22,807 research outputs found
Formation of Low Threshold Voltage Microlasers
Vertical cavity surface emitting lasers (VCSELs) with threshold voltages of 1.7V have been fabricated. The resistance-area product in these new vertical cavity lasers is comparable to that of edge-emitting lasers, and threshold currents as low as 3 mA have been measured. Molecular beam epitaxy was used to grow n-type mirrors, a quantum well active region, and a heavily Be-doped p-contact. After contact definition and alloying, passive high-reflectivity mirrors were deposited by reactive sputter deposition of SiO2/Si3N4 to complete the laser cavity
Communication Theoretic Data Analytics
Widespread use of the Internet and social networks invokes the generation of
big data, which is proving to be useful in a number of applications. To deal
with explosively growing amounts of data, data analytics has emerged as a
critical technology related to computing, signal processing, and information
networking. In this paper, a formalism is considered in which data is modeled
as a generalized social network and communication theory and information theory
are thereby extended to data analytics. First, the creation of an equalizer to
optimize information transfer between two data variables is considered, and
financial data is used to demonstrate the advantages. Then, an information
coupling approach based on information geometry is applied for dimensionality
reduction, with a pattern recognition example to illustrate the effectiveness.
These initial trials suggest the potential of communication theoretic data
analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan.
201
Cellular neural networks for motion estimation and obstacle detection
Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN). It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible
Eigenvector Model Descriptors for Solving an Inverse Problem of Helmholtz Equation: Extended Materials
We study the seismic inverse problem for the recovery of subsurface
properties in acoustic media. In order to reduce the ill-posedness of the
problem, the heterogeneous wave speed parameter to be recovered is represented
using a limited number of coefficients associated with a basis of eigenvectors
of a diffusion equation, following the regularization by discretization
approach. We compare several choices for the diffusion coefficient in the
partial differential equations, which are extracted from the field of image
processing. We first investigate their efficiency for image decomposition
(accuracy of the representation with respect to the number of variables and
denoising). Next, we implement the method in the quantitative reconstruction
procedure for seismic imaging, following the Full Waveform Inversion method,
where the difficulty resides in that the basis is defined from an initial model
where none of the actual structures is known. In particular, we demonstrate
that the method is efficient for the challenging reconstruction of media with
salt-domes. We employ the method in two and three-dimensional experiments and
show that the eigenvector representation compensates for the lack of low
frequency information, it eventually serves us to extract guidelines for the
implementation of the method.Comment: 45 pages, 37 figure
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