1,106 research outputs found
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
Sequential multi-photon strategy for semiconductor-based terahertz detectors
A semiconductor-based terahertz-detector strategy, exploiting a
bound-to-bound-to-continuum architecture, is presented and investigated. In
particular, a ladder of equidistant energy levels is employed, whose step is
tuned to the desired detection frequency and allows for sequential multi-photon
absorption. Our theoretical analysis demonstrates that the proposed
multi-subband scheme could represent a promising alternative to conventional
quantum-well infrared photodetectors in the terahertz spectral region.Comment: Submitted to Journal of Applied Physic
Digital Signal Processing
Contains table of contents for Part III, table of contents for Section 1, an introduction and reports on seventeen research projects.National Science Foundation FellowshipNational Science Foundation (Grant ECS 84-07285)National Science Foundation (Grant MIP 87-14969)U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Scholarship from the Federative Republic of BrazilU.S. Air Force - Electronic Systems Division (Contract F19628-85-K-0028)AT&T Bell Laboratories Doctoral Support ProgramCanada, Bell Northern Research ScholarshipCanada, Fonds pour la Formation de Chercheurs et I'Aide a la Recherche Postgraduate FellowshipSanders Associates, Inc.OKI Semiconductor, Inc.Tel Aviv University, Department of Electronic SystemsU.S. Navy - Office of Naval Research (Contract N00014-85-K-0272)Natural Sciences and Engineering Research Council of Canada, Science and Engineering Scholarshi
Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial
Multirate digital filters and filter banks find application in communications, speech processing, image compression, antenna systems, analog voice privacy systems, and in the digital audio industry. During the last several years there has been substantial progress in multirate system research. This includes design of decimation and interpolation filters, analysis/synthesis filter banks (also called quadrature mirror filters, or QMFJ, and the development of new sampling theorems. First, the basic concepts and building blocks in multirate digital signal processing (DSPJ, including the digital polyphase representation, are reviewed. Next, recent progress as reported by several authors in this area is discussed. Several applications are described, including the following: subband coding of waveforms, voice privacy systems, integral and fractional sampling rate conversion (such as in digital audio), digital crossover networks, and multirate coding of narrow-band filter coefficients. The M-band QMF bank is discussed in considerable detail, including an analysis of various errors and imperfections. Recent techniques for perfect signal reconstruction in such systems are reviewed. The connection between QMF banks and other related topics, such as block digital filtering and periodically time-varying systems, based on a pseudo-circulant matrix framework, is covered. Unconventional applications of the polyphase concept are discussed
An overview on deep learning-based approximation methods for partial differential equations
It is one of the most challenging problems in applied mathematics to
approximatively solve high-dimensional partial differential equations (PDEs).
Recently, several deep learning-based approximation algorithms for attacking
this problem have been proposed and tested numerically on a number of examples
of high-dimensional PDEs. This has given rise to a lively field of research in
which deep learning-based methods and related Monte Carlo methods are applied
to the approximation of high-dimensional PDEs. In this article we offer an
introduction to this field of research, we review some of the main ideas of
deep learning-based approximation methods for PDEs, we revisit one of the
central mathematical results for deep neural network approximations for PDEs,
and we provide an overview of the recent literature in this area of research.Comment: 23 page
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