Adaptive feature-specific imaging: a face recognition example

We present an adaptive feature-specific imaging (AFSI) system and consider its application to a face recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. Using sequential hypothesis testing, we compare AFSI with static-FSI (SFSI) and static or adaptive conventional imaging in terms of the number of measurements required to achieve a specified probability of misclassification (Pe). The AFSI system exhibits significant improvement compared to SFSI and conventional imaging at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses and desired Pe=10(-2), AFSI requires 100 times fewer measurements than the adaptive conventional imager at SNR= -20 dB. We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage, resulting in an optimal value of integration time (equivalent to SNR) per measurement.     

A parallel iterative partitioned coupling analysis system for large-scale acoustic fluid–structure interactions

In many engineering fields, dynamic response in fluid–structure interaction (FSI) is important, and some of the FSI phenomena are treated as acoustic FSI (AFSI) problems. Dynamic interactions between fluids and structures may change dynamic characteristics of the structure and its response to external excitation parameters such as seismic loading. This paper describes a parallel coupling analysis system for large-scale AFSI problems using iterative partitioned coupling techniques. We employ an open source parallel finite element analysis system called ADVENTURE, which adopts an efficient preconditioned iterative linear algebraic solver. In addition, we have recently developed a parallel coupling tool called ADVENTURE_Coupler to efficiently handle interface variables in various parallel computing environments. We also employ the Broyden method for updating interface variables to attain robust and fast convergence of fixed-point iterations. This paper describes key features of the coupling analysis system developed, and we perform tests to validate its performance for several AFSI problems. The system runs efficiently in a parallel environment, and it is capable of analyzing three-dimensional-complex-shaped structures with more than 20 million degrees-of-freedom (DOFs). Its numerical results also show good agreement with experimental results.     

Small-kernel superresolution methods for microscanning imaging systems

Two computationally efficient methods for superresolution reconstruction and restoration of microscanning imaging systems are presented. Microscanning creates multiple low-resolution images with slightly different sample-scene phase shifts. The digital processing methods developed here combine the low-resolution images to produce an image with higher pixel resolution (i.e., superresolution) and higher fidelity. The methods implement reconstruction to increase resolution and restoration to improve fidelity in one-pass convolution with a small kernel. One method uses a small-kernel Wiener filter and the other method uses a parametric cubic convolution filter. Both methods are based on an end-to-end, continuous-discrete-continuous microscanning imaging system model. Because the filters are constrained to small spatial kernels they can be efficiently applied by convolution and are amenable to adaptive processing and to parallel processing. Experimental results with simulated imaging and with real microscanned images indicate that the small-kernel methods efficiently and effectively increase resolution and fidelity.     

Adaptive optics with advanced phase-contrast techniques. II. High-resolution wave-front control

A wave-front control paradigm based on gradient-flow optimization is analyzed. In adaptive systems with gradient-flow dynamics, the output of the wave-front sensor is used to directly control high-resolution wavefront correctors without the need for wave-front phase reconstruction (direct-control systems). Here, adaptive direct-control systems with advanced phase-contrast wave-front sensors are analyzed theoretically, through numerical simulations, and experimentally. Adaptive system performance is studied for atmospheric-turbulence-induced phase distortions in the presence of input field intensity scintillations. The results demonstrate the effectiveness of this approach for high-resolution adaptive optics.     

Space-time compressive imaging

Compressive imaging systems typically exploit the spatial correlation of the scene to facilitate a lower dimensional measurement relative to a conventional imaging system. In natural time-varying scenes there is a high degree of temporal correlation that may also be exploited to further reduce the number of measurements. In this work we analyze space-time compressive imaging using Karhunen-Loève (KL) projections for the read-noise-limited measurement case. Based on a comprehensive simulation study, we show that a KL-based space-time compressive imager offers higher compression relative to space-only compressive imaging. For a relative noise strength of 10% and reconstruction error of 10%, we find that space-time compressive imaging with 8×8×16 spatiotemporal blocks yields about 292× compression compared to a conventional imager, while space-only compressive imaging provides only 32× compression. Additionally, under high read-noise conditions, a space-time compressive imaging system yields lower reconstruction error than a conventional imaging system due to the multiplexing advantage. We also discuss three electro-optic space-time compressive imaging architecture classes, including charge-domain processing by a smart focal plane array (FPA). Space-time compressive imaging using a smart FPA provides an alternative method to capture the nonredundant portions of time-varying scenes.     

