3,720 research outputs found
Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
Many materials have distinct spectral profiles. This facilitates estimation
of the material composition of a scene at each pixel by first acquiring its
hyperspectral image, and subsequently filtering it using a bank of spectral
profiles. This process is inherently wasteful since only a set of linear
projections of the acquired measurements contribute to the classification task.
We propose a novel programmable camera that is capable of producing images of a
scene with an arbitrary spectral filter. We use this camera to optically
implement the spectral filtering of the scene's hyperspectral image with the
bank of spectral profiles needed to perform per-pixel material classification.
This provides gains both in terms of acquisition speed --- since only the
relevant measurements are acquired --- and in signal-to-noise ratio --- since
we invariably avoid narrowband filters that are light inefficient. Given
training data, we use a range of classical and modern techniques including SVMs
and neural networks to identify the bank of spectral profiles that facilitate
material classification. We verify the method in simulations on standard
datasets as well as real data using a lab prototype of the camera
Adaptive optical networks using photorefractive crystals
The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined. Limitations on the density of interconnections and the number of holographic associations which can be stored in photorefractive crystals are derived. Optical architectures for implementing various neural schemes are described. Experimental results are presented for one of these architectures
Adiabatic evolution on a spatial-photonic Ising machine
Combinatorial optimization problems are crucial for widespread applications
but remain difficult to solve on a large scale with conventional hardware.
Novel optical platforms, known as coherent or photonic Ising machines, are
attracting considerable attention as accelerators on optimization tasks
formulable as Ising models. Annealing is a well-known technique based on
adiabatic evolution for finding optimal solutions in classical and quantum
systems made by atoms, electrons, or photons. Although various Ising machines
employ annealing in some form, adiabatic computing on optical settings has been
only partially investigated. Here, we realize the adiabatic evolution of
frustrated Ising models with 100 spins programmed by spatial light modulation.
We use holographic and optical control to change the spin couplings
adiabatically, and exploit experimental noise to explore the energy landscape.
Annealing enhances the convergence to the Ising ground state and allows to find
the problem solution with probability close to unity. Our results demonstrate a
photonic scheme for combinatorial optimization in analogy with adiabatic
quantum algorithms and enforced by optical vector-matrix multiplications and
scalable photonic technology.Comment: 9 pages, 4 figure
Multilayer optical learning networks
A new approach to learning in a multilayer optical neural network based on holographically interconnected nonlinear devices is presented. The proposed network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self-aligning fashioias volume holographic gratings in photorefractive crystals. Parallel arrays of globally space-integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backward propagating error signal to form holographic interference patterns which are time integrated in the volume of a photorefractive crystal to modify slowly and learn the appropriate self-aligning interconnections. This multilayer system performs an approximate implementation of the backpropagation learning procedure in a massively parallel high-speed nonlinear optical network
Quantum-implemented selective reconstruction of high-resolution images
This paper proposes quantum image reconstruction. Input-triggered selection
of an image among many stored ones, and its reconstruction if the input is
occluded or noisy, has been simulated by a computer program implementable in a
real quantum-physical system. It is based on the Hopfield associative net; the
quantum-wave implementation bases on holography. The main limitations of the
classical Hopfield net are much reduced with the new, original --
quantum-optical -- implementation. Image resolution can be almost arbitrarily
increased.Comment: 4 pages, 15 figures, essential
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