Optical waveguides and switches based on periodic arrays of carbon nanotubes

This document presents the modeling and characterization of novel optical devices based on periodic arrays of multiwalled carbon nanotubes. Vertically aligned carbon nanotubes can be grown in the arrangement of two-dimensional arrays of precisely determined dimensions. Having their dimensions comparable to the wavelength of light makes carbon nanotubes good candidates for utilization in nano-scale optical devices. We report that highly dense periodic arrays of multiwalled carbon nanotubes can be utilized as sub-wavelength structures for establishing advanced optical materials, such as metamaterials and photonic crystals. We demonstrate that when carbon nanotubes are grown close together at spacing of the order of few hundred nanometers, they display artificial optical properties towards the incident light, acting as metamaterials. By utilizing these properties we have established micro-scaled plasmonic high pass filter which operates in the optical domain. Highly dense arrays of multiwalled also offer a periodic dielectric constant to the incident light and display interesting photonic band gaps, which are frequency domains within which on wave propagation can take place. We have utilized these band gaps displayed by a periodic nanotube array, having 400 nm spacing, to construct photonic crystals based optical waveguides and switches. © 2011 IEEE.

Liquid crystal multi-mode lenses and axicons based on electronic phase shift control

We report on the principle of operation, construction and testing of a liquid crystal lens which is controlled by distributing voltages across the control electrodes, which are in turn controlled by adjusting the phase of the applied voltages. As well as (positive and negative) defocus, then lenses can be used to control tip/tilt, astigmatism, and to create variable axicons. © 2007 Optical Society of America.

Deconvolution and elastography based on dimensional ultrasound

This paper is in two parts and addresses two of getting more information out of the RF signal from three-dimensional (3D) mechanically-swept medical ultrasound . The first topic is the use of non-blind deconvolution improve the clarity of the data, particularly in the direction to the individual B-scans. The second topic is imaging. We present a robust and efficient approach to estimation and display of axial strain information. deconvolution, we calculate an estimate of the point-spread at each depth in the image using Field II. This is used as of an Expectation Maximisation (EM) framework in which ultrasound scatterer field is modelled as the product of (a) a smooth function and (b) a fine-grain varying function. the E step, a Wiener filter is used to estimate the scatterer based on an assumed piecewise smooth component. In the M , wavelet de-noising is used to estimate the piecewise smooth from the scatterer field. strain imaging, we use a quasi-static approach with efficient based algorithms. Our contributions lie in robust and 3D displacement tracking, point-wise quality-weighted , and a stable display that shows not only strain but an indication of the quality of the data at each point in the . This enables clinicians to see where the strain estimate is and where it is mostly noise. deconvolution, we present in-vivo images and simulations quantitative performance measures. With the blurred 3D taken as OdB, we get an improvement in signal to noise ratio 4.6dB with a Wiener filter alone, 4.36dB with the ForWaRD and S.18dB with our EM algorithm. For strain imaging show images based on 2D and 3D data and describe how full D analysis can be performed in about 20 seconds on a typical . We will also present initial results of our clinical study to explore the applications of our system in our local hospital. © 2008 IEEE.

Multi-Modal Image Retrieval from Construction Databases and Model-Based Systems

In the modern and dynamic construction environment it is important to access information in a fast and efficient manner in order to improve the decision making processes for construction managers. This capability is, in most cases, straightforward with today’s technologies for data types with an inherent structure that resides primarily on established database structures like estimating and scheduling software. However, previous research has demonstrated that a significant percentage of construction data is stored in semi-structured or unstructured data formats (text, images, etc.) and that manually locating and identifying such data is a very hard and time-consuming task. This paper focuses on construction site image data and presents a novel image retrieval model that interfaces with established construction data management structures. This model is designed to retrieve images from related objects in project models or construction databases using location, date, and material information (extracted from the image content with pattern recognition techniques).