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- Title
- A MACHINE LEARNING APPROACH FOR OCEAN EVENT MODELING AND PREDICTION.
- Creator
- Muhamed, Ali Ali Abdullateef, Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
In the last decade, deep learning models have been successfully applied to a variety of applications and solved many tasks. The ultimate goal of this study is to produce deep learning models to improve the skills of forecasting ocean dynamic events in general and those of the Loop Current (LC) system in particular. A specific forecast target is to predict the geographic location of the (LC) extension and duration, LC eddy shedding events for a long lead time with high accuracy. Also, this...
Show moreIn the last decade, deep learning models have been successfully applied to a variety of applications and solved many tasks. The ultimate goal of this study is to produce deep learning models to improve the skills of forecasting ocean dynamic events in general and those of the Loop Current (LC) system in particular. A specific forecast target is to predict the geographic location of the (LC) extension and duration, LC eddy shedding events for a long lead time with high accuracy. Also, this study aims to improve the predictability of velocity fields (or more precisely, velocity volumes) of subsurface currents. In this dissertation, several deep learning based prediction models have been proposed. The core of these models is the Long-Short Term Memory (LSTM) network. This type of recurrent neural network is trained with Sea Surface Height (SSH) and LC velocity datasets. The hyperparameters of these models are tuned according to each model's characteristics and data complexity. Prior to training, SSH and velocity data are decomposed into their temporal and spatial counterparts.A model uses the Robust Principle Component Analysis is first proposed, which produces a six-week lead time in forecasting SSH evolution. Next, the Wavelet+EOF+LSTM (WELL) model is proposed to improve the forecasting capability of a prediction model. This model is tested on the prediction of two LC eddies, namely eddy Cameron and Darwin. It is shown that the WELL model can predict the separation of both eddies 10 and 14 weeks ahead respectively, which is two more weeks than the DAC model. Furthermore, the WELL model overcomes the problem due to the partitioning step involved in the DAC model. For subsurface currents forecasting, a layer partitioning method is proposed to predict the subsurface field of the LC system. A weighted average fusion is used to improve the consistency of the predicted layers of the 3D subsurface velocity field. The main challenge of forecasting of the LC and its eddies is the small number of events that have occurred over time, which is only once or twice a year, which makes the training task difficult. Forecasting the velocity of subsurface currents is equally challenging because of the limited insitu measurements.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013727
- Subject Headings
- Machine learning, Loop Current, Oceanography--Forecasting
- Format
- Document (PDF)
- Title
- Automatic extraction and tracking of eye features from facial image sequences.
- Creator
- Xie, Xangdong., Florida Atlantic University, Sudhakar, Raghavan, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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The dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the...
Show moreThe dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the processing system. A new corner detection algorithm is presented in which the problem of detecting corners is solved by minimizing a cost function. Each cost factor captures a desirable characteristic of the corner using both the gray level information and the geometrical structure of a corner. This approach additionally provides corner orientations and angles along with corner locations. The advantage of the new approach over the existing corner detectors is that it is able to improve the reliability of detection and localization by imposing criteria related to both the gray level data and the corner structure. The extraction of eye features is performed by using an improved method of deformable templates which are geometrically arranged to resemble the expected shape of the eye. The overall energy function is redefined to simplify the minimization process. The weights for the energy terms are selected based on the normalized value of the energy term. Thus the weighting schedule of the modified method does not demand any expert knowledge for the user. Rather than using a sequential procedure, all parameters of the template are changed simultaneously during the minimization process. This reduces not only the processing time but also the probability of the template being trapped in local minima. An efficient algorithm for real-time eye feature tracking from a sequence of eye images is developed in the dissertation. Based on a geometrical model which describes the characteristics of the eye, the measurement equations are formulated to relate suitably selected measurements to the tracking parameters. A discrete Kalman filter is then constructed for the recursive estimation of the eye features, while taking into account the measurement noise. The small processing time allows this tracking algorithm to be used in real-time applications. This tracking algorithm is suitable for an automated, non-intrusive and inexpensive system as the algorithm is capable of measuring the time profiles of the eye movements. The issue of compensating head movements during the tracking of eye movements is also discussed. An appropriate measurement model was established to describe the effects of head movements. Based on this model, a Kalman filter structure was formulated to carry out the compensation. The whole tracking scheme which cascades two Kalman filters is constructed to track the iris movement, while compensating the head movement. The presence of the eye blink is also taken into account and its detection is incorporated into the cascaded tracking scheme. The above algorithms have been integrated to design an automated, non-intrusive and inexpensive system which provides accurate time profile of eye movements tracking from video image frames.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/12377
- Subject Headings
- Kalman filtering, Eye--Movements, Algorithms, Image processing
- Format
- Document (PDF)
- Title
- Camera calibration techniques.
- Creator
- Xu, Xuan., Florida Atlantic University, Roth, Zvi S., Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In order to facilitate the implementation of RAC-based camera calibration technique, several issues are addressed in this thesis. First, a straightforward extension of the RAC-based camera calibration technique to the case of unknown camera specifications is given. Second, to speed up the calibration process and reduce numerical difficulties, a simplified RAC-based method is presented. The simplified RAC-based method provides a closed-form solution to the camera calibration problem. Third,...
