Thesis (M.S.)--University of North Texas, May, 2007. / Title from title page display. Includes bibliographical references.

No description available.

No description available.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2007. / Title from title screen (site viewed July 22, 2008). PDF text: viii, 127 p. : ill. (some col.) ; 2 Mb. UMI publication number: AAT 3296996. Includes bibliographical references. Also available in microfilm and microfiche formats.

The research involves the analysis and design of a new band of horn antennas, namely Frequency Selective Horns (FSHs). Folding a Frequency Selective Surface (FSS) into a shape of a cone makes a FSH. The stimulus behind this research emanates from an idea to use the FSH as a microwave camera for medical diagnostic purposes. FSHs with dipole geometries have been modelled using existing software. A quasi-static approximation models the dipole elements with a dielectric backing as dielectrically loaded cylindrical conducting elements.

Made available in DSpace on 2014-10-09T12:25:48Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:03:33Z (GMT). No. of bitstreams: 1 11313.pdf: 4364467 bytes, checksum: 80dca67e716c2de36cf87cbbab397c48 (MD5) / Dissertacao (Mestrado) / IPEN/D / Intituto de Pesquisas Energeticas e Nucleares, IPEN/CNEN-SP

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Image sensors play a vital role in many image sensing and capture applications. Among the various types of image sensors, complementary metal oxide semiconductor (CMOS) based active pixel sensors (APS), which are characterized by reduced pixel size, give fast readouts and reduced noise. APS are used in many applications such as mobile cameras, digital cameras, Webcams, and many consumer, commercial and scientific applications. With these developments and applications, CMOS APS designs are challenging the old and mature technology of charged couple device (CCD) sensors. With the continuous improvements of APS architecture, pixel designs, along with the development of nanometer CMOS fabrications technologies, APS are optimized for optical sensing. In addition, APS offers very low-power and low-voltage operations and is suitable for monolithic integration, thus allowing manufacturers to integrate more functionality on the array and building low-cost camera-on-a-chip. In this thesis, I explore the current state-of-the-art of CMOS APS by examining various types of APS. I show design and simulation results of one of the most commonly used APS in consumer applications, i.e. photodiode based APS. We also present an approach for technology scaling of the devices in photodiode APS to present CMOS technologies. Finally, I present the most modern CMOS APS technologies by reviewing different design models. The design of the photodiode APS is implemented using commercial CAD tools.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The fairly recent introduction of low-cost depth sensors such as Microsoft’s Xbox Kinect has encouraged a large amount of research on the use of depth sensors for many common Computer Vision problems. Depth images are advantageous over normal colour images because of how easily objects in a scene can be segregated in real-time. Microsoft used the depth images from the Kinect to successfully separate multiple users and track various larger body joints, but has difficulty tracking smaller joints such as those of the fingers. This is a result of the low resolution and noisy nature of the depth images produced by the Kinect. The objective of this project is to use the depth images produced by the Kinect to remotely track the user’s hands and to recognise the static hand poses in real-time. Such a system would make it possible to control an electronic device from a distance without the use of a remote control. It can be used to control computer systems during computer aided presentations, translate sign language and to provide more hygienic control devices in clean rooms such as operating theatres and electronic laboratories. The proposed system uses the open-source OpenNI framework to retrieve the depth images from the Kinect and to track the user’s hands. Random Decision Forests are trained using computer generated depth images of various hand poses and used to classify the hand regions from a depth image. The region images are processed using a Mean-Shift based joint estimator to find the 3D joint coordinates. These coordinates are finally used to classify the static hand pose using a Support Vector Machine trained using the libSVM library. The system achieves a final accuracy of 95.61% when tested against synthetic data and 81.35% when tested against real world data. / AFRIKAANSE OPSOMMING: Die onlangse bekendstelling van lae-koste diepte sensors soos Microsoft se Xbox Kinect het groot belangstelling opgewek in navorsing oor die gebruik van die diepte sensors vir algemene Rekenaarvisie probleme. Diepte beelde maak dit baie eenvoudig om intyds verskillende voorwerpe in ’n toneel van mekaar te skei. Microsoft het diepte beelde van die Kinect gebruik om verskeie persone en hul ledemate suksesvol te volg. Dit kan egter nie kleiner ledemate soos die vingers volg nie as gevolg van die lae resolusie en voorkoms van geraas in die beelde. Die doel van hierdie projek is om die diepte beelde (verkry vanaf die Kinect) te gebruik om intyds ’n gebruiker se hande te volg oor ’n afstand en die statiese handgebare te herken. So ’n stelsel sal dit moontlik maak om elektroniese toestelle oor ’n afstand te kan beheer sonder die gebruik van ’n afstandsbeheerder. Dit kan gebruik word om rekenaarstelsels te beheer gedurende rekenaargesteunde aanbiedings, vir die vertaling van vingertaal en kan ook gebruik word as higiëniese, tasvrye beheer toestelle in skoonkamers soos operasieteaters en elektroniese laboratoriums. Die voorgestelde stelsel maak gebruik van die oopbron OpenNI raamwerk om die diepte beelde vanaf die Kinect te lees en die gebruiker se hande te volg. Lukrake Besluitnemingswoude ("Random Decision Forests") is opgelei met behulp van rekenaar gegenereerde diepte beelde van verskeie handgebare en word gebruik om die verskeie handdele vanaf ’n diepte beeld te klassifiseer. Die 3D koördinate van die hand ledemate word dan verkry deur gebruik te maak van ’n Gemiddelde-Afset gebaseerde ledemaat herkenner. Hierdie koördinate word dan gebruik om die statiese handgebaar te klassifiseer met behulp van ’n Steun-Vektor Masjien ("Support Vector Machine"), opgelei met behulp van die libSVM biblioteek. Die stelsel behaal ’n finale akkuraatheid van 95.61% wanneer dit getoets word teen sintetiese data en 81.35% wanneer getoets word teen werklike data.