Comparison among the variants of subspace-based optimization method for addressing inverse scattering problems: transverse electric case

The subspace-based optimization method (SOM) is an efficient approach to addressing the inverse scattering problem. In this paper, a comparative study, on the basis of numerical experiments, is conducted to evaluate the performances of variants of SOM, so as to find the optimal method for the determination of the ambiguous portion, which has a dominant influence on the computational cost and the reconstruction capability of the algorithm.     

Electromagnetic imaging within the contrast-source formulation by means of the multiscaling inexact Newton method

We introduce a new imaging technique that integrates the inexact Newton method into a multifocusing scheme within the contrast-source formulation of the inverse scattering problem. Representative results from an extensive validation concerned with both synthetic and experimental scattering data are reported to assess, also through comparisons, advantages and limitations of the proposed approach in terms of accuracy, robustness, and computational efficiency.     

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.     

Multi-parameter acoustic imaging of uniform objects in inhomogeneous soft tissue

The problem studied in this paper is ultrasound image reconstruction from frequency-domain measurements of the scattered field from an object with contrast in attenuation and sound speed. The case in which the object has uniform but unknown contrast in these properties relative to the background is considered. Background clutter is taken into account in a physically realistic manner by considering an exact scattering model for randomly located small scatterers that vary in sound speed. The resulting statistical characteristics of the interference are incorporated into the imaging solution, which includes application of a total-variation minimization-based approach in which the relative effect of perturbation in sound speed to attenuation is included as a parameter. Convex optimization methods provide the basis for the reconstruction algorithm. Numerical data for inversion examples are generated by solving the discretized Lippman-Schwinger equation for the object and speckle-forming scatterers in the background. A statistical model based on the Born approximation is used for reconstruction of the object profile. Results are presented for a two-dimensional problem in terms of classification performance and compared with minimum-l2-norm reconstruction. Classification using the proposed method is shown to be robust down to a signal-to-clutter ratio of less than 1 dB.     

Source-detector calibration in three-dimensional Bayesian optical diffusion tomography

Optical diffusion tomography is a method for reconstructing three-dimensional optical properties from light that passes through a highly scattering medium. Computing reconstructions from such data requires the solution of a nonlinear inverse problem. The situation is further complicated by the fact that while reconstruction algorithms typically assume exact knowledge of the optical source and detector coupling coefficients, these coupling coefficients are generally not available in practical measurement systems. A new method for estimating these unknown coupling coefficients in the three-dimensional reconstruction process is described. The joint problem of coefficient estimation and three-dimensional reconstruction is formulated in a Bayesian framework, and the resulting estimates are computed by using a variation of iterative coordinate descent optimization that is adapted for this problem. Simulations show that this approach is an accurate and efficient method for simultaneous reconstruction of absorption and diffusion coefficients as well as the coupling coefficients. A simple experimental result validates the approach.     

Imaging of fluorescent yield and lifetime from multiply scattered light reemitted from random media

The feasibility of employing fluorescent contrast agents to perform optical imaging in tissues and other scattering media has been examined through computational studies. Fluorescence lifetime and yield can give crucial information about local metabolite concentrations or environmental conditions within tissues. This information can be employed toward disease detection, diagnosis, and treatment if noninvasively quantitated from reemitted optical signals. However, the problem of inverse image reconstruction of fluorescence yield and lifetime is complicated because of the highly scattering nature of the tissue. Here a light propagation model employing the diffusion equation is used to account for the scattering of both the excitation and fluorescent light. Simulated measurements of frequency-domain parameters of fluorescent modulated ac amplitude and phase lag are used as inputs to an inverse image-reconstruction algorithm, which employs the diffusion model to predict frequency-domain measurements resulting from a modulated input at the phantom periphery. In the inverse image-reconstruction algorithm, a Newton-Raphson technique combined with a Marquardt algorithm is employed to converge on the fluorescent properties within the medium. The successful reconstruction of both the fluorescence yield and lifetime in the case of a heterogeneous fluorophore distribution within a scattering medium has been demonstrated without a priori information or without the necessity of obtaining absence images.     

Reconstruction of Two-Dimensional Randomly Rough Surfaces Based on Bidirectional Reflectance Distribution Function

This article presents an inverse method for reconstructing two-dimensional randomly rough surfaces based on the available (experimental or given) data of the bidirectional reflectance distribution function (BRDF). The Maxwell’s equations of electromagnetic waves are applied to describe the light scattering process of rough surfaces by accounting for the near-field effect. Such a forward problem is numerically solved with the finite-difference time-domain algorithm. The inverse scattering problem of reconstructing the surface profile is handled by means of an optimization technique—the particle swarm optimizer algorithm. As an example, reconstruction of a Gaussian rough surface is conducted based on the experimental data of BRDFs. The retrieved results of the surface profile are compared with those measured by atomic force microscopy from the samples, which shows that the reconstruction algorithm can provide the credible prediction of surface profiles. The reconstruction approach studied in this study can make reliable predictions of the actual or required surface profiles.     

Electromagnetic field enhancement and spectrum shaping through plasmonically integrated optical vortices

We introduce a new design approach for surface-enhanced Raman spectroscopy (SERS) substrates that is based on molding the optical powerflow through a sequence of coupled nanoscale optical vortices "pinned" to rationally designed plasmonic nanostructures, referred to as Vortex Nanogear Transmissions (VNTs). We fabricated VNTs composed of Au nanodiscs by electron beam lithography on quartz substrates and characterized their near- and far-field responses through combination of computational electromagnetism, and elastic and inelastic scattering spectroscopy. Pronounced dips in the far-field scattering spectra of VNTs provide experimental evidence for an efficient light trapping and circulation within the nanostructures. Furthermore, we demonstrate that VNT integration into periodic arrays of Au nanoparticles facilitates the generation of high E-field enhancements in the VNTs at multiple defined wavelengths. We show that spectrum shaping in nested VNT structures is achieved through an electromagnetic feed-mechanism driven by the coherent multiple scattering in the plasmonic arrays and that this process can be rationally controlled by tuning the array period. The ability to generate high E-field enhancements at predefined locations and frequencies makes nested VNTs interesting substrates for challenging SERS applications.     

Time-resolved Fourier optical diffuse tomography.

Time-resolved Fourier optical diffuse tomography is a novel approach for imaging of objects in a highly scattering turbid medium with use of an incident (near) plane wave. The theory of the propagation of spatial Fourier components of the scattered wave field is presented, along with a fast algorithm for three-dimensional reconstruction in a parallel planar geometry. Examples of successful reconstructions of simulated hidden absorptive or scattering objects embedded inside a human-tissue-like semi-infinite turbid medium are provided.     

Genetically engineered plasmonic nanoarrays

In the present Letter, we demonstrate how the design of metallic nanoparticle arrays with large electric field enhancement can be performed using the basic paradigm of engineering, namely the optimization of a well-defined objective function. Such optimization is carried out by coupling a genetic algorithm with the analytical multiparticle Mie theory. General design criteria for best enhancement of electric fields are obtained, unveiling the fundamental interplay between the near-field plasmonic and radiative photonic coupling. Our optimization approach is experimentally validated by surface-enhanced Raman scattering measurements, which demonstrate how genetically optimized arrays, fabricated using electron beam lithography, lead to order of ten improvement of Raman enhancement over nanoparticle dimer antennas, and order of one hundred improvement over optimal nanoparticle gratings. A rigorous design of nanoparticle arrays with optimal field enhancement is essential to the engineering of numerous nanoscale optical devices such as plasmon-enhanced biosensors, photodetectors, light sources and more efficient nonlinear optical elements for on chip integration.     

Hard-X-ray dark-field imaging using a grating interferometer

Imaging with visible light today uses numerous contrast mechanisms, including bright- and dark-field contrast, phase-contrast schemes and confocal and fluorescence-based methods. X-ray imaging, on the other hand, has only recently seen the development of an analogous variety of contrast modalities. Although X-ray phase-contrast imaging could successfully be implemented at a relatively early stage with several techniques, dark-field imaging, or more generally scattering-based imaging, with hard X-rays and good signal-to-noise ratio, in practice still remains a challenging task even at highly brilliant synchrotron sources. In this letter, we report a new approach on the basis of a grating interferometer that can efficiently yield dark-field scatter images of high quality, even with conventional X-ray tube sources. Because the image contrast is formed through the mechanism of small-angle scattering, it provides complementary and otherwise inaccessible structural information about the specimen at the micrometre and submicrometre length scale. Our approach is fully compatible with conventional transmission radiography and a recently developed hard-X-ray phase-contrast imaging scheme. Applications to X-ray medical imaging, industrial non-destructive testing and security screening are discussed.     

Inverse acoustic scattering by solid obstacles: topological sensitivity and its preliminary application

A to pological sensitivity approach is developed for the imaging of penetrable solid obstacles embedded in a fluid background medium by way of inverse acoustic scattering. To this end, an asymptotic form for the scattered field caused by a nucleating spherical solid obstacle in the reference fluid medium is derived within the boundary integral equation framework, where the required limiting behaviour of the acceleration field inside the perturbation is established based on solutions of two simplified fluid–solid interaction problems. With this result, the equivalence of acoustic scattering by fluid and solid scatterers in terms of asymptotic behaviour is observed and numerically validated. The direct and adjoint-field topological sensitivity expressions for transient and time-harmonic excitations are obtained accordingly. The utility of the proposed method as a tool for preliminary obstacle reconstruction and bulk modulus characterization is illustrated through numerical examples and an exploratory experimental study. On the basis of adjusted topological sensitivity formulas for fluid reference domains containing pre-existing solid inhomogeneities, an iterative 3D obstacle reconstruction approach is established and its performance with synthetic data is demonstrated.     

Image reconstruction scheme that combines modified Newton method and efficient initial guess estimation for optical tomography of finger joints

What we believe to be a novel 3D diffuse optical tomography scheme is developed to reconstruct images of both absorption and scattering coefficients of finger joint systems. Compared with our previous reconstruction method, the improved 3D algorithm employs both modified Newton methods and an enhanced initial value optimization scheme to recover the optical properties of highly heterogeneous media. The developed approach is tested using simulated, phantom, and in vivo measurement data. The recovered results suggest that the improved approach is able to provide quantitatively better images than our previous algorithm for optical tomography reconstruction.     

Concept, modeling, and performance prediction of a low-cost, large deformable mirror

While it is attractive to integrate a deformable mirror (DM) for adaptive optics (AO) into the telescope itself rather than using relay optics within an instrument, the resulting large DM can be expensive, particularly for extremely large telescopes. A low-cost approach for building a large DM is to use voice-coil actuators connected to the back of the DM through suction cups. Use of such inexpensive voice-coil actuators leads to a poorly damped system with many structural modes within the desired bandwidth. Control of the mirror dynamics using electro-mechanical sensors is thus required for integration within an AO system. We introduce a distributed control approach, and we show that the "inner" back sensor control loop does not need to function at low frequencies, leading to significant cost reduction for the sensors. Incorporating realistic models of low-cost actuators and sensors together with an atmospheric seeing model, we demonstrate that the low-cost mirror strategy is feasible within a closed-loop AO system.     

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

Genetic algorithms in optimizing surveillance and maintenance of components

Nowadays the great ability of genetic algorithms (GA) to find solutions in complex optimization problems is known, where other methods used to give poor results. This has opened a wide range of application areas using GA and here we present a new approach aimed at the global and constrained optimization of surveillance and maintenance (S&M) of components based on risk and cost criteria. Also, a case study is performed using this approach which shows the benefits of the integration of S&M tasks of components based on optimized intervals. Moreover, this methodology is completely valid in solving other optimization problems with respect to risk and cost beyond the component level.     

