A recent breakthrough in information theory known as compressive sensing is one component of an ongoing revolution in data acquisition and processing that guides one to acquire less data yet still recover the same amount of information as traditional techniques, meaning less resources such as time, detector cost, or power are required. Starting from these basic principles, this thesis explores the application of these techniques to imaging. The first laboratory example we introduce is a simple infrared camera. Then we discuss the application of compressive sensing techniques to hyperspectral microscopy, specifically Raman microscopy, which should prove to be a powerful technique to bring the acquisition time for such microscopies down from hours to minutes. Next we explore a novel sensing architecture that uses partial circulant matrices as sensing matrices, which results in a simplified, more robust imaging system. The results of these imaging experiments lead to questions about the performance and fundamental nature of sparse signal recovery with partial circulant compressive sensing matrices. Thus, we present the results of a suite of numerical experiments that show some surprising and suggestive results that could stimulate further theoretical and applied research of partial circulant compressive sensing matrices. We conclude with a look ahead to adaptive sensing procedures that allow real-time, interactive optical signal processing to further reduce the resource demands of an imaging system.

In digital signal processing field, for recovering the signal without distortion, Shannon sampling theory must be fulfilled in the traditional signal sampling. However, in some practical applications, it is becoming an obstacle because of the dramatic increase of the costs due to increased volume of the storage and transmission as a function of frequency for sampling. Therefore, how to reduce the number of the sampling in analog to digital conversion (ADC) for wideband and how to compress the large data effectively has been becoming major subject for study. Recently, a novel technique, so-called “compressed sampling”, abbreviated as CS, has been proposed to solve the problem. This method will capture and represent compressible signals at a sampling rate significantly lower than the Nyquist rate.   This paper not only surveys the theory of compressed sampling, but also simulates the CS with the software Matlab. The error between the recovered signal and original signal for simulation is around -200dB. The attempts were made to apply CS. The error between the recovered signal and original one for experiment is around -40 dB which means the CS is realized in a certain extent. Furthermore, some related applications and the suggestions of the further work are discussed.

This dissertation presents the fundamental theory and design procedure of the sub-Nyquist rate sampling receiver front-end that exploits signal sparsity by employing Compressive Sensing (CS) techniques. The CS receiver serves as an Analog-to-Information Conversion (AIC) system that works at sampling rates much lower than the Nyquist rate. The performance of a parallel path CS front-end structure that employs current mode sampling techniques is quantified analytically. Useful and fundamental design guidelines that are unique to CS are provided based on the analytical tools. Simulations with IBM 90nm CMOS process verify the theoretical derivations and the circuit implementations. Based on these results, it is shown that instantaneous receiver signal bandwidth of 1.5 GHz and 44 dB of signal to noise plus distortion ratio (SNDR) are achievable in simulations assuming 0.5 ps clock jitter is present. The ADC and front-end core power consumption is estimated to be 120.8 mW. The front-end is fabricated with IBM 90nm CMOS process, and a BPSK sub-Nyquist rate communication system is realized as a prototype in the testing. A 1.25 GHz reference clock with 4.13 ps jitter variance is employed in the test bench. The signal frequency, phase and amplitude can be correctly reconstructed, and the maximum signal SNR obtained in the testing is 40 dB with single tone input and 30 dB with multi-tones test. The CS system has a better FOM than state-of-art Nyquist rate data acquisition systems taking into account the estimated PLL power.

In this study, trabecular architecture is considered as a cellular solid structure, including both intact and damaged bone models. ??Intact?? bone models were constructed based on ideal versions of 25, 60 and 80-year-old specimens with varying trabecular lengths and orientations to 5%, and 10% covariance of variation (COV). The models were also flipped between longer transverse and longer longitudinal trabeculae. With increasing COV of lengths and orientations of trabecular bone, the apparent modulus is linearly decreased, especially in the longer transverse trabeculae lengths. ??Damaged?? bone models were built from the 25 year old model at 5% COV of longer transverse trabeculae, and with removing trabeculae of 5% and 10% of trabecular volume in transverse and longitudinal directions, respectively, as well as in combination to total 10% and 15%. With increasing percent of trabeculae missing, the apparent modulus decreased, especially dramatically when removal was only in the transverse direction. The trabecular bone models were also connected to a cortical shell and it was found that the apparent modulus of an entire slice was increased in comparison to the modulus of trabecular bone alone. We concluded that the architecture of trabecular bone, especially both lengths and percent of trabecular missing in the longitudinal direction, significantly influences mechanical properties.