Show moreIn order to facilitate the implementation of RAC-based camera calibration technique, several issues are addressed in this thesis. First, a straightforward extension of the RAC-based camera calibration technique to the case of unknown camera specifications is given. Second, to speed up the calibration process and reduce numerical difficulties, a simplified RAC-based method is presented. The simplified RAC-based method provides a closed-form solution to the camera calibration problem. Third, the PTM-based camera calibration technique is presented to give an example of pre-1985 camera calibration technique. Fourth, a method is devised to compute the ratio of scale factors. Finally, an optimization scheme is suggested to estimate image center. These modifications preserve all the advantages possessed by the original RAC-based calibration technique. The experiment results are provided to illustrate the effectiveness of the present methods.
Show less - Date Issued
- 1991
- PURL
- http://purl.flvc.org/fcla/dt/14771
- Subject Headings
- Cameras--Calibration
- Format
- Document (PDF)
- Title
- Camera-aided SCARA arm calibration.
- Creator
- Wu, Wen-chiang., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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The focus of this thesis is the kinematic calibration of a SCARA arm with a hand-mounted camera. Kinematic calibration can greatly improve the accuracy of SCARA arms, which are widely used in electronic assembly lines. Vision-based robot calibration has the potential of being a fast, nonintrusive, low-cost, and autonomous approach. In this thesis, we apply a vision-based technique to calibrate SCARA arms. The robot under investigation is modeled by the modified complete and parametrically...
Show moreThe focus of this thesis is the kinematic calibration of a SCARA arm with a hand-mounted camera. Kinematic calibration can greatly improve the accuracy of SCARA arms, which are widely used in electronic assembly lines. Vision-based robot calibration has the potential of being a fast, nonintrusive, low-cost, and autonomous approach. In this thesis, we apply a vision-based technique to calibrate SCARA arms. The robot under investigation is modeled by the modified complete and parametrically continuous model. By repeatedly calibrating the camera, the pose of the robot end-effector are collected at various robot measurement configurations. A least squares technique is then applied to estimate the geometric error parameters of the SCARA arm using the measured robot poses. In order to improve the robustness of the method, a new approach is proposed to calibrate the hand-mounted camera. The calibration algorithm is designed to deal with the case in which the camera sensor plane is nearly-parallel to the camera calibration board. Practical issues regarding robot calibration in general and SCARA arm calibration in particular are also addressed. Experiment studies reveal that the proposed camera-aided approach is a viable means for accuracy enhancement of SCARA arms.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/15075
- Subject Headings
- Robots--Calibration, Manipulators (Mechanism)--Calibration, Robots--Error detection and recovery, Image processing
- Format
- Document (PDF)
- Title
- Camera-aided self-calibration of robot manipulators.
- Creator
- Meng, Yan., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Robot calibration is a software-based accuracy enhancement process. It is normally implemented in a well-controlled environment. However, for a system that function in a natural environment, it is desirable that the system is capable of performing a calibration task without any external expensive calibration apparatus and elaborate setups, i.e., system self-calibration. Vision systems have become standard automation components as cameras are normally integral components of most robotic...
Show moreRobot calibration is a software-based accuracy enhancement process. It is normally implemented in a well-controlled environment. However, for a system that function in a natural environment, it is desirable that the system is capable of performing a calibration task without any external expensive calibration apparatus and elaborate setups, i.e., system self-calibration. Vision systems have become standard automation components as cameras are normally integral components of most robotic manipulators. This research focuses on camera-aided robot self-calibration. Unlike classical vision-based robot calibration methods, which need both image coordinates and precise 3D world coordinates of calibration points, the self-calibration algorithms proposed in the dissertation only require a sequence of images of objects in a natural environment and a known scale. A new robot self-calibration algorithm using a known scale at every camera pose is proposed in the dissertation. It has been known that, the extrinsic parameters of the camera along with its intrinsic parameters can be obtained up to a scale factor by using the corresponding image points of objects due to the factor that the system is inherently under-determined. Now, if the camera is treated as the tool of the robot, one is then able to compute the corresponding robot pose directly from the camera, extrinsic parameters once the scale factor is available. This scale factor, which changes from one camera pose to another, can be uniquely determined from the known scale at each robot pose. The limitation of the above approach for robot self-calibration is that the known scale has to be utilized at every robot measurement pose. A new algorithm is proposed by using the known scale only once in the entire self-calibration procedure. The prerequisite of this calibration algorithm is a carefully planned optimal measurement trajectory for the estimation of the scale factor. By taking into consideration of the observability of the link error parameters, the problem can be formulated either as a constrained or a weighted minimization problem that can be solved by an optimization procedure. A new method for camera lens distortion calibration by using only point correspondences of two images without knowing the camera movement is described in the dissertation. The images for robot calibration can be shared for lens distortion coefficient calibration. This characteristic saves the user much effort in collecting image data and makes it possible to conduct a robot calibration task on line. Extensive simulations and experiment studies on a PUMA 560 robot at FAU Robotics Center reveal the convenience and effectiveness of the proposed self-calibration approaches. Compared to other robot calibration algorithms, the proposed algorithms in this dissertation are more autonomous and can be applied to a natural environment.
