As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models characterize mobile agent movement patterns. On the other hand, they describe the conditions of the mobile services. The focus of this thesis is on mobility modeling in cyber-physical systems. A macroscopic model that captures the mobility of individuals (people and vehicles) can facilitate an unlimited number of applications. One fundamental and obvious example is traffic profiling. Mobility in most systems is a dynamic process and small non-linearities can lead to substantial errors in the model. Extensive research activities on statistical inference and filtering methods for data modeling in cyber-physical systems exist. In this thesis, several methods are employed for multimodal data fusion, localization and traffic modeling. A novel energy-aware sparse signal processing method is presented to process massive sensory data. At baseline, this research examines the application of statistical filters for mobility modeling and assessing the difficulties faced in fusing massive multi-modal sensory data. A statistical framework is developed to apply proposed methods on available measurements in cyber-physical systems. The proposed methods have employed various statistical filtering schemes (i.e., compressive sensing, particle filtering and kernel-based optimization) and applied them to multimodal data sets, acquired from intelligent transportation systems, wireless local area networks, cellular networks and air quality monitoring systems. Experimental results show the capability of these proposed methods in processing multimodal sensory data. It provides a macroscopic mobility model of mobile agents in an energy efficient way using inconsistent measurements.

Group sparsity reveals underlying sparsity patterns and contains rich structural information in data. Hence, exploiting group sparsity will facilitate more efficient techniques for recovering large and complicated data in applications such as compressive sensing, statistics, signal and image processing, machine learning and computer vision. This thesis develops efficient algorithms for solving a class of optimization problems with group sparse solutions, where arbitrary group configurations are allowed and the mixed L21-regularization is used to promote group sparsity. Such optimization problems can be quite challenging to solve due to the mixed-norm structure and possible grouping irregularities. We derive algorithms based on a variable splitting strategy and the alternating direction methodology. Extensive numerical results are presented to demonstrate the efficiency, stability and robustness of these algorithms, in comparison with the previously known state-of-the-art algorithms. We also extend the existing global convergence theory to allow more generality.

Digital image acquisition can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). In this work, a CS-based data-guided statistical sparse measurements method is presented, implemented and evaluated. This method significantly improves image reconstruction from sparse measurements. In the data-guided statistical sparse measurements approach, signal sampling distribution is optimized for improving image reconstruction performance. The sampling distribution is based on underlying data rather than the commonly used uniform random distribution. The optimal sampling pattern probability is accomplished by learning process through two methods - direct and indirect. The direct method is implemented for learning a nonparametric probability density function directly from the dataset. The indirect learning method is implemented for cases where a mapping between extracted features and the probability density function is required. The unified model is implemented for different representation domains, including frequency domain and spatial domain. Experiments were performed for multiple applications such as optical coherence tomography, bridge structure vibration, robotic vision, 3D laser range measurements and fluorescence microscopy. Results show that the data-guided statistical sparse measurements method significantly outperforms the conventional CS reconstruction performance. Data-guided statistical sparse measurements method achieves much higher reconstruction signal-to-noise ratio for the same compression rate as the conventional CS. Alternatively, Data-guided statistical sparse measurements method achieves similar reconstruction signal-to-noise ratio as the conventional CS with significantly fewer samples.

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demonstrated that structured signals can be acquired with just a small number of linear measurements, on the order of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sampling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs accommodate the otherwise limiting quantizer of conventional ADCs. In this thesis, we consider a different approach to CS ADC by shifting towards lower quantizer bit-depths rather than lower sampling rates. We explore the extreme case where each measurement is quantized to just one bit, representing its sign. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement compression (MC) regime, a high SNR favors acquiring fewer measurements with more bits per measurement (as in conventional CS); in the quantization compression (QC) regime, a low SNR favors acquiring more measurements with fewer bits per measurement (as in this thesis). A surprise from our analysis and experiments is that in many practical applications it is better to operate in the QC regime, even acquiring as few as 1 bit per measurement. The above philosophy extends further to practical CS ADC system designs. We propose two new CS architectures, one of which takes advantage of the fact that the sampling and quantization operations are performed by two different hardware components. The former can be employed at high rates with minimal costs while the latter cannot. Thus, we develop a system that discretizes in time, performs CS preconditioning techniques, and then quantizes at a low rate.

This work was carried out to study the effect of shot-peening on the fatigue behaviour of carbon steels. Differently heat treated medium and high carbon steel specimens were selected. Medium carbon steels, AISI 1141 and AISI 1151, were respectively air cooled and quenched-tempered. A high carbon steel, C70S6 (AISI 1070), was air cooled. The other material was a powder metal (0.5% C) steel. Each group of steels was divided into two. One was shot-peened. The other half remained in their original conditions. All were fatigue tested under fully reversed (R=-1) tension-compression loading conditions. Microhardness tests were carried out on both the grip and gage sections of selected non shot-peened and shot-peened specimens to determine the hardness profile and effect of cycling. Shot-peening was found to be deeper on one side of each specimen. Compressive residual stress profiles and surface roughness measurements were provided. Shot-peening increased the surface roughness from 0.26±0.03µm to 3.60±0.44µm. Compressive residual stresses induced by shot-peening reached a maximum of -463.9MPa at a depth of 0.1mm.The fatigue limit (N≈106 cycles) and microhardness profiles of the non shot-peened and shot-peened specimens were compared to determine the material behaviour changes after shot-peening and cycling. Also their fatigue properties were related to the manufacturing process including heat and surface treatments. Comparing the grip and gage microhardness profiles of each steel showed that neither cyclic softening nor hardening occurred in the non shot-peened condition. Cyclic softening was apparent in the shot-peened regions of all steels except powder metal (PM) steel. The amount of softening in the shot-peened region was 55.0% on the left side and 73.0% on the right in the AISI 1141 steel , 46.0% on the left side and 55.0% on the right in the C70S6AC steel and 31.0% on the right side in AISI 1151QT steel. Softening was accompanied by a decrease in the depth of surface hardness. It is suggested that although the beneficial effects of shot peening, compressive residual stresses and work hardening, were offset by surface roughness, crack initiation was more likely to occur below the surface. Surface roughness was not a significant factor in controlling the fatigue lives of AISI 1141AC and C70S6 steels, since they were essentially the same for the non shot-peened and shot-peened conditions. Shot-peening had very little effect on the push-pull fatigue limit of C70S6 steel (-2.1%), and its effect on AISI 1141AC steel was relatively small (6.0%). However, the influence of shot-peening on the AISI 1151QT and PM steels was more apparent. The fatigue limit of the PM steel increased 14.0% whereas the fatigue limit of the AISI 1151QT steel decreased 11.0% on shot peening.

Reconstruction of continuous signals from a number of their discrete samples is central to digital signal processing. Digital devices can only process discrete data and thus processing the continuous signals requires discretization. After discretization, possibility of unique reconstruction of the source signals from their samples is crucial. The classical sampling theory provides bounds on the sampling rate for unique source reconstruction, known as the Nyquist sampling rate. Recently a new sampling scheme, Compressive Sensing (CS), has been formulated for sparse signals. CS is an active area of research in signal processing. It has revolutionized the classical sampling theorems and has provided a new scheme to sample and reconstruct sparse signals uniquely, below Nyquist sampling rates. A signal is called (approximately) sparse when a relatively large number of its elements are (approximately) equal to zero. For the class of sparse signals, sparsity can be viewed as prior information about the source signal. CS has found numerous applications and has improved some image acquisition devices. Interesting instances of CS can happen, when apart from sparsity, side information is available about the source signals. The side information can be about the source structure, distribution, etc. Such cases can be viewed as extensions of the classical CS. In such cases we are interested in incorporating the side information to either improve the quality of the source reconstruction or decrease the number of the required samples for accurate reconstruction. A general CS problem can be transformed to an equivalent optimization problem. In this thesis, a special case of CS with side information about the feasible region of the equivalent optimization problem is studied. It is shown that in such cases uniqueness and stability of the equivalent optimization problem still holds. Then, an efficient reconstruction method is proposed. To demonstrate the practical value of the proposed scheme, the algorithm is applied on two real world applications: image deblurring in optical imaging and surface reconstruction in the gradient field. Experimental results are provided to further investigate and confirm the effectiveness and usefulness of the proposed scheme.