一种基于压力泊松方程的流体结构紧耦合算法

从流固耦合系统的整体控制方程出发,推导出与流体控制方程一致的耦合等价方程,并得到基于耦合方程的压力泊松方程,通过求解耦合系统压力泊松方程和一致的等价方程就能获得耦合系统的解,而不需要直接求解整体耦合系统的控制方程,有利于降低求解自由度。预估-校正多步迭代格式用于耦合系统的时间推进,克服了传统迭代耦合方法由于时间不同步而产生较大数值误差的不足。应用该方法对附带局部突起的主动脉弓动脉瘤进行流固耦合分析,验证数值方法的可行性。     

Compressive imaging: hybrid measurement basis design

The inherent redundancy in natural scenes forms the basis of compressive imaging where the number of measurements is less than the dimensionality of the scene. The compressed sensing theory has shown that a purely random measurement basis can yield good reconstructions of sparse objects with relatively few measurements. However, additional prior knowledge about object statistics that is typically available is not exploited in the design of the random basis. In this work, we describe a hybrid measurement basis design that exploits the power spectral density statistics of natural scenes to minimize the reconstruction error by employing an optimal combination of a nonrandom basis and a purely random basis. Using simulation studies, we quantify the reconstruction error improvement achievable with the hybrid basis for a diverse set of natural images. We find that the hybrid basis can reduce the reconstruction error up to 77% or equivalently requires fewer measurements to achieve a desired reconstruction error compared to the purely random basis. It is also robust to varying levels of object sparsity and yields as much as 40% lower reconstruction error compared to the random basis in the presence of measurement noise.     

Voxel-Based Full-Field Eigenstrain Reconstruction of Residual Stresses

Inverse eigenstrain (inherent strain) analysis methods are shown to be effective for the reconstruction of residual stresses in plane eigenstrain problems (continuously processed bodies) while conversely residual stress reconstruction in discontinuously processed bodies is extremely challenging and necessitates the use of complex regularizing assumptions. Herein, a new generic inverse eigenstrain method suitable for the reconstruction of residual stresses along with residual elastic strains and displacements in discontinuously processed bodies is introduced. The proposed method uses the superposition of eigenstrain radial basis functions together with a set of limited experimental data for model-free (unconstrained) determination of unknown eigenstrain fields. This approach eliminates the limitations introduced by global basis functions such as polynomials. The novel point of this method is the ability to account for all six components of strain in an isotropic body without using regularizing assumptions. By lifting complex guiding formulation, the fidelity of full-field eigenstrain reconstruction becomes directly related to the quality of experimental data and proper discretisation of the model domain. The FEniCS implementation has been validated using the experimental data of pointwise high-energy synchrotron X-ray diffraction measurements from a bent titanium alloy bar. A hybrid high throughput computing approach is also introduced for effective parallel computing.     

A high accuracy microwave ranging system for industrialapplications

A microwave ranging system that employs a composite frequency modulated continuous wave/continuous wave (FMCW/CW) measurement technique is described. Conventional FMCW radar techniques are employed to find the approximate range of the target. An ambiguous but very accurate set of range solutions is also determined through a CW measurement. The correct, precision CW distance measurement is resolved on the basis of the approximate FMCW solution. An adaptive, spatial digital filtering routine applied to the FMCW radar measurements reduces the influence of clutter, ensuring reliable operation. An X-band prototype system that achieves submillimeter accuracy is described     

Comparison of shortwave and longwave measuring thermal-imaging systems

A comparison study of shortwave (3-5-μm) and longwave (8-12-μm) measuring thermal imaging systems has been conducted. The study was limited to systems working in indoor conditions, as is typical in many industrial and scientific applications. A theory of the influence of measurement conditions and system parameters on the accuracy of temperature measurements has been developed. On the basis of the developed formulas an analysis of the influence of signal disturbances (because of incorrectly assumed emissivity, radiation reflected by the object, radiation emitted by system optics, limited transmittance of the atmosphere, and limited temperature resolution of the system) on the accuracy of temperature measurement has been made. It has been found that the shortwave systems in typical measurement conditions offer generally better accuracy in temperature measurement than the longwave ones do.     

