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Title: SELF DESIGNING PATTERN RECOGNITION SYSTEM EMPLOYING MULTISTAGE CLASSIFICATION.
Creator: ABDELWAHAB, MANAL MAHMOUD, Mikhael, Wasfy, University of Central Florida
Abstract / Description: Recently, pattern recognition/classification has received a considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, etc. In most of these applications, it is desirable to maintain the classification accuracy in the presence of corrupted and/or incomplete data. The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be...
Show moreRecently, pattern recognition/classification has received a considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, etc. In most of these applications, it is desirable to maintain the classification accuracy in the presence of corrupted and/or incomplete data. The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be classified correctly despite any distortion. Some classification techniques that are introduced in the literature are described in Chapter one.In this dissertation, a pattern recognition approach that can be designed to have evolutionary learning by developing the features and selecting the criteria that are best suited for the recognition problem under consideration is proposed. Chapter two presents some of the features used in developing the set of criteria employed by the system to recognize different types of signals. It also presents some of the preprocessing techniques used by the system. The system operates in two modes, namely, the learning (training) mode, and the running mode. In the learning mode, the original and preprocessed signals are projected into different transform domains. The technique automatically tests many criteria over the range of parameters for each criterion. A large number of criteria are developed from the features extracted from these domains. The optimum set of criteria, satisfying specific conditions, is selected. This set of criteria is employed by the system to recognize the original or noisy signals in the running mode. The modes of operation and the classification structures employed by the system are described in details in Chapter three.The proposed pattern recognition system is capable of recognizing an enormously large number of patterns by virtue of the fact that it analyzes the signal in different domains and explores the distinguishing characteristics in each of these domains. In other words, this approach uses available information and extracts more characteristics from the signals, for classification purposes, by projecting the signal in different domains. Some experimental results are given in Chapter four showing the effect of using mathematical transforms in conjunction with preprocessing techniques on the classification accuracy. A comparison between some of the classification approaches, in terms of classification rate in case of distortion, is also given.A sample of experimental implementations is presented in chapter 5 and chapter 6 to illustrate the performance of the proposed pattern recognition system. Preliminary results given confirm the superior performance of the proposed technique relative to the single transform neural network and multi-input neural network approaches for image classification in the presence of additive noise.
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Date Issued: 2004
Identifier: CFE0000020, ucf:46077
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000020

Title: DISCUSSION ON EFFECTIVE RESTORATION OF ORAL SPEECH USING VOICE CONVERSION TECHNIQUES BASED ON GAUSSIAN MIXTURE MODELING.
Creator: Alverio, Gustavo, Mikhael, Wasfy, University of Central Florida
Abstract / Description: Today's world consists of many ways to communicate information. One of the most effective ways to communicate is through the use of speech. Unfortunately many lose the ability to converse. This in turn leads to a large negative psychological impact. In addition, skills such as lecturing and singing must now be restored via other methods. The usage of text-to-speech synthesis has been a popular resolution of restoring the capability to use oral speech. Text to speech synthesizers convert...
Show moreToday's world consists of many ways to communicate information. One of the most effective ways to communicate is through the use of speech. Unfortunately many lose the ability to converse. This in turn leads to a large negative psychological impact. In addition, skills such as lecturing and singing must now be restored via other methods. The usage of text-to-speech synthesis has been a popular resolution of restoring the capability to use oral speech. Text to speech synthesizers convert text into speech. Although text to speech systems are useful, they only allow for few default voice selections that do not represent that of the user. In order to achieve total restoration, voice conversion must be introduced. Voice conversion is a method that adjusts a source voice to sound like a target voice. Voice conversion consists of a training and converting process. The training process is conducted by composing a speech corpus to be spoken by both source and target voice. The speech corpus should encompass a variety of speech sounds. Once training is finished, the conversion function is employed to transform the source voice into the target voice. Effectively, voice conversion allows for a speaker to sound like any other person. Therefore, voice conversion can be applied to alter the voice output of a text to speech system to produce the target voice. The thesis investigates how one approach, specifically the usage of voice conversion using Gaussian mixture modeling, can be applied to alter the voice output of a text to speech synthesis system. Researchers found that acceptable results can be obtained from using these methods. Although voice conversion and text to speech synthesis are effective in restoring voice, a sample of the speaker before voice loss must be used during the training process. Therefore it is vital that voice samples are made to combat voice loss.
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Date Issued: 2007
Identifier: CFE0001793, ucf:47286
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001793

Title: EFFICIENT ALGORITHMS FOR CORRELATION PATTERN RECOGNITION.
Creator: Ragothaman, Pradeep, Mikhael, Wasfy, University of Central Florida
Abstract / Description: The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing...
Show moreThe mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes are predetermined, and examples (templates) are available for comparison. This task naturally lends itself to the application of correlation as a tool to accomplish this goal. Thus the field of Correlation Pattern Recognition has developed over the past few decades as an important area of research. From the signal processing point of view, correlation is nothing but a filtering operation. Thus there has been a great deal of work in using concepts from filter theory to develop Correlation Filters for pattern recognition. While considerable work has been to done to develop linear correlation filters over the years, especially in the field of Automatic Target Recognition, a lot of attention has recently been paid to the development of Quadratic Correlation Filters (QCF). QCFs offer the advantages of linear filters while optimizing a bank of these simultaneously to offer much improved performance. This dissertation develops efficient QCFs that offer significant savings in storage requirements and computational complexity over existing designs. Firstly, an adaptive algorithm is presented that is able to modify the QCF coefficients as new data is observed. Secondly, a transform domain implementation of the QCF is presented that has the benefits of lower computational complexity and computational requirements while retaining excellent recognition accuracy. Finally, a two dimensional QCF is presented that holds the potential to further save on storage and computations. The techniques are developed based on the recently proposed Rayleigh Quotient Quadratic Correlation Filter (RQQCF) and simulation results are provided on synthetic and real datasets.
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Date Issued: 2007
Identifier: CFE0001974, ucf:47429
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001974

