Engineering Commons^™

Grnsight: A Web Application And Service For Visualizing Models Of Small- To Medium-Scale Gene Regulatory Networks, Kam D. Dahlquist, John David N. Dionisio, Ben G. Fitzpatrick, Nicole A. Anguiano, Anindita Varshneya, Britain J. Southwick, Mihir Samdarshi

John David N. Dionisio

GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of …

Perceptions Of Planned Versus Unplanned Malfunctions: A Human-Robot Interaction Scenario, Theresa T. Kessler, Keith R. Macarthur, Manuel Trujillo-Silva, Thomas Macgillivray, Chris Ripa, Peter A. Hancock

Keith Reid MacArthur

Validation Of Orion Cockpit Displays Using Eggplant Functional And Python Programming, M. A. Rafe Biswas

M. A. Rafe Biswas

No abstract provided.

Person Identification From Streaming Surveillance Video Using Mid-Level Features From Joint Action-Pose Distribution, Binu M. Nair, Vijayan K. Asari

Vijayan K. Asari

We propose a real time person identification algorithm for surveillance based scenarios from low-resolution streaming video, based on mid-level features extracted from the joint distribution of various types of human actions and human poses. The proposed algorithm uses the combination of an auto-encoder based action association framework which produces per-frame probability estimates of the action being performed, and a pose recognition framework which gives per-frame body part locations. The main focus in this manuscript is to effectively combine these per-frame action probability estimates and pose trajectories from a short temporal window to obtain mid-level features. We demonstrate that these mid-level …

State Preserving Extreme Learning Machine For Face Recognition, Md. Zahangir Alom, Paheding Sidike, Vijayan K. Asari, Tarek M. Taha

Vijayan K. Asari

Extreme Learning Machine (ELM) has been introduced as a new algorithm for training single hidden layer feed-forward neural networks (SLFNs) instead of the classical gradient-based algorithms. Based on the consistency property of data, which enforce similar samples to share similar properties, ELM is a biologically inspired learning algorithm with SLFNs that learns much faster with good generalization and performs well in classification applications. However, the random generation of the weight matrix in current ELM based techniques leads to the possibility of unstable outputs in the learning and testing phases. Therefore, we present a novel approach for computing the weight matrix …

Video-To-Video Pose And Expression Invariant Face Recognition Using Volumetric Directional Pattern, Vijayan K. Asari, Almabrok Essa

Vijayan K. Asari

Face recognition in video has attracted attention as a cryptic method of human identification in surveillance systems. In this paper, we propose an end-to-end video face recognition system, addressing a difficult problem of identifying human faces in video due to the presence of large variations in facial pose and expression, and poor video resolution. The proposed descriptor, named Volumetric Directional Pattern (VDP), is an oriented and multi-scale volumetric descriptor that is able to extract and fuse the information of multi frames, temporal (dynamic) information, and multiple poses and expressions of faces in input video to produce feature vectors, which are …

Efficient Thermal Image Segmentation Through Integration Of Nonlinear Enhancement With Unsupervised Active Contour Model, Fatema Albalooshi, Evan Krieger, Paheding Sidike, Vijayan K. Asari

Vijayan K. Asari

Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions. Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity …

Gaussian Weighted Neighborhood Connectivity Of Nonlinear Line Attractor For Learning Complex Manifolds, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Vijayan K. Asari

The human brain has the capability to process high quantities of data quickly for detection and recognition tasks. These tasks are made simpler by the understanding of data, which intentionally removes redundancies found in higher dimensional data and maps the data onto a lower dimensional space. The brain then encodes manifolds created in these spaces, which reveal a specific state of the system. We propose to use a recurrent neural network, the nonlinear line attractor (NLA) network, for the encoding of these manifolds as specific states, which will draw untrained data towards one of the specific states that the NLA …

Histogram Of Oriented Phase And Gradient (Hopg) Descriptor For Improved Pedestrian Detection, Hussin Ragb, Vijayan K. Asari

Vijayan K. Asari

This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG). The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more …

Intensity And Resolution Enhancement Of Local Regions For Object Detection And Tracking In Wide Area Surveillance, Evan Krieger, Vijayan K. Asari, Saibabu Arigela, Theus H. Aspiras

Vijayan K. Asari

Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst. First, the focus of …

Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Vijayan K. Asari

The human brain’s ability to extract information from multidimensional data modeled by the Nonlinear Line Attractor (NLA), where nodes are connected by polynomial weight sets. Neuron connections in this architecture assumes complete connectivity with all other neurons, thus creating a huge web of connections. We envision that each neuron should be connected to a group of surrounding neurons with weighted connection strengths that reduces with proximity to the neuron. To develop the weighted NLA architecture, we use a Gaussian weighting strategy to model the proximity, which will also reduce the computation times significantly. Once all data has been trained in …

Brain Machine Interface Using Emotiv Epoc To Control Robai Cyton Robotic Arm, Daniel P. Prince, Mark J. Edmonds, Andrew J. Sutter, Matthew Thomas Cusumano, Wenjie Lu, Vijayan K. Asari

