Trisneopentylgallium as a precursor for atomic layer epitaxy of GaAs

We report the use of a new precursor, trisneopentylgallium (NPG) for the growth of GaAs by atomic layer epitaxy (ALE). In contrast to most other alkyl gallium precursors such as triethylgallium, which decompose via a β-hydride elimination mechanism, this compound undergoes homolysis similar to that of trimethylgallium (TMGa), the normal choice as an ALE precursor. Clear self-limiting growth behavior similar to that of TMGa was observed over a reasonably wide range of growth conditions (430–500°C). Carbon incorporation was not significantly reduced compared with TMGa suggesting that the adsorbed neopentyl radicals undergo decomposition to result in a methyl terminated surface identical to that obtained for growth with TMGa.     

Cadmium sulfide thin films growth by chemical bath deposition

Cadmium sulfide (CdS) thin films have been prepared by a simple technique such as chemical bath deposition (CBD).A set of samples CdS were deposited on glass substrates by varying the bath temperature from 55 to 75 ℃ at fixed deposition time (25 min) in order to investigate the effect of deposition temperature on CdS films physical properties.The determination of growth activation energy suggests that at low temperature CdS film growth is governed by the release of Cd2+ ions in the solution.The structural characterization indicated that the CdS films structure is cubic or hexagonal with preferential orientation along the direction (111) or (002),respectively.The optical characterization indicated that the films have a fairly high transparency,which varies between 55％ and 80％ in the visible range of the optical spectrum,the refractive index varies from 1.85 to 2.5 and the optical gap value of which can reach 2.2 eV.It can be suggested that these properties make these films perfectly suitable for their use as window film in thin films based solar cells.     

Planar p-on-n HgCdTe FPAs by Arsenic Ion Implantation

This paper reviews recent developments in the characterization of planar p-on-n photodiodes fabricated from long- and mid-wavelength Hg_1−x Cd _x Te at␣the Electronics and Information Technology Laboratory (LETI). The Hg_1−x Cd _x Te epitaxial layers were grown by both liquid-phase and molecular-beam epitaxy. Planar p-on-n photodiodes were fabricated by arsenic implantation into an indium-doped Hg_1−x Cd _x Te base layer. Electro-optical characterization on these p-on-n photodiodes showed low leakage currents (shunt resistance > 10 GΩ) and mean R ₀ A values comparable to the state of the art, i.e., equal to 5000 Ω cm² at λ _c = 9.3 μm (λ _c: cutoff wavelength). Results of focal-plane arrays operating in both the long-wavelength infrared (IR) and middle-wavelength IR bands are reported, with noise equivalent delta temperature and responsivity values at λ _c = 9.3 μm in excess of 99.64%. These results demonstrate the viability and technological maturity of both material growth and device processing.     

Selection criteria for the analysis of data-driven clusters in cerebral FMRI

Functional MRI (fMRI) may be possible without a priori models of the cerebral hemodynamic response. First, such data-driven fMRI requires that all cerebral territories with distinct patterns be identified. Second, a systematic selection method is necessary to prevent the subjective interpretation of the identified territories. This paper addresses the second point by proposing a novel method for the automated interpretation of identified territories in data-driven fMRI. Selection criteria are formulated using: 1) the temporal cross-correlation between each identified territory and the paradigm and 2) the spatial contiguity of the corresponding voxel map. Ten event-design fMRI data sets are analyzed with one prominent algorithm, fuzzy c-means clustering, before applying the selection criteria. For comparison, these data are also analyzed with an established, model-based method: statistical parametric mapping. Both methods produced similar results and identified potential activation in the expected territory of the sensorimotor cortex in all ten data sets. Moreover, the proposed method classified distinct territories in separate clusters. Selected clusters have a mean temporal correlation coefficient of 0.39+/-0.07 (n=19) with a mean 2.7+/-1.4 second response delay. At most, four separate contiguous territories were observed in 87% of these clusters. These results suggest that the proposed method may be effective for exploratory fMRI studies where the hemodynamic response is perturbed during cerebrovascular disease.     

Emission of electrons from the ground and first excited states of self-organized InAs/GaAs quantum dot structures

Capacitance- and conductance-voltage studies have been carried out on Schottky barrier structures containing a sheet of self-organized InAs quantum dots. The dots are formed in GaAs n-type matrices after the deposition of four monolayers of InAs. Quasi-static analysis of capacitance-voltage measurements indicates that there are at least two filled electron levels in the quantum dots, located 60 and 140 meV below the GaAs conduction band edge. The conductance of the structure depends on the balance between measurement frequency and the thermionic emission rate of carriers from the quantum dots. An investigation of the temperature-dependent conductance at different frequencies as a function of the reverse bias allows us to study separately the electron emission rates from the ground and first excited levels in the quantum dots. We estimate that the electron escape times from both levels of the quantum dots become comparable at room temperature and equal to about 100 ps.     

Anti-aliasing weighting functions for single-slice helical CT

Spatially variant longitudinal aliasing plagues most volumes reconstructed from single-slice helical computed tomography data, and its presence can degrade resolution and distort image structures. We have recently developed a Fourier-based approach to longitudinal interpolation in helical computed tomography that can, for scans performed at pitch 1 or lower, essentially eliminate this longitudinal aliasing by exploiting a generalization of the Whittaker-Shannon sampling theorem whose conditions are satisfied by the interlaced pairs of direct and complementary longitudinal samples. However, the algorithm is computationally intensive and cannot be pipelined. In this paper, we address this shortcoming by deriving two spatial-domain, projection-data weighting functions that approximate the application of the Fourier-based approach, and preserve its aliasing suppression properties to some degree, while allowing for a pipelined implementation. The first approach, which we call simply 180AA, for anti-aliasing, is a direct spatial-domain approximation of the 180FT approach. The second approach, which we call 180BSP, is based on an approximate generalized interpolation approach making use of B-splines. Studies of aliasing and resolution properties in reconstructions from simulated data indicate that while the 180AA and 180BSP approaches do not perfectly replicate the favorable aliasing suppression and resolution properties of the 180FT approach, they do represent an improvement over the clinically standard 180LI approach on these fronts.     

Image modeling based on a 2-D stochastic subspace system identification algorithm

Fitting a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. In image restoration, for instance, the goal is to recover an estimate of the true image, preferably in the form of a parametric model, given an image that has been degraded by a combination of blur and additive white Gaussian noise. In texture analysis, on the other hand, a model of a particular texture image can serve as a tool for simulating texture patterns. Finally, in image enhancement one computes a model of the true image and the residuals between the image and the modeled image can be interpreted as the result of applying a de-noising filter. There are numerous other applications within the field of image processing that require a causal dynamic model. Such is the case in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average and state-space models (i.e., Kalman filter) are the most commonly used. In this paper we introduce a 2-D stochastic state-space system identification algorithm for fitting a quarter plane causal dynamic Roesser model to an image. The algorithm constructs a causal, recursive, and separable-in-denominator 2-D Kalman filter model. The algorithm is tested with three real images and the quality of the estimated images are assessed.     

A high-level application using belief functions for exchanging and managing uncertain events on the road in vehicular ad hoc networks

This article introduces a high-level system using belief functions for exchanging and managing imperfect information about events on the road in vehicular ad hoc networks. The main purpose of this application is to provide the most reliable information for the driver from multiple messages received informing the driver about events on the roads. This system and some variants are tested using a MATLAB? simulator. An implementation with Android smartphones using a Bluetooth technology to exchange the messages is also introduced.     

On‐Demand Wrinkling Patterns in Thin Metal Films Generated from Self‐Assembling Liquid Crystals

In this work, a new, universal method is described that uses the photopatterning of liquid crystals, which is accurately translated into a controlled, intricately wrinkled metal surface. Remarkably, the patterns have an oscillation in amplitude of the wrinkles. This rapid method allows generation of intricate multidomain patterns and continuous circular structures, including azimuthal, radial, and even higher complexity arrangements as examples. These wrinkled gold surfaces are also strikingly visual, which is interesting for applications ranging from diffractive elements to fine jewelry.     

Fast quasi-flat zones filtering using area threshold and region merging

Quasi-flat zones are morphological operators which segment the image into homogeneous regions according to certain criteria. They are used as an image simplification tool or an image segmentation pre-processing, but they induced a very important oversegmentation. Several filtering methods have been proposed to deal with this issue but they suffer from different drawbacks, e.g., loss of quality or edge deformation. In this article, we propose a new method based on existing approaches which achieves better or similar results than existing approaches, does not suffer from their drawbacks and requires less computation time. It consists of two successive steps. First, small quasi-flat zones are removed according to a minimal area threshold. They are then filled through the growth of remaining zones.