110 research outputs found
Low dark current InAs/GaSb type-II superlattice infrared photodetectors with resonant tunnelling filters
InAs/GaSb type-II strained-layer superlattice (SLS) photovoltaic infrared (IR) detectors are currently of great interest for mid- and long-wave IR detection. A novel technique of reducing detector dark current by inserting resonant tunnelling barriers into a conventional InAs/GaSb SLS is investigated. The GaSb/InAs/GaSb resonant tunnelling double barrier heterostructure was designed to be periodically inserted into a conventional InAs/GaSb SLS detector to block thermally excited electrons, while permitting photo-excited electrons to tunnel through. The measured dark current density of the tunnelling InAs/GaSb SLS detector in the entire negative bias range is lower than that of the conventional SLS detector by a factor of about 3.8 at 77 K. At 84 K, the Johnson-noise-limited detectivity of the tunnelling detector, measured at 4 µm, is 18% higher than that of the conventional detector. Both the conventional and the tunnelling SLS detectors demonstrated high-temperature operation, up to 300 K.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58092/2/d6_23_015.pd
Integral Equation Methods for Electrostatics, Acoustics, and Electromagnetics in Smoothly Varying, Anisotropic Media
[EN] We present a collection of well-conditioned integral equation methods for the solution of electrostatic, acoustic, or electromagnetic scattering problems involving anisotropic, inhomogeneous media. In the electromagnetic case, our approach involves a minor modification of a classical formulation. In the electrostatic or acoustic setting, we introduce a new vector partial differential equation, from which the desired solution is easily obtained. It is the vector equation for which we derive a well-conditioned integral equation. In addition to providing a unified framework for these solvers, we illustrate their performance using iterative solution methods coupled with the FFT-based technique of [F. Vico, L. Greengard, M. Ferrando, J. Comput. Phys., 323 (2016), pp. 191-203] to discretize and apply the relevant integral operators.The work of the authors was partially supported by the Spanish Ministry of Science and Innovation under project TEC2016-78028-C3-3-P and the U.S. Department of Energy under grant DE-FG02-86ER53223.Imbert-Gérard, L.; Vico BondÃa, F.; Greengard, L.; Ferrando Bataller, M. (2019). Integral Equation Methods for Electrostatics, Acoustics, and Electromagnetics in Smoothly Varying, Anisotropic Media. SIAM Journal on Numerical Analysis. 57(3):1020-1035. https://doi.org/10.1137/18M1187039S1020103557
Space Weathering Experiments on Spacecraft Materials
A project to investigate space environment effects on specific materials with interest to remote sensing was initiated in 2016. The goal of the project is to better characterize changes in the optical properties of polymers found in multi-layered spacecraft insulation (MLI) induced by electron bombardment. Previous analysis shows that chemical bonds break and potentially reform when exposed to high energy electrons like those seen in orbit. These chemical changes have been shown to alter a material's optical reflectance, among other material properties. This paper presents the initial experimental results of MLI materials exposed to various fluences of high energy electrons, designed to simulate a portion of the geosynchronous Earth orbit (GEO) space environment. It is shown that the spectral reflectance of some of the tested materials changes as a function of electron dose. These results provide an experimental benchmark for analysis of aging effects on satellite systems which can be used to improve remote sensing and space situational awareness. They also provide preliminary analysis on those materials that are most likely to comprise the high area-to-mass ratio (HAMR) population of space debris in the geosynchronous orbit environment. Finally, the results presented in this paper serve as a proof of concept for simulated environmental aging of spacecraft polymers that should lead to more experiments using a larger subset of spacecraft materials
Quantum Size Effects on the Chemical Sensing Performance of Two-Dimensional Semiconductors
We investigate the role of quantum confinement on the performance of gas
sensors based on two-dimensional InAs membranes. Pd-decorated InAs membranes
configured as H2 sensors are shown to exhibit strong thickness dependence, with
~100x enhancement in the sensor response as the thickness is reduced from 48 to
8 nm. Through detailed experiments and modeling, the thickness scaling trend is
attributed to the quantization of electrons which favorably alters both the
position and the transport properties of charge carriers; thus making them more
susceptible to surface phenomena
Single spin asymmetry measurements for inclusive productions in and \pi^-+\p_{\uparrow}\to \pi^0+X reactions at 70 and 40 GeV respectively
The inclusive asymmetries were measured in reactions and at 70 and 40 GeV/c respectively. The
measurements were made at the central region (for the first reaction) and
asymmetry is compatible with zero in the entire measured region. For the
second reaction the asymmetry is zero for small region () and increases with growth of . Averaged
over the interval the asymmetry was .Comment: 4 pages, 2 figures; Presented at SPIN-2004 at Trieste, October
10-16,200
Preserving Differential Privacy in Convolutional Deep Belief Networks
The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions
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