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Title: An On-orbit Calibration Procedure for Spaceborne Microwave Radiometers Using Special Spacecraft Attitude Maneuvers.
Creator: Farrar, Spencer, Jones, W Linwood, Mikhael, Wasfy, Wahid, Parveen, Gaiser, Peter, University of Central Florida
Abstract / Description: This dissertation revisits, develops, and documents methods that can be used to calibrate spaceborne microwave radiometers once in orbit. The on-orbit calibration methods discussed within this dissertation can provide accurate and early results by utilizing Calibration Attitude Maneuvers (CAM), which encompasses Deep Space Calibration (DSC) and a new use of the Second Stokes (SS) analysis that can provide early and much needed insight on the performance of the instrument. This dissertation...
Show moreThis dissertation revisits, develops, and documents methods that can be used to calibrate spaceborne microwave radiometers once in orbit. The on-orbit calibration methods discussed within this dissertation can provide accurate and early results by utilizing Calibration Attitude Maneuvers (CAM), which encompasses Deep Space Calibration (DSC) and a new use of the Second Stokes (SS) analysis that can provide early and much needed insight on the performance of the instrument. This dissertation describes pre-existing and new methods of using DSC maneuvers as well as a simplified use of the SS procedure. Over TRMM's 17 years of operation it has provided invaluable data and has performed multiple CAMs over its lifetime. These maneuvers are analyzed to implement on-orbit calibration procedures that will be applied for future missions. In addition, this research focuses on the radiometric calibration of TMI that will be incorporated in the final processing (Archive/Legacy of the NASA TMI 1B11 brightness temperature data product). This is of importance since TMI's 17-year sensor data record must be vetted of all known calibration errors so to provide the final stable data for science users, specifically, climatological data records.
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Date Issued: 2015
Identifier: CFE0005611, ucf:50208
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005611

Title: A Roughness Correction for Aquarius Ocean Brightness Temperature Using the CONAE MicroWave Radiometer.
Creator: Hejazin, Yazan, Jones, W Linwood, Wahid, Parveen, Mikhael, Wasfy, Junek, William, Piepmeier, Jeffrey, University of Central Florida
Abstract / Description: Aquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that (")warms(") the ocean Tb. The Aquarius (AQ)...
Show moreAquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that (")warms(") the ocean Tb. The Aquarius (AQ) instrument (L-band radiometer/scatterometer) baseline approach uses the radar scatterometer to provide this ocean roughness correction, through the correlation of radar backscatter with the excess ocean emissivity.In contrast, this dissertation develops an ocean roughness correction for AQ measurements using the MicroWave Radiometer (MWR) instrument Tb measurements at Ka-band to remove the errors that are caused by ocean wind speed and direction. The new ocean emissivity radiative transfer model was tuned using one year (2012) of on-orbit combined data from the MWR and the AQ instruments that are collocated in space and time. The roughness correction in this paper is a theoretical Radiative Transfer Model (RTM) driven by numerical weather forecast model surface winds, combined with ancillary satellite data from WindSat and SSMIS, and environmental parameters from NCEP. This RTM provides an alternative approach for estimating the scatterometer-derived roughness correction, which is independent. The theoretical basis of the algorithm is described and results are compared with the AQ baseline scatterometer method. Also results are presented for a comparison of AQ SSS retrievals using both roughness corrections.
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Date Issued: 2015
Identifier: CFE0005625, ucf:50218
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005625

Title: Transient and Distributed Algorithms to Improve Islanding Detection Capability of Inverter Based Distributed Generation.
Creator: Alhosani, Mohamed, Qu, Zhihua, Mikhael, Wasfy, Haralambous, Michael, Behal, Aman, Xu, Chengying, University of Central Florida
Abstract / Description: Recently, a lot of research work has been dedicated toward enhancing performance, reliability and integrity of distributed energy resources that are integrated into distribution networks. The problem of islanding detection and islanding prevention (i.e. anti-islanding) has stimulated a lot of research due to its role in severely compromising the safety of working personnel and resulting in equipment damages. Various Islanding Detection Methods (IDMs) have been developed within the last ten...
Show moreRecently, a lot of research work has been dedicated toward enhancing performance, reliability and integrity of distributed energy resources that are integrated into distribution networks. The problem of islanding detection and islanding prevention (i.e. anti-islanding) has stimulated a lot of research due to its role in severely compromising the safety of working personnel and resulting in equipment damages. Various Islanding Detection Methods (IDMs) have been developed within the last ten years in anticipation of the tremendous increase in the penetration of Distributed Generation (DG) in distribution system. This work proposes new IDMs that rely on transient and distributed behaviors to improve integrity and performance of DGs while maintaining multi-DG islanding detection capability.In this thesis, the following questions have been addressed: How to utilize the transient behavior arising from an islanding condition to improve detectability and robust performance of IDMs in a distributive manner? How to reduce the negative stability impact of the well-known Sandia Frequency Shift (SFS) IDM while maintaining its islanding detection capability? How to incorporate the perturbations provided by each of DGs in such a way that the negative interference of different IDMs is minimized without the need of any type of communication among the different DGs?It is shown that the proposed techniques are local, scalable and robust against different loading conditions and topology changes. Also, the proposed techniques can successfully distinguish an islanding condition from other disturbances that may occur in power system networks. This work improves the efficiency, reliability and safety of integrated DGs, which presents a necessary advance toward making electric power grids a smart grid.
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Date Issued: 2013
Identifier: CFE0005295, ucf:50567
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005295

