David J. Brady

Adaptive optical networks using photorefractive crystals

The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined. Limitations on the density of interconnections and the number of holographic associations which can be stored in photorefractive crystals are derived. Optical architectures for implementing various neural schemes are described. Experimental results are presented for one of these architectures.

Single disperser design for coded aperture snapshot spectral imaging

We present a single disperser spectral imager that exploits recent theoretical work in the area of compressed sensing to achieve snapshot spectral imaging. An experimental prototype is used to capture the spatiospectral information of a scene that consists of two balls illuminated by different light sources. An iterative algorithm is used to reconstruct the data cube. The average spectral resolution is 3.6 nm per spectral channel. The accuracy of the instrument is demonstrated by comparison of the spectra acquired with the proposed system with the spectra acquired by a nonimaging reference spectrometer.

Single-shot compressive spectral imaging with a dual-disperser architecture

This paper describes a single-shot spectral imaging approach based on the concept of compressive sensing. The primary features of the system design are two dispersive elements, arranged in opposition and surrounding a binary-valued aperture code. In contrast to thin-film approaches to spectral filtering, this structure results in easily-controllable, spatially-varying, spectral filter functions with narrow features. Measurement of the input scene through these filters is equivalent to projective measurement in the spectral domain, and hence can be treated with the compressive sensing frameworks recently developed by a number of groups. We present a reconstruction framework and demonstrate its application to experimental data.

Economic Globalization and the Welfare State in Affluent Democracies, 1975-1998

Previous scholarship is sharply divided over how or if globalization influences welfare states. The effects of globalization may be positive causing expansion, negative triggering crisis and reduction, curvilinear contributing to convergence, or insignificant. We bring new evidence to bear on this debate with an analysis of three welfare state measures and a comprehensive array of economic globalization indicators for 17 affluent democracies from 1975 to 2001. The analysis suggests several conclusions. First, state-of-the-art welfare state models warrant revision in the globalization era. Second, most indicators of economic globalization do not have significant effects, but a few affect the welfare state and improve models of welfare state variation. Third, the few significant globalization effects are in differing directions and often inconsistent with extant theories. Fourth, the globalization effects are far smaller than the effects of domestic political and economic factors. Fifth, the effects of globalization are not systematically different between European and non-European countries, or liberal and non-liberal welfare regimes. Increased globalization and a modest convergence of the welfare state have occurred, but globalization does not clearly cause welfare state expansion, crisis, and reduction or convergence. Ultimately, this study suggests skepticism toward bold claims about globalizations effect on the welfare state.

Metamaterial Apertures for Computational Imaging

Compressed Sampling It is often said that a picture is worth a thousand words. But images often contain a lot of redundant information—effectively creating huge data files of meaningless information. While algorithms can compress the size of a file without loss of information, such processing is done after the picture has been taken. Hunt et al. (p. 310) used a metamaterial sensor to compress the sampled scene directly, obviating the need for postprocessing. Tuning the response of the metamaterial allowed imaging of a scene with a 40:1 compression ratio, which may mean that finding that needle in a haystack may be much easier using a metamaterial camera. Metamaterial-based sensors can be used for compressive image reconstruction. By leveraging metamaterials and compressive imaging, a low-profile aperture capable of microwave imaging without lenses, moving parts, or phase shifters is demonstrated. This designer aperture allows image compression to be performed on the physical hardware layer rather than in the postprocessing stage, thus averting the detector, storage, and transmission costs associated with full diffraction-limited sampling of a scene. A guided-wave metamaterial aperture is used to perform compressive image reconstruction at 10 frames per second of two-dimensional (range and angle) sparse still and video scenes at K-band (18 to 26 gigahertz) frequencies, using frequency diversity to avoid mechanical scanning. Image acquisition is accomplished with a 40:1 compression ratio.

Rethinking the Sociological Measurement of Poverty

Despite serious methodological problems, quantitative studies of poverty by U.S. sociologists predominantly rely on the official U.S. measure. After reviewing the shortcomings of the U.S. measure, this article examines several theoretical and methodological advances in poverty measurement. After synthesizing literature on poverty measurement, I argue that ideal measures of poverty should: (1) measure comparative historical variation effectively; (2) be relative rather than absolute; (3) conceptualize poverty as social exclusion; (4) assess the impact of taxes, transfers, and state benefits; and (5) integrate the depth of poverty and the inequality among the poor. Next, this article evaluates sociological studies published since 1990 for their consideration of these criteria. Due to sociologys neglect of these criteria, this article advocates for three alternative poverty indices: the interval measure, the ordinal measure, and the sum of ordinals measure. Finally, using the Luxembourg Income Study, I examine the empirical patterns with these three measures, across advanced capitalist democracies from 1967 to 1997. Estimates of these poverty indices are made available for future research.

