Preprocessor based approach for cross-platform development with Qt quick components

In the paper we analyze the obstacles of cross-platform mobile development involving Qt Quick Components library. We provide the classification of the most common issues of such a development and propose resolution for them with the use of the specially developed tool - QML preprocessor. Our approach is proven to be successful in development of two mobile applications supporting both Harmattan and Symbian platforms

A framework for non-intrusive load monitoring and diagnostics

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 259-260).The widespread use of electrical and electromechanical systems places increasing demands on monitoring and diagnostic techniques. The non-intrusive load monitor (NILM) provides a low-cost, low-maintenance way to perform this monitoring and diagnostics from a centralized location. This work critically evaluates the current state of the NILM hardware and software in order to develop new techniques and a new hardware and software framework in which to better apply the NILM to real-world systems. New diagnostic indicators are developed on the USCGC SENECA using an improved hardware and software platform. A database-driven framework with the flexibility to create and implement these and future diagnostic indicators is presented.by James Paris.M.Eng

Evaluation of a SoC for Real-time 3D SLAM

SLAM, or Simultaneous Localization and Mapping, is the combined problem of constructing a map of an agent’s environment while localizing, or tracking that same agent’s pose in tandem. It is among the most challenging and fundamental tasks in computer vision, with applications ranging from augmented reality to robotic navigation. With the increasing capability and ubiquity of mobile computers such as cell phones, portable 3D SLAM systems are becoming feasible for widespread use. The Microsoft Hololens, Google Project Tango, and other 3D aware devices are modern day examples of the potential of SLAM and the challenges it has yet to face. The ICP, or Iterative Closest Point Algorithm, is a popular solution for retrieving the relative transformation between two scans of the same object. It has gained a resurgence in popularity due to the rise of affordable depth sensors such as the Kinect in robotics and augmented reality research. ICP, while providing a high certainty of correctness given similar point clouds, is challenging to implement in real time due to its computational complexity. In this thesis, a basic 3D SLAM algorithm is implemented and evaluated, and two proposed FPGA architectures to accelerate the Nearest Neighbor component of ICP for use in a mobile ARM-based System-on-Chip (SoC) are presented. These architectures are predicted to achieve speedups of up to 7.89x and 17.22x over a naive embedded software implementation

Interactive manipulation of three-dimensional images

This thesis is about the design and implementation of an application for editing voxel data. We are primarily working with MRI and CT data in a medical setting, but neither the application nor the problem itself is specific to this field. Most, but not all, of the functionality is geared toward segmented datasets. In addition to being usable as an application in itself, our program should provide a prototyping framework for others who want to test algorithms and tools on three-dimensional datasets. Because of this, we have designed and documented a plugin API, and implemented a number of plugins performing different operations on the dataset. The thesis touches on a lot of problems and choices that were made while implementing the application, from the overall application design down to our choice of libraries and tools. The programming language is C++. We made a choice to rely on libraries where we could, and so we make use of Blitz++, ImageMagick, Autotools, Qt, OpenGL and Open Inventor. The finished application's capabilities are outlined, and the design, tool choices and usability of the application are discussed

Software framework for geophysical data processing, visualization and code development

IGeoS is an integrated open-source software framework for geophysical data processing under development at the UofS seismology group. Unlike other systems, this processing monitor supports structured multicomponent seismic data streams, multidimensional data traces, and employs a unique backpropagation execution logic. This results in an unusual flexibility of processing, allowing the system to handle nearly any geophysical data. In this project, a modern and feature-rich Graphical User Interface (GUI) was developed for the system, allowing editing and submission of processing flows and interaction with running jobs. Multiple jobs can be executed in a distributed multi-processor networks and controlled from the same GUI. Jobs, in their turn, can also be parallelized to take advantage of parallel processing environments such as local area networks and Beowulf clusters. A 3D/2D interactive display server was created and integrated with the IGeoS geophysical data processing framework. With introduction of this major component, the IGeoS system becomes conceptually complete and potentially bridges the gap between the traditional processing and interpretation software. Finally, in a specialized application, network acquisition and relay components were written allowing IGeoS to be used for real-time applications. The completion of this functionality makes the processing and display capabilities of IGeoS available to multiple streams of seismic data from potentially remote sites. Seismic data can be acquired, transferred to the central server, processed, archived, and events picked and placed in database completely automatically

Sistema de Video-on-Demand para IPTV

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Matrix decomposition plays an increasingly significant role in many scientific and engineering applications. Among numerous techniques, Singular Value Decomposition (SVD) and Eigenvalue Decomposition (EVD) are widely used as factorization tools to perform Principal Component Analysis for dimensionality reduction and pattern recognition in image processing, text mining and wireless communications, while QR Decomposition (QRD) and sparse LU Decomposition (LUD) are employed to solve the dense or sparse linear system of equations in bioinformatics, power system and computer vision. Matrix decompositions are computationally expensive and their sequential implementations often fail to meet the requirements of many time-sensitive applications. The emergence of reconfigurable computing has provided a flexible and low-cost opportunity to pursue high-performance parallel designs, and the use of FPGAs has shown promise in accelerating this class of computation. In this research, we have proposed and implemented several highly parallel FPGA-based architectures to accelerate matrix decompositions and their applications in data mining and signal processing. Specifically, in this dissertation we describe the following contributions: • We propose an efficient FPGA-based double-precision floating-point architecture for EVD, which can efficiently analyze large-scale matrices. • We implement a floating-point Hestenes-Jacobi architecture for SVD, which is capable of analyzing arbitrary sized matrices. • We introduce a novel deeply pipelined reconfigurable architecture for QRD, which can be dynamically configured to perform either Householder transformation or Givens rotation in a manner that takes advantage of the strengths of each. • We design a configurable architecture for sparse LUD that supports both symmetric and asymmetric sparse matrices with arbitrary sparsity patterns. • By further extending the proposed hardware solution for SVD, we parallelize a popular text mining tool-Latent Semantic Indexing with an FPGA-based architecture. • We present a configurable architecture to accelerate Homotopy l1-minimization, in which the modification of the proposed FPGA architecture for sparse LUD is used at its core to parallelize both Cholesky decomposition and rank-1 update. Our experimental results using an FPGA-based acceleration system indicate the efficiency of our proposed novel architectures, with application and dimension-dependent speedups over an optimized software implementation that range from 1.5ÃÂ to 43.6ÃÂ in terms of computation time