Show less - Date Issued
- 2000
- PURL
- http://purl.flvc.org/fcla/dt/12651
- Subject Headings
- Manipulators (Mechanism), Robots--Calibration
- Format
- Document (PDF)
- Title
- Design and control of spherical gimbal for laser tracking system.
- Creator
- Wang, Yingli., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
To assess and improve the accuracy of an intelligent machine such as a precision robot and a computer numerically controlled machine tool, it is exceedingly desirable to have a high performance Coordinate Measuring Machine (CMM). Among various coordinate measuring devices, a laser tracking CMM has the advantages of noninvasiveness and extremely high precision over a large workspace. In this dissertation, we concentrate on the design and control of a new type of spherical gimbal for laser...
Show moreTo assess and improve the accuracy of an intelligent machine such as a precision robot and a computer numerically controlled machine tool, it is exceedingly desirable to have a high performance Coordinate Measuring Machine (CMM). Among various coordinate measuring devices, a laser tracking CMM has the advantages of noninvasiveness and extremely high precision over a large workspace. In this dissertation, we concentrate on the design and control of a new type of spherical gimbal for laser tracking system, whose motion is constrained by two spherical surfaces and whose axes are motorized. By this design, principal errors of a conventional tracking gimbal are reduced. To be able to integrate the laser tracking unit into an intelligent machine, a compact optical head is also designed. This laser tracking system is thus capable of being either a stand-alone or an on-line measuring device. An important issue in developing a laser tracking CMM is control. An intelligent control scheme is reported in this dissertation. The controller has the following elements: The entire tracking process of the system is classified into three modes: normal tracking, motionless and change of directions. An artificial neural network is designed to classify on-line which mode the system is in. A Fuzzy Logic Controller (FLC) suitable for the particular tracking mode is then activated to control the system. To deal with the situation in which a target suddenly changes its direction, a feed-forward compensation component is designed. Decoupling units are also added to the control scheme, by which the entire process of tracking controller design can be greatly simplified. To further improve the system performance, various structures of FLCs are analyzed in the dissertation. It is discovered that there is a constraint in the cascade proportional-integral-derivative (PID)-type FLC. Whenever this constraint is violated, the design of the controller will not be optimal. To solve this problem, a parallel PID-type FLC is proposed. Yet another important issue in the system control is parameter tuning. To this end, a mu-law tuning method, which tune both scaling gain and surface of a fuzzy look-up table, is proposed. A new parameter tuning strategy, which combines mu-law with either a Genetic Algorithm (GA) or a downhill simplex algorithm, is introduced. The GA based mu-law tuning of FLCs can automatically tune parameters of the FLCs, while the Simplex-mu-law tuning scheme can reach near optimal results rapidly. To assess the effectiveness of the concepts proposed in this dissertation, a prototype spherical laser tracking gimbal is constructed at the FAU Robotics Center. The control strategy proposed in this dissertation is tested extensively by simulation and experimentation on the prototype system.
Show less - Date Issued
- 1998
- PURL
- http://purl.flvc.org/fcla/dt/12547
- Subject Headings
- Coordinate measuring machines, Optical scanners, Laser inteferometers
- Format
- Document (PDF)
- Title
- Design and implementation of a control system for a laser-tracking measurement system.
- Creator
- Bai, Ying., Florida Atlantic University, Roth, Zvi S., Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
To assess and evaluate the performance of robots and machine tools dynamically, it is desirable to have a precision measuring device that performs dynamic measurement of end-effector positions of such robots and machine tools. Among possible measurement techniques, Laser Tracking Systems (LTSs) exlnbit the capability of high accuracy, large workspace, high sampling rate, and automatic target-tracking,. and thus are well-suited for robot calibration both kinematically and dynamically. In this...
Show moreTo assess and evaluate the performance of robots and machine tools dynamically, it is desirable to have a precision measuring device that performs dynamic measurement of end-effector positions of such robots and machine tools. Among possible measurement techniques, Laser Tracking Systems (LTSs) exlnbit the capability of high accuracy, large workspace, high sampling rate, and automatic target-tracking,. and thus are well-suited for robot calibration both kinematically and dynamically. In this dissertation, the design and implementation of a control system for a homemade laser tracking measurement systems is addressed and calibration of a robot using the laser tracking system is demonstrated Design and development of a control system for a LTS is a challenging task. It involves a deep understanding of laser interferometry,. controls, mechanics and optics,. both in theoretical perspective and in implementation aspect. One of the most important requirements for a successful design and implementation of a control system for the LTS is proper installation and alignment of the laser and optical system,. or laser transducer system. The precision of measurement using the LTS depends highly on the accuracy of the laser transducer system, as well as the accuracy of the installation and alignment of the optical system. Hence, in reference to the experimental alignment method presented in this dissertation, major error sources affecting the system measurement accuracy are identified and analyzed. A manual compensation method is developed to eliminate the effects of these error sources effectively in the measurement system. Considerations on proper design and installation of laser and optical components are indicated in this dissertation. As a part of the conventional control system design, a dynamic system model of the LTS is required. In this study, a detailed derivation and analysis of the dynamic model of the motor gimbal system using Lagrange-Euler equations of motion is developed for both ideal and complete gimbal systems. Based on this system model,. a conventional controller is designed. Fuzzy Logic Controllers (FLC) are designed in order to suppress noise or disturbances that exist in the motor driver subsystem. By using the relevant control strategies. noise and disturbances present in the electrical control channels are shown to reduce significantly. To improve the system performance further, a spectrum analysis of the error sources and disturbances existing in the system is conducted. Major noise sources are effectively suppressed by using a two-stage fuzzy logic control strategy. A comparison study on the performances of different control strategies is given in this dissertation, in reference to the following: An ideal system model, a system with a long time delay, a system with various noise sources and a system model with uncertainties. Both simulation and experimental results are furnished to illustrate the advantages of the FLC in respect of its transient response, steady-state response, and tracking performance. Furthermore, noise reduction in the laser tracking system is demonstrated. Another important issue concerning a successful application of the LTS in the calibration of a robot is the estimation of system accuracy. Hence, a detailed analysis of system accuracy of the LTS is presented in this worL This analysis is also verified by experimental methods by means of tracking a Coordinate Measuring Machine available in the FAU Robotics Center. Using the developed LTS, a PUMA robot in the FAU Robotics Center is calibrated. The results obtained are confirmative with the data available in the literature. In summary, the proposed methodology towards the design and implementation of a control system for LTSs has been shown to be successful by performing experimental tracking and calibration studies at the FAU Robotics Center.
