UMBC Systems Engineering

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Systems Engineering encompasses the principles and practices that guide the engineering of complex systems. As technologies become more complex and integrated, systems engineering professionals bridge traditional disciplines and focus on the system as a whole to find the solution that best meets the needs of the end user.


Recent Submissions

Now showing 1 - 7 of 7
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    Time Series Analysis of Census Internet Response
    (American Statistical Association) Highland, Fred
    Collection of census data over the internet promises to be more respondent friendly, accurate and cost effective than other methods. However, the characteristics of internet response over time are not well understood limiting the ability to accurately model, predict and manage take-up. This work theorizes that census internet response can be modeled as a time series of exponentially decreasing response probabilities associated with stimulating events. The response can be modeled as statistical distributions that characterize the events. This modeling is applied to census internet response data provided by Canada and the US to determine the common characteristics of response events and modeling parameters. The approach provides a means to model internet response patterns over time when calibrated to population and the survey methodology characteristics.
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    Modeling Complexity in Multi-modal Adaptive Survey Systems
    (Elsevier, 2014-11-02) Highland, Fred
    Modern survey data collection systems must balance cost and quality while supporting multiple response modes (paper, internet, telephone and personal interview) and addressing unpredictable respondent behavior. The next generation of survey systems utilizes adaptive methods to address these issues, but this may affect system behavior and introduces new issues. The paper discusses the development of a system model to analyze system behavior, determine the level of complexity present, define the conditions under which complex behaviors occur and explore approaches to manage complexity. The model, built in NetLogo, uses an agent representation for control, response mode management and the respondent. It not only represents control logic, particularly survey strategies, but also realistic stochastic respondent demographics and external influences which are independent of the system. The paper frames the problem as a potential complex adaptive system, discusses the approach and modeling of the system and reviews analysis of the model to date and its impacts on system design. Preliminary results indicate that basic system behavior is complicated (according to the Cynefin framework) but external influences can introduce unpredictable behaviors that make the system complex, requiring careful management in order to achieve system objectives.
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    Adaptation of Spike-Timing-Dependent Plasticity to Unsupervised Learning for Polychronous Wavefront Computing
    (Elsevier, 2015-10-08) Highland, Fred; Hart, Corey B.
    Non-Von Neumann computational architectures have lately aroused significant interest as substrates for complex computation. One recent development in this domain is the Polychronous Wavefront Computing (PWC) computational model based on multiple wavefront dynamics. This model is an abstraction and simplification of the artificial neural network paradigm based on temporal and spatial patterns of activity in a pulse propagating media and their interaction with transponders. While this framework is capable of computing basic logical functions and exhibiting interesting dynamic behaviors, methods for unsupervised training of the framework have not been identified. The lack of input weights and the spatio-temporal nature of the PWC framework make direct application of weight adjusting learning methods (e.g., backpropagation) impractical. The paper will describe research into unsupervised learning for PWCs inspired by Spike-Timing-Dependent Plasticity (STDP) methods used with other types of polychronous models. The method is based on adding Leaky Integrate-and-Fire semantics to the PWC framework allowing analysis of activating wavefronts and determination of the optimal location for future stimulation. The transponder's location is then incrementally adjusted to improve its future response. The paper will discuss the learning approach and examine the results of applying the method over a series of stimulations to sample configurations.
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    Unsupervised Learning of Patterns Using Multilayer Reverberating Configurations of Polychronous Wavefront Computation
    (Elsevier, 2016-10-30) Highland, Fred; Hart, Corey
    Polychronous Wavefront Computation (PWC) is an abstraction of spiking neural networks that has been shown to be capable of basic computational functions and simple pattern recognition through multilayer configurations. The objective of this work is to apply unsupervised learning methods to multilayer PWC configurations to improve performance providing a basis for more advanced applications and deep learning. Previous work on defining multilayer PWC configurations is extended by applying biologically inspired learning methods to dynamically suppress unneeded transponders and improve configuration performance. Simple learning approaches based on concepts from spike-timing-dependent plasticity and potentiation decay models are adapted to PWC transponders and combined with training sequences to optimize the transponder configurations for recognition. Learning is further enhanced by configuring transponders in recurrent structures to activate hidden layer transponders creating reverberations that reinforce learning. A means to classify multiple input patterns into general concepts is also introduced to further enhance the recognition capabilities of the configurations. The concepts are experimentally validated and analyzed through application to a 7-segment display digit recognition problem showing that the approach can improve PWC configuration performance and reduce complexity.
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    Implementing Multilayer Neural Network Behavior Using Polychronous Wavefront Computation
    (Elsevier, 2016-10-30) Highland, Fred; Hart, Corey
    Polychronous Wavefront Computation (PWC) is an abstraction of spiking neural networks that provides a potentially practical model for implementing neuromorphic computing systems. While it's has been shown to exhibit some basic computational capabilities, its use in complex neuro-computational models remains to be explored. The paper presents a model and approach for configuring PWC transponders to implement multilayer neural network behavior to provide a basis for more complex applications of the technology. The model uses a set of input transponders representing pattern features to stimulate hidden layer transponders that combine features and trigger output layer transponders to identify patterns. The input layer transponder geometry is selected to create wavefront intersections for all relevant feature combinations. Hidden layer transponders are positioned by solving the intersection of the circles equations defined by sets of input transponders. Output layer transponders are defined to collect complete sets of features for recognition based on the hidden layer transponder geometry. The approach uses the intersections of three wavefronts to maximize transponder selectivity and increase information density. The concept is experimentally demonstrated and analyzed with a 7-segment display digit recognition application which provides a simple but representative example of more complex pattern recognition problems.
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    Unsupervised Learning of Polychronous Wavefront Computation Configurations for Pattern Recognition
    (Elsevier, 2018-10-23) Highland, Fred
    Polychronous Wavefront Computation (PWC) provides a potentially simple model for large scale implementation of spiking neural networks and deep learning. Although the definition of predefined pattern recognition configurations has been demonstrated, dynamic organization of configurations from examples remains a difficult problem. This paper explores the hypothesis that a properly arranged field of PWC transponders with neuromorphic behaviors can self-organize into recognition configurations based on training examples. The PWC transponders used are augmented with a position learning algorithm based on spike-timing-dependent plasticity, suppression of non-specific transponders using a stimulation fatigue approach and deactivation of unused transponders using potentiation decay. The paper provides the results of initial research demonstrating that pattern recognition configurations can be learned if the initial density and distribution of transponders is properly selected with respect to the learning behavior parameters. The effectiveness of the learning process can be improved by encoding layering information in the wavefronts to focus the transponder activations. The results define a means for PWC transponders to self-organize into recognition configurations providing a basis for development of more complex configurations and deep learning applications.
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    The Definitive Guide to a Digital Business Strategy
    (2020-07-14) Mubako, Albert T.; Ray, Jeffrey S.