Browsing by Author "Du, Yingzi, 1975-"

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    Design and evaluation of a secure, privacy-preserving and cancelable biometric authentication : Bio-Capsule
    (2014-09-04) Sui, Yan; Zou, Xukai, 1963-; Bertino, Elisa; Li, Ninghui; Du, Yingzi, 1975-; Li, Feng; Prabhakar, Sunil; Gorman, William J.
    A large portion of system breaches are caused by authentication failure either during the system login process or even in the post-authentication session, which is further related to the limitations associated with existing authentication approaches. Current authentication methods, whether proxy based or biometrics based, are hardly user-centric; and they either put burdens on users or endanger users' (biometric) security and privacy. In this research, we propose a biometrics based user-centric authentication approach. The main idea is to introduce a reference subject (RS) (for each system), securely fuse the user's biometrics with the RS, generate a BioCapsule (BC) (from the fused biometrics), and employ BCs for authentication. Such an approach is user-friendly, identity-bearing yet privacy-preserving, resilient, and revocable once a BC is compromised. It also supports "one-click sign on" across multiple systems by fusing the user's biometrics with a distinct RS on each system. Moreover, active and non-intrusive authentication can be automatically performed during the user's post-authentication on-line session. In this research, we also formally prove that the proposed secure fusion based BC approach is secure against various attacks and compare the new approach with existing biometrics based approaches. Extensive experiments show that the performance (i.e., authentication accuracy) of the new BC approach is comparable to existing typical biometric authentication approaches, and the new BC approach also possesses other desirable features such as diversity and revocability.
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    Electric utility planning methods for the design of one shot stability controls
    (2012-12) Naghsh Nilchi, Maryam; Rovnyak, Steven; Chen, Yaobin; Du, Yingzi, 1975-
    Reliability of the wide-area power system is becoming a greater concern as the power grid is growing. Delivering electric power from the most economical source through fewest and shortest transmission lines to customers frequently increases the stress on the system and prevents it from maintaining its stability. Events like loss of transmission equipment and phase to ground faults can force the system to cross its stability limits by causing the generators to lose their synchronism. Therefore, a helpful solution is detection of these dynamic events and prediction of instability. Decision Trees (DTs) were used as a pattern recognition tool in this thesis. Based on training data, DT generated rules for detecting event, predicting loss of synchronism, and selecting stabilizing control. To evaluate the accuracy of these rules, they were applied to testing data sets. To train DTs of this thesis, direct system measurements like generator rotor angles and bus voltage angles as well as calculated indices such as the rate of change of bus angles, the Integral Square Bus Angle (ISBA) and the gradient of ISBA were used. The initial method of this thesis included a response based DT only for instability prediction. In this method, time and location of the events were unknown and the one shot control was applied when the instability was predicted. The control applied was in the form of fast power changes on four different buses. Further, an event detection DT was combined with the instability prediction such that the data samples of each case was checked with event detection DT rules. In cases that an event was detected, control was applied upon prediction of instability. Later in the research, it was investigated that different control cases could behave differently in terms of the number of cases they stabilize. Therefore, a third DT was trained to select between two different control cases to improve the effectiveness of the methodology. It was learned through internship at Midwest Independent Transmission Operators (MISO) that post-event steady-state analysis is necessary for better understanding the effect of the faults on the power system. Hence, this study was included in this research.
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    Morphometric analysis of hippocampal subfields : segmentation, quantification and surface modeling
    (2014) Cong, Shan; Rizkalla, Maher E.; Shen, Li (Radiologist); Du, Yingzi, 1975-
    Object segmentation, quantification, and shape modeling are important areas inmedical image processing. By combining these techniques, researchers can find valuableways to extract and represent details on user-desired structures, which can functionas the base for subsequent analyses such as feature classification, regression, and prediction. This thesis presents a new framework for building a three-dimensional (3D) hippocampal atlas model with subfield information mapped onto its surface, with which hippocampal surface registration can be done, and the comparison and analysis can be facilitated and easily visualized. This framework combines three powerful tools for automatic subcortical segmentation and 3D surface modeling. Freesurfer and Functional magnetic resonance imaging of the brain's Integrated Registration and Segmentation Tool (FIRST) are employed for hippocampal segmentation and quantification, while SPherical HARMonics (SPHARM) is employed for parametric surface modeling. This pipeline is shown to be effective in creating a hippocampal surface atlas using the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity and phase 2 (ADNI GO/2) dataset. Intra-class Correlation Coefficients (ICCs) are calculated for evaluating the reliability of the extracted hippocampal subfields. The complex folding anatomy of the hippocampus offers many analytical challenges, especially when informative hippocampal subfields are usually ignored in detailed morphometric studies. Thus, current research results are inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To address this challenge, one contribution of this study is to model the hippocampal surface using a parametric spherical harmonic model, which is a Fourier descriptor for general a 3D surface. The second contribution of this study is to extend hippocampal studies by incorporating valuable hippocampal subfield information. Based on the subfield distributions, a surface atlas is created for both left and right hippocampi. The third contribution is achieved by calculating Fourier coefficients in the parametric space. Based on the coefficient values and user-desired degrees, a pair of averaged hippocampal surface atlas models can be reconstructed. These contributions lay a solid foundation to facilitate a more accurate, subfield-guided morphometric analysis of the hippocampus and have the potential to reveal subtle hippocampal structural damage associated.
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    A new approach for pedestrian tracking and status analysis
    (2013) Jiang, Pingge; Du, Yingzi, 1975-; King, Brian; Rizkalla, Maher E.
    Pedestrian and vehicle interaction analysis in a naturalistic driving environment can provide useful information for designing vehicle-pedestrian crash warning/mitigation systems. Many researchers have used crash data to understand and study pedestrian behaviors and interactions between vehicles and pedestrian during crash. However, crash data may not provide detailed pedestrian-vehicle interaction information for us. In this thesis, we designed an automatic pedestrian tracking and status analysis method to process and study pedestrian and vehicle interactions. The proposed pedestrian tracking and status analysis method includes pedestrian detection, pedestrian tracking and pedestrian status analysis modules. The main contributions of this thesis are: we designed a new pedestrian tracking method by learning the pedestrian appearance and also their motion pattern. We designed a pedestrian status estimation method by using our tracking results and thus helped estimate the possibility of collision. Our preliminary experiment results using naturalistic driving data showed promising results.
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    Text mining of online book reviews for non-trivial clustering of books and users
    (2013-08-14) Lin, Eric; Fang, Shiaofen; Mukhopadhyay, Snehasis; Du, Yingzi, 1975-
    The classification of consumable media by mining relevant text for their identifying features is a subjective process. Previous attempts to perform this type of feature mining have generally been limited in scope due having limited access to user data. Many of these studies used human domain knowledge to evaluate the accuracy of features extracted using these methods. In this thesis, we mine book review text to identify nontrivial features of a set of similar books. We make comparisons between books by looking for books that share characteristics, ultimately performing clustering on the books in our data set. We use the same mining process to identify a corresponding set of characteristics in users. Finally, we evaluate the quality of our methods by examining the correlation between our similarity metric, and user ratings.