Compressive sensing (CS) is a novel sampling methodology representing a paradigm shift from conventional data acquisition schemes. The theory of compressive sensing ensures that under suitable conditions compressible signals or images can be reconstructed from far fewer samples or measurements than what are required by the Nyquist rate. So far in the literature, most works on CS concentrate on one-dimensional or two-dimensional data. However, besides involving far more data, three-dimensional (3D) data processing does have particularities that require the development of new techniques in order to make successful transitions from theoretical feasibilities to practical capacities. This thesis studies several issues arising from the applications of the CS methodology to some 3D image processing tasks. Two specific applications are hyperspectral imaging and video compression where 3D images are either directly unmixed or recovered as a whole from CS samples. The main issues include CS decoding models, preprocessing techniques and reconstruction algorithms, as well as CS encoding matrices in the case of video compression. Our investigation involves three major parts. (1) Total variation (TV) regularization plays a central role in the decoding models studied in this thesis. To solve such models, we propose an efficient scheme to implement the classic augmented Lagrangian multiplier method and study its convergence properties. The resulting Matlab package TVAL3 is used to solve several models. Computational results show that, thanks to its low per-iteration complexity, the proposed algorithm is capable of handling realistic 3D image processing tasks. (2) Hyperspectral image processing typically demands heavy computational resources due to an enormous amount of data involved. We investigate low-complexity procedures to unmix, sometimes blindly, CS compressed hyperspectral data to directly obtain material signatures and their abundance fractions, bypassing the high-complexity task of reconstructing the image cube itself. (3) To overcome the "cliff effect" suffered by current video coding schemes, we explore a compressive video sampling framework to improve scalability with respect to channel capacities. We propose and study a novel multi-resolution CS encoding matrix, and a decoding model with a TV-DCT regularization function. Extensive numerical results are presented, obtained from experiments that use not only synthetic data, but also real data measured by hardware. The results establish feasibility and robustness, to various extent, of the proposed 3D data processing schemes, models and algorithms. There still remain many challenges to be further resolved in each area, but hopefully the progress made in this thesis will represent a useful first step towards meeting these challenges in the future.

<p>This dissertation describes three computational sensors. The first sensor is a scanning multi-spectral aperture-coded microscope containing a coded aperture spectrometer that is vertically scanned through a microscope intermediate image plane. The spectrometer aperture-code spatially encodes the object spectral data and nonnegative</p> <p>least squares inversion combined with a series of reconfigured two-dimensional (2D spatial-spectral) scanned measurements enables three-dimensional (3D) (x, y, &#955) object estimation. The second sensor is a coded aperture snapshot spectral imager that employs a compressive optical architecture to record a spectrally filtered projection</p> <p>of a 3D object data cube onto a 2D detector array. Two nonlinear and adapted TV-minimization schemes are presented for 3D (x,y,&#955) object estimation from a 2D compressed snapshot. Both sensors are interfaced to laboratory-grade microscopes and</p> <p>applied to fluorescence microscopy. The third sensor is a millimeter-wave holographic imaging system that is used to study the impact of 2D compressive measurement on 3D (x,y,z) data estimation. Holography is a natural compressive encoder since a 3D</p> <p>parabolic slice of the object band volume is recorded onto a 2D planar surface. An adapted nonlinear TV-minimization algorithm is used for 3D tomographic estimation from a 2D and a sparse 2D hologram composite. This strategy aims to reduce scan time costs associated with millimeter-wave image acquisition using a single pixel receiver.</p> / Dissertation

<p>Based on the compressed sensing theorem, we present the integrated software and hardware platform for developing a total-variation based image restoration algorithm by applying prior image information and free-form deformation fields for image guided therapy. The core algorithm we developed solves the image restoration problem for handling missing structures in one image set with prior information, and it enhances the quality of the image and the anatomical information of the volume of the on-board computed tomographic (CT) with limited-angle projections. Through the use of the algorithm, prior anatomical CT scans were used to provide additional information to help reduce radiation doses associated with the improved quality of the image volume produced by on-board Cone-Beam CT, thus reducing the total radiation doses that patients receive and removing distortion artifacts in 3D Digital Tomosynthesis (DTS) and 4D-DTS. The proposed restoration algorithm enables the enhanced resolution of temporal image and provides more anatomical information than conventional reconstructed images.</p><p>The performance of the algorithm was determined and evaluated by two built-in parameters in the algorithm, i.e., B-spline resolution and the regularization factor. These parameters can be adjusted to meet different requirements in different imaging applications. Adjustments also can determine the flexibility and accuracy during the restoration of images. Preliminary results have been generated to evaluate the image similarity and deformation effect for phantoms and real patient's case using shifting deformation window. We incorporated a graphics processing unit (GPU) and visualization interface into the calculation platform, as the acceleration tools for medical image processing and analysis. By combining the imaging algorithm with a GPU implementation, we can make the restoration calculation within a reasonable time to enable real-time on-board visualization, and the platform potentially can be applied to solve complicated, clinical-imaging algorithms.</p> / Dissertation

This work investigates the effects of post-fabrication applied mechanical tensile strain on Silicon (Si) Bipolar Junction Transistor (BJT) and Silicon-Germanium (SiGe) Heterojunction Bipolar Transistor (HBT) devices. Applied strain effects on MOSFET transistors are being heavily explored, both in academia and industry, as a possible alternative to dimensional scaling. This thesis focuses on how strain affects Si BJT and SiGe HBTs, where tensile strain is applied after the Integrated Circuit (IC) fabrication has been completed, using a unique mechanical method. The consequence of both biaxial and uniaxial strain application has been examined in this work. Chapter I gives a short introduction to the scope of this work, the motivation for conducting this research and the contributions of this experiment. Chapter II entails a brief discussion on Si bipolar and SiGe heterojunction bipolar device physics, which are key to the understanding of strain induced effects. Chapter III provides a thorough summary of the current state of research regarding applied strain, also known as Strain Engineering. It covers different types, orientations, and application techniques of strain. Chapter IV, highlights the details of this experiment, and also presents the measured results. It is observed that for this particular method of biaxial tensile strain application, the collector current (IC) and current gain degrades for both Si BJT and SiGe HBT. Base current (IB) decreases in Si BJT, though it increases for SiGe HBT after strain. Little or no change is noticed in the dynamic or ac small-signal characteristics like unity-gain cutoff frequency (fT) and base resistance (rBB) after strain. Uniaxially strained SiGe HBT samples showed similar results as the biaxial strain. This chapter also attempts to explain the origin of these strain induced changes. Chapter V, summarizes the finding of this experiment, and concludes the thesis with some future directions for this research.

Fuel cells represent a promising energy alternative to the traditional combustion of fossil fuels. In particular, solid oxide fuel cells (SOFCs) have been of interest due to their high energy densities and potential for stationary power applications. One of the key obstacles precluding the maturation and commercialization of planar SOFCs has been the absence of a robust sealant. A leakage computational model has been developed and refined in conjunction with leakage experiments and material characterization tests at Oak Ridge National Laboratory to predict leakage in a single interface metal-metal compressive seal assembly as well as multi-interface mica compressive seal assemblies. The composite model is applied as a predictive tool for assessing how certain parameters (i.e., temperature, applied compressive stress, surface finish, and elastic thermo physical properties) affect seal leakage rates.

Roller Compacted Concrete (RCC) is new technology gaining popularity in the recent years due to its low cost, rapid construction, and using opportunity of by-products. RCC is widely used in the world. However, the use of RCC has been restricted to construction of few cofferdams, and limited to local use in dam construction up to date. In this thesis, two types of cement, two types of natural pozzolan, aggregates with varying gradations, and a type of water reducing chemical admixture were used. Prior to carrying out the tests, the chemical and physical properties of materials were determined. Additionally, steam curing was applied to the test specimens in order to get long term compressive strength at early ages. Differences between steam cured specimens and normal cured specimens have been discussed in the discussion part. In the study, the results indicate that usage of water reducing chemical admixture improves compressive strength of RCC. Moreover, it is revealed that usage of fine material is essential to obtain desired results since the amount of cementitious materials is considerably low in RCC. Steam curing is known as its property of providing long term compressive strength at early ages. It was observed that application of steam curing in CEM I type cement used RCC mixtures generated expected results. However, in CEM IV type cement used RCC mixtures compressive strength results did not behave in the same manner.

The aim of this thesis is to twofold, in order to demonstrate the variabilities that can be faced within the compressive strength of blended cements, one blended cement namely CEM IV / B (P-V) 32.5N is selected and the 28-day compressive strength is obtained by 16 different laboratories following TS EN 196-1 standard. Later, to show the variabilities that could be faced by different standards, three different cement types were selected and their compressive strengths are determined following two procedures first with TS EN 196-1, later with similar procedure described in ASTM. The strength of cement is determined by TS EN 196-1 in Turkey that is the same for all types of cements. However, American cement producers use different standards for testing the strength of Portland cement and blended cements. The main difference is the amount of water utilized in producing the cement mortar. It was observed that for Portland and Portland composite cements / there is not any significant difference in between the compressive strength results of cement mortars prepared by both methods. However, for pozzolanic cements, there is much deviance in the compressive strength results of cement mortars prepared by TS EN 196-1.

This thesis describes improvements on newly-discovered high-strength pozzolan-based materials fabricated via a low-cost chemical reaction that takes place between 90 and 115 ℃ for 3 to 24 hours. The reported results focus on pozzolan constituents acquired from Coal Combustion Products (CCPs) such as cenospheres, fly ash C and F, as well as bottom ash. The thesis reports on various types of these materials with specific gravity ranging from 0.5 to 1.6; compressive strength ranging from 300 to 3600 psi, and compressive modulus ranging from 50 to 240 ksi. In addition to their good mechanical properties under compression that are attractive for the building and construction industries, the materials further exhibit great potential for applications as energy absorption cores in sandwich construction that could extend their value in other industries including the automotive and aerospace industries. For example, the load-displacement curve exhibits a short elastic zone followed by a long load-plateau; while the materials crush through a controlled vertical cracking process. Additionally, an attempt was made to further decrease the manufacturing cost of the material by investigating incorporation of chemicals that accelerates dehydration of the mixture. One such successful chemical reported in this thesis is aluminum phosphate; while it is not conclusive how the chemical improves any major property.

One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth. One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited. While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference. Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].

Use of imaging systems for performing various situational awareness tasks in militaryand commercial settings has a long history. There is increasing recognition,however, that a much better job can be done by developing non-traditional opticalsystems that exploit the task-specific system aspects within the imager itself. Insome cases, a direct consequence of this approach can be real-time data compressionalong with increased measurement fidelity of the task-specific features. In others,compression can potentially allow us to perform high-level tasks such as direct trackingusing the compressed measurements without reconstructing the scene of interest.In this dissertation we present novel advancements in feature-specific (FS) imagersfor large field-of-view surveillence, and estimation of temporal object-scene changesutilizing the compressive imaging paradigm. We develop these two ideas in parallel.In the first case we show a feature-specific (FS) imager that optically multiplexesmultiple, encoded sub-fields of view onto a common focal plane. Sub-field encodingenables target tracking by creating a unique connection between target characteristicsin superposition space and the target's true position in real space. This isaccomplished without reconstructing a conventional image of the large field of view.System performance is evaluated in terms of two criteria: average decoding time andprobability of decoding error. We study these performance criteria as a functionof resolution in the encoding scheme and signal-to-noise ratio. We also includesimulation and experimental results demonstrating our novel tracking method. Inthe second case we present a FS imager for estimating temporal changes in the objectscene over time by quantifying these changes through a sequence of differenceimages. The difference images are estimated by taking compressive measurementsof the scene. Our goals are twofold. First, to design the optimal sensing matrixfor taking compressive measurements. In scenarios where such sensing matrices arenot tractable, we consider plausible candidate sensing matrices that either use theavailable <italic>a priori</italic> information or are non-adaptive. Second, we develop closed-form and iterative techniques for estimating the difference images. We present results to show the efficacy of these techniques and discuss the advantages of each.