Optical imaging and spectroscopy

Preface. Acknowledgments. 1. Past, present and future. 1.1 Three revolutions. 1.2 Computational imaging. 1.3 Overview. 1.4 The fourth revolution. Problems. 2. Geometric imaging. 2.1 Visibility. 2.2 Optical elements. 2.3 Focal imaging. 2.4 Imaging systems. 2.5 Pinhole and coded aperture imaging. 2.6 Projection tomography. 2.7 Reference structure tomography. Problems. 3. Analysis. 3.1 Analytical tools. 3.2 Fields and transformations. 3.3 Fourier analysis. 3.4 Transfer functions and filters. 3.5 The Fresnel transformation. 3.6 The Whittaker-Shannon sampling theorem. 3.7 Discrete analysis of linear transformations. 3.8 Multiscale sampling. 3.9 B-splines. 3.10 Wavelets. Problems. 4. Wave imaging. 4.1 Waves and fields. 4.2 Wave model for optical fields. 4.3 Wave propagation. 4.4 Diffraction. 4.5 Wave analysis of optical elements. 4.6 Wave propagation through thin lenses. 4.7 Fourier analysis of wave imaging. 4.8 Holography. Problems. 5. Detection. 5.1 The Optoelectronic interface. 5.2 Quantum mechanics of optical detection. 5.3 Optoelectronic detectors. 5.3.1 Photoconductive detectors. 5.3.2 Photodiodes. 5.4 Physical characteristics of optical detectors. 5.5 Noise. 5.6 Charge coupled devices. 5.7 Active pixel sensors. 5.8 Infrared focal plane arrays. Problems. 6. Coherence imaging. 6.1 Coherence and spectral fields. 6.2 Coherence propagation. 6.3 Measuring coherence. 6.4 Fourier analysis of coherence imaging. 6.5 Optical coherence tomography. 6.6 Modal analysis. 6.7 Radiometry. Problems. 7. Sampling. 7.1 Samples and pixels. 7.2 Image plane sampling on electronic detector arrays. 7.3 Color imaging. 7.4 Practical sampling models. 7.5 Generalized sampling. Problems. 8. Coding and inverse problems. 8.1 Coding taxonomy. 8.2 Pixel coding. 8.3 Convolutional coding. 8.4 Implicit coding. 8.5 Inverse problems. Problems. 9. Spectroscopy. 9.1 Spectral measurements. 9.2 Spatially dispersive spectroscopy. 9.3 Coded aperture spectroscopy. 9.4 Interferometric Spectroscopy. 9.5 Resonant spectroscopy. 9.6 Spectroscopic filters. 9.7 Tunable filters. 9.8 2D spectroscopy. Problems. 10. Computational imaging. 10.1 Imaging systems. 10.2 Depth of field. 10.3 Resolution. 10.4 Multiple aperture imaging. 10.5 Generalized sampling revisited. 10.6 Spectral imaging. Problems. References.

Video rate spectral imaging using a coded aperture snapshot spectral imager.

We have previously reported on coded aperture snapshot spectral imagers (CASSI) that can capture a full frame spectral image in a snapshot. Here we describe the use of CASSI for spectral imaging of a dynamic scene at video rate. We describe significant advances in the design of the optical system, system calibration procedures and reconstruction method. The new optical system uses a double Amici prism to achieve an in-line, direct view configuration, resulting in a substantial improvement in image quality. We describe NeAREst, an algorithm for estimating the instantaneous three-dimensional spatio-spectral data cube from CASSIs two-dimensional array of encoded and compressed measurements. We utilize CASSIs snapshot ability to demonstrate a spectral image video of multi-colored candles with live flames captured at 30 frames per second.

Multidimensional tomographic imaging using volume holography

We propose the application of volume holography to four-dimensional (4-D) spatiospectral imaging. The proposed systems use materials and techniques developed for holographic data storage and interconnections to capture three-dimensional (3-D) spatial and one-dimensional (1-D) spectral information about a remote light source or scatterer. We analyze case studies of simple architectures using spherical-reference volume holograms as imaging elements in a fluorescence confocal microscope arrangement and demonstrate the equivalence of the holographic degeneracies with a slicing operation on the reconstructing incoherent source. We develop a general theoretical framework for the diffraction of random fields from volume holograms and show that the formulation can be used as an imaging design tool. Applications and future directions are also discussed.

Compressive Coded Aperture Spectral Imaging: An Introduction

Imaging spectroscopy involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent zones of the underlying spectral scene and merge the results to construct a spectral data cube. Push broom spectral imaging sensors, for instance, capture a spectral cube with one focal plane array (FPA) measurement per spatial line of the scene [1], [2]. Spectrometers based on optical bandpass filters sequentially scan the scene by tuning the bandpass filters in steps. The disadvantage of these techniques is that they require scanning a number of zones linearly in proportion to the desired spatial and spectral resolution. This article surveys compressive coded aperture spectral imagers, also known as coded aperture snapshot spectral imagers (CASSI) [1], [3], [4], which naturally embody the principles of compressive sensing (CS) [5], [6]. The remarkable advantage of CASSI is that the entire data cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot.