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Dive into the research topics where John H. Hong is active.

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Featured researches published by John H. Hong.


Neural Networks | 1988

Higher Order Associative Memories and Their Optical Implementations

Demetri Psaltis; Cheol Hoon Park; John H. Hong

Abstract The properties of higher order memories are described. The non-redundant, up to Nth order polynomial expansion of N-dimensional binary vectors is shown to yield orthogonal feature vectors. The properties of expansions that contain only a single order are investigated in detail and the use of the sum of outer product algorithm for training higher order memories is analyzed. Optical implementations of quadratic associative memories are described using volume holograms for the general case and planar holograms for shift invariant memories.


Optical Engineering | 1995

Volume holographic memory systems: techniques and architectures

John H. Hong; Ian McMichael; Tallis Y. Chang; William R. Christian; Eung Gi Paek

Although the concept of using multiplexed holography for data storage has been considered for some time, recent advances in several critical device technologies along with developments in storage materials have greatly enhanced the likelihood of successful implementations. We review several basic architectural concepts along with various multiplexing options and associated techniques for holographic data storage.


Journal of The Optical Society of America A-optics Image Science and Vision | 1992

Cross-talk-limited storage capacity of volume holographic memory

Claire Gu; John H. Hong; Ian McMichael; Ragini Saxena; Fai Mok

We consider the storage density limited by the cross talk between stored pages during readout. We review some earlier work done in this area and present new theoretical work that characterizes the storage capacity limitations due to the cross talk. The results show that because of the presence of degeneracy noise, storage capacity does not benefit from expanding the reference points from one dimension to two dimensions. An optimum configuration that fully utilizes storage capacity and completely eliminates degeneracy noise is given for an angularly multiplexed volume holographic memory system.


Optics Communications | 1997

Interferometric optical tweezers

Arthur E. Chiou; Wen Wang; Greg J. Sonek; John H. Hong; Michael W. Berns

Abstract We report the first experimental demonstration of an optical trap that uses interference fringes for the trapping and micro-manipulation of microscopic objects. The finges can be generated either by two-beam interference or by projecting a reduced image of a Ronchi ruling on the sample plane. Polystyrene beads of a few microns in diameter can be trapped and held in a bright region of a set of interference fringes and subsequently moved to a designated position by sweeping the fringes across the field of view. The potential advantages of extending this technique to the trapping of rod-shaped samples and for independent micro-manipulation of two or more particles are discussed.


Applied Optics | 1988

Optical interconnection using photorefractive dynamic holograms

Pochi Yeh; Arthur E. T. Chiou; John H. Hong

On decrit une nouvelle methode pour definir une nouvelle configuration des interconnexions optiques en utilisant des hologrammes dynamiques dans des cristaux photorefractifs


Applied Optics | 1996

Compact holographic storage demonstrator with rapid access

Ian McMichael; William Christian; David Pletcher; Tallis Y. Chang; John H. Hong

We discuss the design of a complete, compact holographic storage demonstrator based on a combination of spatial and angular multiplexing and using acousto-optic deflectors for rapid nonmechanical access. We also describe the implementation of this design and preliminary results of both the analog storage and the retrieval of 20,000 holograms in twenty 1-mm-thick layers of a lithium niobate crystal and of the digital storage and error-free retrieval of color images by using error-correcting techniques.


Applied Optics | 1990

Optical pattern classifier with Perceptron learning

John H. Hong; Scott Campbell; Pochi Yeh

An optical realization of a single layer pattern classifier is described in which Perceptron learning is implemented to train the system weights. Novel use of the Stokess principle of reversability for light is made to realize both additive and subtractive weight modifications necessary for true Perceptron learning. This is achieved by using a double Mach-Zehnder interferometer in conjunction with photorefractive hologram recording. Experimental results are given which show the high quality subtractive changes that can be made.


Optics Letters | 2002

Holographic recording of fast events on a CCD camera

Zhiwen Liu; Martin Centurion; George Panotopoulos; John H. Hong; Demetri Psaltis

We report on holographic recording of nanosecond events on a conventional CCD camera. Three frames of an air-discharge event, with resolution of 5.9 ns and frame interval of 12 ns, are recorded in a single CCD frame. Each individual frame is reconstructed by digital filtering of the CCD frame, since successively recorded holograms are centered at different carrier frequencies in the spatial frequency domain.


Optical Engineering | 1989

Photorefractive Nonlinear Optics And Optical Computing

Pochi Yeh; Arthur E. T. Chiou; John H. Hong; Paul H. Beckwith; Tallis Y. Chang

This paper describes various nonlinear optical phenomena in photorefractive media and selected applications in optical computing. These phenomena include optical phase conjugation, two- and four-wave mixing, and real-time holography. The applications include image amplification and subtraction, logic and matrix operations, and optical interconnection.


Optics Letters | 1990

Diffraction efficiency of strong volume holograms

John H. Hong; Pochi Yeh; Demetri Psaltis; David J. Brady

We investigate the diffraction efficiency of strong volume holograms in which the coupling parameter is several times that needed for maximum diffraction efficiency. We discuss the implications of our findings on photorefractive implementations of various neural network systems.

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Demetri Psaltis

École Polytechnique Fédérale de Lausanne

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Claire Gu

Pennsylvania State University

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Eung Gi Paek

National Institute of Standards and Technology

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