We consider three problems, motivated by questions in computational neuroscience, related to recovering continuous quantities from binary or discrete data or measurements in the context of sparse structure. First, we show that it is possible to recover the norms of sparse vectors given one-bit compressive measurements, and provide associated guarantees. Second, we present a novel algorithm for spike-sorting in neural data, which involves recovering continuous times and amplitudes of events using discrete bases. This method, Continuous Orthogonal Matching Pursuit, builds on algorithms used in compressive sensing. It exploits the sparsity of the signal and proceeds greedily, achieving gains in speed and accuracy over previous methods. Lastly, we present a Bayesian method making use of hierarchical priors for entropy rate estimation from binary sequences. / text

The goal of this study was to revise the development length equation of ACI 318- 05 and to better reflect test results for high-strength concrete. The revision of the equation was accomplished using test results tabulated in the Database 10-2001maintained by ACI committee 408. Equations for development length in ACI 318-05 and ACI 408.3 examined to understand the issues to be considered for revision on the variability of test data. The development length equation in ACI 318-05 was very conservative for [compressive strength of concrete][less than or equal to]14,000 psi based on the experimental data in Database 10-2001 of ACI Committee 408. On the contrary, the ACI 318-05 may be less conservative for high-strength concrete, [compressive strength of concrete] [greater than or equal to]14,000 psi . Thus, modified design equations were proposed to provide realistic values for normal strength concrete and conservatively for high-strength concrete. The ACI 318-05 equation was modified for 1) compressive strength of concrete and 2) confinement as expressed by the term [minimum side cover, cover over the bar or wire, or one-half the center-to-center spacing of the bars or wires] + [contribution of confining reinforcement across potential splitting planes] / [normal diameter of bar] in ACI 318-05. The basic assumption is that bar stress is a linear function of development length, and development length is the length required for bar stresses to reach the yield. / text

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / Spread Spectrum (SS) techniques are methods used to deliberately spread the spectrum of transmitted signals in communication systems. The increased bandwidth makes detection of these signals challenging for non-cooperative receivers. In this paper, we investigate detection of Frequency Hopping Spread Spectrum (FHSS) signals from compressive measurements. The theoretical and simulated performances of the proposed methods are compared to those of the conventional methods.

Third generation full-waveform (FW) LIDAR systems image an entire scene by emitting laser pulses in particular directions and measuring the echoes. Each of these echoes provides range measurements about the objects intercepted by the laser pulse along a specified direction. By scanning through a specified region using a series of emitted pulses and observing their echoes, connected 1D profiles of 3D scenes can be readily obtained. This extra information has proven helpful in providing additional insight into the scene structure which can be used to construct effective characterizations and classifications. Unfortunately, massive amounts of data are typically collected which impose storage, processing and transmission limitations. To address these problems, a number of compression approaches have been developed in the literature. These, however, generally require the initial acquisition of large amounts of data only to later discard most of it by exploiting redundancies, thus sampling inefficiently. Based on this, our main goal is to apply efficient and effective LIDAR sampling schemes that achieve acceptable reconstruction quality of the 3D scenes. To achieve this goal, we propose on using compressive sampling by emitting pulses only into random locations within the scene and collecting only the corresponding returned FW signals. Under this framework, the number of emissions would typically be much smaller than what traditional LIDAR systems require. Application of this requires, however, that scenes contain many degrees of freedom. Fortunately, such a requirement is satisfied in most natural and man-made scenes. Here, we propose to use a measure of rank as the measure of degrees of freedom. To recover the connected 1D profiles of the 3D scene, matrix completion is applied to the tensor slices. In this paper, we test our approach by showing that recovery of compressively sampled 1D profiles of actual 3D scenes is possible using only a subset of measurements.

A finite element analysis approach is used to determine the susceptibility to wrinkles for thin sheets with clamped ends when subjected to tensile loading. The model problem chosen to do this analysis is the stretching of a thin sheet with clamped-ends. In the preliminary analysis, a stress analysis of thin sheets is done to study the stresses that develop under these boundary conditions. The analysis shows that there is a stretch-induced compressive stress in the transverse direction to the applied load that causes wrinkles. Then, the parametric study is conducted to determine the effect of aspect ratio and strain on the compressive stress. Based on the results of the parametric study, a critical strain value for each aspect ratio is determined for which the corresponding compressive stress is zero. Further buckling analysis is performed to find the buckling modes of the model problem that shows a limit of aspect ratio below which buckling is not possible under given conditions. Finally, post-buckling analysis shows the nature of wrinkles observed in the model problem for different aspect ratios. / text

Spread Spectrum (SS) techniques are methods used in communication systems where the spectra of the signal is spread over a much wider bandwidth. The large bandwidth of the resulting signals make SS signals difficult to intercept using conventional methods based on Nyquist sampling. Recently, a novel concept called compressive sensing has emerged. Compressive sensing theory suggests that a signal can be reconstructed from much fewer measurements than suggested by the Shannon Nyquist theorem, provided that the signal can be sparsely represented in a dictionary. In this work, motivated by this concept, we study compressive approaches to detect and decode SS signals. We propose compressive detection and decoding systems based both on random measurements (which have been the main focus of the CS literature) as well as designed measurement kernels that exploit prior knowledge of the SS signal. Compressive sensing methods for both Frequency-Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) systems are proposed.