<p>This dissertation describes two computational sensors that were used to demonstrate applications of generalized sampling of the optical field. The first sensor was an incoherent imaging system designed for compressive measurement of the power spectral density in the scene (spectral imaging). The other sensor was an interferometer used to compressively measure the mutual intensity of the optical field (coherence imaging) for imaging through turbulence. Each sensor made anisomorphic measurements of the optical signal of interest and digital post-processing of these measurements was required to recover the signal. The optical hardware and post-processing software were co-designed to permit acquisition of the signal of interest with sub-Nyquist rate sampling, given the prior information that the signal is sparse or compressible in some basis.</p> <p>Compressive spectral imaging was achieved by a coded aperture snapshot spectral imager (CASSI), which used a coded aperture and a dispersive element to modulate the optical field and capture a 2D projection of the 3D spectral image of the scene in a snapshot. Prior information of the scene, such as piecewise smoothness of objects in the scene, could be enforced by numerical estimation algorithms to recover an estimate of the spectral image from the snapshot measurement.</p> <p>Hypothesizing that turbulence between the scene and CASSI would introduce spectral diversity of the point spread function, CASSI's snapshot spectral imaging capability could be used to image objects in the scene through the turbulence. However, no turbulence-induced spectral diversity of the point spread function was observed experimentally. Thus, coherence functions, which are multi-dimensional functions that completely determine optical fields observed by intensity detectors, were considered. These functions have previously been used to image through turbulence after extensive and time-consuming sampling of such functions. Thus, compressive coherence imaging was attempted as an alternative means of imaging through turbulence.</p> <p>Compressive coherence imaging was demonstrated by using a rotational shear interferometer to measure just a 2D subset of the 4D mutual intensity, a coherence function that captures the optical field correlation between all the pairs of points in the aperture. By imposing a sparsity constraint on the possible distribution of objects in the scene, both the object distribution and the isoplanatic phase distortion induced by the turbulence could be estimated with the small number of measurements made by the interferometer.</p> / Dissertation

Articular cartilage functions to maintain joint mobility. The loss of healthy, functional articular cartilage due to osteoarthritis or injury can severely compromise quality of life. To address this issue, cartilage tissue engineering approaches are currently in development. Bone marrow-derived mesenchymal progenitor cells (MPCs) hold much promise as an alternative cell source for cartilage tissue engineering. While previous studies have established that MPCs from humans and multiple other species undergo in vitro chondrogenic differentiation, additional research is needed to define conditions that will enhance MPC differentiation, increase matrix production by differentiating cultures, and support development of functional tissue-engineered cartilage constructs. Mechanical loading may be an important factor regulating chondrogenic differentiation of MPCs and cartilage matrix formation by chondrogenic MPCs. This thesis work evaluated the influence of oscillatory unconfined compressive mechanical loading on in vitro MPC chondrogenic activity and biosynthesis within hydrogel suspension. Loading was conducted using MPCs cultured in media supplements supporting chondrogenic differentiation. Possible interactions between the number of days in chondrogenic media preceding loading initiation and the ability of the MPC culture to respond to mechanical stimulation were explored in two different loading studies. The first loading study investigated the effects of 3 hour periods of daily oscillatory mechanical stimulation on subsequent chondrogenic activity, where chondrogenic activity represented an assessment of cartilage matrix production by differentiating MPCs. This study found that oscillatory compression of MPCs initiated during the first seven days of culture did not enhance chondrogenic activity above the level supported by media supplements alone. The second loading study evaluated changes in biosynthesis during a single 20 hour period of oscillatory mechanical stimulation to assess mechanoresponsiveness of the MPC cultures. This study found that MPCs modulated proteoglycan and protein synthesis in a culture time-dependent and frequency-dependent manner upon application of oscillatory compression. Together the two loading studies provide an assessment of dynamic compressive mechanical loading influences on MPC cultures undergoing chondrogenic differentiation. The information gained through in vitro studies of differentiating MPC cultures will increase basic knowledge about progenitor cells and may also prove valuable in guiding the future development of cartilage tissue engineering approaches.

In past years, legislative pressures (particularly in Japan and Europe) had forced the electronics industry to eliminate Pb from their end products and manufacturing processes. With respect to factors such as ease of converting existing tin-lead plating systems, ease of manufacture and compatibility with existing assembly methods, pure tin plating is seen by many in the industry as a potentially simple and cost effective alternative to SnPb-based systems. The problem of spontaneous tin whisker formation, a characteristic of pure tin, still needs to be addressed, as it can lead to device failure by shorting two terminals on electronic devices. This possibility gives rise to major reliability concerns. The study relates to an electronic component with pure tin deposit layer on the part for electric connection, wherein pure tin deposit layer is a fine grained tin deposit layer composed of grains with smaller size in the direction perpendicular to the deposit surface than in the direction parallel to the deposit surface. It is called irregular tin grain structure. It applies a process for plating an electronic component, so as to form a pure tin deposit layer on the part for electric connection, comprising the steps of: adjusting the composition of tin plating solution in which starter additive and brighter additive are included; moving the electronic component through the tin plating solution, so as to form a fine grained tin deposit layer on the part for electric connection. We performed a DoE by depositing different tin grain structures with variant thickness. After whisker test in high temperature/high humidity and room condition, we confirmed corrosion mechanism, intermetallic morphology, and different behaviour of tin atoms. To summarize the studies, as compared with the prior arts, irregular grain structure can validly inhibit the whisker growth.