Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Computer-Based Stereoscopic Parts Recognition for Robotic Applications

Most of robotic handling and assembly operations are based on sensors such as range and touch sensors. In certain circumstances, such as in the presence of ionizing radiation where most customary sensors will degrade over time due to radiation exposure, these sensors won\u27t function properly. Utilizing two or more cameras (stereo vision) located outside the target zone and analyzing their images to identify location and dimensions of parts within the robot workspace is an alternative for using sensors. Object Recognition is affected by the light condition which oftentimes causes the gray-scale or red, green, and blue values to have a relatively small dynamic range. With this small dynamic range, edge detection algorithms fail to detect the proper edges and therefore cause improper image segmentation. To tackle this problem, a transformation on the (r,g,b) values of the pixels is introduced and applied prior to the edge detection and segmentation process. A stereoscopic computer vision system with multiple cameras is then used to compute the distance of the object from the origin of a global Euclidean coordinate system with high resolution. As an application of computer vision, a classifier for testing remote solar panels for cleanness condition, and performing cleaning when necessary, is introduced. A classification algorithm consisting of: the classification vector, the metric used, the training of the classifier, the testing of the classifier, and the classifier is put into play for everyday use. A smart cleaning robot is being designed based on this system to perform the cleaning autonomously when necessary. Another application of computer vision is inspecting the degree of air pollution. A real time classification algorithm that uses a quantization algorithm based on prior calibration is applied to evaluate the quality of air. The intelligent system, based on this algorithm, classifies the air using a numeric system from 1 to 10 which is then transformed to a qualitative scale

CONVR 2011 : Proceedings of the 11th International Conference on Construction Applications of Virtual Reality

Proceedings of the 11th International Conference on Construction Applications of Virtual Realit

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Virtual reality in industry

The industry is undergoing a digital transformation of automation and data exchange in manufacturing caused by the advance of information technologies and software. This transformation is called the 4thIndustry and implies a generation of smart factories which represent a leap forward from the automationto a fullyconnected, flexible and optimized system. Nowadays, every company is aware that a constant renewal and learning areessential to business success. And, among other advanced systems, Virtual Reality (VR) is a future digitization opportunity. Literally, VR makes possible to experience anything, anywhere, anytime. It is the most immersive type of computer-simulatedtechnology and can convince the human brain that it is somewhere else. That is the reason why the largest enterprisesin the worldare currently investing huge amounts of money in VRcompanies and startups. It is clear that the 4thIndustryis bringing with itself elements such as innovation, entrepreneurship, sustainability and internationalization, requirements that every engineer should possess to be competitive in a market increasingly globalized.As an Industrial Engineer, I decided to work on this research project because I am convinced that Virtual Reality will be a disruptive and innovative technology and part of our everyday lives in the coming years.Outgoin

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts