Current Search: Zhuang, Hanqi (x)
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- Title
- Sparse representation classification of dolphin whistles using local binary patterns.
- Creator
- Esfahanian, Mahdi, Zhuang, Hanqi, Erdol, Nurgun, Graduate College
- Date Issued
- 2013-04-12
- PURL
- http://purl.flvc.org/fcla/dt/3361295
- Subject Headings
- Dolphins, Dolphin sounds, Bioacoustics
- Format
- Document (PDF)
- Title
- Sparse Representation Classification of Dolphin Whistles Using Gabor Wavelets.
- Creator
- Esfahanian, Mahdi, Zhuang, Hanqi, Graduate College, Erdol, Nurgun
- Abstract/Description
-
This research presents a novel approach to categorize dolphin whistles into various types. Most accurate methods to identify dolphin whistles are tedious and not robust, especially in the presence of ocean noise. One of the biggest challenges of dolphin whistle extraction is the coexistence of short-time duration wide-band echo clicks with the whistles. In this research, a subspace of select orientation parameters of the 2D Gabor wavelet frames is utilized to enhance or suppress signals by...
Show moreThis research presents a novel approach to categorize dolphin whistles into various types. Most accurate methods to identify dolphin whistles are tedious and not robust, especially in the presence of ocean noise. One of the biggest challenges of dolphin whistle extraction is the coexistence of short-time duration wide-band echo clicks with the whistles. In this research, a subspace of select orientation parameters of the 2D Gabor wavelet frames is utilized to enhance or suppress signals by their orientation. The result is a Gabor image that contains a noise free grayscale representation of the fundamental dolphin whistle which is resampled and fed into the Sparse Representation Classifier. The classifier uses the l1 norm to select a match. Experimental studies conducted demonstrate: a a robust technique based on the Gabor wavelet filters in extracting reliable call patterns, and b the superior performance of Sparse Representation Classifier for identifying dolphin whistles by their call type.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00005146
- Format
- Document (PDF)
- Title
- A biologically inspired myelinated neuron axon model using a system identification approach.
- Creator
- Morales, George J., Zhuang, Hanqi, Pavlovic, Mirjana, Graduate College
- Date Issued
- 2011-04-08
- PURL
- http://purl.flvc.org/fcla/dt/3164633
- Subject Headings
- Axons, Nodes of Ranvier, Models, Neurological
- Format
- Document (PDF)
- Title
- A Study in Implementing Autonomous Video Surveillance Systems Based on Optical Flow Concept.
- Creator
- Fonseca, Alvaro A., Zhuang, Hanqi, Marques, Oge, Florida Atlantic University
- Abstract/Description
-
Autonomous video surveillance systems are usually built with several functional blocks such as motion detection, foreground and background separation, object tracking, depth estimation, feature extraction and behavioral analysis of tracked objects. Each of those blocks is usually designed with different techniques and algorithms, which may need significant computational and hardware resources. In this thesis we present a surveillance system based on an optical flow concept, as a main unit on...
Show moreAutonomous video surveillance systems are usually built with several functional blocks such as motion detection, foreground and background separation, object tracking, depth estimation, feature extraction and behavioral analysis of tracked objects. Each of those blocks is usually designed with different techniques and algorithms, which may need significant computational and hardware resources. In this thesis we present a surveillance system based on an optical flow concept, as a main unit on which other functional blocks depend. Optical flow limitations, capabilities and possible problem solutions are discussed in this thesis. Moreover, performance evaluation of various methods in handling occlusions, rigid and non-rigid object classification, segmentation and tracking is provided for a variety of video sequences under different ambient conditions. Finally, processing time is measured with software that shows an optical flow hardware block can improve system performance and increase scalability while reducing the processing time by more than fifty percent.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012516
- Subject Headings
- Electronic surveillance, Optical pattern recognition, Computer vision, Optical flow--Image analysis
- Format
- Document (PDF)
- Title
- An Application of Artificial Neural Networks for Hand Grip Classification.
- Creator
- Gosine, Robbie R., Zhuang, Hanqi, Florida Atlantic University
- Abstract/Description
-
The gripping action as performed by an average person is developed over their life and changes over time. The initial learning is based on trial and error and becomes a natural action which is modified as the physiology of the individual changes. Each grip type is a personal expression and as the grip changes over time to accommodate physiologically changes, it can be considered to be a grip-signature. lt is postulated that an ANN can deliver a classification mechanism that is able to make...
Show moreThe gripping action as performed by an average person is developed over their life and changes over time. The initial learning is based on trial and error and becomes a natural action which is modified as the physiology of the individual changes. Each grip type is a personal expression and as the grip changes over time to accommodate physiologically changes, it can be considered to be a grip-signature. lt is postulated that an ANN can deliver a classification mechanism that is able to make sense of the varying gripping inputs that are linearly inseparable and uniquely attributed to user physiology. Succinctly, in this design, the stifnulus is characterized by a voltage that represents the applied force in a grip. This signature of forces is then used to train an ANN to recognize the grip that produced the signature, the ANN in turn is used to successfully classify three unique states of grip-signatures collected from the gripping action of various individuals as they hold, lift and crush a paper coffee-cup. A comparative study is done for three types of classification: K-Means, Backpropagation Feedforward Neural Networks and Recurrent Neural Networks, with recommendations made in selecting more effective classification methods.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012522
- Subject Headings
- Neural networks (Computer science), Pattern perception, Back propagation (Artificial intelligence), Multivariate analysis (Computer programs)
- Format
- Document (PDF)
- Title
- Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning.
- Creator
- Mohammadi Khoroushadi, Mohammad Sadegh, Leventouri, Theodora, Zhuang, Hanqi, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
- Abstract/Description
-
Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient...
Show moreReducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013115
- Subject Headings
- Tomography--Image quality, Wavelets (Mathematics), Deep learning, Tomography, X-Ray Computed
- 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
- 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
-
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
- 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 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
- 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
-
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
- 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
-
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
- Performance analysis of the genetic algorithm and its applications.
- Creator
- Liu, Xinggang., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Research and applications of genetic algorithms have become increasingly important in a wide variety of scientific fields. In this thesis, we present an empirical analysis of genetic algorithms in the function optimization area. As a focus of our research, a novel empirical analysis approach to various genetic algorithms is provided. The research starts from the survey of current trends in genetic algorithms, followed by exploring the characteristics of the simple genetic algorithm, the...
Show moreResearch and applications of genetic algorithms have become increasingly important in a wide variety of scientific fields. In this thesis, we present an empirical analysis of genetic algorithms in the function optimization area. As a focus of our research, a novel empirical analysis approach to various genetic algorithms is provided. The research starts from the survey of current trends in genetic algorithms, followed by exploring the characteristics of the simple genetic algorithm, the modified genetic algorithm and hybridized genetic algorithm. A number of typical function optimization problems are solved by these genetic algorithms. Ample empirical data associated with various modifications to the simple genetic algorithm is also provided. Results from this research can be used to assist practitioners in their applications of genetic algorithms.
Show less - Date Issued
- 1995
- PURL
- http://purl.flvc.org/fcla/dt/15210
- Subject Headings
- Genetic algorithms, Combinatorial optimization
- 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
-
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)
- 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
- Self-calibration of parallel-link mechanisms.
- Creator
- Liu, Lixin., 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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Self-calibration is a desirable feature for an intelligent machine such as a robot that must function outside of controlled laboratory conditions. This is because it is inevitable that variations in the kinematic model arise from imperfections in the manufacturing process and changes of environment conditions. Self-calibration has the potential of (a) removing the dependence on external pose sensing, (b) producing high accuracy measurement data over the entire workspace of the system with an...
Show moreSelf-calibration is a desirable feature for an intelligent machine such as a robot that must function outside of controlled laboratory conditions. This is because it is inevitable that variations in the kinematic model arise from imperfections in the manufacturing process and changes of environment conditions. Self-calibration has the potential of (a) removing the dependence on external pose sensing, (b) producing high accuracy measurement data over the entire workspace of the system with an extremely fast measurement rate, (c) being automated and completely non invasive, (d) facilitating on-line accuracy compensation, and (e) being cost effective. This dissertation concentrates on the study of self-calibrating parallel-link mechanisms. A framework of self-calibration of a parallel-link mechanism is created, which is based on kinematic analysis and the construction of measurement residuals utilizing the information provided by redundant sensors embedded in the system. Forward and inverse kinematic measurement residuals of the mechanisms are proposed. To avoid the estimation of redundant kinematic parameters of the mechanism, the concept of relative residuals is introduced. Guidelines for placement of sensors for self-calibration are presented. An approach to determining the number of independent kinematic parameters of the mechanism is introduced. Extensive simulation and experimental studies conducted on a parallel-link mechanism, the Stewart platform built in the Robotics Center at Florida Atlantic University, confirm the effectiveness of the proposed approach.
Show less - Date Issued
- 1997
- PURL
- http://purl.flvc.org/fcla/dt/12539
- Subject Headings
- Manipulators (Mechanism)--Calibration, Robots--Control systems, Robotics
- 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
- 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
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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)