Vijayan K. Asari

The initial framework for an electroencephalography (EEG) thought recognition software suite is developed, built, and tested. This suite is designed to recognize human thoughts and pair them to actions for controlling a robotic arm. Raw EEG brain activity data is collected using an Emotiv EPOC headset. The EEG data is processed through linear discriminant analysis (LDA), where an intended action is identified. The EEG classification suite is being developed to increase the number of distinct actions that can be identified compared to the Emotiv recognition software. The EEG classifier was able to correctly distinguish between two separate physical movements. Future …

A Modular Approach For Key-Frame Selection In Wide Area Surveillance Video Analysis, Almabrok Essa, Paheding Sidike, Vijayan K. Asari

Vijayan K. Asari

This paper presents an efficient preprocessing algorithm for big data analysis. Our proposed key-frame selection method utilizes the statistical differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames.

We anticipate that such key frame selection technique will have significant impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery. Three real-world datasets are used for evaluation and testing and the observed results are encouraging.

Constructing Virtual Asymmetric Opponents From Data And Models In The Literature: Case Of Crowd Rioting, Barry G. Silverman, Michael Johns, Kevin O'Brien, Ransom Weaver, Jason Cornwell

Barry G Silverman

This paper describes an effort to integrate human behavior models from a range of ability, stress, emotion, decision theoretic, and motivation literatures into a game-theoretic framework appropriate for representing synthetic asymmetric agents and scenarios. Our goal is to create a common mathematical framework (CMF) and an open agent architecture that allows one to research and explore alternative behavior models to add realism to software agents - e.g., physiology and stress, personal values and emotive states, and cultural influences. Our CMF is based on a dynamical, game-theoretic approach to evolution and equilibria in Markov chains representing states of the world that …

A Demonstration Of The Pmf-Extraction Approach: Modeling The Effects Of Sound On Crowd Behavior, Jason Cornwell, Barry G. Silverman, Kevin O'Brien, Michael Johns

Barry G Silverman

The vast majority of psychology, sociology, and other social-science literature describing human behavior and performance does not reach the eyes of those of us working in the modeling and simulation community. Our recent work has been concerned with the extraction and implementation of Human Behavior Models(HBMs)/ Performance Moderator Functions(PMFs) from this literature. This paper demonstrates how our methodology was applied to extract models of the effects of music and sound on both individuals and groups and to implement them in a simulated environment. PMFs describing how several classes of sound affect decision-making and performance were constructed based on well-established psychological …

A Demonstration Of The Pmf-Extraction Approach: Modeling The Effects Of Sound On Crowd Behavior, Jason Cornwell, Barry G. Silverman, Kevin O'Brien, Michael Johns

Barry G Silverman

The vast majority of psychology, sociology, and other social-science literature describing human behavior and performance does not reach the eyes of those of us working in the modeling and simulation community. Our recent work has been concerned with the extraction and implementation of Human Behavior Models(HBMs)/ Performance Moderator Functions(PMFs) from this literature. This paper demonstrates how our methodology was applied to extract models of the effects of music and sound on both individuals and groups and to implement them in a simulated environment. PMFs describing how several classes of sound affect decision-making and performance were constructed based on well-established psychological …

Constructing Virtual Asymmetric Opponents From Data And Models In The Literature: Case Of Crowd Rioting, Barry G. Silverman, Michael Johns, Kevin O'Brien, Ransom Weaver, Jason Cornwell

Barry G Silverman

This paper describes an effort to integrate human behavior models from a range of ability, stress, emotion, decision theoretic, and motivation literatures into a game-theoretic framework appropriate for representing synthetic asymmetric agents and scenarios. Our goal is to create a common mathematical framework (CMF) and an open agent architecture that allows one to research and explore alternative behavior models to add realism to software agents - e.g., physiology and stress, personal values and emotive states, and cultural influences. Our CMF is based on a dynamical, game-theoretic approach to evolution and equilibria in Markov chains representing states of the world that …

Human-Robot Versus Human-Human Relationship Impact On Comfort Levels Regarding In Home Privacy, Keith R. Macarthur, Thomas G. Macgillivray, Eva L. Parkhurst, Peter A. Hancock

Keith Reid MacArthur

Platform-Specific Code Generation From Platform-Independent Timed Models, Baekgyu Kim, Lu Feng, Oleg Sokolsky, Insup Lee

Oleg Sokolsky

Many safety-critical real-time embedded systems need to meet stringent timing constraints such as preserving delay bounds between input and output events. In model-based development, a system is often implemented by using a code generator to automatically generate source code from system models, and integrating the generated source code with a platform. It is challenging to guarantee that the implemented systems preserve required timing constraints, because the timed behavior of the source code and the platform is closely intertwined. In this paper, we address this challenge by proposing a model transformation approach for the code generation. Our approach compensates the platform-processing …

From Requirements To Code: Model Based Development Of A Medical Cyber Physical System, Anitha Murugesan, Mats Heimdahl, Michael Whalen, Sanjai Rayadurgam, John Komp, Lian Duan, Baekgyu Kim, Oleg Sokolsky, Insup Lee

Oleg Sokolsky

The advanced use of technology in medical devices has improved the way health care is delivered to patients. Unfortunately, the increased complexity of modern medical devices poses challenges for development, assurance, and regulatory approval. In an e ort to improve the safety of advanced medical devices, organizations such as FDA have supported exploration of techniques to aid in the development and regulatory approval of such systems. In an ongoing research project, our aim is to provide effective development techniques and exemplars of system development artifacts that demonstrate state of the art development techniques.

