qing liu - US grants

The goals of this career program are to develop efficient forward and inverse techniques to solve electromagnetic and elastodynamic problems in geophysical subsurface sensing, and to foster an effective, interdisciplinary educational program. In geophysical subsurface sensing, electromagnetic and acoustic sensors are widely used to probe the complex geological structures from within a borehole. The interpretation of these important measurements, however, remains a challenging problem because of the complexity of the underground medium and of the presence of the wellbore. Simulating realistic three-dimensional models encountered in these problems can easily exceed the capacity of any modern supercomputer if conventional methods are used. Therefore, there is a pressing demand for more efficient numerical techniques to simulate large-scale electromagnetic and acoustic measurements. These simulations are also critical in processing the collected data and in computer-aided design of new measurement systems. In this research, efficient forward and inverse solutions will be developed for electrodetype, induction, and sonic well logging tools in three-dimensional inhomogeneous media. In forward solutions, the measurement data are simulated given the physical properties of the medium. In inverse problems, on the other hand, the physical properties of the medium are determined from the measurement data, which is the ultimate goal of geophysical subsurface sensing. As the focus of research, a series of efficient forward models will be developed as a backbone of nonlinear inversion for 3-D electromagnetic and elastodynamic problems. The techniques proposed will allow one to solve much larger problems than conventional finite-difference and finite-element methods on a supercomputer. The forward solutions will be effectively coupled with the inverse algorithms. In the educational program, the principal investigator (PI) proposes to strengthen the interactions among faculty members and students in electrical engineering, geophysics, and mechanical engineering departments by developing interdisciplinary courses and by collaboration. Three new cross-department courses will be developed, and the use of computers and network in electromagnetic education will be incorporated to improve the teaching effectiveness. The research program will be fully integrated with the undergraduate and graduate electrical engineering education. This integrated career program Will significantly advance the capability of simulating large-scale forward and inverse electromagnetic and elastodynamic problems in geophysical subsurface sensing. The petroleum industry will benefit from this research program through the publication of knowledge and software developed. This program will also benefit subsurface mapping of underground buried waste and the medical imaging industry. The students involved in this research will have the unique opportunity to acquire interdisciplinary knowledge on the applications of electrical engineering in geophysical exploration.

This is the first 4 years funding of a five-year continuing award. Humans are very good at discerning the spatial origin of sound using a mixture of frequency-dependent interaural time difference (ITD), interaural level difference (ILD), and pinna spectral cues in disparate environments ranging from open spaces to small crowded rooms. This ability helps us to interact with others and the environment by sorting out individual sounds from a mixture, and helps us to survive by warning us of danger over a wider region of space compared to vision. These advantages of spatial sound are important for human-computer interaction.

While the frequency-independent ITD cues (delays) associated with the two ears are relatively easy to render over headphones, the ILD (level difference) and pinna elevation cues are not. For a given source location and frequency content, the sound scattered by the person's torso, head and pinnae, and is received differently at the two ears, leading to differences in the intensity and spectral features of the received sound. These effects are encoded in an extremely individual "Head Related Transfer Function" (HRTF) that depends on the person's anatomical features (structure of the torso, head and pinnae). This individuality has made it difficult to use the HRTF in the proposed applications. Recent research, including that of members of this team, has focused on measuring the HRTFs for individuals in specific environments, on constructing models of the HRTF, on understanding how the geometry of the body is related to the characteristics of HRTF, and how the brain processes the cues to derive spatial information. However, this research has also indicated that the brain is extraordinarily perceptive to errors in cues that result when sound is rendered with an incorrect HRTF.

In this project the PI and his team will use numerical methods to compute individualized HRTFs from accurate 3-D surface models of the body. They will use multiview, multiframe computational vision techniques to extract the surface models from imagery. They will then use boundary element methods employing fast multipole/ transform techniques and parallel processing to compute the HRTFs from the surface models. The resulting HRTFs will be evaluated both by objective comparisons with acoustically measured HRTFs and by psychoacoustic testing, and will be used in demonstrations of virtual reality, augmented reality, and teleconferencing. A major advantage of this vision-based approach is that it will allow the PI and his team to investigate and model the way that HRTFs change with body posture, providing the potential of tracking dynamic environments. Thus, the project will include fundamental research to extend the static HRTF measurements to dynamic situations in different environments, using a combination of visual tracking to locate the person in real space, and construction of in-room HRTFs from free-field HRTFs using fast iterative techniques. This will provide a scientific foundation for HCI applications of audio rendering. The research will in addition yield algorithms and understanding that will have an impact on varied fields, including computer vision based model creation; scientific computing; computational acoustics for noise control and land mine detection; neurophysiological understanding of human audition; etc.