Title: Time and Space Efficient Techniques for Facial Recognition.
Creator: Alrasheed, Waleed, Mikhael, Wasfy, DeMara, Ronald, Haralambous, Michael, Wei, Lei, Myers, Brent, University of Central Florida
Abstract / Description: In recent years, there has been an increasing interest in face recognition. As a result, many new facial recognition techniques have been introduced. Recent developments in the field of face recognition have led to an increase in the number of available face recognition commercial products. However, Face recognition techniques are currently constrained by three main factors: recognition accuracy, computational complexity, and storage requirements. The problem is that most of the current face...
Show moreIn recent years, there has been an increasing interest in face recognition. As a result, many new facial recognition techniques have been introduced. Recent developments in the field of face recognition have led to an increase in the number of available face recognition commercial products. However, Face recognition techniques are currently constrained by three main factors: recognition accuracy, computational complexity, and storage requirements. The problem is that most of the current face recognition techniques succeed in improving one or two of these factors at the expense of the others.In this dissertation, four novel face recognition techniques that improve the storage and computational requirements of face recognition systems are presented and analyzed. Three of the four novel face recognition techniques to be introduced, namely, Quantized/truncated Transform Domain (QTD), Frequency Domain Thresholding and Quantization (FD-TQ), and Normalized Transform Domain (NTD). All the three techniques utilize the Two-dimensional Discrete Cosine Transform (DCT-II), which reduces the dimensionality of facial feature images, thereby reducing the computational complexity. The fourth novel face recognition technique is introduced, namely, the Normalized Histogram Intensity (NHI). It is based on utilizing the pixel intensity histogram of poses' subimages, which reduces the computational complexity and the needed storage requirements. Various simulation experiments using MATLAB were conducted to test the proposed methods. For the purpose of benchmarking the performance of the proposed methods, the simulation experiments were performed using current state-of-the-art face recognition techniques, namely, Two Dimensional Principal Component Analysis (2DPCA), Two-Directional Two-Dimensional Principal Component Analysis ((2D)^2PCA), and Transform Domain Two Dimensional Principal Component Analysis (TD2DPCA). The experiments were applied to the ORL, Yale, and FERET databases.The experimental results for the proposed techniques confirm that the use of any of the four novel techniques examined in this study results in a significant reduction in computational complexity and storage requirements compared to the state-of-the-art techniques without sacrificing the recognition accuracy.
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Date Issued: 2013
Identifier: CFE0005297, ucf:50566
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005297

Title: Speech Detection using Gammatone Features and One-Class Support Vector Machine.
Creator: Cooper, Douglas, Mikhael, Wasfy, Wahid, Parveen, Behal, Aman, Richie, Samuel, University of Central Florida
Abstract / Description: A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD's rely on time-domain features and simple thresholds for...
Show moreA network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD's rely on time-domain features and simple thresholds for efficient speech detection however this doesn't say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and non- speech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5dB.
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Date Issued: 2013
Identifier: CFE0005091, ucf:50731
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005091

Title: Vehicle Tracking and Classification via 3D Geometries for Intelligent Transportation Systems.
Creator: Mcdowell, William, Mikhael, Wasfy, Jones, W Linwood, Haralambous, Michael, Atia, George, Mahalanobis, Abhijit, Muise, Robert, University of Central Florida
Abstract / Description: In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D...
Show moreIn this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA (&) LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification.
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Date Issued: 2015
Identifier: CFE0005976, ucf:50790
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005976

Title: Understanding images and videos using context.
Creator: Vaca Castano, Gonzalo, Da Vitoria Lobo, Niels, Shah, Mubarak, Mikhael, Wasfy, Jones, W Linwood, Wiegand, Rudolf, University of Central Florida
Abstract / Description: In computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also...
Show moreIn computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also investigate the influence of the semantic context, determined by objects, relationships, locations, and global composition, to achieve a general understanding of the image content as a whole. In our research, we also attempt to solve the related problem of finding the context associated with visual media. Given a set of visual elements (images), we want to extract the context that can be commonly associated with these images in order to remove ambiguity. The first part of this dissertation concentrates on achieving image understanding using semantic context.(&)nbsp; In spite of the recent success in tasks such as image classi?cation, object detection, image segmentation, and the progress on scene understanding, researchers still lack clarity about computer comprehension of the content of the image as a whole. Hence, we propose a Top-Down Visual Tree (TDVT) image representation that allows the encoding of the content of the image as a hierarchy of objects capturing their importance, co-occurrences, and type of relations. A novel Top-Down Tree LSTM network is presented to learn about the image composition from the training images and their TDVT representations. Given a test image, our algorithm detects objects and determine the hierarchical structure that they form, encoded as a TDVT representation of the image.A single image could have multiple interpretations that may lead to ambiguity about the intentionality of an image.(&)nbsp; What if instead of having only a single image to be interpreted, we have multiple images that represent the same topic. The second part of this dissertation covers how to extract the context information shared by multiple images. We present a method to determine the topic that these images represent. We accomplish this task by transferring tags from an image retrieval database, and by performing operations in the textual space of these tags. As an application, we also present a new image retrieval method that uses multiple images as input. Unlike earlier works that focus either on using just a single query image or using multiple query images with views of the same instance, the new image search paradigm retrieves images based on the underlying concepts that the input images represent.Finally, in the third part of this dissertation, we analyze the influence of context in videos. In this case, the temporal context is utilized to improve scene identification and object detection. We focus on egocentric videos, where agents require some time to change from one location to another. Therefore, we propose a Conditional Random Field (CRF) formulation, which penalizes short-term changes of the scene identity to improve the scene identity accuracy.(&)nbsp; We also show how to improve the object detection outcome by re-scoring the results based on the scene identity of the tested frame. We present a Support Vector Regression (SVR) formulation in the case that explicit knowledge of the scene identity is available during training time. In the case that explicit scene labeling is not available, we propose an LSTM formulation that considers the general appearance of the frame to re-score the object detectors.
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Date Issued: 2017
Identifier: CFE0006922, ucf:51703
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006922

Title: Investigation of the effect of rain on sea surface salinity.
Creator: Santos Garcia, Andrea, Jones, W Linwood, Mikhael, Wasfy, Wahid, Parveen, Junek, William, Asher, William, Wilheit, Thomas, University of Central Florida
Abstract / Description: The Aquarius/SAC-D mission provided Sea Surface Salinity (SSS), globally over the ocean, for almost 4 years. As a member of the AQ/SAC-D Cal/Val team, the Central Florida Remote Sensing Laboratory has analyzed these salinity measurements in the presence of precipitation and has noted the high correlation between the spatial patterns of reduced SSS and the spatial distribution of rain. It was determined that this is the result of a cause and effect relation, and not SSS measurement errors....
Show moreThe Aquarius/SAC-D mission provided Sea Surface Salinity (SSS), globally over the ocean, for almost 4 years. As a member of the AQ/SAC-D Cal/Val team, the Central Florida Remote Sensing Laboratory has analyzed these salinity measurements in the presence of precipitation and has noted the high correlation between the spatial patterns of reduced SSS and the spatial distribution of rain. It was determined that this is the result of a cause and effect relation, and not SSS measurement errors. Thus, it is important to understand these salinity changes due to seawater dilution by rain and the associated near-surface salinity strati?cation. This research addresses the effects of rainfall on the Aquarius (AQ) SSS retrieval using a macro-scale Rain Impact Model (RIM). This model, based on the superposition of a one-dimension eddy diffusion (turbulent diffusion) model, relates SSS to depth, rainfall accumulation and time since rain. To identify instantaneous and prior rainfall accumulations, a Rain Accumulation product was developed. This product, based on the NOAA CMORPH precipitation data set, provides the rainfall history for 24 hours prior to the satellite observation time, which is integrated over each AQ IFOV. In this research results of the RIM validation are presented by comparing AQ and SMOS measured and RIM simulated SSS. The results show the high cross correlation for these comparisons and also with the corresponding SSS anomalies relative to HYCOM.
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Date Issued: 2016
Identifier: CFE0006175, ucf:51133
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006175

Title: Autonomous Recovery of Reconfigurable Logic Devices using Priority Escalation of Slack.
Creator: Imran, Syednaveed, DeMara, Ronald, Mikhael, Wasfy, Lin, Mingjie, Yuan, Jiann-Shiun, Geiger, Christopher, University of Central Florida
Abstract / Description: Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases.To extend these concepts to semiconductor aging and process variation in the deep...
Show moreField Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases.To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Reconfigurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric.FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria.
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Date Issued: 2013
Identifier: CFE0005006, ucf:50005
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005006

