Protease activity has been demonstrated to be an important prognostic and predictive marker in diseases such as cancer and stroke. As such, much attention has been given to the development of diagnostic tools that would allow one to assay their activity in living tissues. Protease activity is regulated at many levels including transcription, translation, activation, and inhibition and in order to derive the maximum prognostic benefit, it is essential to study their activity within this complex environment. Initial attempts to accomplish this goal involved the use of organic fluorophores pairs that utilize Fluorescence Resonance Energy Transfer (FRET) but suffered many drawbacks. Quantum Dots (QD) have addressed many of the drawbacks of organic fluorophores in various optical imaging applications. These include a decreased sensitivity to photobleaching and chemical degradation, size-tunable narrow emission peaks, and broad absorbance allowing excitation of multiple peak emission QD by one excitation source. In this work, we propose to utilize Quantum Dots (QD) linked to gold nanoparticles (AuNPs) by protease cleavable peptide sequences to serve as probes for assaying protease activity both in vivo and in vitro. This work involved the synthesis and characterization of the various components necessary for the probe design as well as the optimization of probe characteristics to achieve highly biocompatible probes that exhibit both a high level of quenching and maximum fluorescence recovery in the presence of protease. Probe functionality was optimized and it was determined that probes with AuNP:QD ratio of 10.1 and peptide linker length of 6.5 nm resulted in highest and most linear fluorescent signal gain. The ability to multiplex probes was also validated by developing spectrally orthogonal probes sensitive to collagenase and cathepsin K. Our design is expected to have many applications in the research and understanding of the role of proteases in disease and as a predictive tool for the prognosis of diseases such as cancer.

A great research effort has been invested on finding alternatives to CMOS that have better process variation and subthreshold leakage. From possible candidates, FinFET is the most compatible with respect to CMOS and it has shown promising leakage and speed performance. This thesis introduces basic characteristics of FinFETs and the effects of FinFET physical parameters on their performance are explained quantitatively. I show how dual- V th independent-gate FinFETs can be fabricated by optimizing their physical parameters. Optimum values for these physical parameters are derived using the physics-based University of Florida SPICE model for double-gate devices, and the optimized FinFETs are simulated and validated using Sentaurus TCAD simulations. Dual-14, FinFETs with independent gates enable series and parallel merge transformations in logic gates, realizing compact low power alternative gates with competitive performance and reduced input capacitance in comparison to conventional FinFET gates. Furthermore, they also enable the design of a new class of compact logic gates with higher expressive power and flexibility than CMOS gates. Synthesis results for 16 benchmark circuits from the ISCAS and OpenSPARC suites indicate that on average at 2GHz and 75°C, the library that contains the novel gates reduces total power and the number of fins by 36% and 37% respectively, over a conventional library that does not have novel gates in the 32nm technology.

Pose estimation of neighboring robots is a key requirement for configuration control behaviors in multi-robot systems. Estimating pose is difficult without system constraints, it is even more challenging when using minimalistic sensing alongside limited bandwidth. Minimal sensing models are a well studied field in robotics and are relevant to our particular hardware platform, the r-one, which has sensors that only measure a low-resolution bearing to neighboring robots. These bearing-only sensors are simpler to design with and cheaper to deploy in large numbers. In this thesis, I focus on the r-one multi-robot system which is capable of coarsely measuring the bearing, but not the distance, to neighbors. These sensors have a angular resolution of only 22.5 degrees due to the construction of the infrared system. I develop a particle filter algorithm that allows the r-one robot to estimate the pose of a neighbor using the infrared communication system and odometry measurements. This algorithm relies on the fusion of a coarse bearing measurement and neighbor velocities and is optimized to use the smallest communications bandwidth possible. I tested this algorithm with a simulation to demonstrate its effectiveness across varying sensor setups, neighbor update periods, and number of particles.

This thesis formulates and analyzes low-order penalty-free discontinuous Galerkin methods for solving the incompressible Stokes and Navier-Stokes equations. Some symmetric and non-symmetric discontinuous Galerkin methods for incompressible Stokes and Navier-Stokes equations require penalizing jump terms for stability and convergence of the methods. These discontinuous Galerkin methods are called interior penalty methods as the penalizing jump terms involve a penalty parameter. It is known that the penalty parameter has to be large enough to prove coercivity of the bilinear form and therefore to obtain existence of the solution for the symmetric case. The momentum equation is satisfied locally on each mesh element, and it depends on the penalty parameter. Setting the penalty parameter equal to zero yields a singular linear system, if piecewise linears are used. To overcome this instability, this thesis discusses an enrichment of the velocity space with locally supported quadratic functions called bubbles. First, the penalty-free non-symmetric discontinuous Galerkin method is analyzed for the Stokes equations. Second, the main contribution of this thesis is the analysis of both symmetric and non-symmetric penalty-free discontinuous Galerkin methods for the incompressible Varier-Stokes equations. Since a direct application of the generalized Lax-Milgram theorem is not possible, the numerical solution is shown to be the solution as a fixed-point of a problem-related map. A priori error estimate is derived.

As multicore computing becomes the norm, exploiting parallelism in applications becomes a requirement for all software. Many applications exhibit different kinds of parallelism, but most parallel programming languages are biased towards a specific paradigm, of which two common ones are task and streaming parallelism. This results in a dilemma for programmers who would prefer to use the same language to exploit different paradigms for different applications. Our thesis is an integration of stream-parallel and task-parallel paradigms can be achieved in a single language with high programmability and high resource efficiency, when a general dataflow programming model is used as the foundation. The dataflow model used in this thesis is Intel's Concurrent Collections (CnC). While CnC is general enough to express both task-parallel and stream-parallel paradigms, all current implementations of CnC use task-based runtime systems that do not deliver the resource efficiency expected from stream-parallel programs. For streaming programs, this use of a task-based runtime system is wasteful of computing cycles and makes memory management more difficult than it needs to be. We propose Streaming Concurrent Collections (SCnC), a streaming system that can execute a subset of applications supported by Concurrent Collections, a general macro data-flow coordination language. Integration of streaming and task models allows application developers to benefit from the efficiency of stream parallelism as well as the generality of task parallelism, all in the context of an easy-to-use and general dataflow programming model. To achieve this integration, we formally define streaming access patterns that, if respected, allow CnC task based applications to be executed using the streaming model. We specify conditions under which an application can run safely, meaning with identical result and without deadlocks using the streaming runtime. A static analysis that verifies if an application respects these patterns is proposed and we describe algorithmic transformations to bring a larger set of CnC applications to a form that can be run using the streaming runtime. To take advantage of dynamic parallelism opportunities inside streaming applications, we propose a simple tuning annotation for streaming applications, that have traditionally been considered with fixed parallelism. Our dynamic parallelism construct, the dynamic splitter, which allows fission of stateful filters with little guidance from the programmer is based on the idea of different places where computations are distributed. Finally, performance results show that transitioning from the task parallel runtime to streaming runtime leads to a throughput increase of up to 40×. In summary, this thesis shows that stream-parallel and task-parallel paradigms can be integrated in a single language when a dataflow model is used as the foundation, and that this integration can be achieved with high programmability and high resource efficiency. Integration of these models allows application developers to benefit from the efficiency of stream parallelism as well as the generality of task parallelism, all in the context of an easy-to-use dataflow programming model.

We develop a new method for tracking narrowband signals acquired through compressive sensing, called the compressive sensing phase-locked loop (CS-PLL). The CS-PLL enables one to track oscillating signals in very large bandwidths using a small number of measurements. Not only does the CS-PLL potentially operate below the Nyquist rate, it can extract phase and frequency information without the computational complexity normally associated with compressive sensing signal re-construction. The CS-PLL has a wide variety of applications, including but not limited to communications, phase tracking, robust control, sensing, and FM demodulation. In particular we emphasize the advantages of using this system in wideband surveillence systems. Our design modifies classical PLL designs to operate with CS-based sampling systems. Performance results are shown for PLLs operating on both real and complex data. In addition to explaining general performance tradeoffs, implementations using several different CS sampling systems are explored.

Previous studies have demonstrated that surgical patches comprised of small intestinal submucosa (SIS)-derived extracellular matrix (ECM) have biological remodeling potential in various tissues. In this series of experiments, the remodeling potential of a commercially available cardiac SIS-ECM patch was examined in both in vivo and in vitro models. This thesis begins by introducing the clinical need for a tissue-engineered cardiovascular scaffold that can grow with the patient and avoid the morbidity associated with currently available synthetic and biological materials. Such a patch would transform the surgical repair of congenital heart disease, in particular of patients with tetralogy of Fallot and in the repair of mitral regurgitation. The in vivo study investigated histological, mechanical, and bioelectrical properties of an SIS-ECM patch implanted in the ovine right-ventricular outflow tract (RVOT), and the histological and mechanical properties of the same patch implanted in the descending aorta and main pulmonary artery of a juvenile ovine. We found the juvenile ovine model to be a suitable model for evaluation of SIS-ECM patch remodeling, as seen by in vivo echocardiography, electrical mapping, and ex vivo optical mapping for the RVOT patch and mechanical testing, histology and immunohistochemistry for patches placed in all three positions. The in vitro study looked at an SIS-ECM patch pretreated with pepsin, seeded with mitral valve interstitial cells (MVICs), and exposed to mechanical stimulation in a splashing bioreactor for one week. Greater cell integration and proliferation and greater tissue cohesion was seen in the pepsin-treated SIS-ECM, while groups without mechanical stimulation demonstrated a stiffening effect for the bioreactor. In sheep, the SIS-ECM patch appears capable of remodeling to resemble native, functional ventricular tissue, but further validation of this patch material is required. Bioreactors can play an important role in validation of this promising scaffold material. Tissue-engineered scaffolds are unique in their complete ontological metamorphosis (from scaffold material to part of the patient's own tissue) and pose distinctive ethical challenges that must be responsibly managed. Contract studies, such as the research presented in chapters 3-5, that are funded by the medical device industry require close scrutiny and precautions to avoid conflicts of interest.

Though multicore systems are ubiquitous, parallel programming models for these systems are generally not accessible to a wide programmer community. The macro-dataflow model is an attractive stepping stone to implicit parallelism for domain experts who are not the target audience for explicit parallel programming models. We use Intel's Concurrent Collections (CnC) programming model as a concrete exemplar of the macro-dataflow model in this work. CnC is a high level coordination language that can be implemented on top of lower-level task-parallel frameworks. In this thesis, we study an implementation of CnC, based on Habanero-Java as the underlying task-parallel runtime system. A unique feature of CnC, first-class decoupling of data and control dependences, allows us to experiment with schedulers by taking these data and control dependences into account for better scheduling decisions. Our observations led to the proposal and implementation of a new task-parallel synchronization construct for Habanero-Java, namely Data-Driven Futures. We obtained two kinds of experimental results from our implementation. First, we compare the effectiveness of task scheduling policies for CnC programs. Secondly, we show that data-driven futures not only reduce execution time but also shrink memory footprint. In summary, this thesis shows a macro-dataflow programming model can deliver productivity and performance on modern multicore processors.

The objective of this research was to elucidate how specific membrane surface functionalities affect fouling by alginate in seawater desalination. Alginate adsorption on self-assembled mono-layers with -COOH, -NH 2 , -CONH 2 groups under different solution conditions was studied using quartz crystal microbalance with dissipation monitoring. Cross-flow filtration experiments were performed using four commercial RO membranes to study the correlation of alginate fouling to membrane surface properties. Experimental results revealed the importance of solution condition, but surface functionality did not have significant impact on alginate adsorption equilibrium. The initial adsorption/deposition rate on the -COOH surface was the highest., while adsorption on the -NH 2 surface was most difficult to remove by surfactant cleaning. Filtration experiments showed that alginate fouling was more closely related to surface roughness than surface chemistry. The results suggest that surface chemistry is not an important factor for short term membrane fouling in seawater RO processes. / page 42 is missing from hardcopy

A new sampling theory, called compressive sensing (CS), has recently emerged and its fundamental idea can be distilled as: it is possible to obtain near complete recovery of a signal/image from a small set of mixed measurements of it if the signal/image possesses properties akin to sparseness. Based on this theory, we have developed a unique hardware platform for imaging and spectroscopy applications which incorporates a spatial modulator and a single pixel detector. Random projections of the signal/image are applied to the light modulator and the modulated light is focused on the single detector generating a series of photovoltage values which are later used in the image reconstruction. For wavelengths outside the visible spectrum, where it is especially expensive to produce the large detector arrays, this scheme provides a far better solution using a single detector element.