Computer Engineering Commons^™

Modeling Energy Consumption Of High-Performance Applications On Heterogeneous Computing Platforms, Gary D. Lawson Jr.

Computational Modeling & Simulation Engineering Theses & Dissertations

Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit.

Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship …

Development Of A Data Acquisition System For Unmanned Aerial Vehicle (Uav) System Identification, Donald Joseph Lear

Mechanical & Aerospace Engineering Theses & Dissertations

Aircraft system identification techniques are developed for fixed wing Unmanned Aerial Vehicles (UAV). The use of a designed flight experiment with measured system inputs/outputs can be used to derive aircraft stability derivatives. This project set out to develop a methodology to support an experiment to model pitch damping in the longitudinal short-period mode of a UAV. A Central Composite Response Surface Design was formed using angle of attack and power levels as factors to test for the pitching moment coefficient response induced by a multistep pitching maneuver.

Selecting a high-quality data acquisition platform was critical to the success of the …

Design Of A Virtual Laboratory For Automation Control, Zelin Zhu

Computational Modeling & Simulation Engineering Theses & Dissertations

In the past, only students who studied on campus were able to access laboratory equipment in traditional lab courses; distance learning students, enrolled in online courses, were at a disadvantage for they could learn basic lab experiment principles but could never experience hands-on learning. Modeling and simulation can be a powerful tool for generating virtual laboratories for distance learning students. This thesis describes the design and development of a virtual laboratory for automation control using mechanical, electrical, and pneumatic components for an automation and control course at Old Dominion University. This virtual laboratory application was implemented for two platforms — …

Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee

Electrical & Computer Engineering Theses & Dissertations

Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a …

Scalable And Fully Distributed Localization In Large-Scale Sensor Networks, Miao Jin, Su Xia, Hongyi Wu, Xianfeng David Gu

Electrical & Computer Engineering Faculty Publications

This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of …

Controlling The Error On Target Motion Through Real-Time Mesh Adaptation: Applications To Deep Brain Stimulation, Huu Phuoc Bui, Satyendra Tomar, Hadrien Courtecuisse, M. Audette, Stéphane Cotin, Stéphane P.A. Bordas

Computational Modeling & Simulation Engineering Faculty Publications

We present an error-controlled mesh refinement procedure for needle insertion simulation and apply it to the simulation of electrode implantation for deep brain stimulation, including brain shift.

Our approach enables to control the error in the computation of the displacement and stress fields around the needle tip and needle shaft by suitably refining the mesh, whilst maintaining a coarser mesh in other parts of the domain.

We demonstrate through academic and practical examples that our approach increases the accuracy of the displacement and stress fields around the needle without increasing the computational expense. This enables real-time simulations.

The proposed methodology …

A Mobile Platform Using Software Defined Radios For Wireless Communication Systems Experimentation, Otilia Popescu, Shiny Abraham, Samy El-Tawab

Engineering Technology Faculty Publications

A distinctive feature of wireless communication systems is implied by the fact that there is no physical connection between the transmitter and its corresponding receiver, which enables user mobility. However, experimenting with wireless communication systems is mostly done in the lab, where transmitters and receivers are setup on benches, in stationary settings. This prevents students from experiencing fading and other propagation effects associated with mobile wireless channels. This paper describes a mobile platform for wireless communication experimentation that enables students to run experiments beyond the confines of a traditional lab, in realistic settings that cover indoor and outdoor scenarios with …

Hands-On Learning Environment And Educational Curriculum On Collaborative Robotics, Ana Djuric, Jeremy L. Rickli, Vukica M. Jovanovic, Daniel Foster

Engineering Technology Faculty Publications

The objective of this paper is to describe teaching modules developed at Wayne State University integrate collaborative robots into existing industrial automation curricula. This is in alignment with Oakland Community College and WSU’s desire to create the first industry-relevant learning program for the use of emerging collaborative robotics technology in advanced manufacturing systems. The various learning program components will prepare a career-ready workforce, train industry professionals, and educate academicians on new technologies. Preparing future engineers to work in highly automated production, requires proper education and training in CoBot theory and applications. Engineering and Engineering Technology at Wayne State University offer …

Industrial Wireless Sensor Networks 2016, Qindong Sun, Schancang Li, Shanshan Zhao, Hongjian Sun, Li Xu, Arumugam Nallamathan

Information Technology & Decision Sciences Faculty Publications

The industrial wireless sensor network (IWSN) is the next frontier in the Industrial Internet of Things (IIoT), which is able to help industrial organizations to gain competitive advantages in industrial manufacturing markets by increasing productivity, reducing the costs, developing new products and services, and deploying new business models.

Qos Recommendation In Cloud Services, Xianrong Zheng, Li Da Xu, Sheng Chai

Information Technology & Decision Sciences Faculty Publications

As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud …

Secrecy Rates And Optimal Power Allocation For Full-Duplex Decode-And-Forward Relay Wire-Tap Channels, Lubna Elsaid, Leonardo Jimenez-Rodriguez, Nghi H. Tran, Sachin Shetty, Shivakumar Sastry

Computational Modeling & Simulation Engineering Faculty Publications

This paper investigates the secrecy rates and optimal power allocation schemes for a decode-and-forward wiretap relay channel where the transmission from a source to a destination is aided by a relay operating in a full-duplex (FD) mode under practical residual self-interference. By first considering static channels, we address the non-convex optimal power allocation problems between the source and relay nodes under individual and joint power constraints to establish closed-form solutions. An asymptotic analysis is then given to provide important insights on the derived power allocation solutions. Specifically, by using the method of dominant balance, it is demonstrated that full power …

Hoeffding Tree Algorithms For Anomaly Detection In Streaming Datasets: A Survey, Asmah Muallem, Sachin Shetty, Jan W. Pan, Juan Zhao, Biswajit Biswal

Computational Modeling & Simulation Engineering Faculty Publications

This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions …

Impact Of Trucking Network Flow On Preferred Biorefinery Locations In The Southern United States, Timothy M. Young, Lee D. Han, James H. Perdue, Stephanie R. Hargrove, Frank M. Guess, Xia Huang, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The "median family income," "timberland annual growth-to-removal ratio," and "transportation delays" were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site …

Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li

Electrical & Computer Engineering Faculty Publications

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled …