful devices, e.g. Google Glass and other wearable devices. However, as portable electronic products become smaller, energy consumption becomes an issue that limits the development of portable systems due to battery lifetime. In general, simply reducing device size cannot fully address the energy issue. To tackle this problem, we propose an on-chip interconnect infrastructure and pro- gram storage structure for a coarse-grained reconfigurable architecture (CGRA) with emerging non-volatile embedded memory (MRAM). The interconnect is composed of a matrix of time-multiplexed switchboxes which can be dynamically reconfigured with the goal of energy reduction. The number of processors performing computation can also be adapted. The use of MRAM provides access to high-density storage and lower memory energy consumption versus more standard SRAM technologies. The combination of CGRA, MRAM, and flexible on-chip interconnection is considered for signal processing. This application domain is of interest based on its time-varying computing demands. To evaluate CGRA architectural features, prototype architectures have been pro- totyped in a field-programmable gate array (FPGA). Measurements of energy, power, instruction count, and execution time performance are considered for a scalable num- ber of processors. Applications such as adaptive Viterbi decoding and Reed Solomon coding are used for evaluation. To complete this thesis, a time-scheduled switchbox was integrated into our CGRA model. This model was prototyped on an FPGA. It is shown that energy consumption can be reduced by about 30% if dynamic design reconfiguration is performed.