General-purpose acousto-optic connectionist processor

Naughton, Thomas J.

doi:10.1117/1.602167

Cited by 17 publications

(14 citation statements)

References 16 publications

(25 reference statements)

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“…Numerous physical implementations of the latter class exist, and example applications include fast pattern recognition and matrix-vector algebra [35,90]. There have been much resources devoted to designs, implementations and algorithms for such optical information processing architectures (for example see [4,15,29,35,52,55,57,62,77,90,101,27] and their references).…”

Section: Optical Models Of Computation and Computational Complexitymentioning

confidence: 99%

“…The model was originally proposed by Naughton [63,64]. The CSM is inspired by analog Fourier optical computing architectures, specifically pattern recognition and matrix algebra processors [35,62]. For example, these architectures have the ability to do unit time Fourier transformation using coherent (laser) light and lenses.…”

Section: Continuous Space Machine (Csm)mentioning

confidence: 99%

“…A SLM could be used to encode the image onto the expanded and collimated laser beam. One could write to a SLM offline (expose photographic film, or laser print or relief etch a transparency) or online (in the case of a liquid-crystal display [102,62,91] or holographic material [24,74]). The functions h and v could be effected using two convex cylindrical lenses, oriented horizontally and vertically, respectively [90,35,62,34].…”

Section: Optical Realisationmentioning

confidence: 99%

“…An application that, further, was tolerant to the inherent inaccuracies and noise of analog optics was optical neural networks [28,20] including online neural learning in the presence of noise [62].…”

Section: Analog Optical Numerical Computationmentioning

confidence: 99%

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Optical computing

Woods¹,

Naughton²

2009

Applied Mathematics and Computation

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We consider optical computers that encode data using images and compute by transforming such images. We give an overview of a number of such optical computing architectures, including descriptions of the type of hardware commonly used in optical computing, as well as some of the computational efficiencies of optical devices. We go on to discuss optical computing from the point of view of computational complexity theory, with the aim of putting some old, and some very recent, results in context. Finally, we focus on a particular optical model of computation called the continuous space machine. We describe some results for this model including characterisations in terms of well-known complexity classes.

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Section: Optical Models Of Computation and Computational Complexitymentioning

confidence: 99%

Section: Continuous Space Machine (Csm)mentioning

confidence: 99%

Section: Optical Realisationmentioning

confidence: 99%

Section: Analog Optical Numerical Computationmentioning

confidence: 99%

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Optical computing

Woods¹,

Naughton²

2009

Applied Mathematics and Computation

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“…Applications requiring matrix algebra benefited greatly from the tightly-coupled parallelism afforded by optics. An application that, further, was tolerant to the inherent inaccuracies and noise of analog optics was optical neural networks [40] including online neural learning in the presence of noise [41].…”

Section: Analog Optical Numerical Computationmentioning

confidence: 99%

Phase in Optical Image Processing

Naughton¹,

Rastogi²,

Hack³

2010

AIP Conference Proceedings

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Abstract. The use of phase has a long standing history in optical image processing, with early milestones being in the field of pattern recognition, such as VanderLugt's practical construction technique for matched filters, and (implicitly) Goodman's joint Fourier transform correlator. In recent years, the flexibility afforded by phase-only spatial light modulators and digital holography, for example, has enabled many processing techniques based on the explicit encoding and decoding of phase. One application area concerns efficient numerical computations. Pushing phase measurement to its physical limits, designs employing the physical properties of phase have ranged from the sensible to the wonderful, in some cases making computationally easy problems easier to solve and in other cases addressing mathematics' most challenging computationally hard problems. Another application area is optical image encryption, in which, typically, a phase mask modulates the fractional Fourier transformed coefficients of a perturbed input image, and the phase of the inverse transform is then sensed as the encrypted image. The inherent linearity that makes the system so elegant mitigates against its use as an effective encryption technique, but we show how a combination of optical and digital techniques can restore confidence in that security. We conclude with the concept of digital hologram image processing, and applications of same that are uniquely suited to optical implementation, where the processing, recognition, or encryption step operates on full field information, such as that emanating from a coherently illuminated real-world three-dimensional object.

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Optical Computing and Computational Complexity

Woods

2006

Lecture Notes in Computer Science

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This work concerns the computational complexity of a model of computation that is inspired by optical computers. The model is called the continuous space machine and operates in discrete timesteps over a number of two-dimensional images of fixed size and arbitrary spatial resolution. The (constant time) operations on images include Fourier transformation, multiplication, addition, thresholding, copying and scaling. We survey some of the work to date on the continuous space machine. This includes a characterisation of the power of an important discrete restriction of the model. Parallel time corresponds, within a polynomial, to sequential space on Turing machines, thus satisfying the parallel computation thesis. A characterisation of the complexity class NC in terms of the model is also given. Thus the model has computational power that is (polynomially) equivalent to that of many well-known parallel models. Such characterisations give a method to translate parallel algorithms to optical algorithms and facilitate the application of the complexity theory toolbox to optical computers. In the present work we improve on these results. Specifically we tighten a lower bound and present some new resource trade-offs.1 That is, finding lower and upper bounds on computational power in terms of known complexity classes.

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General-purpose acousto-optic connectionist processor

Cited by 17 publications

References 16 publications

Optical computing

Optical computing

Phase in Optical Image Processing

Optical Computing and Computational Complexity

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