Development of robust high performance face detector under different environmental conditions.

足利工業大学201

Data Analysis for the E and B EXperiment and Instrumentation Development for Cosmic Microwave Background Polarimetry

The E and B EXperiment (EBEX) was a balloon-borne instrument designed to measure the polarization of the cosmic microwave background (CMB) while simultaneously characterizing Galactic dust emission. The instrument was based on a two-mirror ambient temperature Gregorian-Dragone telescope coupled with cooled refractive optics to a kilo-pixel array of transition edge sensor (TES) bolometeric detectors. To achieve sensitivity to both the CMB signal and Galactic foregrounds, EBEX observed in three signal bands centered on 150, 250, and 410 GHz. Polarimetry was achieved via a stationary wire-grid polarizer and a continuously rotating achromatic half-wave plate (HWP) based on a superconducting magnetic bearing (SMB). EBEX launched from McMurdo station, Antarctica on December 29, 2012 and collected ~ 1.3 TB of data during 11 days of observation. This thesis is presented in two Parts. Part I reviews the data analysis we performed to transform the raw EBEX data into maps of temperature and polarization sky signals, with a particular focus on post-flight pointing reconstruction; time stream cleaning and map making; the generation of model sky maps of the expected signal for each of the three EBEX signal bands; removal of the HWP-synchronous signal from the detector time streams; and our attempts to identify, characterize, and correct for non-linear detector responsivity. In Part II we present recent developments in instrumentation for the next generation of CMB polarimeters. The developments we describe, including advances in lumped-element kinetic inductance detector (LEKID) technology and the development of a hollow-shaft SMB-based motor for use in HWP polarimetry, were motivated in part by the design for a prospective ground-based CMB polarimeter based in Greenland

Articulated human tracking and behavioural analysis in video sequences

Recently, there has been a dramatic growth of interest in the observation and tracking of human subjects through video sequences. Arguably, the principal impetus has come from the perceived demand for technological surveillance, however applications in entertainment, intelligent domiciles and medicine are also increasing. This thesis examines human articulated tracking and the classi cation of human movement, rst separately and then as a sequential process. First, this thesis considers the development and training of a 3D model of human body structure and dynamics. To process video sequences, an observation model is also designed with a multi-component likelihood based on edge, silhouette and colour. This is de ned on the articulated limbs, and visible from a single or multiple cameras, each of which may be calibrated from that sequence. Second, for behavioural analysis, we develop a methodology in which actions and activities are described by semantic labels generated from a Movement Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was developed for human tracking that allows multi-level parameter search consistent with the body structure. This tracker relies on the articulated motion prediction provided by the MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to generate a probabilistic activity description with action labels. The implemented algorithms for tracking and behavioural analysis are tested extensively and independently against ground truth on human tracking and surveillance datasets. Dynamic models are shown to predict and generate synthetic motion, while MCM recovers both periodic and non-periodic activities, de ned either on the whole body or at the limb level. Tracking results are comparable with the state of the art, however the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS

The E and B EXperiment: A balloon-borne cosmic microwave background anisotropy probe

The E and B Experiment (EBEX), is a balloon-borne sub-orbital cosmic microwave background polarimeter, designed to measure polarization levels in the microwave spectrum. EBEX recently completed an 11-day Antarctic long duration balloon (LDB) science flight in January, 2013. ~1000 transition edge sensor bolometric detectors in three frequency bands centered at 150, 250 and 410 GHz sampled a large segment of the southern sky. Over 1.5TB of data were collected during the LDB flight. In this thesis, we describe the design and performance of the EBEX software components monitoring and controlling the system during the flight, including automation, telemetry, data storage and readout array management. We also describe the design and development of a novel attitude reconstruction system for a balloon-borne pointed observation platform based on a daytime star camera and 3-axis gyroscopes. The data gathered during the LDB flight are analyzed and the results presented showing attitude reconstruction error at less than 20" RMS for an 80 second interval

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community

AGENT AUTONOMY APPROACH TO PROBABILISTIC PHYSICS-OF-FAILURE MODELING OF COMPLEX DYNAMIC SYSTEMS WITH INTERACTING FAILURE MECHANISMS

A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018