Title: NOVEL FACIAL IMAGE RECOGNITION TECHNIQUES EMPLOYINGPRINCIPAL COMPONENT ANALYSIS.
Creator: ABDELWAHAB, MOATAZ, Mikhael, Wasfy, University of Central Florida
Abstract / Description: Recently, pattern recognition/classification has received considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, and face recognition, etc. This study contributes novel techniques for facial image recognition based on the Two dimensional principal component analysis in the transform domain. These algorithms reduce the storage requirements by an order of magnitude and the computational complexity by a factor of 2 while...
Show moreRecently, pattern recognition/classification has received considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, and face recognition, etc. This study contributes novel techniques for facial image recognition based on the Two dimensional principal component analysis in the transform domain. These algorithms reduce the storage requirements by an order of magnitude and the computational complexity by a factor of 2 while maintaining the excellent recognition accuracy of the recently reported methods. The proposed recognition systems employ different structures, multicriteria and multitransform. In addition, principal component analysis in the transform domain in conjunction with vector quantization is developed which result in further improvement in the recognition accuracy and dimensionality reduction. Experimental results confirm the excellent properties of the proposed algorithms.
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Date Issued: 2007
Identifier: CFE0001977, ucf:47465
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001977

Title: FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS APPLIED TO WIRELESS COMMUNICATIONS OVER FREQUENCY-SELECTIVE CHANNELS.
Creator: Liu, Yuan, Mikhael, Wasfy, University of Central Florida
Abstract / Description: In wireless communications, frequency-selective fading is a major source of impairment for wireless communications. In this research, a novel Frequency-Domain Independent Component Analysis (ICA-F) approach is proposed to blindly separate and deconvolve signals traveling through frequency-selective, slow fading channels. Compared with existing time-domain approaches, the ICA-F is computationally efficient and possesses fast convergence properties. Simulation results confirm the effectiveness...
Show moreIn wireless communications, frequency-selective fading is a major source of impairment for wireless communications. In this research, a novel Frequency-Domain Independent Component Analysis (ICA-F) approach is proposed to blindly separate and deconvolve signals traveling through frequency-selective, slow fading channels. Compared with existing time-domain approaches, the ICA-F is computationally efficient and possesses fast convergence properties. Simulation results confirm the effectiveness of the proposed ICA-F. Orthogonal Frequency Division Multiplexing (OFDM) systems are widely used in wireless communications nowadays. However, OFDM systems are very sensitive to Carrier Frequency Offset (CFO). Thus, an accurate CFO compensation technique is required in order to achieve acceptable performance. In this dissertation, two novel blind approaches are proposed to estimate and compensate for CFO within the range of half subcarrier spacing: a Maximum Likelihood CFO Correction approach (ML-CFOC), and a high-performance, low-computation Blind CFO Estimator (BCFOE). The Bit Error Rate (BER) improvement of the ML-CFOC is achieved at the expense of a modest increase in the computational requirements without sacrificing the system bandwidth or increasing the hardware complexity. The BCFOE outperforms the existing blind CFO estimator [25, 128], referred to as the YG-CFO estimator, in terms of BER and Mean Square Error (MSE), without increasing the computational complexity, sacrificing the system bandwidth, or increasing the hardware complexity. While both proposed techniques outperform the YG-CFO estimator, the BCFOE is better than the ML-CFOC technique. Extensive simulation results illustrate the performance of the ML-CFOC and BCFOE approaches.
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Date Issued: 2005
Identifier: CFE0000756, ucf:46560
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000756

Title: GENERAL INTERFERENCE SUPPRESSION TECHNIQUE FOR DIVERSITY WIRELESS RECEIVERS IN FADING CHANNELS BASED ON INDEPENDENT COMPONENT ANALYSIS.
Creator: Yang, Tianyu, Mikhael, Wasfy, University of Central Florida
Abstract / Description: The area of wireless transceiver design is becoming increasingly important due to the rapid growth of wireless communications market as well as diversified design specifications. Research efforts in this area concentrates on schemes that are capable of increasing the system capacity, providing reconfigurability/reprogrammability and reducing the hardware complexity. Emerging topics related to these goals include Software Defined Radio, Multiple-Input-Multiple-Output (MIMO) Systems, Code...
Show moreThe area of wireless transceiver design is becoming increasingly important due to the rapid growth of wireless communications market as well as diversified design specifications. Research efforts in this area concentrates on schemes that are capable of increasing the system capacity, providing reconfigurability/reprogrammability and reducing the hardware complexity. Emerging topics related to these goals include Software Defined Radio, Multiple-Input-Multiple-Output (MIMO) Systems, Code Division Multiple Access, Ultra-Wideband Systems, etc. This research adopts space diversity and statistical signal processing for digital interference suppression in wireless receivers. The technique simplifies the analog front-end by eliminating the anti-aliasing filters and relaxing the requirements for IF bandpass filters and A/D converters. Like MIMO systems, multiple antenna elements are used for increased frequency reuse. The suppression of both image signal and Co-Channel Interference (CCI) are performed in DSP simultaneously. The signal-processing algorithm used is Independent Component Analysis (ICA). Specifically, the fixed-point Fast-ICA is adopted in the case of static or slow time varying channel conditions. In highly dynamic environment that is typically encountered in cellular mobile communications, a novel ICA algorithm, OBAI-ICA, is developed, which outperforms Fast-ICA for both linear and abrupt time variations. Several practical implementation issues are also considered, such as the effect of finite arithmetic and the possibility of reducing the number of antennas.
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Date Issued: 2004
Identifier: CFE0000231, ucf:46260
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000231