Proposal #0098140
Duke University
Liu, Qing Huo

In this interdisciplinary project, we propose to develop fast algorithms for electromagnetic and elastic wave scattering in layered media. The impetus for such a joint effort is the ever increasing demand for efficient and accurate numerical simulation tools for electronic packaging and geophysical exploration where wave phenomenon plays an important role for design, evaluation, prediction and production. In both applications, there is a pressing need for fast solution techniques for full wave equations in layered media, namely, Maxwell's equations for electronic packaging and both electromagnetic and elastic wave equations for geophysical exploration. As the numerical issues involved in the solution of both wave equations share many common features, a concerted effort to develop fast algorithms for wave scattering in layered media will have a significant impact in both areas.

In a high-speed electronic package, interconnects are one of the determining factors for the speed performance of the system. Such a high order effect is not easily captured in either equations or tables, rendering conventional timing driven layout techniques inaccurate and obsolete. One must fully characterize
the interconnect structures to ensure on-chip signal integrity and to achieve the expected high-speed system performance. Therefore, there is a strong need for faster and more accurate full-wave electromagnetic analysis tools to extract parasitic parameters such as resistance, capacitance, and inductance.

On the other hand, in geophysical exploration for oil and gas, electromagnetic and acoustic sensors are widely used to probe complex geologic structures. The goal of electromagnetic and acoustic subsurface sensing is to infer from these measurements the electromagnetic and mechanical properties of the formation,
and to combine with other, such as nuclear, measurements to determine the petrophysical characteristics of the reservoir. The interpretation of these easurements, however, remains a challenging problem because of the complicated interaction of waves with the complex geologic structures and wellbore. The interpretation and processing of these measurements depend on fast and accurate forward and inverse solutions of lectromagnetic and acoustic waves in large-scale, highly heterogeneous media.

The main emphasis of this proposal is on numerical algorithm development relevant to direct problems for electromagnetic and elastic waves propagation in layered media. A frequency domain integral equation formulation will be used. Major tasks include fast calculation of dyadic Green's functions for general
layered media; fast matrix-vector multiplication and robust preconditioner for matrix solver; construction and study of high order basis functions for large targets; application of the obtained numerical algorithms in electronic packaging and geophysical exploration.

Both PI's have extensive experience in the proposed application areas---parameter extraction for VLSI and RF component design (Cai) and geophysical subsurface sensing and electronic packaging (Liu). The
collaborated research will greatly benefit the electronics and oil exploration industry, and our research and educational programs in electrical engineering and applied mathematics and scientific computation.

Qing Huo Liu
Duke University

This research aims to develop a novel framework for rapid and accurate processing of
geophysical subsurface measurements in multilayer media. At present, only 2-D mappings
(i.e., the low-resolution images on the ground surface) are available, greatly limiting the target
depth information. Due to the excessive computational demand, previous inverse scattering
algorithms for these sensors have been developed, but only for objects in a homogeneous
background medium. This new computational framework is expected to dramatically improve
the way environmental site characterization is being performed.
The investigator will develop rapid and accurate computational algorithms for the process-
ing of some of the arguably most important measurements in environmental site characteriza-
tion and oil exploration, namely electromagnetic induction (EMI), ground penetrating radar
(GPR), surface seismic, and borehole sonic imaging measurements in 3-D heterogeneous,
multilayer media. These algorithms, coupled with large-scale computing and visualization
platforms, form a novel framework for current and future environmental site characterization
where 3-D subsurface structures can be directly investigated, and sensor arrays can be adap-
tively optimized. The main contributions of this research are: (1) Rapid inversion methods
based on accelerated extended Born/Rytov approximations in layered media; (2) High-fidelity
imaging algorithms based on preconditioned contrast source inversion methods in a multi-
layer background; (3) A 3-D imaging and adaptive optimization framework using these fast
inverse scattering algorithms for multi-sensors. These approaches are expected to yield a
high resolution because they account for the important multiple scattering effects that cause
the resolution degradation in previous linear inversion methods. The development of the al-
gorithms for objects embedded in multilayer media represents significant advances in inverse
scattering and its applications in environmental site characterization and oil exploration.

ABSTRACT NOT PROVIDED

DESCRIPTION (provided by applicant): The proposed research would develop a full three-dimensional array microwave imaging system for breast imaging, with fast 3-D forward and nonlinear inverse scattering algorithms for image reconstruction. Extensive studies reveal that the electrical properties of malignant and normal mammary tissues have a large contrast. In particular, at microwave frequencies, the dielectric constant of breast tumor is about 3.5 times that in normal tissue, while this ratio is approximately 6.7 for electrical conductivity. This large contrast provides a strong scattering signal to incoming microwaves, leading to a high sensitivity of microwaves to early-stage, even small-size breast tumors undetectable by X-ray mammography. Furthermore, microwaves are a non-ionizing radiation, and the required energy is well below the safety level. This new technique is also favorable because there is no breast compression. Ongoing microwave imaging techniques using twodimensional microwave arrays have shown very encouraging results in clinical tests. Our preliminary investigation of a 2-D microwave imaging system shows promising results of images reconstructed from this experimental imaging system with nonlinear inverse scattering algorithms. This research is based on 3-D array sensors and on recent progress in computational electromagnetics that leads to fast numerical techniques. The 3-D hardware system is expected to significantly accelerate the data acquisition time. The inverse scattering algorithms unravel the complicated multiple scattering effects, leading to a high resolution in microwave image reconstruction. We have demonstrated that our algorithms can achieve a superresolution that is better than one-quarter wavelength (2.8 mm at 6 GHz). The product of our data processing is a high-resolution digital image containing the physical properties of the tissue and potential tumors. A physics-based statistical decision algorithm is applied to aid in arriving at a diagnostic result. The full three-dimensional integrated system is also expected to improve the detection of tumors in dense breasts and near the chest wall and underarm. This 3-D microwave imaging system thus combines hardware, forward simulation methods, nonlinear inverse scattering algorithms, and statistical decision algorithms for breast cancer diagnosis and screening, and has the potential to become an effective modality complementary to X-ray mammography.

