Optimal surface profile design of deployable mesh reflectors via a force density strategy

Based on a force density method coupled with optimal design of node positions, a novel approach for optimal surface profile design of mesh reflectors is presented. Uniform tension is achieved by iterations on coefficients of force density. The positions of net nodes are recalculated in each iteration so that the faceting RMS error of the reflector surface is minimized. Applications of both prime focus and offset configurations are demonstrated. The simulation results show the effectiveness of the proposed approach

Energy Efficient Hardware Design for Securing the Internet-of-Things

The Internet of Things (IoT) is a rapidly growing field that holds potential to transform our everyday lives by placing tiny devices and sensors everywhere. The ubiquity and scale of IoT devices require them to be extremely energy efficient. Given the physical exposure to malicious agents, security is a critical challenge within the constrained resources. This dissertation presents energy-efficient hardware designs for IoT security. First, this dissertation presents a lightweight Advanced Encryption Standard (AES) accelerator design. By analyzing the algorithm, a novel method to manipulate two internal steps to eliminate storage registers and replace flip-flops with latches to save area is discovered. The proposed AES accelerator achieves state-of-art area and energy efficiency. Second, the inflexibility and high Non-Recurring Engineering (NRE) costs of Application-Specific-Integrated-Circuits (ASICs) motivate a more flexible solution. This dissertation presents a reconfigurable cryptographic processor, called Recryptor, which achieves performance and energy improvements for a wide range of security algorithms across public key/secret key cryptography and hash functions. The proposed design employs circuit techniques in-memory and near-memory computing and is more resilient to power analysis attack. In addition, a simulator for in-memory computation is proposed. It is of high cost to design and evaluate new-architecture like in-memory computing in Register-transfer level (RTL). A C-based simulator is designed to enable fast design space exploration and large workload simulations. Elliptic curve arithmetic and Galois counter mode are evaluated in this work. Lastly, an error resilient register circuit, called iRazor, is designed to tolerate unpredictable variations in manufacturing process operating temperature and voltage of VLSI systems. When integrated into an ARM processor, this adaptive approach outperforms competing industrial techniques such as frequency binning and canary circuits in performance and energy.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147546/1/zhyiqun_1.pd

Interpretation of transient temperature data from Permanent Down-hole Gauges (PDGs)

With the installation of Permanent Down-hole Gauges (PDGs) during oil field development, a large volume of high resolution pressure, temperature and sometimes flow-rate data are available for real-time and continuous reservoir monitoring. In practice, interpretations of these data can optimize well performance, provide information about the reservoir and continuously calibrate the reservoir model. Although the wellbore is in a non-isothermal environment, heat transfer between the fluid in the wellbore and the formation is often ignored and temperature is usually assumed to be constant in the process of data interpretation, leading to misunderstanding of the pressure profile. Furthermore, the pressure transient analysis (PTA) often fails to determine accurate flow regimes, and may be erroneously applied in nonlinear reservoir-well systems. These problems motivated my detailed analysis of temperature data. In this thesis, firstly, a non-isothermal wellbore model that is capable of predicting the temperature, pressure, and flow-rate profiles under multi-rate and multiphase production scenarios is established. Then this numerical wellbore model is coupled with a reservoir model to reproduce the transient temperature behaviour at gauge locations. Secondly, a new workflow for integrating transient down-hole data processing is introduced. The relationship between temperature change and flow-rate change is interpreted and a new nonlinearity diagnostic function () is presented. Thirdly, new procedures of model-independent transient temperature analysis are performed, followed by diagnosing the wellbore storage regime, verifying the PTA interpretation results, and reconstructing the flow-rate history using transient temperature data. Several case studies are conducted to demonstrate how transient temperature analysis, along with the transient pressure analysis can greatly reduce the uncertainties in well testing interpretation. The applications of both synthetic datasets which are simulated by the fully coupled wellbore-reservoir model and real field datasets demonstrated that the temperature data can provide additional constraints for pressure analysis. Additionally, the reliability of the developed methods which reveal complementary reservoir information from transient temperature data has also been verified

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling