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The refractive index of GaAs has been measured in the wavelength range from 0.97 to 17 μm, which covers nearly the entire transmission range of the material. Linear and quadratic temperature coefficients of the refractive index have been fitted to data measured between room temperature and 95 °C. In the midinfrared, the refractive index and temperature dependence are obtained from analysis of etalon fringes measured by Fourier-transform spectroscopy in undoped GaAs wafers. In the near infrared, the refractive index is deduced from the quasiphasematching (QPM) wavelengths of second-harmonic generation in orientation-patterned GaAs crystals. Two alternative empirical expressions are fitted to the data to give the refractive index as a function of wavelength and temperature. These dispersion relations agree with observed QPM conditions for midinfrared difference-frequency generation and second-harmonic generation. Predictions for various nonlinear optical interactions are presented, including tuning curves for optical parametric oscillators and amplifiers. Also, accurate values are predicted for QPM conditions in which extremely large parametric gain bandwidths can be obtained.

Metal-oxide-semiconductor field-effect transistors (MOSFETs) on CdTe/HgTe/CdTe heterostructures are fabricated with silicon dioxide gate insulators. In these devices, the density of the quasi two-dimensional electron gas in the HgTe quantum well can be tuned in a wide range. In low magnetic fields we observe beating patterns in the Shubnikov-de Haas oscillations that render possible the determination of the coefficient α of the Rashba term in the Hamiltonian as a function of electron density. This coefficient consistently describes the splittings observed in cyclotron resonance in low magnetic fields.

The control of thermal radiation is of great current importance for applications such as energy conversions and radiative cooling. Here we show theoretically that the thermal emission of a finite-size blackbody emitter can be enhanced in a thermal extraction scheme, where one places the emitter in optical contact with an extraction device consisting of a transparent object, as long as both the emitter and the extraction device have an internal density of state higher than vacuum, and the extraction device has an area larger than the emitter and moreover has a geometry that enables light extraction. As an experimental demonstration of the thermal extraction scheme, we observe a four-fold enhancement of the far-field thermal emission of a carbon-black emitter having an emissivity of 0.85.

Quasi-phase-matched (QPM) GaAs structures, 0.5 mm thick, 10 mm long, and with 61-mum grating periods, were grown by a combination of molecular-beam epitaxy and hydride vapor phase epitaxy. These were characterized by use of mid-IR second-harmonic generation (SHG) with a ZnGeP(2) (ZGP) optical parametric oscillator as a pump source. The SHG efficiencies of QPM GaAs and QPM LiNbO(3) were directly compared, and a ratio of nonlinear coefficients d(14)(GaAs)/d(33) (LiNbO(3))=5.01+/-0.3 was found at 4.1-mum fundamental wavelength. For input pulse energies as low as 50muJ and approximately 60-ns pulse duration, an internal SHG conversion efficiency of 33% was measured in QPM GaAs.

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Coregistration errors in multi- and hyperspectral imaging sensors arise when the spatial sensitivity pattern differs between bands or when the spectral response varies across the field of view, potentially leading to large errors in the recorded image data. In imaging spectrometers, spectral and spatial offset errors are customarily specified as "smile" and "keystone" distortions. However these characteristics do not account for errors resulting from variations in point spread function shape or spectral bandwidth. This paper proposes improved metrics for coregistration error both in the spatial and spectral dimensions. The metrics are essentially the integrated difference between point spread functions. It is shown that these metrics correspond to an upper bound on the error in image data. The metrics enable estimation of actual data errors for a given image, and can be used as part of the merit function in optical design optimization, as well as for benchmarking of spectral image sensors.

Hyperspectral imaging, which records a detailed spectrum of light arriving in each pixel, has many potential uses in remote sensing as well as other application areas. Practical applications will typically require real-time processing of large data volumes recorded by a hyperspectral imager. This paper investigates the use of graphics processing units (GPU) for such real-time processing. In particular, the paper studies a hyperspectral anomaly detection algorithm based on normal mixture modelling of the background spectral distribution, a computationally demanding task relevant to military target detection and numerous other applications. The algorithm parts are analysed with respect to complexity and potential for parallellization. The computationally dominating parts are implemented on an Nvidia GeForce 8800 GPU using the Compute Unified Device Architecture programming interface. GPU computing performance is compared to a multicore central processing unit implementation. Overall, the GPU implementation runs significantly faster, particularly for highly data-parallelizable and arithmetically intensive algorithm parts. For the parts related to covariance computation, the speed gain is less pronounced, probably due to a smaller ratio of arithmetic to memory access. Detection results on an actual data set demonstrate that the total speedup provided by the GPU is sufficient to enable realtime anomaly detection with normal mixture models even for an airborne hyperspectral imager with high spatial and spectral resolution.