Master of Science

thesisAnalysis and visualization of flow is an important part of many scientific endeavors. Computation of streamlines is fundamental to many of these analysis and visualization tasks. A streamline is the path a massless particle traces under the instantenous velocities of a given vector field. Flow data are often stored as a sampled vector field over a mesh. We propose a new representation of flow defined by such a vector field. Given a triangulation and a vector field defined over its vertices, we represent flow in the form of its transversal behavior over the edges of the triangulation. A streamline is represented as a set of discrete jumps over these edges. Any information about the actual path taken through the interior of the triangles is discarded. We eliminate the necessity to compute actual paths of streamlines through the interior of each triangle while maintaining the aggregate behavior of flow within each of them. We discretize each edge uniformly into a fixed number of bins and use this discretization to form a combinatorial representation of flow in the form of a directed graph whose nodes are the set of all bins and its edges represent the discrete jumps between these bins. This representation is a combinatorial structure that provides robustness and consistency in expressing flow features like the critical points, streamlines, separatrices and closed streamlines which are otherwise hard to compute consistently

Some Contribution of Statistical Techniques in Big Data: A Review

Big Data is a popular topic in research work. Everyone is talking about big data, and it is believed that science, business, industry, government, society etc. will undergo a through change with the impact of big data.Big data is used to refer to very huge data set having large, more complex, hidden pattern, structured and unstructured nature of data with the difficulties to collect, storage, analysing for process or result. So proper advanced techniques to use to gain knowledge about big data. In big data research big challenge is created in storage, process, search, sharing, transfer, analysis and visualizing. To deeply discuss on introduction of big data, issue, management and all used big data techniques. Also in this paper present a review of various advanced statistical techniques to handling the key application of big data have large data set. These advanced techniques handle the structure as well as unstructured big data in different area

Annotations for Rule-Based Models

The chapter reviews the syntax to store machine-readable annotations and describes the mapping between rule-based modelling entities (e.g., agents and rules) and these annotations. In particular, we review an annotation framework and the associated guidelines for annotating rule-based models of molecular interactions, encoded in the commonly used Kappa and BioNetGen languages, and present prototypes that can be used to extract and query the annotations. An ontology is used to annotate models and facilitate their description

Visualization of Time-Varying Data from Atomistic Simulations and Computational Fluid Dynamics

Time-varying data from simulations of dynamical systems are rich in spatio-temporal information. A key challenge is how to analyze such data for extracting useful information from the data and displaying spatially evolving features in the space-time domain of interest. We develop/implement multiple approaches toward visualization-based analysis of time-varying data obtained from two common types of dynamical simulations: molecular dynamics (MD) and computational fluid dynamics (CFD). We also make application case studies. Parallel first-principles molecular dynamics simulations produce massive amounts of time-varying three-dimensional scattered data representing atomic (molecular) configurations for material system being simulated. Rendering the atomic position-time series along with the extracted additional information helps us understand the microscopic processes in complex material system at atomic length and time scales. Radial distribution functions, coordination environments, and clusters are computed and rendered for visualizing structural behavior of the simulated material systems. Atom (particle) trajectories and displacement data are extracted and rendered for visualizing dynamical behavior of the system. While improving our atomistic visualization system to make it versatile, stable and scalable, we focus mainly on atomic trajectories. Trajectory rendering can represent complete simulation information in a single display; however, trajectories get crowded and the associated clutter/occlusion problem becomes serious for even moderate data size. We present and assess various approaches for clutter reduction including constrained rendering, basic and adaptive position merging, and information encoding. Data model with HDF5 and partial I/O, and GLSL shading are adopted to enhance the rendering speed and quality of the trajectories. For applications, a detailed visualization-based analysis is carried out for simulated silicate melts such as model basalt systems. On the other hand, CFD produces temporally and spatially resolved numerical data for fluid systems consisting of a million to tens of millions of cells (mesh points). We implement time surfaces (in particular, evolving surfaces of spheres) for visualizing the vector (flow) field to study the simulated mixing of fluids in the stirred tank

Multi-resource management in embedded real-time systems

This thesis addresses the problem of online multi-resource management in embedded real-time systems. It focuses on three research questions. The first question concentrates on how to design an efficient hierarchical scheduling framework for supporting independent development and analysis of component based systems, to provide temporal isolation between components. The second question investigates how to change the mapping of resources to tasks and components during run-time efficiently and predictably, and how to analyze the latency of such a system mode change in systems comprised of several scalable components. The third question deals with the scheduling and analysis of a set of parallel-tasks with real-time constraints which require simultaneous access to several different resources. For providing temporal isolation we chose a reservation-based approach. We first focused on processor reservations, where timed events play an important role. Common examples are task deadlines, periodic release of tasks, budget replenishment and budget depletion. Efficient timer management is therefore essential. We investigated the overheads in traditional timer management techniques and presented a mechanism called Relative Timed Event Queues (RELTEQ), which provides an expressive set of primitives at a low processor and memory overhead. We then leveraged RELTEQ to create an efficient, modular and extensible design for enhancing a real-time operating system with periodic tasks, polling, idling periodic and deferrable servers, and a two-level fixed-priority Hierarchical Scheduling Framework (HSF). The HSF design provides temporal isolation and supports independent development of components by separating the global and local scheduling, and allowing each server to define a dedicated scheduler. Furthermore, the design addresses the system overheads inherent to an HSF and prevents undesirable interference between components. It limits the interference of inactive servers on the system level by means of wakeup events and a combination of inactive server queues with a stopwatch queue. Our implementation is modular and requires only a few modifications of the underlying operating system. We then investigated scalable components operating in a memory-constrained system. We first showed how to reduce the memory requirements in a streaming multimedia application, based on a particular priority assignment of the different components along the processing chain. Then we investigated adapting the resource provisions to tasks during runtime, referred to as mode changes. We presented a novel mode change protocol called Swift Mode Changes, which relies on Fixed Priority with Deferred preemption Scheduling to reduce the mode change latency bound compared to existing protocols based on Fixed Priority Preemptive Scheduling. We then presented a new partitioned parallel-task scheduling algorithm called Parallel-SRP (PSRP), which generalizes MSRP for multiprocessors, and the corresponding schedulability analysis for the problem of multi-resource scheduling of parallel tasks with real-time constraints. We showed that the algorithm is deadlock-free, derived a maximum bound on blocking, and used this bound as a basis for a schedulability test. We then demonstrated how PSRP can exploit the inherent parallelism of a platform comprised of multiple heterogeneous resources. Finally, we presented Grasp, which is a visualization toolset aiming to provide insight into the behavior of complex real-time systems. Its flexible plugin infrastructure allows for easy extension with custom visualization and analysis techniques for automatic trace verification. Its capabilities include the visualization of hierarchical multiprocessor systems, including partitioned and global multiprocessor scheduling with migrating tasks and jobs, communication between jobs via shared memory and message passing, and hierarchical scheduling in combination with multiprocessor scheduling. For tracing distributed systems with asynchronous local clocks Grasp also supports the synchronization of traces from different processors during the visualization and analysis