Chii-Maw Uang

Submicrometer displacement sensing using inner-product multimode fiber speckle fields.

A multimode fiber sensor using the intensity inner product of speckle fields is presented. The sensitivity and the dynamic range of the displacement sensing are quantitatively analyzed. We show that the sensitivity of displacement can be in the submicrometer range. Experimental performances show that the results are consistent with the calculated results.

Multimode fiber sensing by using mean-absolute speckle-intensity variation

We present a fiber-sensing technique that detects the mean-absolute speckle-intensity variation between the updated and the reference speckle pattern for determining the environmental perturbation factor (e.g., displacement, temperature, pressure, acoustic wave). We show that the proposed technique is highly sensitive and simple. One of the major advantages of the proposed technique is that it can perform a fast-response measurement with off-the-shelf electronic hardware. Experimental data for submicrometer displacement as well as temperature measurement are provided. To extend the dynamic range of the proposed technique, one can use an updated reference speckle pattern.

Phase modulation depth for a real-time kinoform using a liquid crystal television

Interest in the liquid crystal television (LCTV) kinoform has been reported recently by several investigators. The technique requires a modulation depth of about 2π, which most of the commercially available LCTVs cannot attain. We discuss an experimental study of generating an LCTV kinoform in which the the modulation depth is limited by π. The major drawbacks of the ir phase variation are that, compared with 2π variation, it reduces the diffraction efficiency as well as the space bandwidth product to a degree of about one-fourth. Verification of these findings is reported.

Effects of thresholding in joint-transform correlation

The joint-transform power spectrum of two identical objects can be represented as a one-dimensional sinusoidal grating modulated by a Fourier transform, and the correlation peaks can be regarded as the first-order diffraction of the grating. The peak intensity and the width are then determined by the aperture and the modulation of the grating. Based on this analysis, it is shown that dc blocking, hard clipping, or binarization of the power spectrum results in higher correlation peak intensity and a narrower peak width. Direct-current blocking is also found to be preferable if the input pattern to the correlator is corrupted by noise.

Fiber specklegram sensing by means of an adaptive joint transform correlator

An adaptive joint transform correlator is used to process the speckle fields derived from a multimode optical fiber. The system is applied to evaluate changes in the speckle field when the fiber is used as a sensor element. We experimentally demonstrate that the sensitivity for temperature measurement and for submicrometer displacement measurement can be as high as 0.1°C and 0.2 μm, respectively. The duty cycle of the proposed fiber sensing system is about 2.5 s, which is limited by the microcomputer that we have used. To extend the dynamic range of sensing measurement, an updated algorithm can be used, by which the dynamic range of sensing capability can be further extended.

Gray-level discrete associative memory.

A gray-level discrete associative-memory neural network based on object decomposition and composition is presented. By decomposing a gray-level pattern into bipolar/binary subpatterns, a gray-level discrete associative memory can be constructed from the composition of the subpattern channel results. Preprocessing for removing dc bias and normalizing the gray-level scale is performed on the input gray-level pattern. This eliminates the mismatching and saturation problems caused by bias level, which shifts the pattern gray levels throughout the pattern. Computer-simulation and optical-experimental results for a gray-level interpattern association model are shown to be consistent with the theoretical model.

Unipolar interpattern association neural networks.

A method for constructing a unipolar interconnection weight matrix (IWM) of the interpattern association (IPA) model is presented. By a search of the redundant interconnection links, a method that removes all negative links is introduced. Since the IWM is unipolar, the difficulty of implementing positive and negative IWMs can be avoided. Computer simulation and experimental results show that the unipolar IWM IPA neural network performs better than the bipolar IWM IPA model.

SHIFT- AND ROTATION-INVARIANT INTERPATTERN HETEROASSOCIATION (IHA) MODEL

Abstract A shift- and rotation-invariant netral work using an interpattern heteroassociation (IHA) model is illustrated. The shift- and rotation-invariant properties are achived by using a set of binarized-encoded circular harmonic expansion (CHE) functions in the Fourier domain as the network training set. Because of the shift-invariant and symmetric properties of the modulus of Fourier spectrum, the problem of locating the center of the CHE functions can be avoided. Computer simulations and experimental demonstrations that demonstrate the shift- and the rotation-invariant properties of the proposed IHA neural net are provided.

A content-addressable polychromatic neural net using a (Ce:Fe)-doped LiNbO3 photorefractive crystal

Abstract A polychromatic neural net using a (Ce:Fe)-doped LiNbO 3 photorefractive crystal is presented. This neural net is a two-level high-content addressable memory. The first level is a polychromatic Hamming net for color image classification, and the second level is a mapping net, based on a photorefractive crystal associative memory. The major advantage of this neural net is the large storage capacity and it requires a fewer interconnection links. Experimental demonstrations are provided, in which is shown that the proposed neural net is consistent with the theoretical model.

IMPLEMENTATION OF A POLYCHROMATIC HAMMING NET FOR COLOR IMAGE CLASSIFICATION

A color exemplar-based Hamming net as applied to a color image classifier is presented. The color decomposition and composition techniques for constructing a polychromatic Hamming layer and winner-take-all (WTA) layer interconnection weight matrix (IWM) are discussed. We have simplified the WTA algorithm to reduce the number of iteration cycles in the WTA layer. To confirm the operation of the color Hamming net we provide simulation and experimental demonstrations.