Title: SPEAKER IDENTIFICATION BASED ON DISCRIMINATIVE VECTOR QUANTIZATION AND DATA FUSION.
Creator: Zhou, Guangyu, Mikhael, Wasfy, University of Central Florida
Abstract / Description: Speaker Identification (SI) approaches based on discriminative Vector Quantization (VQ) and data fusion techniques are presented in this dissertation. The SI approaches based on Discriminative VQ (DVQ) proposed in this dissertation are the DVQ for SI (DVQSI), the DVQSI with Unique speech feature vector space segmentation for each speaker pair (DVQSI-U), and the Adaptive DVQSI (ADVQSI) methods. The difference of the probability distributions of the speech feature vector sets from various...
Show moreSpeaker Identification (SI) approaches based on discriminative Vector Quantization (VQ) and data fusion techniques are presented in this dissertation. The SI approaches based on Discriminative VQ (DVQ) proposed in this dissertation are the DVQ for SI (DVQSI), the DVQSI with Unique speech feature vector space segmentation for each speaker pair (DVQSI-U), and the Adaptive DVQSI (ADVQSI) methods. The difference of the probability distributions of the speech feature vector sets from various speakers (or speaker groups) is called the interspeaker variation between speakers (or speaker groups). The interspeaker variation is the measure of template differences between speakers (or speaker groups). All DVQ based techniques presented in this contribution take advantage of the interspeaker variation, which are not exploited in the previous proposed techniques by others that employ traditional VQ for SI (VQSI). All DVQ based techniques have two modes, the training mode and the testing mode. In the training mode, the speech feature vector space is first divided into a number of subspaces based on the interspeaker variations. Then, a discriminative weight is calculated for each subspace of each speaker or speaker pair in the SI group based on the interspeaker variation. The subspaces with higher interspeaker variations play more important roles in SI than the ones with lower interspeaker variations by assigning larger discriminative weights. In the testing mode, discriminative weighted average VQ distortions instead of equally weighted average VQ distortions are used to make the SI decision. The DVQ based techniques lead to higher SI accuracies than VQSI. DVQSI and DVQSI-U techniques consider the interspeaker variation for each speaker pair in the SI group. In DVQSI, speech feature vector space segmentations for all the speaker pairs are exactly the same. However, each speaker pair of DVQSI-U is treated individually in the speech feature vector space segmentation. In both DVQSI and DVQSI-U, the discriminative weights for each speaker pair are calculated by trial and error. The SI accuracies of DVQSI-U are higher than those of DVQSI at the price of much higher computational burden. ADVQSI explores the interspeaker variation between each speaker and all speakers in the SI group. In contrast with DVQSI and DVQSI-U, in ADVQSI, the feature vector space segmentation is for each speaker instead of each speaker pair based on the interspeaker variation between each speaker and all the speakers in the SI group. Also, adaptive techniques are used in the discriminative weights computation for each speaker in ADVQSI. The SI accuracies employing ADVQSI and DVQSI-U are comparable. However, the computational complexity of ADVQSI is much less than that of DVQSI-U. Also, a novel algorithm to convert the raw distortion outputs of template-based SI classifiers into compatible probability measures is proposed in this dissertation. After this conversion, data fusion techniques at the measurement level can be applied to SI. In the proposed technique, stochastic models of the distortion outputs are estimated. Then, the posteriori probabilities of the unknown utterance belonging to each speaker are calculated. Compatible probability measures are assigned based on the posteriori probabilities. The proposed technique leads to better SI performance at the measurement level than existing approaches.
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Date Issued: 2005
Identifier: CFE0000720, ucf:46621
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000720

