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Design patterns and object oriented model of a biometric service system
Title
Design patterns and object oriented model of a biometric service system.
Creator
Blandon, Jatni., Florida Atlantic University, Han, Chingping (Jim)
Abstract/Description
Continuous changes in the software development community require challenging conventional approaches resulting in techniques that allow for early decisions at the design level. This project is a demonstration of the use of design patterns as a common way to organize objects to make practical design decisions helping to generate flexible, manageable and agile software architectures. Due to the continuity and unpredictability of its requirements, the Biometric Industry is appropriate to...
Show moreContinuous changes in the software development community require challenging conventional approaches resulting in techniques that allow for early decisions at the design level. This project is a demonstration of the use of design patterns as a common way to organize objects to make practical design decisions helping to generate flexible, manageable and agile software architectures. Due to the continuity and unpredictability of its requirements, the Biometric Industry is appropriate to illustrate of the use of design patterns and object oriented analysis. First, the conceptual model of an Electronic Fingerprint Service establishes the vocabulary for discussing how a system is constructed. Since good design decisions eventually result in a good design model, this model is used to leverage the object reused when requirements change. The Electronic Biometric Services model demonstrates how by applying design patterns, the system can gain the flexibility and agility required to grow and change according to new requirements.
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Date Issued
2006
PURL
http://purl.flvc.org/fcla/dt/13351
Subject Headings
Computer software--Development, Software architecture, Object-oriented programming (Computer science), Biometric identification, Pattern recognition systems--Development
Format
Document (PDF)
Face Processing Using Mobile Devices
Title
Face Processing Using Mobile Devices.
Creator
James, Jhanon, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Image Processing and Computer Vision solutions have become commodities for software developers, thanks to the growing availability of Application Program- ming Interfaces (APIs) that encapsulate rich functionality, powered by advanced al- gorithms. To understand and create an e cient method to process faces in images by computers, one must understand how the human visual system processes them. Face processing by computers has been an active research area for about 50 years now. Face detection...
Show moreImage Processing and Computer Vision solutions have become commodities for software developers, thanks to the growing availability of Application Program- ming Interfaces (APIs) that encapsulate rich functionality, powered by advanced al- gorithms. To understand and create an e cient method to process faces in images by computers, one must understand how the human visual system processes them. Face processing by computers has been an active research area for about 50 years now. Face detection has become a commodity and is now incorporated into simple devices such as digital cameras and smartphones. An iOS app was implemented in Objective-C using Microsoft Cognitive Ser- vices APIs, as a tool for human vision and face processing research. Experimental work on image compression, upside-down orientation, the Thatcher e ect, negative inversion, high frequency, facial artifacts, caricatures and image degradation were completed on the Radboud and 10k US Adult Faces Databases along with other images.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004770, http://purl.flvc.org/fau/fd/FA00004770
Subject Headings
Image processing--Digital techniques., Mobile communication systems., Mobile computing., Artificial intelligence., Human face recognition (Computer science), Computer vision., Optical pattern recognition.
Format
Document (PDF)
Context-based Image Concept Detection and Annotation
Title
Context-based Image Concept Detection and Annotation.
Creator
Zolghadr, Esfandiar, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Scene understanding attempts to produce a textual description of visible and latent concepts in an image to describe the real meaning of the scene. Concepts are either objects, events or relations depicted in an image. To recognize concepts, the decision of object detection algorithm must be further enhanced from visual similarity to semantical compatibility. Semantically relevant concepts convey the most consistent meaning of the scene. Object detectors analyze visual properties (e.g., pixel...
