Temporal Coding of Volumetric Imagery

'Image volumes' refer to realizations of images in other dimensions such as time, spectrum, and focus. Recent advances in scientific, medical, and consumer applications demand improvements in image volume capture. Though image volume acquisition continues to advance, it maintains the same sampling mechanisms that have been used for decades; every voxel must be scanned and is presumed independent of its neighbors. Under these conditions, improving performance comes at the cost of increased system complexity, data rates, and power consumption.

This dissertation explores systems and methods capable of efficiently improving sensitivity and performance for image volume cameras, and specifically proposes several sampling strategies that utilize temporal coding to improve imaging system performance and enhance our awareness for a variety of dynamic applications.

Video cameras and camcorders sample the video volume (x,y,t) at fixed intervals to gain understanding of the volume's temporal evolution. Conventionally, one must reduce the spatial resolution to increase the framerate of such cameras. Using temporal coding via physical translation of an optical element known as a coded aperture, the compressive temporal imaging (CACTI) camera emonstrates a method which which to embed the temporal dimension of the video volume into spatial (x,y) measurements, thereby greatly improving temporal resolution with minimal loss of spatial resolution. This technique, which is among a family of compressive sampling strategies developed at Duke University, temporally codes the exposure readout functions at the pixel level.

Since video cameras nominally integrate the remaining image volume dimensions (e.g. spectrum and focus) at capture time, spectral (x,y,t,\lambda) and focal (x,y,t,z) image volumes are traditionally captured via sequential changes to the spectral and focal state of the system, respectively. The CACTI camera's ability to embed video volumes into images leads to exploration of other information within that video; namely, focal and spectral information. The next part of the thesis demonstrates derivative works of CACTI: compressive extended depth of field and compressive spectral-temporal imaging. These works successfully show the technique's extension of temporal coding to improve sensing performance in these other dimensions.

Geometrical optics-related tradeoffs, such as the classic challenges of wide-field-of-view and high resolution photography, have motivated the development of mulitscale camera arrays. The advent of such designs less than a decade ago heralds a new era of research- and engineering-related challenges. One significant challenge is that of managing the focal volume (x,y,z) over wide fields of view and resolutions. The fourth chapter shows advances on focus and image quality assessment for a class of multiscale gigapixel cameras developed at Duke.

Along the same line of work, we have explored methods for dynamic and adaptive addressing of focus via point spread function engineering. We demonstrate another form of temporal coding in the form of physical translation of the image plane from its nominal focal position. We demonstrate this technique's capability to generate arbitrary point spread functions.

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.

l0-deconvolution for compressive diffusion MRI

Diffusion MRI is a well established imaging modality providing a powerful way to non-invasively probe the structure of the white matter. Despite the potential of the technique, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a wide variety of methods have been proposed to shorten acquisition times. [...] We here review a recent work where we propose to further exploit the versatility of compressed sensing and convex optimization with the aim to characterize the fiber orientation distribution sparsity more optimally. We re-formulate the spherical deconvolution problem as a constrained l0 minimization.

Ligament balancing in TKA: Evaluation of a force-sensing device and the influence of patellar eversion and ligament release

Ligament balancing in total knee arthroplasty may have an important influence on joint stability and prosthesis lifetime. In order to provide quantitative information and assistance during ligament balancing, a device that intraoperatively measures knee joint forces and moments was developed. Its performance and surgical advantages were evaluated on six cadaver specimens mounted on a knee joint loading apparatus allowing unconstrained knee motion as well as compression and varus-valgus loading. Four different experiments were performed on each specimen. (1) Knee joints were axially loaded. Comparison between applied and measured compressive forces demonstrated the accuracy and reliability of in situ measurements (1.8N). (2) Assessment of knee stability based on condyle contact forces or varus-valgus moments were compared to the current surgical method (difference of varus-valgus loads causing condyle lift-off). The force-based approach was equivalent to the surgical method while the moment-based, which is considered optimal, showed a tendency of lateral imbalance. (3) To estimate the importance of keeping the patella in its anatomical position during imbalance assessment, the effect of patellar eversion on the mediolateral distribution of tibiofemoral contact forces was measured. One fourth of the contact force induced by the patellar load was shifted to the lateral compartment. (4) The effect of minor and major medial collateral ligament releases was biomechanically quantified. On average, the medial contact force was reduced by 20% and 46%, respectively. Large variation among specimens reflected the difficulty of ligament release and the need for intraoperative force monitoring. This series of experiments thus demonstrated the device's potential to improve ligament balancing and survivorship of total knee arthroplasty.

Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown.

Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals

Many problems in digital communications involve wideband radio signals. As the most recent example, the impressive advances in Cognitive Radio systems make even more necessary the development of sampling schemes for wideband radio signals with spectral holes. This is equivalent to considering a sparse multiband signal in the framework of Compressive Sampling theory. Starting from previous results on multicoset sampling and recent advances in compressive sampling, we analyze the matrix involved in the corresponding reconstruction equation and define a new method for the design of universal multicoset codes, that is, codes guaranteeing perfect reconstruction of the sparse multiband signal.

Self-sensing properties of alkali activated blast furnace slag (BFS) composites reinforced with carbon fibers

In recent years, several researchers have shown the good performance of alkali activated slag cement and concretes. Besides their good mechanical properties and durability, this type of cement is a good alternative to Portland cements if sustainability is considered. Moreover, multifunctional cement composites have been developed in the last decades for their functional applications (self-sensing, EMI shielding, self-heating, etc.). In this study, the strain and damage sensing possible application of carbon fiber reinforced alkali activated slag pastes has been evaluated. Cement pastes with 0, 0.29 and 0.58 vol % carbon fiber addition were prepared. Both carbon fiber dosages showed sensing properties. For strain sensing, function gage factors of up to 661 were calculated for compressive cycles. Furthermore, all composites with carbon fibers suffered a sudden increase in their resistivity when internal damages began, prior to any external signal of damage. Hence, this material may be suitable as strain or damage sensor.

Nested Sampling and its Applications in Stable Compressive Covariance Estimation and Phase Retrieval with Near-Minimal Measurements

Compressed covariance sensing using quadratic samplers is gaining increasing interest in recent literature. Covariance matrix often plays the role of a sufficient statistic in many signal and information processing tasks. However, owing to the large dimension of the data, it may become necessary to obtain a compressed sketch of the high dimensional covariance matrix to reduce the associated storage and communication costs. Nested sampling has been proposed in the past as an efficient sub-Nyquist sampling strategy that enables perfect reconstruction of the autocorrelation sequence of Wide-Sense Stationary (WSS) signals, as though it was sampled at the Nyquist rate. The key idea behind nested sampling is to exploit properties of the difference set that naturally arises in quadratic measurement model associated with covariance compression. In this thesis, we will focus on developing novel versions of nested sampling for low rank Toeplitz covariance estimation, and phase retrieval, where the latter problem finds many applications in high resolution optical imaging, X-ray crystallography and molecular imaging. The problem of low rank compressive Toeplitz covariance estimation is first shown to be fundamentally related to that of line spectrum recovery. In absence if noise, this connection can be exploited to develop a particular kind of sampler called the Generalized Nested Sampler (GNS), that can achieve optimal compression rates. In presence of bounded noise, we develop a regularization-free algorithm that provably leads to stable recovery of the high dimensional Toeplitz matrix from its order-wise minimal sketch acquired using a GNS. Contrary to existing TV-norm and nuclear norm based reconstruction algorithms, our technique does not use any tuning parameters, which can be of great practical value. The idea of nested sampling idea also finds a surprising use in the problem of phase retrieval, which has been of great interest in recent times for its convex formulation via PhaseLift, By using another modified version of nested sampling, namely the Partial Nested Fourier Sampler (PNFS), we show that with probability one, it is possible to achieve a certain conjectured lower bound on the necessary measurement size. Moreover, for sparse data, an l1 minimization based algorithm is proposed that can lead to stable phase retrieval using order-wise minimal number of measurements.

Human Dental Pulp Cells: A New Source of Cell Therapy in a Mouse Model of Compressive Spinal Cord Injury

Strategies aimed at improving spinal cord regeneration after trauma are still challenging neurologists and neuroscientists throughout the world. Many cell-based therapies have been tested, with limited success in terms of functional outcome. In this study, we investigated the effects of human dental pulp cells (HDPCs) in a mouse model of compressive spinal cord injury (SCI). These cells present some advantages, such as the ease of the extraction process, and expression of trophic factors and embryonic markers from both ecto-mesenchymal and mesenchymal components. Young adult female C57/BL6 mice were subjected to laminectomy at T9 and compression of the spinal cord with a vascular clip for 1 min. The cells were transplanted 7 days or 28 days after the lesion, in order to compare the recovery when treatment is applied in a subacute or chronic phase. We performed quantitative analyses of white-matter preservation, trophic-factor expression and quantification, and ultrastructural and functional analysis. Our results for the HDPC-transplanted animals showed better white-matter preservation than the DMEM groups, higher levels of trophic-factor expression in the tissue, better tissue organization, and the presence of many axons being myelinated by either Schwann cells or oligodendrocytes, in addition to the presence of some healthy-appearing intact neurons with synapse contacts on their cell bodies. We also demonstrated that HDPCs were able to express some glial markers such as GFAP and S-100. The functional analysis also showed locomotor improvement in these animals. Based on these findings, we propose that HDPCs may be feasible candidates for therapeutic intervention after SCI and central nervous system disorders in humans.

Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature

Cloud-aerosol interaction is a key issue in the climate system, affecting the water cycle, the weather, and the total energy balance including the spatial and temporal distribution of latent heat release. Information on the vertical distribution of cloud droplet microphysics and thermodynamic phase as a function of temperature or height, can be correlated with details of the aerosol field to provide insight on how these particles are affecting cloud properties and their consequences to cloud lifetime, precipitation, water cycle, and general energy balance. Unfortunately, today's experimental methods still lack the observational tools that can characterize the true evolution of the cloud microphysical, spatial and temporal structure in the cloud droplet scale, and then link these characteristics to environmental factors and properties of the cloud condensation nuclei. Here we propose and demonstrate a new experimental approach (the cloud scanner instrument) that provides the microphysical information missed in current experiments and remote sensing options. Cloud scanner measurements can be performed from aircraft, ground, or satellite by scanning the side of the clouds from the base to the top, providing us with the unique opportunity of obtaining snapshots of the cloud droplet microphysical and thermodynamic states as a function of height and brightness temperature in clouds at several development stages. The brightness temperature profile of the cloud side can be directly associated with the thermodynamic phase of the droplets to provide information on the glaciation temperature as a function of different ambient conditions, aerosol concentration, and type. An aircraft prototype of the cloud scanner was built and flew in a field campaign in Brazil. The CLAIM-3D (3-Dimensional Cloud Aerosol Interaction Mission) satellite concept proposed here combines several techniques to simultaneously measure the vertical profile of cloud microphysics, thermodynamic phase, brightness temperature, and aerosol amount and type in the neighborhood of the clouds. The wide wavelength range, and the use of multi-angle polarization measurements proposed for this mission allow us to estimate the availability and characteristics of aerosol particles acting as cloud condensation nuclei, and their effects on the cloud microphysical structure. These results can provide unprecedented details on the response of cloud droplet microphysics to natural and anthropogenic aerosols in the size scale where the interaction really happens.

On the compressive strength prediction for concrete masonry prisms

The results of a combined experimental program and numerical modeling program to evaluate the behavior of ungrouted hollow concrete blocks prisms under uniaxial compression are addressed. In the numerical program, three distinct approaches have been considered using a continuum model with a smeared approach, namely plane-stress, plane-strain and three-dimensional conditions. The response of the numerical simulations is compared with experimental data of masonry prisms using concrete blocks specifically designed for this purpose. The elastic and inelastic parameters were acquired from laboratory tests on concrete and mortar samples that constitute the blocks and the bed joint of the prisms. The results from the numerical simulations are discussed with respect to the ability to reproduce the global response of the experimental tests, and with respect to the failure behavior obtained. Good agreement between experimental and numerical results was found for the peak load and for the failure mode using the three-dimensional model, on four different sets of block/mortar types. Less good agreement was found for plain stress and plain strain models.

Structural behaviour of three-pile caps subjected to axial compressive loading

This work presents a comparative analysis about the behaviour of pile caps supported by 3 piles subjected to axial loading. Piles with 20 cm and 30 cm diameters were analysed. The main reinforcement was maintained in all the specimens, however, the arrangement of the secondary reinforcement varied. The main reinforcement consisted of steel bars connecting the piles. The secondary reinforcement was made up of: (a) bars going through the piles and through the projection of the column, (b) bars forming a network, and (c) vertical and horizontal stirrups. The main objective was the observation of the pile cap behaviour regarding the cracks and the modes of rupture. The real scale specimens were subjected to experimental tests until failure by rupture. Instruments were placed with the aim to obtain the displacement of the bases, the strains in the main and secondary reinforcement bars, in the compression struts, in the lower and upper nodal zones and in the sides of the caps. None of the caps reached failure by rupture with a load less than 1.12 times the theoretical load. The specimens ruptured due to the cracking of the compression strut and/or the yielding of the reinforcement bars in one direction.

Determination of in situ uniaxial compressive strength of coal seams based on geophysical data

The central issue for pillar design in underground coal mining is the in situ uniaxial compressive strength (sigma (cm)). The paper proposes a new method for estimating in situ uniaxial compressive strength in coal seams based on laboratory strength and P wave propagation velocity. It describes the collection of samples in the Bonito coal seam, Fontanella Mine, southern Brazil, the techniques used for the structural mapping of the coal seam and determination of seismic wave propagation velocity as well as the laboratory procedures used to determine the strength and ultrasonic wave velocity. The results obtained using the new methodology are compared with those from seven other techniques for estimating in situ rock mass uniaxial compressive strength.