Modeling of in situ monitored laser reflectance during MOCVD growth of HgCdTe

An effective way to in situ monitor the metalorganic chemical vapor deposition (MOCVD) of HgCdTe/CdTe/ZnTe on GaAs or GaAs/Si substrates is presented. Specular He-Ne laser reflectance was used to in situ monitor the growth rates, layer thickness, and morphology for each layer in the grown multilayer structure. In situ monitoring has enabled precise measurements of ZnTe nucleation and CdTe buffer layer thicknesses. Monitoring the constancy of reflectance during the thicker CdTe buffer growth where absorption in the CdTe reduces reflectance to just the surface component has led to optimum buffer growth ensuring good quality of subsequently grown HgCdTe. During the interdiffused multilayer process (IMP) HgCdTe growth, because multiple interfaces are present within the absorption length, a periodic reflectance signal is maintained throughout this growth cycle. A theoretical model was developed to extract IMP layer thicknesses from in situ recorded experimental data. For structures that required the growth of a larger band gap HgCdTe cap layer on top of a smaller band gap active layer, in situ monitored reflectance data allowed determination of alloy composition in the cap layer as well. Continuous monitoring of IMP parameters established the stability of growth conditions, translating into depth uniformity of the grown material, and allowed diagnosis of growth rate instabilities in terms of changes in the HgTe and CdTe parts of the IMP cycle. A unique advantage of in situ laser monitoring is the opportunity to perform “interactive” crystal growth, a development that is a key to real time MOCVD HgCdTe feedback growth control.     

Performance measures for video object segmentation and tracking

We propose measures to evaluate quantitatively the performance of video object segmentation and tracking methods without ground-truth (GT) segmentation maps. The proposed measures are based on spatial differences of color and motion along the boundary of the estimated video object plane and temporal differences between the color histogram of the current object plane and its predecessors. They can be used to localize (spatially and/or temporally) regions where segmentation results are good or bad; and/or they can be combined to yield a single numerical measure to indicate the goodness of the boundary segmentation and tracking results over a sequence. The validity of the proposed performance measures without GT have been demonstrated by canonical correlation analysis with another set of measures with GT on a set of sequences (where GT information is available). Experimental results are presented to evaluate the segmentation maps obtained from various sequences using different segmentation approaches.     

Multidirectional and multiscale edge detection via M-band wavelettransform

In this correspondence, the problem of directional and multiscale edge detection is considered. Orthogonal and linear-phase M-band wavelet transform is used to decompose the image into MxM channels. These channels are then combined such that each combination, which we refer to as decomposition filter, results in zero-crossings at the locations of edges corresponding to different directions and resolutions, and inherently performs regularization against noise. By applying a zero-crossing detector on the outputs of the decomposition filters, edge maps of desired resolution and direction are obtained. In addition, with the application of the Teager's energy operator at the analysis stage, it is possible to obtain a reduction in unwanted zero-crossings. Final edge maps of images are obtained through simple combinations of directional edge maps.     

VQ-adaptive block transform coding of images

Two new design techniques for adaptive orthogonal block transforms based on vector quantization (VQ) codebooks are presented. Both techniques start from reference vectors that are adapted to the characteristics of the signal to be coded, while using different methods to create orthogonal bases. The resulting transforms represent a signal coding tool that stands between a pure VQ scheme on one extreme and signal-independent, fixed block transformation-like discrete cosine transform (DCT) on the other. The proposed technique has superior compaction performance as compared to DCT both in the rendition of details of the image and in the peak signal-to-noise ratio (PSNR) figures.     

Multilevel statistical inference from functional near-infrared spectroscopy data during stroop interference

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy- and deoxy-hemoglobin (oxy-Hb and deoxy-Hb) in the brain. An important consideration in fNIRS-based neuroimaging modality is to conduct group-level analysis from a set of time series measured from a group of subjects. We investigate the feasibility of multilevel statistical inference for fNIRS. As a case study, we search for hemodynamic activations in the prefrontal cortex during Stroop interference. Hierarchical general linear model (GLM) is used for making this multilevel analysis. Activation patterns both at the subject and group level are investigated on a comparative basis using various classical and Bayesian inference methods. All methods showed consistent left lateral prefrontal cortex activation for oxy-Hb during interference condition, while the effects were much less pronounced for deoxy-Hb. Our analysis showed that mixed effects or Bayesian models are more convenient for faithful analysis of fNIRS data. We arrived at two important conclusions. First, fNIRS has the capability to identify activations at the group level, and second, the mixed effects or Bayesian model is the appropriate mechanism to pass from subject to group-level inference.     

Spatio–Temporal Transform Based Video Hashing

Identification and verification of a video clip via its fingerprint find applications in video browsing, database search and security. For this purpose, the video sequence must be collapsed into a short fingerprint using a robust hash function based on signal processing operations. We propose two robust hash algorithms for video based both on the discrete cosine transform (DCT), one on the classical basis set and the other on a novel randomized basis set (RBT). The robustness and randomness properties of the proposed hash functions are investigated in detail. It is found that these hash functions are resistant to signal processing and transmission impairments, and therefore can be instrumental in building database search, broadcast monitoring and watermarking applications for video. The DCT hash is more robust, but lacks security aspect, as it is easy to find different video clips with the same hash value. The RBT based hash, being secret key based, does not allow this and is more secure at the cost of a slight loss in the receiver operating curves     

Multiresolution biological transient extraction applied to respiratory crackles

Ketoprofen was encapsulated within poly-epsilon-caprolactone (PCL) and hydroxypropyl methylcellulose phthalate 50 (HPMCP50) microspheres (MS). Scanning electron microscopy (SEM) studies showed spherical particles without surface crystal formation and differential scanning calorimetry (DSC) supported these results. MS of PCL or HPMCP50 had a mean particle size of 10.7 +/- 2.2 and 10.9 +/- 2.0 mu m respectively, whereas a mixture of these polymers increased the MS particle size to 30 mu m. Greater incorporation efficiencies were found for HPMCP50 MS (98.1 +/- 0.7). MS of PCL and HPMCP50 mixtures showed a decreased drug entrapment as the amount of PCL was increased (96.0 +/- 0.2 for 25% PCL, 95.6 +/- 1.8 for 50% PCL, 80.2 +/- 0.7 for 75% PCL and 78.9 +/- 9.0 for 100% PCL). Size exclusion chromatography (SEC) studies revealed a weak interaction between ketoprofen and PCL and some polymer degradation was found during HPMCP50 MS storage, probably by breaking of the phthalic anhydride bond to be anyhydroglucose backbone. Four types of cryoprotectors (glucose, trehalose, mannitol and sorbitol, at 5 and 10% W/V) and two freezing conditions (-196 and -20 degrees C) were evaluated in freeze-drying studies. For HPMCP50, the sizes of MS after reconstitution of liophylizates were nearly the same as the initial ones. For PCL MS only, those formulations with sorbitol or glucose at 10% and frozen at -196 degrees C showed acceptable results. In contrast to the rapid release rate of ketoprofen from PCL MS as a result of carrier porosity (80% released within 15 min), the release from HPMCP50 MS could be controlled by means of pH (40% released in the first 15 min in simulated gastric fluid and nearly 100% ketoprofen delivered in the same time in simulated intestinal fluid).     

Robust classification of face and head gestures in video

Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in human-computer interfaces. We develop a face and head gesture detector in video streams. The detector is based on face landmark paradigm in that appearance and configuration information of landmarks are used. First we detect and track accurately facial landmarks using adaptive templates, Kalman predictor and subspace regularization. Then the trajectories (time series) of facial landmark positions during the course of the head gesture or facial expression are converted in various discriminative features. Features can be landmark coordinate time series, facial geometric features or patches on expressive regions of the face. We use comparatively, two feature sequence classifiers, that is, Hidden Markov Models (HMM) and Hidden Conditional Random Fields (HCRF), and various feature subspace classifiers, that is, ICA (Independent Component Analysis) and NMF (Non-negative Matrix Factorization) on the spatiotemporal data. We achieve 87.3% correct gesture classification on a seven-gesture test database, and the performance reaches 98.2% correct detection under a fusion scheme. Promising and competitive results are also achieved on classification of naturally occurring gesture clips of LIlir TwoTalk Corpus.     

Robust facial landmarking for registration

Finding landmark positions on facial images is an important step in face registration and normalization, for both 2D and 3D face recognition. In this paper, we inspect shortcomings of existing approaches in the literature and compare several methods for performing automatic landmarking on near-frontal faces in different scales. Two novel methods have been employed to analyze facial features in coarse and fine scales successively. The first method uses a mixture of factor analyzers to learn Gabor filter outputs on a coarse scale. The second method is a template matching of block-based Discrete Cosine Transform (DCT) features. In addition, a structural analysis subsystem is proposed that can determine false matches, and correct their positions.