Khan M. Iftekharuddin

Optical interconnection networks

Publisher Summary This chapter discusses the optical interconnection networks. Ongoing advances in high-speed integrated circuit device technology along with increasing demand in data communication, digital signal and image processing, neural network, and machine vision systems necessitate the design of efficient interconnections topology. In electrical interconnections, the scope of the systems is limited by interference and the planar nature of very large-scale integrated (VLSI) circuits. The chip speed—in particular, is limited by interconnect delays. Intrachip connections pose further difficulty. Optical interconnects, because of their inherent parallelism, high speed, and negligible mutual interference, offer a reasonable solution to this intricate problem. Optical techniques may provide an alternative means for fast, secure interconnections of the devices directly to the interior of a chip. Optical computing research involving optical bistable devices, nonlinear Fabry–Perots, hybrid electrooptic devices, and optical interconnects has contributed to two distinct, parallel interconnect architectures. One approach uses integrated optics to interconnect optical logic devices, while the other involves 2D arrays of devices interconnected in free-space. Free-space interconnection, in turn, is classified as either focused (obtained by lens, beamsplitter, etc.) or unfocused (obtained by imaging light through holographic elements). The index-guided waveguide interconnects may be either space variant or space invariant.

Feature-based neural wavelet optical character recognition system

A hybrid character recognition system that uses a feature-extraction method is proposed. The features are extracted using a wavelet transform, preclassified using a k-nearest-neighbor-based neural net and subsequently postprocessed using an optical correlator. This feature-based neural wavelet optical architecture is then tested on blurred character images.

Guest Editorial: Special Section on Computer Arithmetic for Optical Computing

Around the same time, non-binary systemssuch as multiple valued logic ~MVL!~Ref. 3! alsoachieved prominence both in optics and digital comput-ing. Since then a large number of papers have been pub-lished in optical computer arithmetic. This special sectionis an attempt to capture current research in computerarithmetic for optical computing. The five major areasthat are presented in this section are MSD-based algo-rithm and systems, optimization of MVL, novel architec-tures for binary optical computing, high accuracy analogoptical system implementations, and system studies forfault-tolerance and accuracy. Some of the papers mayhave overlap of two or more areas with one primary fo-cus; they are pointed out in the following discussion.The largest cluster of papers appears in the area ofsigned-digit arithmetic and its implementation. A numberof different techniques for addition, multiplication and di-vision are proposed by several authors. The number sys-tems addressed include redundant binary, MSD binary,negabinary, MSD trinary, recoded trinary and MSD qua-ternary. In terms of number of steps, addition/subtractionin single, dual and triple step has been proposed. Whilethe MSD number system leads to higher information den-sity, if the number of steps is reduced, the truth tablesmay become humongous, which may impose challengingrequirements on the actual implementation. Techniquesfor reducing the cost of such implementations have beenaddressed by some authors. Proposed implementations in-clude space-variant logic array, correlator~composite andpseudo-inverse filter! and non-holographic content ad-dressable memory ~CAM! using electron-trapping mate-rial. Several authors have proposed novel algorithms andtheir possible optical implementations while others havesuggested implementations and/or optimization on knownalgorithms.The first paper in the area of signed-digit arithmetic byLi et al. presents negabinary arithmetic operations for ad-dition, subtraction and multiplication and implementsthem using electron trapping material. A carry free addi-tion technique in signed-digit negabinary ~SDN! is pre-sented with a conversion technique from SDN to normalnegabinary. Zhang and Karim propose modified two-step,one-step, canonical and three-input algorithms for addi-tion of redundant binary numbers and provide architectureand encoding for corresponding optical space-variantimplementation. Cherri demonstrates single step trinaryand quaternary signed-digit circuits. In general, the reduc-tion in step increases the complexity of the truth table.However, Cherri overcomes the problem by smart digitgrouping to reduce the number of rules and an intelligentpixel encoding to implement the system within a certainspace-bandwidth product. Huang, Itoh and Yatagai pro-pose a new technique for high-speed 2-D data array addi-tion and multiplication based on binary MSD addition anddigit-decomposition-plane representation. Huang, Itohand Yatagai generate all the partial products in paralleland propose to add them using an MSD adder tree. It isinteresting to note that they perform multiplication opera-tion using five elementary operations such as bitwiseproduct, duplication, shifting, masking and magnification.In the next paper, Alam introduces trinary division tech-nique based on recoded trinary addition and multiplica-tion. The proposed implementation uses a pseudo-inversefilter correlator. The last two papers in this group byAhmed, Awwal and Power and by Zhang and Karim pro-pose novel implementations of trinary and binary MSDalgorithm. Ahmed, Awwal and Power implement an MSDtrinary adder using composite phase-only-filter correlatorarchitecture. In this framework, the truth table rules are