Title: DESIGN OF POLYNOMIAL-BASED FILTERS FOR CONTINUOUSLY VARIABLE SAMPLE RATE CONVERSION WITH APPLICATIONS IN SYNTHETIC INSTRUMENTATION AND SOFTWARE DEFINED RADIO.
Creator: Hunter, Matthew, Mikhael, Wasfy, University of Central Florida
Abstract / Description: In this work, the design and application of Polynomial-Based Filters (PBF) for continuously variable Sample Rate Conversion (SRC) is studied. The major contributions of this work are summarized as follows. First, an explicit formula for the Fourier Transform of both a symmetrical and nonsymmetrical PBF impulse response with variable basis function coefficients is derived. In the literature only one explicit formula is given, and that for a symmetrical even length filter with fixed basis...
Show moreIn this work, the design and application of Polynomial-Based Filters (PBF) for continuously variable Sample Rate Conversion (SRC) is studied. The major contributions of this work are summarized as follows. First, an explicit formula for the Fourier Transform of both a symmetrical and nonsymmetrical PBF impulse response with variable basis function coefficients is derived. In the literature only one explicit formula is given, and that for a symmetrical even length filter with fixed basis function coefficients. The frequency domain optimization of PBFs via linear programming has been proposed in the literature, however, the algorithm was not detailed nor were explicit formulas derived. In this contribution, a minimax optimization procedure is derived for the frequency domain optimization of a PBF with time-domain constraints. Explicit formulas are given for direct input to a linear programming routine. Additionally, accompanying Matlab code implementing this optimization in terms of the derived formulas is given in the appendix. In the literature, it has been pointed out that the frequency response of the Continuous-Time (CT) filter decays as frequency goes to infinity. It has also been observed that when implemented in SRC, the CT filter is sampled resulting in CT frequency response aliasing. Thus, for example, the stopband sidelobes of the Discrete-Time (DT) implementation rise above the CT designed level. Building on these observations, it is shown how the rolloff rate of the frequency response of a PBF can be adjusted by adding continuous derivatives to the impulse response. This is of great advantage, especially when the PBF is used for decimation as the aliasing band attenuation can be made to increase with frequency. It is shown how this technique can be used to dramatically reduce the effect of alias build up in the passband. In addition, it is shown that as the number of continuous derivatives of the PBF increases the resulting DT implementation more closely matches the Continuous-Time (CT) design. When implemented for SRC, samples from a PBF impulse response are computed by evaluating the polynomials using a so-called fractional interval, µ. In the literature, the effect of quantizing µ on the frequency response of the PBF has been studied. Formulas have been derived to determine the number of bits required to keep frequency response distortion below prescribed bounds. Elsewhere, a formula has been given to compute the number of bits required to represent µ to obtain a given SRC accuracy for rational factor SRC. In this contribution, it is shown how these two apparently competing requirements are quite independent. In fact, it is shown that the wordlength required for SRC accuracy need only be kept in the µ generator which is a single accumulator. The output of the µ generator may then be truncated prior to polynomial evaluation. This results in significant computational savings, as polynomial evaluation can require several multiplications and additions. Under the heading of applications, a new Wideband Digital Downconverter (WDDC) for Synthetic Instruments (SI) is introduced. DDCs first tune to a signal's center frequency using a numerically controlled oscillator and mixer, and then zoom-in to the bandwidth of interest using SRC. The SRC is required to produce continuously variable output sample rates from a fixed input sample rate over a large range. Current implementations accomplish this using a pre-filter, an arbitrary factor resampler, and integer decimation filters. In this contribution, the SRC of the WDDC is simplified reducing the computational requirements to a factor of three or more. In addition to this, it is shown how this system can be used to develop a novel computationally efficient FFT-based spectrum analyzer with continuously variable frequency spans. Finally, after giving the theoretical foundation, a real Field Programmable Gate Array (FPGA) implementation of a novel Arbitrary Waveform Generator (AWG) is presented. The new approach uses a fixed Digital-to-Analog Converter (DAC) sample clock in combination with an arbitrary factor interpolator. Waveforms created at any sample rate are interpolated to the fixed DAC sample rate in real-time. As a result, the additional lower performance analog hardware required in current approaches, namely, multiple reconstruction filters and/or additional sample clocks, is avoided. Measured results are given confirming the performance of the system predicted by the theoretical design and simulation.
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Date Issued: 2008
Identifier: CFE0002292, ucf:47844
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002292

Title: NOVEL COMPLEX ADAPTIVE SIGNAL PROCESSING TECHNIQUES EMPLOYING OPTIMALLY DERIVED TIME-VARYING CONVERGENCE FACTORS WITH APPLICATIONS IN DIGITAL SIGNAL PROCESSING AND WIRELESS COMMUNICATIONS.
Creator: Ranganathan, Raghuram, Mikhael, Wasfy, University of Central Florida
Abstract / Description: In digital signal processing in general, and wireless communications in particular, the increased usage of complex signal representations, and spectrally efficient complex modulation schemes such as QPSK and QAM has necessitated the need for efficient and fast-converging complex digital signal processing techniques. In this research, novel complex adaptive digital signal processing techniques are presented, which derive optimal convergence factors or step sizes for adjusting the adaptive...
Show moreIn digital signal processing in general, and wireless communications in particular, the increased usage of complex signal representations, and spectrally efficient complex modulation schemes such as QPSK and QAM has necessitated the need for efficient and fast-converging complex digital signal processing techniques. In this research, novel complex adaptive digital signal processing techniques are presented, which derive optimal convergence factors or step sizes for adjusting the adaptive system coefficients at each iteration. In addition, the real and imaginary components of the complex signal and complex adaptive filter coefficients are treated as separate entities, and are independently updated. As a result, the developed methods efficiently utilize the degrees of freedom of the adaptive system, thereby exhibiting improved convergence characteristics, even in dynamic environments. In wireless communications, acceptable co-channel, adjacent channel, and image interference rejection is often one of the most critical requirements for a receiver. In this regard, the fixed-point complex Independent Component Analysis (ICA) algorithm, called Complex FastICA, has been previously applied to realize digital blind interference suppression in stationary or slow fading environments. However, under dynamic flat fading channel conditions frequently encountered in practice, the performance of the Complex FastICA is significantly degraded. In this dissertation, novel complex block adaptive ICA algorithms employing optimal convergence factors are presented, which exhibit superior convergence speed and accuracy in time-varying flat fading channels, as compared to the Complex FastICA algorithm. The proposed algorithms are called Complex IA-ICA, Complex OBA-ICA, and Complex CBC-ICA. For adaptive filtering applications, the Complex Least Mean Square algorithm (Complex LMS) has been widely used in both block and sequential form, due to its computational simplicity. However, the main drawback of the Complex LMS algorithm is its slow convergence and dependence on the choice of the convergence factor. In this research, novel block and sequential based algorithms for complex adaptive digital filtering are presented, which overcome the inherent limitations of the existing Complex LMS. The block adaptive algorithms are called Complex OBA-LMS and Complex OBAI-LMS, and their sequential versions are named Complex HA-LMS and Complex IA-LMS, respectively. The performance of the developed techniques is tested in various adaptive filtering applications, such as channel estimation, and adaptive beamforming. The combination of Orthogonal Frequency Division Multiplexing (OFDM) and the Multiple-Input-Multiple-Output (MIMO) technique is being increasingly employed for broadband wireless systems operating in frequency selective channels. However, MIMO-OFDM systems are extremely sensitive to Intercarrier Interference (ICI), caused by Carrier Frequency Offset (CFO) between local oscillators in the transmitter and the receiver. This results in crosstalk between the various OFDM subcarriers resulting in severe deterioration in performance. In order to mitigate this problem, the previously proposed Complex OBA-ICA algorithm is employed to recover user signals in the presence of ICI and channel induced mixing. The effectiveness of the Complex OBA-ICA method in performing ICI mitigation and signal separation is tested for various values of CFO, rate of channel variation, and Signal to Noise Ratio (SNR).
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Date Issued: 2008
Identifier: CFE0002431, ucf:47765
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002431

Title: High frequency communication system modeling and performance enhancement, employing novel adaptive DSP techniques.
Creator: Qahwash, Murad M., Mikhael, Wasfy, Engineering and Computer Science
Abstract / Description: University of Central Florida College of Engineering Thesis; High Frequency (HF) communication has been shown to be a useful communication technique from the very beginning of World War I and it accelerated during World War II. This is attributed to its simplicity, ability to provide near globe connectivity at low power without repeaters, moderate cost, and ease of proliferation [I]. In fact, the HF communication system utilizes the ionosphere [2][3][4] to refract the skywave signals to a...
Show moreUniversity of Central Florida College of Engineering Thesis; High Frequency (HF) communication has been shown to be a useful communication technique from the very beginning of World War I and it accelerated during World War II. This is attributed to its simplicity, ability to provide near globe connectivity at low power without repeaters, moderate cost, and ease of proliferation [I]. In fact, the HF communication system utilizes the ionosphere [2][3][4] to refract the skywave signals to a distant receiver. This ionospheric channel has some disadvantages. First, it is a non-stationary channel as the HF frequency propagation is a function of the sun spot activities, solar winds, and diurnal variations of the ionization level [5]. Second, the channel produces distortion in both signal amplitude and phase. As the different ionospheric layers move up or down, independent Doppler shifts on each propagation mode are introduced. Multipath fading [6] caused by multiple refractions of the signal fiom the ionosphere with or without ground reflection causes performance degradation in the HF system. Some techniques have been developed to improve HF performance [I]. One example is Space-Diversity [7], which uses more than one antenna at distant spaces to combine the received signal. Angle-of-Arrival Diversity that takes advantage of the fact that different modes have different arrival angles at the receiver, and so, highly directional antenna for example, can be used to improve the system performance. Another method of improving HF performance is to use different frequencies to transmit and receive messages. This method is known as Frequency diversity. Using timediversity, one can add a degree of redundancy to the transmitted message through the use of different types of coding, interleaving, etc. In the military standard, MIL-STD- 1 88- 1 1 OA [8], a convolutional encoder [9][10] followed by interleaver [Ill-[14] was used to scramble and transmit the data in different bit rates. In the presence of multipath fading [ 1 51, a training sequence is transmitted in an interleaved fashion with the data symbols with a 50% duty cycle. This has the disadvantage of losing half the bandwidth. At present, the recent advances of the Digital Signal Processing (DSP) [16][17] make it possible to reduce the bit-error-rate BEY and increase the transmission bit rate [18] through the usage of adaptive equalization [ 191-[2 11 which will be the focus of this dissertation. Equalizers such as, Transversal Equalizer [ 1 61, Blind Equalizer [22], Training waveform Equalizer [23], and Minimum Mean Square Error (MMSE) [20] Adaptive Equalizer have been applied into various communication systems. This proposal work will be to initially apply some of the previous developed equalizer to the HF channel specifically. Thereafter, new adaptive channel equalization [24],[25] will be developed to compensate for transmission channel impairments due to bandwidth limitations, multipath propagation, and rayleigh fading [2 11 conditions in mobile environments. A new technique for frequency offset prediction has been developed and finally, a new approach for MIL-STD- 1 88- 1 1 0A high frequency single-tone modem employing orthogonal Walsh-PN codes has been implemented.
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Date Issued: 2002
Identifier: CFR0000759, ucf:52934
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFR0000759

Title: BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING.
Creator: Vartak, Aniket, Mikhael, Wasfy, University of Central Florida
Abstract / Description: User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of...
Show moreUser-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development beyond the use of traditional user performance metrics to include system intelligence measures that are based on current neuroscience theories. These advances are of paramount importance in the success and wide spread use of learning systems that are automated and intelligent. Emotion is considered an important aspect of how learning occurs, and yet estimating it and making adaptive adjustments are not part of most learning systems. In this research we focus on one specific aspect of constructing an adaptive and intelligent learning system, that is, estimation of the emotion of the learner as he/she is using the automated training system. The challenge starts with the definition of the emotion and the utility of it in human life. The next challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find consistent features from these measures that are valid across wide population. In this research we use four physiological sensors that are non-invasive, and establish a methodology of utilizing the data from these sensors using different signal processing tools. A validated set of visual stimuli used worldwide in the research of emotion and attention, called International Affective Picture System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to elicit emotions from these validated visual stimuli. We describe a novel wavelet method to calculate hemispheric asymmetry metric using electroencephalography data. This method is tested against typically used power spectral density method. We show overall improvement in accuracy in classifying specific emotions using the novel method. We also show distinctions between different discrete emotions from the autonomic nervous system activity using electrocardiography, electrodermal activity and pupil diameter changes. Findings from different features from these sensors are used to give guidelines to use each of the individual sensors in the adaptive learning environment.
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Date Issued: 2010
Identifier: CFE0003301, ucf:48503
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003301

Title: Ultra-Efficient Cascaded Buck-Boost Converter.
Creator: Ashok Pise, Anirudh, Batarseh, Issa, Mikhael, Wasfy, Sun, Wei, Kutkut, Nasser, University of Central Florida
Abstract / Description: This thesis presents various techniques to achieve ultra-high-efficiency for Cascaded-Buck-Boost converter. A rigorous loss model with component non linearity is developed and validated experimentally. An adaptive-switching-frequency control is discussed to optimize weighted efficiency. Some soft-switching techniques are discussed. A low-profile planar-nanocrystalline inductor is developed and various design aspects of core and copper design are discussed. Finite-element-method is used to...
Show moreThis thesis presents various techniques to achieve ultra-high-efficiency for Cascaded-Buck-Boost converter. A rigorous loss model with component non linearity is developed and validated experimentally. An adaptive-switching-frequency control is discussed to optimize weighted efficiency. Some soft-switching techniques are discussed. A low-profile planar-nanocrystalline inductor is developed and various design aspects of core and copper design are discussed. Finite-element-method is used to examine and visualize the inductor design. By implementing the above, a peak efficiency of over 99.2 % is achieved with a power density of 6 kW/L and a maximum profile height of 7 mm is reported. This converter finds many applications because of its versatility: allowing bidirectional power flow and the ability to step-up or step-down voltages in either direction.
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Date Issued: 2017
Identifier: CFE0007277, ucf:52181
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007277

Title: A Microwave Radiometer Roughness Correction Algorithm For Sea Surface Salinity Retrieval.
Creator: Hejazin, Yazan, Jones, W, Mikhael, Wasfy, Wei, Lei, University of Central Florida
Abstract / Description: The Aquarius/SAC-D is an Earth Science remote sensing satellite mission to measure global Sea Surface Salinity (SSS) that is sponsored by the NASA and the Argentine Space Agency (CONAE). The prime remote sensor is the Aquarius (AQ) L-band radiometer/scatterometer, which measures the L-band emitted blackbody radiation (brightness temperature) from the ocean. The brightness temperature at L-band is proportional to the ocean salinity as well as a number of physical parameters including ocean...
Show moreThe Aquarius/SAC-D is an Earth Science remote sensing satellite mission to measure global Sea Surface Salinity (SSS) that is sponsored by the NASA and the Argentine Space Agency (CONAE). The prime remote sensor is the Aquarius (AQ) L-band radiometer/scatterometer, which measures the L-band emitted blackbody radiation (brightness temperature) from the ocean. The brightness temperature at L-band is proportional to the ocean salinity as well as a number of physical parameters including ocean surface wind speed. The salinity retrieval algorithm make corrections for all other parameters before retrieving salinity, and the greatest of these is the increased brightness temperature due to roughness caused by surface wind speed. This thesis presents an independent approach for the AQ roughness correction, which is derived using simultaneous measurements from the CONAE Microwave Radiometer (MWR). When the wind blows over the ocean's surface, the brightness temperature is increased because of the ocean wave surface roughness. The MWR provides a semi-empirical approach by measuring the excess ocean emissivity at 36.5 GHz and then applying radiative transfer theory (improved ocean surface emissivity model) to translate this to the AQ 1.4 GHz frequency (L-band). The theoretical basis of the MWR algorithm is described and empirical results are presented that demonstrate the effectiveness in reducing the salinity measurement error due to surface roughness.
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Date Issued: 2012
Identifier: CFE0004212, ucf:49007
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004212

Title: Engineering Evaluation of Multi-beam Satellite Antenna Boresight Pointing using Land/Water Crossings.
Creator: May, Catherine, Jones, W, Mikhael, Wasfy, Wahid, Parveen, University of Central Florida
Abstract / Description: The Microwave Radiometer (MWR) on the Aquarius/SAC-D mission measures microwave radiation from earth and intervening atmosphere in terms of brightness temperature (Tb). It takes measurements in a push-broom fashion at K (23.8GHz) and Ka (36.5 GHz) band frequencies using two separate antenna systems, each producing eight antenna beams. Pre-launch knowledge of the alignment of these beams with respect to the space-craft is used to geolocate the antenna footprints on ground. As a part of MWR's...
Show moreThe Microwave Radiometer (MWR) on the Aquarius/SAC-D mission measures microwave radiation from earth and intervening atmosphere in terms of brightness temperature (Tb). It takes measurements in a push-broom fashion at K (23.8GHz) and Ka (36.5 GHz) band frequencies using two separate antenna systems, each producing eight antenna beams. Pre-launch knowledge of the alignment of these beams with respect to the space-craft is used to geolocate the antenna footprints on ground. As a part of MWR's on-orbit engineering check-out, the verification of MWR's pointing accuracy is discussed here. The technique used to assess MWR's pointing involved comparing the radiometer image of land with high-resolution maps. When the beam's instantaneous field of view (IFOV) passes over a land water boundary, the brightness temperature changes from a radiometrically hot land scene to a radiometrically cold ocean scene. This (")step-function(") change in brightness temperature provides a very sensitive way to characterize the mispointing error of the MWR sensor antenna footprints. This thesis describes the algorithm used for the MWR geolocation calibration. MWR sensor observed boundaries are determined by the absolute maximum Tb slope location. A system of linear equations is produced for each sensor observed land/water crossing to determine the true intersection of the MWR track with the coastline. The observed and expected boundary locations are compared by means of an error distance. Results, presented for all eight beams of the three MWR channels, show that the mispointing error (standard deviations) are overall less than 15 km from the true coastline.
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Date Issued: 2012
Identifier: CFE0004245, ucf:49523
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004245

Title: A Triangulation Based Coverage Path Planning For a Mobile Robot With Circular Sensing Range.
Creator: An, Vatana, Qu, Zhihua, Haralambous, Michael, Mikhael, Wasfy, University of Central Florida
Abstract / Description: In this dissertation, two coverage path planning (CPP) approaches for a nonholonomic mobile robot are proposed. The first approach is the Local Coverage Path Planning (LCPP) approach which is designed for all sensing ranges. The second approach is the Global Coverage Path Planning (GCPP) approach which is designed for sufficient sensing range that can observe all points of interests in the target region (TR). The LCPP approach constructs CP after finding observer points for all local regions...
Show moreIn this dissertation, two coverage path planning (CPP) approaches for a nonholonomic mobile robot are proposed. The first approach is the Local Coverage Path Planning (LCPP) approach which is designed for all sensing ranges. The second approach is the Global Coverage Path Planning (GCPP) approach which is designed for sufficient sensing range that can observe all points of interests in the target region (TR). The LCPP approach constructs CP after finding observer points for all local regions in the TR. The GCPP approach computes observer points after CP construction. Beginning with the sample TR, the LCPP approach requires 8 algorithms to find a smooth CP and sufficient number of observers for complete coverage. The Global Coverage Path Planning approach requires 17 algorithms to find the smooth CP with sufficient number of observers for completed coverage. The worst case running time for both approaches are quadratic which is consider to be very fast as compared to previous works reported in the literature. The main technical contributions of both approaches are to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and then compute the smooth and collision-free CP. Both approaches provide localization, speed control, curvature control, CP length control, and smooth CP control. The first approach has applications in automate vacuum cleaning, search and rescue mission, spray painting, and etc. The second approach is best used in military and space applications as it requires infinite sensing range which only resource rich organizations can afford. At the very least, the second approach provides simulation opportunity and upper bound cost estimate for CPP. Both approaches will lead to a search strategy that provides the shortest CP with the minimum number of observer and with the shortest running time for any sensing range.
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Date Issued: 2017
Identifier: CFE0006853, ucf:51745
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006853

Title: Virtual resistance based DC-link voltage regulation for Microgrid DG inverters.
Creator: Shinde, Siddhesh, Batarseh, Issa, Mikhael, Wasfy, Kutkut, Nasser, University of Central Florida
Abstract / Description: This research addresses the practical issues faced by Microgrid Distributed Generation (DG) inverters when operated in islanded mode. A Microgrid (MG) is an interconnection of domestic distributed loads and low voltage distributed energy sources such as micro-turbine, wind-turbine, PVs and storage devices. These energy sources are power limited in nature and constrain the operation of DG inverters to which they are coupled. DG inverters operated in islanded mode should maintain the power...
Show moreThis research addresses the practical issues faced by Microgrid Distributed Generation (DG) inverters when operated in islanded mode. A Microgrid (MG) is an interconnection of domestic distributed loads and low voltage distributed energy sources such as micro-turbine, wind-turbine, PVs and storage devices. These energy sources are power limited in nature and constrain the operation of DG inverters to which they are coupled. DG inverters operated in islanded mode should maintain the power balance between generation and demand. If DG inverter operating in islanded mode drains its source power below a certain limit or if it is incapable of supplying demanded power due to its hardware rating, it turns on its safety mechanism and isolates itself from the MG. This, in turn, increases the power demand on the rest of the DG units and can have a catastrophic impact on the viability of the entire system. This research presents a Virtual Resistance based DC Link Voltage Regulation technique which will allow DG inverters to continue to source their available power even when the power demand by the load is higher than their capacity without shutting off and isolating from the MG.
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Date Issued: 2016
Identifier: CFE0006503, ucf:51403
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006503

Title: Microwave Radiometer (MWR) Evaluation of Multi-Beam Satellite Antenna Boresight Pointing Using Land-Water Crossings, for the Aquarius/SAC-D Mission.
Creator: Clymer, Bradley, Jones, W Linwood, Mikhael, Wasfy, Flitsiyan, Elena, University of Central Florida
Abstract / Description: This research concerns the CONAE Microwave Radiometer (MWR), on board the Aquarius/SAC-D platform. MWR's main purpose is to provide measurements that are simultaneous and spatially collocated with those of NASA's Aquarius radiometer/scatterometer. For this reason, knowledge of the MWR antenna beam footprint geolocation is crucial to mission success.In particular, this thesis addresses an on-orbit validation of the MWR antenna beam pointing, using calculated MWR instantaneous field of view ...
Show moreThis research concerns the CONAE Microwave Radiometer (MWR), on board the Aquarius/SAC-D platform. MWR's main purpose is to provide measurements that are simultaneous and spatially collocated with those of NASA's Aquarius radiometer/scatterometer. For this reason, knowledge of the MWR antenna beam footprint geolocation is crucial to mission success.In particular, this thesis addresses an on-orbit validation of the MWR antenna beam pointing, using calculated MWR instantaneous field of view (IFOV) centers, provided in the CONAE L-1B science data product. This procedure compares L-1B MWR IFOV centers at land/water crossings against high-resolution coastline maps. MWR IFOV locations versus time are computed from knowledge of the satellite's instantaneous location relative to an earth-centric coordinate system (provided by on-board GPS receivers), and a priori measurements of antenna gain patterns and mounting geometry.Previous conical scanning microwave radiometer missions (e.g., SSM/I) have utilized observation of rapid change in brightness temperatures (T_B) to estimate the location of land/water boundaries, and subsequently to determine the antenna beam-pointing accuracy. In this thesis, results of an algorithm to quantify the geolocation error of MWR beam center are presented, based upon two-dimensional convolution between each beam's gain pattern and land-water transition. The analysis procedures have been applied to on-orbit datasets that represent land-water boundaries bearing specific desirable criteria, which are also detailed herein. The goal of this research is to gain a better understanding of satellite radiometer beam-pointing error and thereby to improve the geolocation accuracy for MWR science data products.
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Date Issued: 2015
Identifier: CFE0005591, ucf:50269
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005591