Show less - Date Issued
- 2000
- PURL
- http://purl.flvc.org/fcla/dt/12622
- Subject Headings
- Robots--Calibration, Robots--Control systems, Fuzzy logic
- Format
- Document (PDF)
- Title
- Design and tuning of fuzzy control surface with Bezier functions.
- Creator
- Wongsoontorn, Songwut., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Design and Tuning a fuzzy logic controller (FLCs) are usually done in two stages. In the first stage, the structure of a FLC is determined based on physical characteristics of the system. In the second stage, the parameters of the FLC are selected to optimize the performance of the system. The task of tuning FLCs can be performed by a number of methods such as adjusting control gains, changing membership functions, modifying control rules and varying control surfaces. A method for the design...
Show moreDesign and Tuning a fuzzy logic controller (FLCs) are usually done in two stages. In the first stage, the structure of a FLC is determined based on physical characteristics of the system. In the second stage, the parameters of the FLC are selected to optimize the performance of the system. The task of tuning FLCs can be performed by a number of methods such as adjusting control gains, changing membership functions, modifying control rules and varying control surfaces. A method for the design and tuning of FLCs through modifying their control surfaces is presented in this dissertation. The method can be summarized as follows. First, fuzzy control surfaces are modeled with Bezier functions. Shapes of the control surface are then adjusted through varying Bezier parameters. A Genetic Algorithm (GA) is used to search for the optimal set of parameters based on the control performance criteria. Then, tuned control surfaces are sampled to create rule-based FLCs. To further improve the system performance, continuity constraints of the curves are imposed. Under the continuity constraints with the same number of tunable parameters, one can obtain more flexible curves that have the potential to improve the overall system performance. An important issue is to develop a new method to self-tune a fuzzy PD controller. The method is based on two building blocks: (I) Bezier functions used to model the control surfaces of the fuzzy PD controller; and, shapes of control surfaces are then adjusted by varying Bezier parameters. (II) The next step involves using a gradient-based optimization algorithm with which the input scaling factors and Bezier parameters are on-line tuned until the controller drives the output of the process as close as possible to the reference position. To protect vendors and consumers from being victimized, various trust models have been used in e-commerce practices. However, a strict verification and authentication process may pose unnecessary heavy cost to the vendor. As an application of the control strategy proposed, this dissertation presents a solution to the reduction of costs of a vendor. With two fuzzy variables (price, credit-history), a trust-surface can be tuned to achieve an optimal solution in terms of profit margin of the vendor. With this new approach, more realistic trust decisions can be reached.
Show less - Date Issued
- 2005
- PURL
- http://purl.flvc.org/fcla/dt/12172
- Subject Headings
- Fuzzy systems, Nonlinear control theory, Process control--Data processing, Fuzzy logic, Intelligent control systems
- Format
- Document (PDF)
- Title
- Detection and classification of marine mammal sounds.
- Creator
- Esfahanian, Mahdi, Zhuang, Hanqi, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Ocean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods...
Show moreOcean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods that automatically detect and classify vocalization patterns of marine mammals. The first work performed is the classification of bottlenose dolphin calls by type. The extraction of salient and distinguishing features from recordings is a major part of this endeavor. To this end, two strategies are evaluated with real datasets provided by Woods Hole Oceanographic Institution: The first strategy is to use contour-based features such as Time-Frequency Parameters and Fourier Descriptors and the second is to employ texture-based features such as Local Binary Patterns (LBP) and Gabor Wavelets. Once dolphin whistle features are extracted for spectrograms, selection of classification procedures is crucial to the success of the process. For this purpose, the performances of classifiers such as K-Nearest Neighbor, Support Vector Machine, and Sparse Representation Classifier (SRC) are assessed thoroughly, together with those of the underlined feature extractors.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00004282, http://purl.flvc.org/fau/fd/FA00004282
- Subject Headings
- Acoustic phenomena in nature, Marine mammals -- Effect of noise on, Marine mammals -- Vocalization, Signal processing -- Mathematics, Underwater acoustics, Wavelets (Mathematics)
- Format
- Document (PDF)
- Title
- Development of A Portable Impedance Based Flow Cytometer for Diagnosis of Sickle Cell Disease.
- Creator
- Dieujuste, Darryl, Zhuang, Hanqi, Du, Sarah, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Sickle cell disease is an inherited blood cell disorder that affects about 100,000 people in the US and results in high cost of medical care exceeding $1.1 billion annually. Sickle cell patients suffer from unpredictable, painful vaso-occlusive crises. Portable, costeffective approaches for diagnosis and monitoring sickle blood activities are important for a better management of the disease and reducing the medical cost. In this research, a mobile application controlled, impedance-based flow...
Show moreSickle cell disease is an inherited blood cell disorder that affects about 100,000 people in the US and results in high cost of medical care exceeding $1.1 billion annually. Sickle cell patients suffer from unpredictable, painful vaso-occlusive crises. Portable, costeffective approaches for diagnosis and monitoring sickle blood activities are important for a better management of the disease and reducing the medical cost. In this research, a mobile application controlled, impedance-based flow cytometer is developed for the diagnosis of sickle cell disease. Calibration of the portable device is performed using a component of known impedance value. The preliminary test results are then compared to those obtained by a commercial benchtop impedance analyzer for further validation. With the developed portable flow cytometer, experiments are performed on two sickle cell samples and a healthy cell sample. The acquired results are subsequently analyzed with MATLAB scripts to extract single-cell level impedance information as well as statistics of different cell conditions. Significant differences in cell impedance signals are observed between sickle cells and normal cells, as well as between sickle cells under hypoxia and normoxia conditions.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013145
- Subject Headings
- Sickle cell disease, Sickle cell anemia--Diagnosis, Flow cytometry--Diagnostic use, Mobile Applications
- Format
- Document (PDF)
- Title
- DSP implementation of turbo decoder using the Modified-Log-MAP algorithm.
- Creator
- Khan, Zeeshan Haneef., Florida Atlantic University, Zhuang, Hanqi, Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The design of any communication receiver needs to addresses the issues of operating under the lowest possible signal-to-noise ratio. Among various algorithms that facilitate this objective are those used for iterative decoding of two-dimensional systematic convolutional codes in applications such as spread spectrum communications and Code Division Multiple Access (CDMA) detection. A main theme of any decoding schemes is to approach the Shannon limit in signal-to-noise ratio. All these...
Show moreThe design of any communication receiver needs to addresses the issues of operating under the lowest possible signal-to-noise ratio. Among various algorithms that facilitate this objective are those used for iterative decoding of two-dimensional systematic convolutional codes in applications such as spread spectrum communications and Code Division Multiple Access (CDMA) detection. A main theme of any decoding schemes is to approach the Shannon limit in signal-to-noise ratio. All these decoding algorithms have various complexity levels and processing delay issues. Hence, the optimality depends on how they are used in the system. The technique used in various decoding algorithms is termed as iterative decoding. Iterative decoding was first developed as a practical means for decoding turbo codes. With the Log-Likelihood algebra, it is shown that a decoder can be developed that accepts soft inputs as a priori information and delivers soft outputs consisting of channel information, a posteriori information and extrinsic information to subsequent stages of iteration. Different algorithms such as Soft Output Viterbi Algorithm (SOVA), Maximum A Posteriori (MAP), and Log-MAP are compared and their complexities are analyzed in this thesis. A turbo decoder is implemented on the Digital Signal Processing (DSP) chip, TMS320C30 by Texas Instruments using a Modified-Log-MAP algorithm. For the Modified-Log-MAP-Algorithm, the optimal choice of the lookup table (LUT) is analyzed by experimenting with different LUT approximations. A low complexity decoder is proposed for a (7,5) code and implemented in the DSP chip. Performance of the decoder is verified under the Additive Wide Gaussian Noise (AWGN) environment. Hardware issues such as memory requirements and processing time are addressed for the chosen decoding scheme. Test results of the bit error rate (BER) performance are presented for a fixed number of frames and iterations.
Show less - Date Issued
- 2002
- PURL
- http://purl.flvc.org/fcla/dt/12948
- Subject Headings
- Error-correcting codes (Information theory), Signal processing--Digital techniques, Coding theory, Digital communications
- Format
- Document (PDF)
- Title
- Implementation of a fuzzy logic controller for laser tracking system.
- Creator
- Wu, Xiaomin., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
With the strikingly fast development of industrial applications and research projects, control systems have become more and more complex than ever. Intelligent control techniques, featuring their being more robust and their availability when system mathematical models are unknown, have proven to be one of the most attractive and highlighted areas in the automatic control arena. This thesis concentrates first on the design of a laser tracking system. A standard design procedure of Fuzzy Logic...
Show moreWith the strikingly fast development of industrial applications and research projects, control systems have become more and more complex than ever. Intelligent control techniques, featuring their being more robust and their availability when system mathematical models are unknown, have proven to be one of the most attractive and highlighted areas in the automatic control arena. This thesis concentrates first on the design of a laser tracking system. A standard design procedure of Fuzzy Logic Controllers (FLCs) is followed, which is then realized in a PC-based environment in the design. An essential issue in this thesis study is the auto tuning of the Fuzzy Logic Controller. An efficient tuning method, mu-law functions, which can adjust both the shape and scaling gain of fuzzy controller's decision table is adopted. Also a search process called Downhill Simplex Search is chosen. Combining these two methods, a Simplex-mu-law auto-tuning algorithm that fits our application is applied to tune the FLC for the laser tracking system. Another issue covered in this research is to modify the Fuzzy Logic Controller structure by changing the distribution of the membership functions. Based on the analysis of the real time error histogram of the system, a novel method is proposed in the thesis for the modification of the membership functions To assess the effectiveness of the methods proposed in this thesis, a prototype laser tracking system is constructed at the FAU Robotics Center. The control strategy proposed in this thesis is tested extensively by simulations and experimentations on the prototype system.
Show less - Date Issued
- 1999
- PURL
- http://purl.flvc.org/fcla/dt/15717
- Subject Headings
- Fuzzy logic, Automatic control, Fuzzy systems
- Format
- Document (PDF)
- Title
- Implementation of a fuzzy-logic-based trust model.
- Creator
- Zhao, Yuanhui., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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In the last 10 years, due to the rapid developments in computers and Internet, the Electronic Commerce has advanced significantly. More and more companies have shifted their businesses activities to the Internet. However, the popular use of ecommerce has also raised serious security problems. Therefore developing security schemes has become a key issue both in the academic as well as industrial research. Since the Internet is open to the public, the associated security issue is challenging. A...
Show moreIn the last 10 years, due to the rapid developments in computers and Internet, the Electronic Commerce has advanced significantly. More and more companies have shifted their businesses activities to the Internet. However, the popular use of ecommerce has also raised serious security problems. Therefore developing security schemes has become a key issue both in the academic as well as industrial research. Since the Internet is open to the public, the associated security issue is challenging. A good security strategy should not only protect the vendors' interest, but also enhance the mutual trust between vendors and customers. As a result, the people will feel more confident in conducting e-commerce. This thesis is dedicated to develop a fuzzy-logic based trust model. In general, the ecommerce transactions need costly verification and authentication process. In some cases, it is not cost effective to verify and authenticate each transaction, especially for transactions involving only small amount of money and for customers having an excellent transaction history. In view of this, in this research a model that distinguishes potentially safe transactions from unsafe transactions is developed. Only those potentially unsafe transactions need to be verified and authenticated. The model takes a number of fuzzy variables as inputs. However, this poses problems in constructing the trust table since the number of fuzzy rules will increase exponentially as the number of fuzzy variables increase. To make the problem more trackable, the variables are divided into several groups, two for each table. Each table will produce a decision on trust. The final decision is made based on the "intersection" of all these outputs. Simulation studies have been conducted to validate the effectiveness of the proposed trust model. Therefore simulations, however, need to be tested in a real business environment using real data. Relevant limitations on the proposed model are hence discussed and future research direction is indicated.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/12812
- Subject Headings
- Fuzzy logic, Electronic commerce--Security measures
- Format
- Document (PDF)
- Title
- Integrating Multi-user Scheduling with Retransmission Diversity over Wireless Links.
- Creator
- Li, Irena, Zhuang, Hanqi, Wang, Xin, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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Research presented in this thesis develops a mainly theoretical basis and computer models for enhancing the throughput of multi-user wireless communication networks. The cross-layer combination of an adaptive modulation and coding (AMC) scheme at the physical layer and the use of automatic repeat request (ARQ) retransmi ssions at the data link layer is integrated into a scheduling framework for multi-user networks. Scheduling algorithms incorporating retransmission diversity are derived for...
Show moreResearch presented in this thesis develops a mainly theoretical basis and computer models for enhancing the throughput of multi-user wireless communication networks. The cross-layer combination of an adaptive modulation and coding (AMC) scheme at the physical layer and the use of automatic repeat request (ARQ) retransmi ssions at the data link layer is integrated into a scheduling framework for multi-user networks. Scheduling algorithms incorporating retransmission diversity are derived for three cases of typical network traffic: best-effort, non-realtime, and realtime. For each case, numeric computer si mulations of wireless communications over Nakagami-m block fading channels are developed to examine the effectiveness of the formulated schemes.
Show less - Date Issued
- 2008
- PURL
- http://purl.flvc.org/fau/fd/FA00012533
- Subject Headings
- Wireless communication networks, Code division multiple access, Modulation (Electronics), Signal processing (Digital techniques)
- Format
- Document (PDF)
- Title
- Kinematic modeling, identification and compensation of robot manipulators.
- Creator
- Zhuang, Hanqi, Florida Atlantic University, Hamano, Fumio, Roth, Zvi S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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Theoretical and practical issues of kinematic modeling, measurement, identification and compensation are addressed in this dissertation. A comprehensive robot calibration methodology using a new Complete and Parametrically Continuous (CPC) kinematic model is presented. The dissertation focuses on model-based robot calibration techniques. Parametric continuity of a kinematic model is defined and discussed to characterize model singularity. Irreducibility is defined to facilitate error model...
Show moreTheoretical and practical issues of kinematic modeling, measurement, identification and compensation are addressed in this dissertation. A comprehensive robot calibration methodology using a new Complete and Parametrically Continuous (CPC) kinematic model is presented. The dissertation focuses on model-based robot calibration techniques. Parametric continuity of a kinematic model is defined and discussed to characterize model singularity. Irreducibility is defined to facilitate error model reduction. Issues of kinematic parameter identification are addressed by utilizing generic forms of linearized kinematic error models. The CPC model is a complete and parametrically continuous kinematic model capable of describing geometry and motion of a robot manipulator. Owing to the completeness of the CPC model, the transformation from the base frame to the world frame and from the tool frame to the last link frame can be modeled with the same modeling convention as the one used for internal link transformations. Due to the parametric continuity of the CPC model, numerical difficulties in kinematic parameter identification using error models are reduced. The CPC model construction, computation of the link parameters from a given link transformation, inverse kinematics, transformations between the CPC model and the Denavit-Hartenberg model, and linearized CPC error model construction are investigated. New methods for self-calibration of a laser tracking coordinate-measuring-machine are reported. Two calibration methods, one based on a four-tracker system and the other based on three trackers with a precision plane, are proposed. Iterative estimation algorithms along with simulation results are presented. Linear quadratic regulator (LQR) theory is applied to design robot accuracy compensators. In the LQR algorithm, additive corrections of joint commands are found without explicitly solving the inverse kinematic problem for an actual robot; a weighting matrix and coefficients in the cost function can be chosen systematically to achieve specific objective such as emphasizing the positioning accuracy of the end-effector over its orientation accuracy and vice versa and taking into account joint travelling limits as well as singularity zones of the robot. The results of the kinematic identification and compensation experiments using the PUMA robot have shown that the CPC modeling technique presented in this dissertation is a convenient and effective means for accuracy improvements of industrial robots.
Show less - Date Issued
- 1989
- PURL
- http://purl.flvc.org/fcla/dt/12243
- Subject Headings
- Robotics, Manipulators (Mechanism)
- Format
- Document (PDF)
- Title
- MEASUREMENT, ANALYSIS, CLASSIFICATION AND DETECTION OF GUNSHOT AND GUNSHOT-LIKE SOUNDS.
- Creator
- Baliram, Rajesh Singh, Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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The recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity...
Show moreThe recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity of machine learning (ML) processes, systems are being developed and optimized to assist personnel in highly dangerous situations. In addition to saving innocent lives, supporting the capture of the responsible criminals is part of the AI algorithm that can be hosted in acoustic gunshot detection systems (AGDSs). Although there has been some speculation that these AGDSs produce a higher false positive rate (FPR) than reported in their specifications, optimizing the dataset used for the model’s training and testing will enhance its performance. This dissertation proposes a new gunshot-like sound database that can be incorporated into a dataset for improved training and testing of a ML gunshot detection model. Reduction of the sample bias (that is, a bias in ML caused by an incomplete database) is achievable. The Mel frequency cepstral coefficient (MFCC) feature extraction process was utilized in this research. The uniform manifold and projection (UMAP) algorithm revealed that the MFCCs of this newly created database were the closest sounds to a gunshot sound, as compared to other gunshot-like sounds reported in literature. The UMAP algorithm reinforced the outcome derived from the calculation of the distances of the centroids of various gunshot-like sounds in MFCCs’ clusters. Further research was conducted into the feature reduction aspect of the gunshot detection ML model. Reducing a feature set to a minimum, while also maintaining a high accuracy rate, is a key parameter of a highly efficient model. Therefore, it is necessary for field deployed ML applications to be computationally light weight and highly efficient. Building on the discoveries of this research can lead to the development of highly efficient gunshot detection models.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014110
- Subject Headings
- Firearms, Sound, Detectors, Machine learning
- Format
- Document (PDF)
- Title
- A method to create three-dimensional facial image from two-dimensional facial data set.
- Creator
- Theerawong, Teerapat., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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A method to create 3D-face image using 2D-face images is the objective of this research. The 3D-face image is constructed using a set of 3D-face images of other persons available in a face database. The 3D-face image actually depicts a parameterized form in terms of depth and texture. This concept can be used to facilitate creating a 3D-face image from 2D database. For this purpose, a 3D-face database is first developed. When a 2D-face image is presented to the system, a 3D-face image that...
Show moreA method to create 3D-face image using 2D-face images is the objective of this research. The 3D-face image is constructed using a set of 3D-face images of other persons available in a face database. The 3D-face image actually depicts a parameterized form in terms of depth and texture. This concept can be used to facilitate creating a 3D-face image from 2D database. For this purpose, a 3D-face database is first developed. When a 2D-face image is presented to the system, a 3D-face image that starts with an average 3D-face image (derived from the 3D-face database) is projected onto the 2D-image plane, with necessary rotation, translation, scaling and interpolation. The projected image is then compared with the input image; and, an optimization algorithm is applied to minimize an error index by selecting 3D-depth and texture parameters. Hence, the projected image is derived. Once the algorithm converges, the resulting 3D-depth and the texture parameters can be employed to construct a 3D-face image of the subject photographed in the 2D-images. A merit of this method is that only the depth and texture parameters of the compared images are required to be stored in the database. Such data can be used either for the recreation of a 3D-image of the test subject or for any biometric authentication (based on 3D face recognition). Results from an experimental study presented in the thesis illustrate the effectiveness of the proposed approach, which has applications in biometric authentication and 3D computer graphics areas.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/13407
- Subject Headings
- Image processing--Digital techniques, Computervision, Computer graphics, Three-dimensional display systems, Computer-aided design
- Format
- Document (PDF)
- Title
- MULTI-MODEL DEEP LEARNING FOR GROUPER SOUND CLASSIFICATION AND SEIZURE PREDICTION.
