Integrated System Technologies for Modular Trapped Ion Quantum Information Processing

Although trapped ion technology is well-suited for quantum information science, scalability of the system remains one of the main challenges. One of the challenges associated with scaling the ion trap quantum computer is the ability to individually manipulate the increasing number of qubits. Using micro-mirrors fabricated with micro-electromechanical systems (MEMS) technology, laser beams are focused on individual ions in a linear chain and steer the focal point in two dimensions. Multiple single qubit gates are demonstrated on trapped 171Yb+ qubits and the gate performance is characterized using quantum state tomography. The system features negligible crosstalk to neighboring ions (< 3e-4), and switching speeds comparable to typical single qubit gate times (< 2 us). In a separate experiment, photons scattered from the 171Yb+ ion are coupled into an optical fiber with 63% efficiency using a high numerical aperture lens (0.6 NA). The coupled photons are directed to superconducting nanowire single photon detectors (SNSPD), which provide a higher detector efficiency (69%) compared to traditional photomultiplier tubes (35%). The total system photon collection efficiency is increased from 2.2% to 3.4%, which allows for fast state detection of the qubit. For a detection beam intensity of 11 mW/cm2, the average detection time is 23.7 us with 99.885(7)% detection fidelity. The technologies demonstrated in this thesis can be integrated to form a single quantum register with all of the necessary resources to perform local gates as well as high fidelity readout and provide a photon link to other systems.

SAFE: A Declarative Trust-Agile System with Linked Credentials

Secure Access For Everyone (SAFE), is an integrated system for managing trust

using a logic-based declarative language. Logical trust systems authorize each

request by constructing a proof from a context---a set of authenticated logic

statements representing credentials and policies issued by various principals

in a networked system. A key barrier to practical use of logical trust systems

is the problem of managing proof contexts: identifying, validating, and

assembling the credentials and policies that are relevant to each trust

decision.

SAFE addresses this challenge by (i) proposing a distributed authenticated data

repository for storing the credentials and policies; (ii) introducing a

programmable credential discovery and assembly layer that generates the

appropriate tailored context for a given request. The authenticated data

repository is built upon a scalable key-value store with its contents named by

secure identifiers and certified by the issuing principal. The SAFE language

provides scripting primitives to generate and organize logic sets representing

credentials and policies, materialize the logic sets as certificates, and link

them to reflect delegation patterns in the application. The authorizer fetches

the logic sets on demand, then validates and caches them locally for further

use. Upon each request, the authorizer constructs the tailored proof context

and provides it to the SAFE inference for certified validation.

Delegation-driven credential linking with certified data distribution provides

flexible and dynamic policy control enabling security and trust infrastructure

to be agile, while addressing the perennial problems related to today's

certificate infrastructure: automated credential discovery, scalable

revocation, and issuing credentials without relying on centralized authority.

We envision SAFE as a new foundation for building secure network systems. We

used SAFE to build secure services based on case studies drawn from practice:

(i) a secure name service resolver similar to DNS that resolves a name across

multi-domain federated systems; (ii) a secure proxy shim to delegate access

control decisions in a key-value store; (iii) an authorization module for a

networked infrastructure-as-a-service system with a federated trust structure

(NSF GENI initiative); and (iv) a secure cooperative data analytics service

that adheres to individual secrecy constraints while disclosing the data. We

present empirical evaluation based on these case studies and demonstrate that

SAFE supports a wide range of applications with low overhead.

Generalized sampling using a compound-eye imaging system for multi-dimensional object acquisition.

In this paper, we propose generalized sampling approaches for measuring a multi-dimensional object using a compact compound-eye imaging system called thin observation module by bound optics (TOMBO). This paper shows the proposed system model, physical examples, and simulations to verify TOMBO imaging using generalized sampling. In the system, an object is modulated and multiplied by a weight distribution with physical coding, and the coded optical signal is integrated on to a detector array. A numerical estimation algorithm employing a sparsity constraint is used for object reconstruction.

The increasing impact of human immunodeficiency virus infections, sexually transmitted diseases, and viral hepatitis in Durham County, North Carolina: a call for coordinated and integrated services.

