Computer Engineering Commons^™

Ultrafast Optical Response And Ablation Mechanisms Of Molybdenum Disulfide Under Intense Femtosecond Laser Irradiation, Changji Pan, Lan Jiang, Jingya Sun, Qingsong Wang, Feifei Wang, Ka Wang, Yongfeng Lu, Yeliang Wang, Liangti Qu, Tianhong Cui

Department of Electrical and Computer Engineering: Faculty Publications

Numerous valuable studies on electron dynamics have focussed on the extraordinary properties of molybdenum disulfide (MoS2); however, most of them were confined to the level below the damage threshold. Here the electron dynamics of MoS2 under intense ultrafast laser irradiation was investigated by experiments and simulations. Two kinds of ablation mechanisms were revealed, which led to two distinct types of electron dynamics and final ablation morphology. At a higher fluence, the emergence of superheated liquid induced a dramatic change in the transient reflectivity and micro-honeycomb structures. At a lower fluence, the material was just removed by sublimation, and the ablation …

Far-Field Transient Absorption Nanoscopy With Sub-50 Nm Optical Super-Resolution, Yali Bi, Chi Yang, Lei Tong, Haozheng Li, Boyu Yu, Shuai Yan, Guang Yang, Meng Deng, Yi Wang, Wei Bao, Lei Ye, Ping Wang

Department of Electrical and Computer Engineering: Faculty Publications

Nanoscopic imaging or characterizing is the mainstay of the development of advanced materials. Despite great progress in electronic and atomic force microscopies, label-free and far-field characterization of materials with deep sub- wavelength spatial resolution has long been highly desired. Herein, we demonstrate far-field super-resolution transient absorption (TA) imaging of two-dimensional material with a spatial resolution of sub-50 nm. By introducing a donut- shaped blue saturation laser, we effectively suppress the TA transition driven by near-infrared (NIR) pump–probe photons, and push the NIR-TA microscopy to sub-diffraction-limited resolution. Specifically, we demonstrate that our method can image the individual nano-grains in graphene with …

Precursor-Surface Interactions Revealed During Plasma-Enhanced Atomic Layer Deposition Of Metal Oxide Thin Films By In-Situ Spectroscopic Ellipsometry, Ufuk Kilic, Alyssa Mock, Derek Sekora, Simeon Gilbert, Shah R. Valloppilly, Natale J. Ianno, Marjorie Langell, Eva Schubert, Mathias Schubert

Department of Electrical and Computer Engineering: Faculty Publications

We find that a five-phase (substrate, mixed native oxide and roughness interface layer, metal oxide thin film layer, surface ligand layer, ambient) model with two-dynamic (metal oxide thin film layer thickness and surface ligand layer void fraction) parameters (dynamic dual box model) is sufficient to explain in-situ spectroscopic ellipsometry data measured within and across multiple cycles during plasma-enhanced atomic layer deposition of metal oxide thin films. We demonstrate our dynamic dual box model for analysis of in-situ spectroscopic ellipsometry data in the photon energy range of 0.7–3.4 eV measured with time resolution of few seconds over large numbers of cycles …

Transcriptional Analysis Of Cleft Palate In Tgfβ3 Mutant Mice, J. Liu, S.K. Chanumolu, K.M. White, M. Albahrani, Hasan H. Otu, A. Nawshad

Department of Electrical and Computer Engineering: Faculty Publications

Cleft palate (CP) is one of the most common craniofacial birth defects, impacting about 1 in 800 births in the USA. Tgf-β3 plays a critical role in regulating murine palate development, and Tgf-β3 null mutants develop cleft palate with 100% penetrance. In this study, we compared global palatal transcriptomes of wild type (WT) and Tgf-β3 −/− homozygous (HM) mouse embryos at the crucial palatogenesis stages of E14.5, and E16.5, using RNA-seq data. We found 1,809 and 2,127 differentially expressed genes at E16.5 vs. E14.5 in the WT and HM groups, respectively (adjusted p < 0.05; |fold change|> 2.0). We focused on the genes that …

Scalable Universalspace Vector Pulse Width Modulation Scheme For Multilevel Inverters, Wei Qiao, Fa Chen, Liyan Qu

Department of Electrical and Computer Engineering: Faculty Publications

A scalable universal space vector pulse-width modulation (SVPWM) scheme for multilevel inverters is disclosed. In the disclosed SVPWM scheme, the modulation triangle is quickly identified based on a coordinate transformation from an a-ß coordinate system to a 120° oblique coordinate system. Then, the duty cycles and switching states of the three vertices of the modulation triangle are determined by simple algebric computations. In a switching period, any vertex of the modulation triangle can be flexibly selected as the start point to optimize the switching sequence with flexibly adjustable duty cycles(s) for the redundant switching state(s) according to specific applications.