Characterization of harmonic wavelet joint transform (HWJT) in image de-noising

Harmonic Wavelet Joint Transform (HWJT) has recently been proposed for automatic target recognition applications. The preliminary study shows improved noise tolerance for HWJT. In this paper, we investigate the application of JHWT in image de-noising. De-noising characteristics of this HWJTC have been studied and noise removal performances are demonstrated using HWJT correlator model for clutter image. To benchmark its performance, we compare its performance with the performances of well-established image de-noising techniques, such as Wavelet Packet. The image used for our simulation is a Synthetic Aperture Radar image from DARPA Moving and Stationary Target Recognition Program with angle of depression of 15 degree(s). Simulation results are presented to validate the performance of the proposed technique.

Front Matter: Volume 9970

A wide range of chemical compositions are possible to design photopolymers. These materials are also appealing for diffractive and holographic applications due to their capability to modulate the refractive index and/or the thickness when illuminated. Some of the most interesting applications for photopolymers are the optical data storage, security systems, surface relief photo-embossing, diffractive and refractive optical elements, holographic elements, solar concentrators, optical detectors and hybrid optoelectronic 3-D circuitry. Looking for an optimized chemical composition for each application many different photopolymers compositions may be needed enabling a variety of materials properties: materials with low or high rates of monomer diffusion, low or high values of shrinkage, long or short length of polymer chains and low or high light absorption. In parallel many models are presented in order to predict the photopolymers recording and the post exposure evolution. In this work we use one of these experimentally checked models to study the influence of the material characteristics in the final diffractive optical element recorded in the material. We study the changes in the surface relief and in the refractive index in order to understand the importance of each material property in the final diffractive optical element recorded.

Front Matter: Volume 8498

This PDF file contains the front matter associated with SPIE Proceedings Volume 8498, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Low-Pass and High-Pass Filtering

True images are usually degraded during image acquisition. Image restoration is for restoring true images from their observed but degraded versions; it is often used for preprocessing observed images so that subsequent image processing and analysis becomes more reliable. Among many different types of degradations, point degradations (or, noise) and spatial degradations (or, blurring) are most common in applications. This article introduces some fundamental image denoising and image deblurring methods. ——————————————– Observed images generated by image acquisition devices are usually not exactly the same as the true images, but are instead degraded versions of their true images[10][19]. Degradations can occur in the entire process of image acquisition, and there are many different sources of degradation. For instance, in aerial reconnaissance, astronomy, and remote sensing, images are often degraded by atmospheric turbulence, aberrations of the optical system, or relative motion between the camera and the object. Image degradations can be classified into several categories, among which point degradations (or, noise) and spatial degradations (or, blurring) are most common in applications. Other types of degradations involve chromatic or temporal effects. For a detailed discussion about formation and description of various degradations, read [1]. Image restoration is a process to restore an original image f from its observed but degraded version Z. Since edges are important structures of the true image, they should be preserved during image restoration. In the literature, a commonly used model for describing the relationship between f and Z is Z(x, y) = h ⊗ f(x, y) + ε(x, y), for (x, y) ∈ Ω, (1) where h is a 2-D point spread function (psf) describing the spatial blur, ε(x, y) is a pointwise noise at (x, y), Ω is the design space, and h ⊗ f denotes the convolution between h and f . In model (1), the spatial blur is assumed to be linear and location invariant, and the pointwise noise is additive, which may

Wavelet Series Expansion

Discrete frequency transforms provide a method to obtain a global view of data within a window. Discrete cosine transform is the frequency transform for practical image processing because of its excellent energy compaction property. Another reason for its popularity is the existence of a fast implementation for the algorithm. In this article, the basics of the discrete cosine transform, its use for image compression, and a few variants of the transform coding for compression are considered.

Discrete Cosine Transform

Discrete frequency transforms provide a method to obtain a global view of data within a window. Discrete cosine transform is the frequency transform for practical image processing because of its excellent energy compaction property. Another reason for its popularity is the existence of a fast implementation for the algorithm. In this article, the basics of the discrete cosine transform, its use for image compression, and a few variants of the transform coding for compression are considered.

Front Matter: Volume 8134

This PDF file contains the front matter associated with SPIE Proceedings Volume 8134, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.