Show moreScene understanding attempts to produce a textual description of visible and latent concepts in an image to describe the real meaning of the scene. Concepts are either objects, events or relations depicted in an image. To recognize concepts, the decision of object detection algorithm must be further enhanced from visual similarity to semantical compatibility. Semantically relevant concepts convey the most consistent meaning of the scene. Object detectors analyze visual properties (e.g., pixel intensities, texture, color gradient) of sub-regions of an image to identify objects. The initially assigned objects names must be further examined to ensure they are compatible with each other and the scene. By enforcing inter-object dependencies (e.g., co-occurrence, spatial and semantical priors) and object to scene constraints as background information, a concept classifier predicts the most semantically consistent set of names for discovered objects. The additional background information that describes concepts is called context. In this dissertation, a framework for building context-based concept detection is presented that uses a combination of multiple contextual relationships to refine the result of underlying feature-based object detectors to produce most semantically compatible concepts. In addition to the lack of ability to capture semantical dependencies, object detectors suffer from high dimensionality of feature space that impairs them. Variances in the image (i.e., quality, pose, articulation, illumination, and occlusion) can also result in low-quality visual features that impact the accuracy of detected concepts. The object detectors used to build context-based framework experiments in this study are based on the state-of-the-art generative and discriminative graphical models. The relationships between model variables can be easily described using graphical models and the dependencies and precisely characterized using these representations. The generative context-based implementations are extensions of Latent Dirichlet Allocation, a leading topic modeling approach that is very effective in reduction of the dimensionality of the data. The discriminative contextbased approach extends Conditional Random Fields which allows efficient and precise construction of model by specifying and including only cases that are related and influence it. The dataset used for training and evaluation is MIT SUN397. The result of the experiments shows overall 15% increase in accuracy in annotation and 31% improvement in semantical saliency of the annotated concepts.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004745, http://purl.flvc.org/fau/fd/FA00004745
Subject Headings
Computer vision--Mathematical models., Pattern recognition systems., Information visualization., Natural language processing (Computer science), Multimodal user interfaces (Computer systems), Latent structure analysis., Expert systems (Computer science)
Format
Document (PDF)
Design of an Aquatic Quadcopter with Optical Wireless Communications
Title
Design of an Aquatic Quadcopter with Optical Wireless Communications.
Creator
Haller, Patterson, Abtahi, Homayoon, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
With a focus on dynamics and control, an aquatic quadcopter with optical wireless communications is modeled, designed, constructed, and tested. Optical transmitter and receiver circuitry is designed and discussed. By utilization of the small angle assumption, the nonlinear dynamics of quadcopter movement are linearized around an equilibrium state of zero motion. The set of equations are then tentatively employed beyond limit of the small angle assumption, as this work represents an initial...
Show moreWith a focus on dynamics and control, an aquatic quadcopter with optical wireless communications is modeled, designed, constructed, and tested. Optical transmitter and receiver circuitry is designed and discussed. By utilization of the small angle assumption, the nonlinear dynamics of quadcopter movement are linearized around an equilibrium state of zero motion. The set of equations are then tentatively employed beyond limit of the small angle assumption, as this work represents an initial explorative study. Specific constraints are enforced on the thrust output of all four rotors to reduce the multiple-input multiple-output quadcopter dynamics to a set of single-input single-output systems. Root locus and step response plots are used to analyze the roll and pitch rotations of the quadcopter. Ultimately a proportional integral derivative based control system is designed to control the pitch and roll. The vehicle’s yaw rate is similarly studied to develop a proportional controller. The prototype is then implemented via an I2C network of Arduino microcontrollers and supporting hardware.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004786, http://purl.flvc.org/fau/fd/FA00004786
Subject Headings
Autonomous robots--Design and construction., Embedded computer systems--Design and construction., Wireless communication systems., Artificial intelligence., Optical pattern recognition., Computer vision.
Format
Document (PDF)
Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States
Title
Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States.
Creator
Lloyd, Eric, Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from...
Show moreA self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004764, http://purl.flvc.org/fau/fd/FA00004764
Subject Headings
Cognitive neuroscience., Neural networks (Computer science), Pattern recognition systems., Artificial intelligence., Self-organizing systems., Human-computer interaction., Human information processing.
Format
Document (PDF)

Pages