Title: Modeling and fault detection in DC side of Photovoltaic Arrays.
Creator: Akram, Mohd, Lotfifard, Saeed, Mikhael, Wasfy, Wu, Thomas, University of Central Florida
Abstract / Description: Fault detection in PV systems is a key factor in maintaining the integrity of any PV system. Faults in photovoltaic systems can cause irrevocable damages to the stability of the PV system and substantially decrease the power output generated from the array of PV modules. Among'st the various AC and DC faults in a PV system, the clearance of the AC side faults is achieved by conventional AC protection schemes,the DC side, however , there still exists certain faults which are difficult to...
Show moreFault detection in PV systems is a key factor in maintaining the integrity of any PV system. Faults in photovoltaic systems can cause irrevocable damages to the stability of the PV system and substantially decrease the power output generated from the array of PV modules. Among'st the various AC and DC faults in a PV system, the clearance of the AC side faults is achieved by conventional AC protection schemes,the DC side, however , there still exists certain faults which are difficult to detect and clear. This paper deals with the modeling, detection and classification of these types of DC faults. It is essential to be able to simulate the PV characteristics and faults through software. In this thesis a comprehensive literature survey of fault detection methods for DC side of a PV system is presented. The disparities in the techniques employed for fault detection are studied . A new method for modeling the PV systems information only from manufacturers datasheet using both the Normal Operating Cell temperature conditions (NOCT) and Standard Operating Test Conditions (STC) conditions is then proposed.The input parameters for modeling the system are Isc,Voc,Impp,Vmpp and the temperature coefficients of Isc and Voc for both STC and NOCT conditions. The model is able to analyze the variations of PV parameters such as ideality factor, Series resistance, thermal voltage and Band gap energy of the PV module with temperature. Finally a novel intelligent method based on Probabilistic Neural Network for fault detection and classification for PV farm with string inverter technology is proposed.
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Date Issued: 2014
Identifier: CFE0005293, ucf:50571
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005293

Title: Practical Issues in GPRAM Development.
Creator: Li, Yin, Wei, Lei, Wu, Xinzhang, Mikhael, Wasfy, University of Central Florida
Abstract / Description: In this thesis, two parts of practical issues in the GPRAM system design are included. The first part is the coding part. The sum-product decoding algorithm of LDPC codes has been refined to fit for the GPRAM hardware implementation. As we all know, communication channel has noise. The noise in telecom system is different from that in GPRAM systems. So the noise should be handled well in the GPRAM design. A noise look-up table was created for FPGA and those noises in the table are quantized....
Show moreIn this thesis, two parts of practical issues in the GPRAM system design are included. The first part is the coding part. The sum-product decoding algorithm of LDPC codes has been refined to fit for the GPRAM hardware implementation. As we all know, communication channel has noise. The noise in telecom system is different from that in GPRAM systems. So the noise should be handled well in the GPRAM design. A noise look-up table was created for FPGA and those noises in the table are quantized. The second part of the thesis is to convert perfect images in video stream to those similar to the coarse images in human vision. GPRAM is an animal like robot in which coarse images are needed more than the fine images in order for us to understand how to GPRAM progresses those images to generate as clear image as we experienced. We use three steps, Point Spread function, inserting Poisson Noise, and introducing Eye fixation movements to mimic the coarse images seen merely from our eyes at the retinal photo-receptor level, i.e., without any brain processing.
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Date Issued: 2014
Identifier: CFE0005200, ucf:50632
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005200

Title: An Emissive Antenna Correction for The Tropical Rainfall Measuring Mission Microwave Imager (TMI).
Creator: Alquaied, Faisal, Jones, W Linwood, Mikhael, Wasfy, Wei, Lei, Zec, Josko, Wilheit, Thomas, University of Central Florida
Abstract / Description: This dissertation deals with the radiometric calibration of a satellite microwave radiometer known as the TRMM Microwave Imager (TMI), which operated on NASA's Tropical Rainfall Measuring Mission (TRMM). This multi-frequency, conical-scanning, passive microwave, remote sensor measures the earth's blackbody emissions (brightness temperature, Tb) from a low earth orbit and covers the tropics ((&)#177;35(&)deg; latitude). The original scientific objective for TRMM's 3-year mission was to measure...
Show moreThis dissertation deals with the radiometric calibration of a satellite microwave radiometer known as the TRMM Microwave Imager (TMI), which operated on NASA's Tropical Rainfall Measuring Mission (TRMM). This multi-frequency, conical-scanning, passive microwave, remote sensor measures the earth's blackbody emissions (brightness temperature, Tb) from a low earth orbit and covers the tropics ((&)#177;35(&)deg; latitude). The original scientific objective for TRMM's 3-year mission was to measure the statistics of rainfall in the tropics. However, the mission was quite successful, and TRMM was extended for greater than 17 years to provide a long-term satellite rain measurements, which has contributed significantly to the study of global climate change.A significant part of the extended TRMM mission was the establishment of a constellation of satellite radiometer that provide frequent global rainfall measurements that enable severe storm warnings for operational hazard forecast by the international weather community. TRMM played a key role by serving as the radiometric calibration standard for the TRMM constellation microwave radiometers.The objective of this dissertation is to improve the radiometric calibration of TMI and to provide to NASA a new robust, physics-based algorithm for the legacy data processing of the TRMM brightness temperature data product, which will be called TMI 1B11 V8. Moreover, the results of this new procedure have been validated using the double difference techniques with the Global Precipitation Mission Microwave Imager (GMI), which is the replacement satellite mission to TRMM.
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Date Issued: 2017
Identifier: CFE0006711, ucf:51900
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006711

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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