BACKGROUND: Durham County, North Carolina, faces high rates of human immunodeficiency virus (HIV) infection (with or without progression to AIDS) and sexually transmitted diseases (STDs). We explored the use of health care services and the prevalence of coinfections, among HIV-infected residents, and we recorded community perspectives on HIV-related issues. METHODS: We evaluated data on diagnostic codes, outpatient visits, and hospitalizations for individuals with HIV infection, STDs, and/or hepatitis B or C who visited Duke University Hospital System (DUHS). Viral loads for HIV-infected patients receiving care were estimated for 2009. We conducted geospatial mapping to determine disease trends and used focus groups and key informant interviews to identify barriers and solutions to improving testing and care. RESULTS: We identified substantial increases in HIV/STDs in the southern regions of the county. During the 5-year period, 1,291 adults with HIV infection, 4,245 with STDs, and 2,182 with hepatitis B or C were evaluated at DUHS. Among HIV-infected persons, 13.9% and 21.8% were coinfected with an STD or hepatitis B or C, respectively. In 2009, 65.7% of HIV-infected persons receiving care had undetectable viral loads. Barriers to testing included stigma, fear, and denial of risk, while treatment barriers included costs, transportation, and low medical literacy. LIMITATIONS: Data for health care utilization and HIV load were available from different periods. Focus groups were conducted among a convenience sample, but they represented a diverse population. CONCLUSIONS: Durham County has experienced an increase in the number of HIV-infected persons in the county, and coinfections with STDs and hepatitis B or C are common. Multiple barriers to testing/treatment exist in the community. Coordinated care models are needed to improve access to HIV care and to reduce testing and treatment barriers.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Investigation and Development of a Fully 3D Tilt Capable Hybrid SPECT - CT System for Dedicated Breast Imaging

X-ray mammography has been the gold standard for breast imaging for decades, despite the significant limitations posed by the two dimensional (2D) image acquisitions. Difficulty in diagnosing lesions close to the chest wall and axilla, high amount of structural overlap and patient discomfort due to compression are only some of these limitations. To overcome these drawbacks, three dimensional (3D) breast imaging modalities have been developed including dual modality single photon emission computed tomography (SPECT) and computed tomography (CT) systems. This thesis focuses on the development and integration of the next generation of such a device for dedicated breast imaging. The goals of this dissertation work are to: [1] understand and characterize any effects of fully 3-D trajectories on reconstructed image scatter correction, absorbed dose and Hounsifeld Unit accuracy, and [2] design, develop and implement the fully flexible, third generation hybrid SPECT-CT system capable of traversing complex 3D orbits about a pendant breast volume, without interference from the other. Such a system would overcome artifacts resulting from incompletely sampled divergent cone beam imaging schemes and allow imaging closer to the chest wall, which other systems currently under research and development elsewhere cannot achieve.

The dependence of x-ray scatter radiation on object shape, size, material composition and the CT acquisition trajectory, was investigated with a well-established beam stop array (BSA) scatter correction method. While the 2D scatter to primary ratio (SPR) was the main metric used to characterize total system scatter, a new metric called ‘normalized scatter contribution’ was developed to compare the results of scatter correction on 3D reconstructed volumes. Scatter estimation studies were undertaken with a sinusoidal saddle (±15° polar tilt) orbit and a traditional circular (AZOR) orbit. Clinical studies to acquire data for scatter correction were used to evaluate the 2D SPR on a small set of patients scanned with the AZOR orbit. Clinical SPR results showed clear dependence of scatter on breast composition and glandular tissue distribution, otherwise consistent with the overall phantom-based size and density measurements. Additionally, SPR dependence was also observed on the acquisition trajectory where 2D scatter increased with an increase in the polar tilt angle of the system.

The dose delivered by any imaging system is of primary importance from the patient’s point of view, and therefore trajectory related differences in the dose distribution in a target volume were evaluated. Monte Carlo simulations as well as physical measurements using radiochromic film were undertaken using saddle and AZOR orbits. Results illustrated that both orbits deliver comparable dose to the target volume, and only slightly differ in distribution within the volume. Simulations and measurements showed similar results, and all measured dose values were within the standard screening mammography-specific, 6 mGy dose limit, which is used as a benchmark for dose comparisons.

Hounsfield Units (HU) are used clinically in differentiating tissue types in a reconstructed CT image, and therefore the HU accuracy of a system is very important, especially when using non-traditional trajectories. Uniform phantoms filled with various uniform density fluids were used to investigate differences in HU accuracy between saddle and AZOR orbits. Results illustrate the considerably better performance of the saddle orbit, especially close to the chest and nipple region of what would clinically be a pedant breast volume. The AZOR orbit causes shading artifacts near the nipple, due to insufficient sampling, rendering a major portion of the scanned phantom unusable, whereas the saddle orbit performs exceptionally well and provides a tighter distribution of HU values in reconstructed volumes.

Finally, the third generation, fully-suspended SPECT-CT system was designed in and developed in our lab. A novel mechanical method using a linear motor was developed for tilting the CT system. A new x-ray source and a custom made 40 x 30 cm2 detector were integrated on to this system. The SPECT system was nested, in the center of the gantry, orthogonal to the CT source-detector pair. The SPECT system tilts on a goniometer, and the newly developed CT tilting mechanism allows ±15° maximum polar tilting of the CT system. The entire gantry is mounted on a rotation stage, allowing complex arbitrary trajectories for each system, without interference from the other, while having a common field of view. This hybrid system shows potential to be used clinically as a diagnostic tool for dedicated breast imaging.