DESCRIPTION (provided by applicant): Current MRI, CT, and PET image reconstruction algorithms are limited by the large interpolation errors of the nonuniform data in the Fourier space. This is becoming a computational bottleneck in large-scale 3D imaging where the size of data grows rapidly with the size of volume and the increasing demand on high resolution. The proposed research would develop multidimensional nonuniform fast Fourier transform (NUFFT) algorithms. Such algorithms will significantly improve both the resolution and the speed of image reconstruction because of the highly accurate and efficient interpolation. The NUFFT will be applied to and evaluated by magnetic resonance imaging (MRI), positron emission tomography (PET), and X-ray computerized tomography (CT). A joint inversion framework will also be developed for the MRI/PET combination and for the CT/PET combination to improve the information obtained by individual modalities. In the R21 Phase I of this research, we will (a) develop new 2D nonuniform fast Fourier transform (NUFFT) algorithms; (b) evaluate the NUFFT algorithms with synthetic and real MRI and CT data from the Duke Center for In Vivo Microscopy. In the R33 Phase II of this research, we will (a) develop 3D NUFFT algorithms for MRT, CT and PET imaging modalities, (b) develop a joint inversion framework based on NUFFT for multi-modality imaging in the MRI/PET and CT/PET combinations, and (c) evaluate the 3D NUFFT and joint inversion framework with synthetic and real MRI/PET and CT/PET data. Such a framework can be considered as a shift in computational paradigm for image reconstruction in MRI, PET, CT, and potentially many other modalities. For rapidly growing 3D imaging applications, the new NUFFT algorithms would improve the resolution and computation speed over the conventional methods by orders of magnitude. The joint inversion method will improve the information obtained by the individual modalities.

Recent research activities in quantum information science have advanced our understanding of quantum mechanics and proposed essential components used in quantum information processing. Quantum decoherence is a major factor affecting the quality of quantum information processing devices, including on-demand single photon sources and quantum nodes. The team conducts a collaborative and interdisciplinary research program for improving the quantum coherence of single-quantum-dot photonic-crystal-cavity systems with the help of state-of-the-art nanofabrication and computational modeling. On-chip solid-state cavity quantum electrodynamics systems, to be constructed by the team, are compact and scalable on a semiconductor wafer. The hardware is essentially integrated optics consisting of compact cavities and planar waveguides, so photonic crystals will provide a practical means of constructing compact integrated quantum information-processing chips. Constructing such ultimate photon localization systems in photonic crystals will open up many possibilities in relevant fields in both science and engineering, much as engineering electronic bands in semiconductor crystals has done. The team's interdisciplinary efforts to resolve the computational and experimental challenges will facilitate the realization of the full potential of nanotechnology in the quantum information science field. The development of high quantum-coherence components will also advance technology of other applications including WDM chips, optical logic circuits, and sensors on a chip, and enhance the understanding of the nature of light.

The objective of this program is to demonstrate current injection, continues-wave lasing action at room temperature from sub-wavelength-scale disk resonators by developing high-quality modes in the proximity of low-loss conductive media and provide CMOS-compatible buildings blocks based on nanocavities that require current injection and electric field application. The developed nano-resonators will also be used as external nano-modulators.

Intellectual merit is in developing practical, planar nanolasers that is smaller than one wavelength of lasing mode in vacuum. A challenge to build current injection nanolasers arises from the difficulty of suppressing absorption of light by electrode metal. The PI?s approach to build sub-wavelength-scale current-injection nanolasers is to use simple, high-Q disk nanocavity designs with transparent conductive oxide (TCO) electrodes such as indium tin oxide (ITO). The PI?s designs with TCO media have advantages, including large thermal conductance, small electric resistance, and large quantum efficiency via the direct current injection to small lasing modes.

The broader impacts are in inducing the development of on-chip photonics systems, such as optical interconnect chips, lab-on-chip, quantum information chips, imaging devices, and data storage chips, via the development of breakthrough technology in nanolasers. The electrically-controlled light localization technology would break the boundary set by present light localization technology and open up possibilities of creating new light localization devices and enhance the performance and functionality of optical devices. The program will provide excellent outreach opportunities by creating online, hands-on, free scientific curriculums.