Application of the sinc basis moment method to the reconstruction of infinite circular cylinders

A solution of the ultrasonic scattering and inverse scattering problem has been obtained by solving the inhomogeneous Helmholtz wave equation by the sinc basis moment method. In this numerical study, the algorithm of S.A. Johnson and M.L. Tracy (1983) has been applied to the reconstruction of an infinite circular cylinder that is subject to an incident cylindrical wave of ultrasound and is surrounded by a homogeneous coupling medium. For weak scattering cylinders, successful reconstructions have been obtained using the known exact solution for the scattered field as the input data for the algorithm. A detailed discussion of sampling requirements for this algorithm is presented, and the threshold derived correlates well with results of a numerical study of variation of the sampling density. Effects of varying object contrast, object size, grid size, sampling density, and method of iteration are investigated. Because the algorithm is slow, optimization of computation is described.     

A two-stage approach for multi-objective decision making with applications to system reliability optimization

This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.     

Optimized sequencing of analysis components in multidisciplinary systems

System analysis of complex engineering systems is synthesized from a collection of analysis components that have data dependencies on each other. Sequencing interdependent analysis components in order to reduce the execution time has been addressed by multidisciplinary design optimization researchers. Representation of interdependency of analysis components is accomplished as a design structure matrix or as a graph made of nodes and edges. Sequencing of interdependent analysis components that form a directed acyclic graph is trivial. Aggregation (i.e., group of components) of some of the components into a single super-component that can render a directed cyclic graph to a directed acyclic graph is important in sequencing. Identification of components that form an aggregation is the first step in sequencing. We argue that the best form of aggregation is the strongly connected component of the graph. Challenge essentially is in sequencing within aggregations. An aggregation having n components presents a search space of n! candidate sequences. The current state of the art is to use evolutionary algorithms for this search. An aggregation requires repeated traversal (cycle/loop) of components within it for convergence. The central aim of sequencing is to reduce/minimize the overall execution time for achieving convergence through iterations. Several objective functions have been proposed for the associated optimization problems like minimize the number of feedback paths, minimize the weighted sum of feedback paths, minimize feedback and crossovers, etc. These are proxy objectives as they are not backed by mathematically established relation between the proxy objective and the aim. An objective method of predicting the number of iterations based on the sensitivity of components is proposed here. It is shown that the best sequence that takes least time to execute has a particular ordering of components, which we call one-hop-sequence. The one-hop-sequencing of components is easily achieved using a small extension to Tarjans depth first search algorithm, a standard tool in graph theory. Extended TDFS does not use sensitivity information and is much faster than evolutionary algorithms that use sensitivity information. System analysis can have simple aggregation, recursive aggregations (i.e., aggregation within aggregation) or overlapping aggregations. One-hop-sequence is shown to be the best sequence for all three cases. After sequencing of the components is done, we investigate whether an inner aggregation must retain its loop or it must be severed for speed up. This step uses sensitivity information and can offer further speed up. The proposed methodology is implemented as a tool named CASeq. Ideas discussed here may be useful to other design structure matrix applicable domains like software design, systems engineering, organizational design, product development, multidisciplinary design, product architecture, project management, building construction, manufacturing and so on.     

The diagonalized contrast source inversion approach for elastic wave inversion

This study focuses on the inverse scattering of objects embedded in a homogeneous elastic background. The medium is probed by ultrasonic sources, and the scattered fields are observed along a receiver array. The goal is to retrieve the shape, location, and constitutive parameters of the objects through an inversion procedure. The problem is formulated using a vector integral equation. As is well-known, this inverse scattering problem is nonlinear and ill-posed. In a realistic configuration, this nonlinear inverse scattering problem involves a large number of unknowns, hence the application of full nonlinear inversion approaches such as Gauss-Newton or nonlinear gradient methods might not be feasible, even with present-day computer power. Hence, in this study we use the so-called diagonalized contrast source inversion (DCSI) method in which the nonlinear problem is approximately transformed into a number of linear problems. We will show that, by using a three-step procedure, the nonlinear inverse problem can be handled at the cost of solving three constrained linear inverse problems. The robustness and efficiency of this approach is illustrated using a number of synthetic examples.