Bipolar optical neural networks using ferroelectric liquid crystal devices

Gomes, Cleber Magalhaes; Sekine, H.; Yamazaki, Takashi; Kobayashi, Shunsuke

doi:10.1016/s0893-6080(05)80016-4

Cited by 14 publications

(2 citation statements)

References 5 publications

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“…On the other hand, ferroelectric liquid crystal ͑FLC͒ devices have been used for an optical neural network as an optically addressed SLM. 13 The FLC SLM has the attractive features of high sensitivity, high gain, fast response, multiple-exposure capability, and high resolution as a real-time optical spatial light modulator. By using such FLC devices, a size of 50ϫ50 neurons can be available, and one iteration of the TA network will be performed within less than 1 ms.…”

Section: Input Patternsmentioning

confidence: 99%

Optical neural networks with terminal attractors for pattern recognition

Lin

Ohtsubo

1996

Opt. Eng

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The neural network system with terminal attractors is proposed for pattern recognition. By the introduction of the terminal attractors, the spurious states of the energy function in the Hopfield neural networks can be avoided and a unique solution with global minimum is obtained. The computer simulations show the usefulness of the method for pattern recognition. Based on the terminal attractor neural network, we proposed the optical neural network with the terminal attractor model. The results show that the recognition rate is considerably enhanced by the introduction of the terminal attractors.

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Section: Input Patternsmentioning

confidence: 99%

Optical neural networks with terminal attractors for pattern recognition

Lin

Ohtsubo

1996

Opt. Eng

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“…One of the promising devices is a ferroelectric liquid-crystal spatial light modulator 1FLC SLM2 that can be used in various fields of optical information processing such as real-time speckle metrology, 4,5 real-time optical correlation, 6 optical phase conjugation, 7 realtime holography, 8,9 and optoelectronic neural networks. 10 The FLC SLM has attractive features of high sensitivity, high gain, fast response, multipleexposure capability, and high resolution as a realtime optical SLM.…”

Section: Introductionmentioning

confidence: 99%

Real-time optical image subtraction and edge enhancement using ferroelectric liquid-crystal devices based on speckle modulation

1996

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We carried out real-time optical image subtraction and edge enhancement based on a speckle modulation technique by using ferroelectric liquid-crystal polarization switches and a ferroelectric liquid-crystal spatial light modulator. A ferroelectric liquid-crystal spatial light modulator is employed as a real-time and multiple-exposure optical device, and successful results are obtained from three-exposure images modulated by speckles. Thus, image subtraction and edge enhancement are realized in real time. The whole operation is performed within several milliseconds with modest operating conditions. Because the spatial light modulator has a high resolution of greater than 100 line pairs/mm and can store fine speckle patterns, the image qualities we obtained are quite satisfactory.

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Optical neural networks with unipolar weights

Dickey

DeLaurentis

1993

Optics Communications

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Bipolar optical neural networks using ferroelectric liquid crystal devices

Cited by 14 publications

References 5 publications

Optical neural networks with terminal attractors for pattern recognition

Optical neural networks with terminal attractors for pattern recognition

Real-time optical image subtraction and edge enhancement using ferroelectric liquid-crystal devices based on speckle modulation

Optical neural networks with unipolar weights

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