- Creator
- Ibrahim, Ali K., Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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Deep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and...
Show moreDeep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and forecasting performances of individual classifiers by fusing results of participating deep learning models. The performance of such an ensemble model, however, depends heavily on the following two design features. Firstly, the diversity of the participating (or base) deep learning models is crucial. If all base deep learning models produce similar classification results, then combining these results will not provide much improvement. Thus, diversity is considered to be a key design feature of any successful MMDL system. Secondly, the selection of a fusion function, namely, a suitable function to integrate the results of all the base models, is important. In short, building an effective MMDL system is a complex and challenging process which requires deep knowledge of the problem context and a well-defined prediction process. The proposed MMDL method utilizes a bank of Convolutional Neural Networks (CNNs) and Stacked AutoEncoders (SAEs). To reduce the design complexity, a randomized generation process is applied to assign values to hyperparameters of base models. To speed up the training process, new feature extraction procedures which captures time-spatial characteristics of input signals are also explored. The effectiveness of the MMDL method is validated in this dissertation study with three real-world case studies. In the first case study, the MMDL model is applied to classify call types of groupers, an important fishery resource in the Caribbean that produces sounds associated with reproductive behaviors during yearly spawning aggregations. In the second case study, the MMDL model is applied to detect upcalls of North Atlantic Right Whales (NARWs), a type of endangered whales. NARWs use upcalls to communicate among themselves. In the third case study, the MMDL model is modified to predict seizure episodes. In all these cases, the proposed MMDL model outperforms existing state-of-the-art methods.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013382
- Subject Headings
- Deep Learning, Machine Learning, Neural networks (Computer science), Groupers, Whales, Vocalization, Animal, Seizures
- Format
- Document (PDF)
- Title
- NEIGHBORING NEAR MINIMUM-TIME CONTROLS WITH DISCONTINUITIES AND THE APPLICATION TO THE CONTROL OF MANIPULATORS (PATH-PLANNING, TRACKING, FEEDBACK).
- Creator
- Zhuang, Hanqi, Florida Atlantic University, Hamano, Fumio, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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This thesis presents several algorithms to treat the problem of closed-loop near minimum-time controls with discontinuities. First, a neighboring control algorithm is developed to solve the problem in which controls are bounded by constant constraints. Secondly, the scheme is extended to account for state-dependent control constraints. And finally, a path tracking algorithm for robotic manipulators is presented, which is also a neighboring control algorithm. These algorithms are suitable for...
Show moreThis thesis presents several algorithms to treat the problem of closed-loop near minimum-time controls with discontinuities. First, a neighboring control algorithm is developed to solve the problem in which controls are bounded by constant constraints. Secondly, the scheme is extended to account for state-dependent control constraints. And finally, a path tracking algorithm for robotic manipulators is presented, which is also a neighboring control algorithm. These algorithms are suitable for real time controls because the on-line computations involved are relatively simple. Simulation results show that these algorithms work well despite the fact that the prescribed final points can not be reached exactly.
Show less - Date Issued
- 1986
- PURL
- http://purl.flvc.org/fcla/dt/14326
- Subject Headings
- Manipulators (Mechanism), Control theory, Algorithms
- Format
- Document (PDF)
- Title
- Object recognition by genetic algorithm.
- Creator
- Li, Jianhua., Florida Atlantic University, Han, Chingping (Jim), Zhuang, Hanqi, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
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Vision systems have been widely used for parts inspection in electronics assembly lines. In order to improve the overall performance of a visual inspection system, it is important to employ an efficient object recognition algorithm. In this thesis work, a genetic algorithm based correlation algorithm is designed for the task of visual electronic parts inspection. The proposed procedure is composed of two stages. In the first stage, a genetic algorithm is devised to find a sufficient number of...
Show moreVision systems have been widely used for parts inspection in electronics assembly lines. In order to improve the overall performance of a visual inspection system, it is important to employ an efficient object recognition algorithm. In this thesis work, a genetic algorithm based correlation algorithm is designed for the task of visual electronic parts inspection. The proposed procedure is composed of two stages. In the first stage, a genetic algorithm is devised to find a sufficient number of candidate image windows. For each candidate window, the correlation is performed between the sampled template and the image pattern inside the window. In the second stage, local searches are conducted in the neighborhood of these candidate windows. Among all the searched locations, the one that has a highest correlation value with the given template is selected as the best matched location. To apply the genetic algorithm technique, a number of important issues, such as selection of a fitness function, design of a coding scheme, and tuning of genetic parameters are addressed in the thesis. Experimental studies have confirmed that the proposed GA-based correlation method is much more effective in terms of accuracy and speed in locating the desired object, compared with the existing Monte-Carlo random search method.
Show less - Date Issued
- 1995
- PURL
- http://purl.flvc.org/fcla/dt/15225
- Subject Headings
- Genetic algorithms, Robots--Control systems, Computer vision, Quality control--Optical methods
- Format
- Document (PDF)