Feature-specific imaging

We analyze the performance of feature-specific imaging systems. We study incoherent optical systems that directly measure linear projects of the optical irradiance distribution. Direct feature measurement exploit, the multiplex advantage, and for small numbers of projections can provide higher feature-fidelity than those systems that postprocess a conventional image. We examine feature-specific imaging using Wavelet, Karhunen-Loeve (KL), Hadamard, and independent-component features, quantifying feature fidelity in Gaussian-, shot-, and quantization-noise environments. An example of feature-specific imaging based on KL projections is analyzed and demonstrates that within a high-noise environment it is possible to improve image fidelity via direct feature measurement. A candidate optical system is presented and a preliminary implementational study is undertaken.     

A new coded-excitation ultrasound imaging system. I. Basicprinciples

A new approach to ultrasound imaging with coded-excitation is presented. The imaging is performed by reconstruction of the scatterer strength on an assumed grid covering the region of interest (ROI). Our formulation is based on an assumed discretized signal model which represents the received sampled data vector as a superposition of impulse responses of all scatterers in the ROI. The reconstruction operator is derived from the pseudo-inverse of the linear operator (system matrix) that produces the received data vector. The singular value decomposition (SVD) method with appropriate regularization techniques is used for obtaining a robust realization of the pseudo-inverse. Under simplifying (but realistic) assumptions, the pseudo-inverse operator (PIO) can be implemented using a bank of transversal filters with each filter designed to extract echoes from a specified image line. This approach allows for the simultaneous acquisition of a large number of image lines. This could be useful in increasing frame rates for two-dimensional imaging systems or allowing for real-time implementation of three-dimensional imaging systems. When compared to the matched filtering approach to similar coded-excitation systems, our approach eliminates correlation artifacts that are known to plague such systems. Furthermore, the lateral resolution of the new system can exceed the diffraction limit imposed on conventional imaging systems utilizing delay-and-sum beamformers. The range resolution is compared to that of conventional pulse-echo systems with resolution enhancement (our PIO behaves as a pseudo-inverse Wiener filter in the range direction). Both simulation and experimental verification of these statements are given     

Dual imaging modality of granular flow based on ECT sensors

In this study, a previously developed dual modality imaging system is applied to image the flow of granular matter with different electrical properties in cylindrical vessels. The imaging system is based on both capacitance and power measurements acquired by an electrical capacitance tomography (ECT) sensor located around the vessel. The measurement data are then used to reconstruct cross-sectional images of both permittivity and conductivity distributions. A neural network multi-criterion optimization reconstruction technique (NN-MOIRT) is used for the inverse (reconstruction) problem. The contribution of this technology to the field of granular matters is explored through review of research articles that can be a direct application of this development. We discuss the capabilities of this dual-modality acquisition system using synthetic data for granular matter with different electrical properties.     

Toward in-cylinder absorption tomography in a production engine

Design requirements for an 8000 frame/s dual-wavelength ratiometric chemical species tomography system, intended for hydrocarbon vapor imaging in one cylinder of a standard automobile engine, are examined. The design process is guided by spectroscopic measurements on iso-octane and by comprehensive results from laboratory phantoms and research engines, including results on temporal resolution performance. Novel image reconstruction techniques, necessary for this application, are presented. Recent progress toward implementation, including details of the optical access arrangement employed and signal-to-noise issues, is described. We present first cross-cylinder IR absorption measurements from a reduced channel-count (nontomographic) system and discuss the prospects for imaging.     

Optimized compressive sampling for passive millimeter-wave imaging

In this paper, we briefly describe a single detector passive millimeter-wave imaging system, which has been previously presented. The system uses a cyclic sensing matrix to acquire incoherent measurements of the observed scene and then reconstructs the image using a Bayesian approach. The cyclic nature of the sensing matrix allows for the design of a single unified and compact mask that provides all the required random masks in a convenient way, such that no mechanical mask exchange is needed. Based on this setup, we primarily propose the optimal adaptive selection of sampling submasks out of the full cyclic mask to obtain improved reconstruction results. The reconstructed images show the feasibility of the imaging system as well as its improved performance through the proposed sampling scheme.     

Analysis of space mapping algorithms for application to partitioned fluid–structure interaction problems

Fluid–structure interactions (FSI) play a crucial role in many engineering fields. However, the computational cost associated with high‐fidelity aeroelastic models currently precludes their direct use in industry, especially for strong interactions. The strongly coupled segregated problem—that results from domain partitioning—can be interpreted as an optimization problem of a fluid–structure interface residual. Multi‐fidelity optimization techniques can therefore directly be applied to this problem in order to obtain the solution efficiently. In previous work, it is already shown that aggressive space mapping (ASM) can be used in this context. In this contribution, we extend the research towards the use of space mapping for FSI simulations. We investigate the performance of two other approaches, generalized space mapping and output space mapping, by application to both compressible and incompressible 2D problems. Moreover, an analysis of the influence of the applied low‐fidelity model on the achievable speedup is presented. The results indicate that output space mapping is a viable alternative to ASM when applied in the context of solver coupling for partitioned FSI, showing similar performance as ASM and resulting in reductions in computational cost up to 50% with respect to the reference quasi‐Newton method. Copyright © 2015 John Wiley & Sons, Ltd.     

