A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^

Adaptive fault-tolerant teleoperation

While the robots gradually become a part of our daily lives, they already play vital roles in many critical operations. Some of these critical tasks include surgeries, battlefield operations, and tasks that take place in hazardous environments or distant locations such as space missions. ^ In most of these tasks, remotely controlled robots are used instead of autonomous robots. This special area of robotics is called teleoperation. Teleoperation systems must be reliable when used in critical tasks; hence, all of the subsystems must be dependable even under a subsystem or communication line failure. ^ These systems are categorized as unilateral or bilateral teleoperation. A special type of bilateral teleoperation is described as force-reflecting teleoperation, which is further investigated as limited- and unlimited-workspace teleoperation. ^ Teleoperation systems configured in this study are tested both in numerical simulations and experiments. A new method, Virtual Rapid Robot Prototyping, is introduced to create system models rapidly and accurately. This method is then extended to configure experimental setups with actual master systems working with system models of the slave robots accompanied with virtual reality screens as well as the actual slaves. Fault-tolerant design and modeling of the master and slave systems are also addressed at different levels to prevent subsystem failure. ^ Teleoperation controllers are designed to compensate for instabilities due to communication time delays. Modifications to the existing controllers are proposed to configure a controller that is reliable in communication line failures. Position/force controllers are also introduced for master and/or slave robots. Later, controller architecture changes are discussed in order to make these controllers dependable even in systems experiencing communication problems. ^ The customary and proposed controllers for teleoperation systems are tested in numerical simulations on single- and multi-DOF teleoperation systems. Experimental studies are then conducted on seven different systems that included limited- and unlimited-workspace teleoperation to verify and improve simulation studies. ^ Experiments of the proposed controllers were successful relative to the customary controllers. Overall, by employing the fault-tolerance features and the proposed controllers, a more reliable teleoperation system is possible to design and configure which allows these systems to be used in a wider range of critical missions. ^

A theory of constraints service systems improvement method: Case of the airline turnaround problem

This dissertation develops a process improvement method for service operations based on the Theory of Constraints (TOC), a management philosophy that has been shown to be effective in manufacturing for decreasing WIP and improving throughput. While TOC has enjoyed much attention and success in the manufacturing arena, its application to services in general has been limited. The contribution to industry and knowledge is a method for improving global performance measures based on TOC principles. The method proposed in this dissertation will be tested using discrete event simulation based on the scenario of the service factory of airline turnaround operations. To evaluate the method, a simulation model of aircraft turn operations of a U.S. based carrier was made and validated using actual data from airline operations. The model was then adjusted to reflect an application of the Theory of Constraints for determining how to deploy the scarce resource of ramp workers. The results indicate that, given slight modifications to TOC terminology and the development of a method for constraint identification, the Theory of Constraints can be applied with success to services. Bottlenecks in services must be defined as those processes for which the process rates and amount of work remaining are such that completing the process will not be possible without an increase in the process rate. The bottleneck ratio is used to determine to what degree a process is a constraint. Simulation results also suggest that redefining performance measures to reflect a global business perspective of reducing costs related to specific flights versus the operational local optimum approach of turning all aircraft quickly results in significant savings to the company. Savings to the annual operating costs of the airline were simulated to equal 30% of possible current expenses for misconnecting passengers with a modest increase in utilization of the workers through a more efficient heuristic of deploying them to the highest priority tasks. This dissertation contributes to the literature on service operations by describing a dynamic, adaptive dispatch approach to manage service factory operations similar to airline turnaround operations using the management philosophy of the Theory of Constraints.

Adaptive software fault prediction approach using object-oriented metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

Adaptive Fault-Tolerant Teleoperation

While the robots gradually become a part of our daily lives, they already play vital roles in many critical operations. Some of these critical tasks include surgeries, battlefield operations, and tasks that take place in hazardous environments or distant locations such as space missions. In most of these tasks, remotely controlled robots are used instead of autonomous robots. This special area of robotics is called teleoperation. Teleoperation systems must be reliable when used in critical tasks; hence, all of the subsystems must be dependable even under a subsystem or communication line failure. These systems are categorized as unilateral or bilateral teleoperation. A special type of bilateral teleoperation is described as force-reflecting teleoperation, which is further investigated as limited- and unlimited-workspace teleoperation. Teleoperation systems configured in this study are tested both in numerical simulations and experiments. A new method, Virtual Rapid Robot Prototyping, is introduced to create system models rapidly and accurately. This method is then extended to configure experimental setups with actual master systems working with system models of the slave robots accompanied with virtual reality screens as well as the actual slaves. Fault-tolerant design and modeling of the master and slave systems are also addressed at different levels to prevent subsystem failure. Teleoperation controllers are designed to compensate for instabilities due to communication time delays. Modifications to the existing controllers are proposed to configure a controller that is reliable in communication line failures. Position/force controllers are also introduced for master and/or slave robots. Later, controller architecture changes are discussed in order to make these controllers dependable even in systems experiencing communication problems. The customary and proposed controllers for teleoperation systems are tested in numerical simulations on single- and multi-DOF teleoperation systems. Experimental studies are then conducted on seven different systems that included limited- and unlimited-workspace teleoperation to verify and improve simulation studies. Experiments of the proposed controllers were successful relative to the customary controllers. Overall, by employing the fault-tolerance features and the proposed controllers, a more reliable teleoperation system is possible to design and configure which allows these systems to be used in a wider range of critical missions.

A framework for policy based coordinated adaptation in mobile systems

Adaptation is an important requirement for mobile applications due to the varying levels of resource availability that characterizes mobile environments. However without proper control, multiple applications can each adapt independently in response to a range of different adaptive stimuli, causing conflicts or sub optimal performance. In this thesis we presented a framework, which enables multiple adaptation mechanisms to coexist on one platform. The key component of this framework was the 'Policy Server', which has all the system policies and governs the rules for adaptation. We also simulated our framework and subjected it to various adaptation scenarios to demonstrate the working of the system as a whole. With the help of the simulation it was shown that our framework enables seamless adaptation of multiple applications.

Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire

Acknowledgements This study was possible by partial financial support from the following Brazilian government agencies: CNPq, CAPES, and FAPESP (2011/19296-1 and 2015/07311-7). We also wish thank Newton Fund and COFAP.

Effective video content distribution by combining TCP with adaptive FEC coding

This paper will look at the benefits and limitations of content distribution using Forward Error Correction (FEC) in conjunction with the Transmission Control Protocol (TCP). FEC can be used to reduce the number of retransmissions which would usually result from a lost packet. The requirement for TCP to deal with any losses is then greatly reduced. There are however side-effects to using FEC as a countermeasure to packet loss: an additional requirement for bandwidth. When applications such as real-time video conferencing are needed, delay must be kept to a minimum, and retransmissions are certainly not desirable. A balance, therefore, between additional bandwidth and delay due to retransmissions must be struck. Our results show that the throughput of data can be significantly improved when packet loss occurs using a combination of FEC and TCP, compared to relying solely on TCP for retransmissions. Furthermore, a case study applies the result to demonstrate the achievable improvements in the quality of streaming video perceived by end users.

Investigation of PHY, MAC and APP layers for adaptive and cross-layer optimization in IEEE802.11 WLAN

Due to huge popularity of portable terminals based on Wireless LANs and increasing demand for multimedia services from these terminals, the earlier structures and protocols are insufficient to cover the requirements of emerging networks and communications. Most research in this field is tailored to find more efficient ways to optimize the quality of wireless LAN regarding the requirements of multimedia services. Our work is to investigate the effects of modulation modes at the physical layer, retry limits at the MAC layer and packet sizes at the application layer over the quality of media packet transmission. Interrelation among these parameters to extract a cross-layer idea will be discussed as well. We will show how these parameters from different layers jointly contribute to the performance of service delivery by the network. The results obtained could form a basis to suggest independent optimization in each layer (an adaptive approach) or optimization of a set of parameters from different layers (a cross-layer approach). Our simulation model is implemented in the NS-2 simulator. Throughput and delay (latency) of packet transmission are the quantities of our assessments. © 2010 IEEE.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Development of Swept Source Optical Coherence Tomography and Adaptive Optics Scanning Laser Ophthalmoscopy: Improved Imaging Speed and Handheld Applications

Optical coherence tomography (OCT) is a noninvasive three-dimensional interferometric imaging technique capable of achieving micrometer scale resolution. It is now a standard of care in ophthalmology, where it is used to improve the accuracy of early diagnosis, to better understand the source of pathophysiology, and to monitor disease progression and response to therapy. In particular, retinal imaging has been the most prevalent clinical application of OCT, but researchers and companies alike are developing OCT systems for cardiology, dermatology, dentistry, and many other medical and industrial applications.

Adaptive optics (AO) is a technique used to reduce monochromatic aberrations in optical instruments. It is used in astronomical telescopes, laser communications, high-power lasers, retinal imaging, optical fabrication and microscopy to improve system performance. Scanning laser ophthalmoscopy (SLO) is a noninvasive confocal imaging technique that produces high contrast two-dimensional retinal images. AO is combined with SLO (AOSLO) to compensate for the wavefront distortions caused by the optics of the eye, providing the ability to visualize the living retina with cellular resolution. AOSLO has shown great promise to advance the understanding of the etiology of retinal diseases on a cellular level.

Broadly, we endeavor to enhance the vision outcome of ophthalmic patients through improved diagnostics and personalized therapy. Toward this end, the objective of the work presented herein was the development of advanced techniques for increasing the imaging speed, reducing the form factor, and broadening the versatility of OCT and AOSLO. Despite our focus on applications in ophthalmology, the techniques developed could be applied to other medical and industrial applications. In this dissertation, a technique to quadruple the imaging speed of OCT was developed. This technique was demonstrated by imaging the retinas of healthy human subjects. A handheld, dual depth OCT system was developed. This system enabled sequential imaging of the anterior segment and retina of human eyes. Finally, handheld SLO/OCT systems were developed, culminating in the design of a handheld AOSLO system. This system has the potential to provide cellular level imaging of the human retina, resolving even the most densely packed foveal cones.

Development of Multi-modal and Super-resolved Retinal Imaging Systems

Advancements in retinal imaging technologies have drastically improved the quality of eye care in the past couple decades. Scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) are two examples of critical imaging modalities for the diagnosis of retinal pathologies. However current-generation SLO and OCT systems have limitations in diagnostic capability due to the following factors: the use of bulky tabletop systems, monochromatic imaging, and resolution degradation due to ocular aberrations and diffraction.

Bulky tabletop SLO and OCT systems are incapable of imaging patients that are supine, under anesthesia, or otherwise unable to maintain the required posture and fixation. Monochromatic SLO and OCT imaging prevents the identification of various color-specific diagnostic markers visible with color fundus photography like those of neovascular age-related macular degeneration. Resolution degradation due to ocular aberrations and diffraction has prevented the imaging of photoreceptors close to the fovea without the use of adaptive optics (AO), which require bulky and expensive components that limit the potential for widespread clinical use.

In this dissertation, techniques for extending the diagnostic capability of SLO and OCT systems are developed. These techniques include design strategies for miniaturizing and combining SLO and OCT to permit multi-modal, lightweight handheld probes to extend high quality retinal imaging to pediatric eye care. In addition, a method for extending true color retinal imaging to SLO to enable high-contrast, depth-resolved, high-fidelity color fundus imaging is demonstrated using a supercontinuum light source. Finally, the development and combination of SLO with a super-resolution confocal microscopy technique known as optical photon reassignment (OPRA) is demonstrated to enable high-resolution imaging of retinal photoreceptors without the use of adaptive optics.

RF MIMO Systems for Wide-Area Indoor Human Motion Monitoring

Human motion monitoring is an important function in numerous applications. In this dissertation, two systems for monitoring motions of multiple human targets in wide-area indoor environments are discussed, both of which use radio frequency (RF) signals to detect, localize, and classify different types of human motion. In the first system, a coherent monostatic multiple-input multiple-output (MIMO) array is used, and a joint spatial-temporal adaptive processing method is developed to resolve micro-Doppler signatures at each location in a wide-area for motion mapping. The downranges are obtained by estimating time-delays from the targets, and the crossranges are obtained by coherently filtering array spatial signals. Motion classification is then applied to each target based on micro-Doppler analysis. In the second system, multiple noncoherent multistatic transmitters (Tx's) and receivers (Rx's) are distributed in a wide-area, and motion mapping is achieved by noncoherently combining bistatic range profiles from multiple Tx-Rx pairs. Also, motion classification is applied to each target by noncoherently combining bistatic micro-Doppler signatures from multiple Tx-Rx pairs. For both systems, simulation and real data results are shown to demonstrate the ability of the proposed methods for monitoring patient repositioning activities for pressure ulcer prevention.

Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.