12,775 research outputs found
Roadmap on optical security
Postprint (author's final draft
dARTMAP: A Neural Network for Fast Distributed Supervised Learning
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning, An implementation algorithm here describes one class of dARTMAP networks. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule for improved contrast control at the coding field. A dARTMAP system reduces to fuzzy ARTMAP when coding is winner-take-all. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657
Optimal Radiometric Calibration for Camera-Display Communication
We present a novel method for communicating between a camera and display by
embedding and recovering hidden and dynamic information within a displayed
image. A handheld camera pointed at the display can receive not only the
display image, but also the underlying message. These active scenes are
fundamentally different from traditional passive scenes like QR codes because
image formation is based on display emittance, not surface reflectance.
Detecting and decoding the message requires careful photometric modeling for
computational message recovery. Unlike standard watermarking and steganography
methods that lie outside the domain of computer vision, our message recovery
algorithm uses illumination to optically communicate hidden messages in real
world scenes. The key innovation of our approach is an algorithm that performs
simultaneous radiometric calibration and message recovery in one convex
optimization problem. By modeling the photometry of the system using a
camera-display transfer function (CDTF), we derive a physics-based kernel
function for support vector machine classification. We demonstrate that our
method of optimal online radiometric calibration (OORC) leads to an efficient
and robust algorithm for computational messaging between nine commercial
cameras and displays.Comment: 10 pages, Submitted to CVPR 201
Distributed ARTMAP
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression. The model's computational learning rules correspond to paradoxical cortical data.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Analytical and experimental studies of graphite-epoxy and boron-epoxy angle ply laminates in shear
The results of a comparison study between a test program on the inelastic response under inplane shear over a wide range of graphite-epoxy and boron-epoxy angle-ply laminates are reported. This investigation was aimed at evaluating the applicability and adequacy of these analyses to predict satisfactorily the responses of angle-ply laminates. It was observed that these analytical tools are inadequate for this purpose as they fail to predict with sufficient confidence the shape of response and in particular the strength values associated with a given laminate configuration. Consequently, they do not provide the sought-after information about failure mechanisms which trigger failure of a particular designed laminate
Study of information transfer optimization for communication satellites
The results are presented of a study of source coding, modulation/channel coding, and systems techniques for application to teleconferencing over high data rate digital communication satellite links. Simultaneous transmission of video, voice, data, and/or graphics is possible in various teleconferencing modes and one-way, two-way, and broadcast modes are considered. A satellite channel model including filters, limiter, a TWT, detectors, and an optimized equalizer is treated in detail. A complete analysis is presented for one set of system assumptions which exclude nonlinear gain and phase distortion in the TWT. Modulation, demodulation, and channel coding are considered, based on an additive white Gaussian noise channel model which is an idealization of an equalized channel. Source coding with emphasis on video data compression is reviewed, and the experimental facility utilized to test promising techniques is fully described
Earthquake resistance of composite beam-columns.
Imperial Users onl
Ternary Weight Networks
We introduce ternary weight networks (TWNs) - neural networks with weights
constrained to +1, 0 and -1. The Euclidian distance between full (float or
double) precision weights and the ternary weights along with a scaling factor
is minimized. Besides, a threshold-based ternary function is optimized to get
an approximated solution which can be fast and easily computed. TWNs have
stronger expressive abilities than the recently proposed binary precision
counterparts and are thus more effective than the latter. Meanwhile, TWNs
achieve up to 16 or 32 model compression rate and need fewer
multiplications compared with the full precision counterparts. Benchmarks on
MNIST, CIFAR-10, and large scale ImageNet datasets show that the performance of
TWNs is only slightly worse than the full precision counterparts but
outperforms the analogous binary precision counterparts a lot.Comment: 5 pages, 3 fitures, conferenc
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