Real-time turbulence profiling with a pair of laser guide star Shack-Hartmann wavefront sensors for wide-field adaptive optics systems on large to extremely large telescopes

Real-time turbulence profiling is necessary to tune tomographic wavefront reconstruction algorithms for wide-field adaptive optics (AO) systems on large to extremely large telescopes, and to perform a variety of image post-processing tasks involving point-spread function reconstruction. This paper describes a computationally efficient and accurate numerical technique inspired by the slope detection and ranging (SLODAR) method to perform this task in real time from properly selected Shack-Hartmann wavefront sensor measurements accumulated over a few hundred frames from a pair of laser guide stars, thus eliminating the need for an additional instrument. The algorithm is introduced, followed by a theoretical influence function analysis illustrating its impulse response to high-resolution turbulence profiles. Finally, its performance is assessed in the context of the Thirty Meter Telescope multi-conjugate adaptive optics system via end-to-end wave optics Monte Carlo simulations.     

Neurofeedback using functional spectroscopy

Neurofeedback based on real‐time measurement of the blood oxygenation level‐dependent (BOLD) signal has potential for treatment of neurological disorders and behavioral enhancement. Commonly used methods are based on functional magnetic resonance imaging (fMRI) sequences that sacrifice speed and accuracy for whole‐brain coverage, which is unnecessary in most applications. We present multivoxel functional spectroscopy (MVFS): a system for computing the BOLD signal from multiple volumes of interest (VOI) in real‐time that improves speed and accuracy of neurofeedback. MVFS consists of a FS pulse sequence, a BOLD reconstruction component, a neural activation estimator, and a stimulus system. The FS pulse sequence is a single‐voxel, magnetic resonance spectroscopy sequence without water suppression that has been extended to allow acquisition of a different VOI at each repetition and real‐time subject head motion compensation. The BOLD reconstruction component determines the T2* decay rate, which is directly related to BOLD signal strength. The neural activation estimator discounts nuisance signals and scales the activation relative to the amount of ROI noise. Finally, the neurofeedback system presents neural activation‐dependent stimuli to experimental subjects with an overall delay of less than 1 s. Here, we present the MVFS system, validation of certain components, examples of its usage in a practical application, and a direct comparison of FS and echo‐planar imaging BOLD measurements. We conclude that in the context of realtime BOLD imaging, MVFS can provide superior accuracy and temporal resolution compared with standard fMRI methods. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 138–148, 2014     

Image reconstruction in three-dimensional magnetostatic permeability tomography

Magnetostatic permeability tomography is an imaging technique that attempts to reconstruct the permeability distribution of an object using magnetostatic measurement data. The data for image reconstruction are external magnetic field measurements on the surface of the object due to an applied magnetostatic field. Theoretically, the normal and tangential components of the magnetic field in the surface uniquely define the internal isotropic permeability distributions. However, the inverse permeability problem is an ill-posed nonlinear problem. Regularization is needed for a stable solution. In this paper, we present a numerical method to solve the reconstruction problem in three dimensions using a regularized Gauss-Newton scheme. We have solved the forward problem using an edge finite-element method and we have employed an efficient technique to calculate the Jacobian matrix. The permeability of the object is assumed to be linear and isotropic. We present the reconstruction results for the permeability using synthetically generated data with additive noise.     

Signal interpretation for monitoring and diagnosis, a coolingsystem testbed

This paper discusses a method for fault detection and isolation (FDI) in continuous dynamic systems. A key aspect of this approach is the coupling of a qualitative diagnosis engine and a monitoring system that computes symbolic feature values through a signal-to-symbol transformation on the continuously sampled measurement data. Signal analysis techniques with a sound statistical basis are employed to generate reliable symbolic data. The methodology is evaluated on the diagnosis of engineered faults in the cooling system of an automobile engine that has been instrumented with temperature and pressure sensors. Results show the interdependency between modeling for diagnosis and the feature extraction system