In this paper we present an end-to-end …

Verified Ros-Based Deployment Of Platform-Independent Control Systems, Wenrui Meng, Junkil Park, Oleg Sokolsky, Stephanie Weirich, Insup Lee

Oleg Sokolsky

The paper considers the problem of model-based deployment of platform-independent control code on a specific platform. The approach is based on automatic generation of platform-specific glue code from an architectural model of the system. We present a tool, ROSGen, that generates the glue code based on a declarative specification of platform interfaces. Our implementation targets the popular Robot Operating System (ROS) platform. We demonstrate that the code generation process is amenable to formal verification. The code generator is implemented in Coq and relies on the infrastructure provided by the CompCert and VST tool. We prove that the generated code always …

Verified Ros-Based Deployment Of Platform-Independent Control Systems, Wenrui Meng, Junkil Park, Oleg Sokolsky, Stephanie Weirich, Insup Lee

Oleg Sokolsky

The paper considers the problem of model-based deployment of platform-independent control code on a specific platform. The approach is based on automatic generation of platform-specific glue code from an architectural model of the system. We present a tool, ROSGen, that generates the glue code based on a declarative specification of platform interfaces. Our implementation targets the popular Robot Operating System (ROS) platform. We demonstrate that the code generation process is amenable to formal verification. The code generator is implemented in Coq and relies on the infrastructure provided by the CompCert and VST tool. We prove that the generated code always …

Automatic Verification Of Linear Controller Software, Miroslav Pajic, Junkil Park, Insup Lee, George Pappas, Oleg Sokolsky

Oleg Sokolsky

We consider the problem of verification of software implementations of linear time-invariant controllers. Commonly, different implementations use different representations of the controller’s state, for example due to optimizations in a third-party code generator. To accommodate this variation, we exploit input-output controller specification captured by the controller’s transfer function and show how to automatically verify correctness of C code controller implementations using a Frama-C/Why3/Z3 toolchain. Scalability of the approach is evaluated using randomly generated controller specifications of realistic size.

From Requirements To Code: Model Based Development Of A Medical Cyber Physical System, Anitha Murugesan, Mats Heimdahl, Michael Whalen, Sanjai Rayadurgam, John Komp, Lian Duan, Baekgyu Kim, Oleg Sokolsky, Insup Lee

Oleg Sokolsky

The advanced use of technology in medical devices has improved the way health care is delivered to patients. Unfortunately, the increased complexity of modern medical devices poses challenges for development, assurance, and regulatory approval. In an e ort to improve the safety of advanced medical devices, organizations such as FDA have supported exploration of techniques to aid in the development and regulatory approval of such systems. In an ongoing research project, our aim is to provide effective development techniques and exemplars of system development artifacts that demonstrate state of the art development techniques.

In this paper we present an end-to-end …

Platform-Specific Code Generation From Platform-Independent Timed Models, Baekgyu Kim, Lu Feng, Oleg Sokolsky, Insup Lee

Oleg Sokolsky

Many safety-critical real-time embedded systems need to meet stringent timing constraints such as preserving delay bounds between input and output events. In model-based development, a system is often implemented by using a code generator to automatically generate source code from system models, and integrating the generated source code with a platform. It is challenging to guarantee that the implemented systems preserve required timing constraints, because the timed behavior of the source code and the platform is closely intertwined. In this paper, we address this challenge by proposing a model transformation approach for the code generation. Our approach compensates the platform-processing …

A Data-Driven Behavior Modeling And Analysis Framework For Diabetic Patients On Insulin Pumps, Sanjian Chen, Lu Feng, Michael Rickels, Amy Peleckis, Oleg Sokolsky, Insup Lee

Oleg Sokolsky

About 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control.

In this paper, we propose an “Eat, Trust, and Correct” (ETC) framework to model the T1D insulin pump users’ behavior. We use machine learning …

A Virtual Environment For Enterprise Engineering Education, Can Saygin, Benjamin Dow, Raymond Kluczny, Majdi Najm, Scott Grasman

Dow Scott

Several resources highlight the need to effectively use modern technology to gain more productive and rewarding undergraduate science, mathematics, engineering, and technology education. In addition to the growth of information technology, the importance of hands-on practice and active learning has been highlighted in various resources. These factors, coupled with inadequate and insufficient real-world experiences in undergraduate education, have become a major reason for under-qualified and under-employed graduates. This paper discusses the creation of the University of Missouri Virtual Enterprise, which provides context for development of learning modules for enterprise engineering education. This approach will improve the undergraduate education experience by …