Title: CONAE MicroWave Radiometer (MWR) Counts to Brightness Temperature Algorithm.
Creator: Ghazi, Zoubair, Jones, W Linwood, Wei, Lei, Mikhael, Wasfy, Wu, Thomas, Junek, William, Piepmeier, Jeffrey, University of Central Florida
Abstract / Description: This dissertation concerns the development of the MicroWave Radiometer (MWR) brightness temperature (Tb) algorithm and the associated algorithm validation using on-orbit MWR Tb measurements. This research is sponsored by the NASA Earth Sciences Aquarius Mission, a joint international science mission, between NASA and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE). The MWR is a CONAE developed passive microwave instrument operating at 23.8 GHz (K-band) H-pol...
Show moreThis dissertation concerns the development of the MicroWave Radiometer (MWR) brightness temperature (Tb) algorithm and the associated algorithm validation using on-orbit MWR Tb measurements. This research is sponsored by the NASA Earth Sciences Aquarius Mission, a joint international science mission, between NASA and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE). The MWR is a CONAE developed passive microwave instrument operating at 23.8 GHz (K-band) H-pol and 36.5 GHz (Ka-band) H- (&) V-pol designed to complement the Aquarius L-band radiometer/scatterometer, which is the prime sensor for measuring sea surface salinity (SSS). MWR measures the Earth's brightness temperature and retrieves simultaneous, spatially collocated, environmental measurements (surface wind speed, rain rate, water vapor, and sea ice concentration) to assist in the measurement of SSS.This dissertation research addressed several areas including development of: 1) a signal processing procedure for determining and correcting radiometer system non-linearity; 2) an empirical method to retrieve switch matrix loss coefficients during thermal-vacuum (T/V) radiometric calibration test; and 3) an antenna pattern correction (APC) algorithm using Inter-satellite radiometric cross-calibration of MWR with the WindSat satellite radiometer. The validation of the MWR counts-to-Tb algorithm was performed using two years of on-orbit data, which included special deep space calibration measurements and routine clear sky ocean/land measurements.
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Date Issued: 2014
Identifier: CFE0005496, ucf:50366
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005496

Calibration (3)	+ -
Remote Sensing (3)	+ -
Aquarius (2)	+ -
Carrier Frequency Offset (2)	+ -
DCT (2)	+ -

Efficiency (2)	+ -
Face Recognition (2)	+ -
HIRAD (2)	+ -
ICA (2)	+ -
Independent Component Analysis (2)	+ -
Machine Learning (2)	+ -
Soft Switching (2)	+ -
TMI (2)	+ -
TRMM (2)	+ -
adaptive filter (2)	+ -
brightness temperature (2)	+ -
remote sensing (2)	+ -
(2D)^2PCA (1)	+ -
2DPCA (1)	+ -
Academic (1)	+ -
Academic -- Engineering (1)	+ -
Activities of Daily Living (1)	+ -
Adaptive architectures (1)	+ -
Algorithms (1)	+ -
Along-scan bias (1)	+ -
Amplitude Estimation (1)	+ -
Automatic Target Recognition (1)	+ -
Autonomous recovery (1)	+ -
BaTiO3 (1)	+ -
Big Data Analysis (1)	+ -

Mikhael, Wasfy (50)	+ -
University of Central Florida (49)	+ -
Jones, W Linwood (15)	+ -
Batarseh, Issa (10)	+ -
Kutkut, Nasser (8)	+ -

Wahid, Parveen (8)	+ -
Wei, Lei (8)	+ -
Haralambous, Michael (6)	+ -
Atia, George (5)	+ -
Myers, Brent (4)	+ -
Wilheit, Thomas (4)	+ -
Wu, Xinzhang (4)	+ -
Behal, Aman (3)	+ -
DeMara, Ronald (3)	+ -
Junek, William (3)	+ -
Qu, Zhihua (3)	+ -
Sun, Wei (3)	+ -
Zec, Josko (3)	+ -
Gong, Xun (2)	+ -
Hejazin, Yazan (2)	+ -
Hunter, Matthew (2)	+ -
Jones, W (2)	+ -
Lin, Mingjie (2)	+ -
Lotfifard, Saeed (2)	+ -
Piepmeier, Jeffrey (2)	+ -
Sundaram, Kalpathy (2)	+ -
Vosoughi, Azadeh (2)	+ -
Wu, Thomas (2)	+ -
Yuan, Jiann-Shiun (2)	+ -
ABDELWAHAB, MANAL MAHMOUD (1)	+ -

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