Study of Morphological Defects on Dual-Band HgCdTe on CdZnTe

HgCdTe dual-band epitaxial layers on lattice-matched CdZnTe substrates often have morphological defects. These defects, unlike normal void and microvoid defects, do not contain a polycrystalline core and, therefore, do not offer a good contrast for observation using optical and electron microscopes. This paper reports a way of identifying these defects by using a Nomarski optical microscopy image overlay on focused ion beam microscopy images for preparation of thin cross-sectional foils of these defects. Transmission electron microscopy was used to study the defect cross-sections to identify the origin and evolution of the morphological defects and their effect on the epitaxial layer. This paper reports cross-sectional analysis of four morphological defects of different shape and size.     

Biomechanical measurements to characterize the oral phase ofdysphagia

Dysphagia is a disorder of the swallowing mechanism and presents a major problem in the rehabilitation of stroke patients and head injured patients. The authors have identified several biomechanical parameters that characterize the oral musculature and have developed techniques to quantify these parameters in normal and dysphagic patients. These parameters include lip closure pressure, lip interface shear force, tongue thrust, and swallow pressure. Significant differences were found in each of these parameters measured in normal and dysphagic patients. The quantitative measurements may aid the physician in choosing the appropriate therapy during the course of recovery.     

Improving the stuck-at fault coverage of functional test sequences by using limited-scan operations

Functional test sequences were shown to detect unique defects in VLSI circuits. This is thought to be due to the fact that they are applied at-speed. However, functional test sequences do not achieve complete stuck-at fault coverage. Therefore, scan-based stuck-at tests, as well as other types of tests, are typically also applied. This increases the amount of test resources required for test application. We describe a procedure for inserting (limited) scan operations into a functional sequence in order to improve its stuck-at fault coverage, thus reducing or eliminating the need for separate scan-based stuck-at tests. Between scan operations, the functional test sequence can still be applied at-speed; however, a higher stuck-at fault coverage is achieved.     

Robust ego-motion estimation and 3-D model refinement using surface parallax.

We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse depth map using parametric surface parallax models and brightness derivatives extracted from an image pair. Given a coarse depth map acquired by a range-finder or extracted from a digital elevation map (DEM), ego-motion is estimated by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth estimate, motion of the three-dimensional (3-D) points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field, and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. The parallax magnitude is estimated using a constant parallax model (CPM) which assumes a smooth parallax field and a depth based parallax model (DBPM), which models the parallax magnitude using the given depth map. We obtain confidence measures for determining the accuracy of the estimated depth values which are used to remove regions with potentially incorrect depth estimates for robustly estimating ego-motion in subsequent iterations. Experimental results using both synthetic and real data (both indoor and outdoor sequences) illustrate the effectiveness of the proposed algorithm.     

Humidity Sustained Wearable Pouch‐Type Triboelectric Nanogenerator for Harvesting Mechanical Energy from Human Activities

Triboelectric nanogenerators (TENGs) are gaining much research interest recently owing to their facile and cost‐effective device structure. However, the effect of relative humidity (in moisture atmosphere) on the output performance still needs to be resolved. Herein, a pouch‐type TENG is proposed to significantly reduce the effect of relative humidity on its electrical output and a stable performance is also attained in a humid environment. In this regard, a dielectric and dielectric materials‐based TENG (DD‐TENG) is first developed using nanoarchitecture polydimethylsiloxane (NA‐PDMS) and multiwalled carbon nanotube/nylon composite layers as a triboelectric material with the negative and positive tendencies, respectively. The NA‐PDMS and nylon composite layers play a key role in increasing the surface contact area and surface charge density between the dielectric/triboelectric materials as well as the output performance of DD‐TENG. However, the DD‐TENG device exhibits a stable and high output performance with the effective output power density of ≈25.35 W m^?2. Additionally, the performance of the pouch‐type DD‐TENG device is not almost affected even though the relative humidity is increased from 35 to 81%, while it is dramatically decreased for the nonpouch‐type device. Finally, the pouch‐type DD‐TENG is employed as a wearable device to effectively harvest the mechanical energy from daily human activities.     

A 60 GHz phased array with measurement and de-embedding techniques

We present a 60 GHz phased array system that combines several key technologies to realize 10 GHz bandwidth coverage. Particularly, a tightly coupled dipole array centered at 60 GHz is designed and tested for its wideband performance. The tightly coupled dipole elements offer excellent wideband behavior of 10 GHz with voltage standing wave ratio?<?3 with scanning to 45°, as well as low cost printed circuit board fabrication. Additionally, we demonstrate a measurement setup with de-embedding procedure to measure gain at the antenna feed point. A feeding structure was designed and fabricated for de-embedding gain pattern measurements. Recovered measurements are shown to be in agreement with simulation.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

Large-Format HgCdTe Dual-Band Long-Wavelength Infrared Focal-Plane Arrays

Raytheon Vision Systems (RVS) continues to further its capability to deliver state-of-the-art high-performance, large-format, HgCdTe focal-plane arrays (FPAs) for dual-band long-wavelength infrared (L/LWIR) detection. Specific improvements have recently been implemented at RVS in molecular-beam epitaxy (MBE) growth and wafer fabrication and are reported in this paper. The aim of the improvements is to establish producible processes for 512 × 512 30-μm-unit-cell L/LWIR FPAs, which has resulted in: the growth of triple-layer heterojunction (TLHJ) HgCdTe back-to-back photodiode detector designs on 6 cm × 6 cm CdZnTe substrates with 300-K Fourier-transform infrared (FTIR) cutoff wavelength uniformity of ±0.1 μm across the entire wafer; demonstration of detector dark-current performance for the longer-wavelength detector band approaching that of single-color liquid-phase epitaxy (LPE) LWIR detectors; and uniform, high-operability, 512 × 512 30-μm-unit-cell FPA performance in both LWIR bands.     

Principal components null space analysis for image and video classification.

We present a new classification algorithm, principal component null space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i," an Mi-dimensional subspace along which the class' intraclass variance is the smallest. We call this subspace an approximate null space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class' mean in the class' ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video--an action retrieval problem and abnormal activity detection.     

Adherence of Acanthamoeba to human corneal epithelial cells recovered from normal non-lens wearers and asymptomatic contact lens wearers.

To increase our knowledge of factors leading to Acanthamoeba keratitis in contact lens wearers, we determined the ability of this organism to adhere to corneal epithelial cells (EC) recovered from non-lens wearers (NL) and from subjects using hydrogel contact lenses on a daily (DW) and extended wear (EW) schedule. ECs were incubated with trophozoites of Acanthamoeba and, after 3 h, the median per cent of cells exhibiting adherence was 24, 23 and 23 for NL, DW and EW groups respectively (P=0.552, Kruskal-Wallis Test). There were no differences between the groups for the number of adherent amoebae and a significant majority had only one adherent trophozoite per EC. No difference in adherence was seen with increasing exposure time. Factors other than amoebic adherence to superficial corneal EC are responsible for the increased incidence of Acanthamoeba keratitis in lens wearers.