Detecting Faults In Wind Turbines, Wei Qiao, Xiang Gong

Department of Electrical and Computer Engineering: Faculty Publications

A wind turbine generator faultdetection method is described.The method includes acquiring curent data from a wind turbine generator during operation, determining frequency demodulated signals and amplitude demodulated signals by frequency demodulating and amplitude demodulating the current data, resampling the frequency and amplitude demodulated signals corresponding to the current data, monitoring a frequency spectra of the resampled frequency and amplitude demodulated signals corresponding to the current data to identify one or more excitations in the frequency spectra. In response to identifying one or more excitations in the frequnecy spectra at one or more of the variable fault characteristic frequecies, the method …

Long-Term Tracking Of Group-Housed Livestock Using Keypoint Detection And Map Estimation For Individual Animal Identification, Eric T. Psota, Ty Schmidt, Benny Mote, Lance C. Pérez

Department of Electrical and Computer Engineering: Faculty Publications

Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a …

Ieee Access Special Section Editorial: Security And Privacy For Cloud And Iot, Kuo-Hui Yeh, Weizhi Meng, Sk Hafizul Islam, Kuan Zhang, Ennan Zhai

Department of Electrical and Computer Engineering: Faculty Publications

No abstract provided.

Ieee Access Special Section Editorial: Energy Management In Buildings, Kun Wang, Yi Qian, Joel J.P.C. Rodrigues, Fei Richard Yu, Peng Li

Department of Electrical and Computer Engineering: Faculty Publications

No abstract provided.

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean …

Fiber-Optic Temperature And Flow Sensory System And Methods, Ming Han, Guigen Liu, Weilin Hou, Qiwen Shen

Department of Electrical and Computer Engineering: Faculty Publications

A fiber optic sensor, a process for utilizing a fiber optic sensor, and a process for fabricating a fiber optic sensor are described, where a double-side-polished silicon pillar is attacked to an optical fiber tip and forms, a Fabry-Perot cavity. In an implementation, a fiber optic sensor in accordance with an examplary embodiment includes an optical fiber configured to be coupled to a light source and a spectrometer; and a single silicon layer or multiple silicon layers disposed on an end face of the optical fiber, where each of the silicon layer(s) defines a Fabry-Perot interferometer, and where the sensor …

Amodal Instance Segmentation And Multi-Object Tracking With Deep Pixel Embedding, Yanfeng Liu

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

This thesis extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches.

The model is further extended to produce consistent pixel-level embeddings across two consecutive image frames from a video to simultaneously perform amodal instance segmentation and multi-object tracking. No post-processing …

Aluminum/Carbon Composites Materials Fabricated By The Powder Metallurgy Process, Amélie Veillère, Hiroki Kurita, Akira Kawasaki, Yongfeng Lu, Jean-Marc Heintz, Jean-François Silvain

Department of Electrical and Computer Engineering: Faculty Publications

Aluminum matrix composites reinforced with carbon fibers or diamond particles have been fabricated by a powder metallurgy process and characterized for thermal management applications. Al/C composite is a nonreactive system (absence of chemical reaction between the metallic matrix and the ceramic reinforcement) due to the presence of an alumina layer on the surface of the aluminum powder particles. In order to achieve fully dense materials and to enhance the thermo-mechanical properties of the Al/C composite materials, a semi-liquid method has been carried out with the addition of a small amount of Al-Si alloys in the Al matrix. Thermal conductivity and …

Advanced Security Analysis For Emergent Software Platforms, Mohannad Alhanahnah

School of Computing: Dissertations, Theses, and Student Research

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems.

This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the …

Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis (FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …

Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque

School of Computing: Dissertations, Theses, and Student Research

Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …

Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, …

Formal Modeling And Analysis Of A Family Of Surgical Robots, Niloofar Mansoor

School of Computing: Dissertations, Theses, and Student Research

Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counterexample showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality and building family-based techniques for both modeling and analysis. This thesis presents a novel approach for building an end to end dependability case for a software product line, where a property is formally modeled, a counterexample is found and then …

Water Pipeline Leakage Detection Based On Machine Learning And Wireless Sensor Networks, Yang Liu, Xuehui Ma, Yong Tie, Yinghui Zhang, Jing Gao

Department of Electrical and Computer Engineering: Faculty Publications

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and …

Machine Current Sensor Fdi Strategy In Pmsms, Haibo Li, Yi Qian, Sohrab Asgarpoor, Hamid Sharif

Department of Electrical and Computer Engineering: Faculty Publications

This work proposes a machine current sensor fault detection and isolation (FDI) strategy in permanent magnet synchronous machines (PMSMs) resilient to multiple faults. The fault detection is performed by comparing the measured and estimated DC link currents. The fault isolation is achieved according to machine phase signal estimation and the corresponding residual examination. Single sensor fault, multiple sensor faults and non-sensor fault are covered by the proposed FDI method. The proposed sensor FDI method is not influenced by machine imbalance, feasible for FDI of both single and multiple machine current sensor faults, and capable of distinguishing between machine current sensor …

A Co-Optimal Coverage Path Planning Method For Aerial Scanning Of Complex Structures, Zhexiong Shang, Justin Bradley, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm – but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to …

The Stability Analysis For Wind Turbines With Doubly Fed Induction Generators, Baohua Dong

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability.

This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis.

Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different …

Hybrid Superhydrophilic–Superhydrophobic Micro/ Nanostructures Fabricated By Femtosecond Laserinduced Forward Transfer For Sub-Femtomolar Raman Detection, Xiaodan Ma, Lan Jiang, Xiaowei Li, Bohong Li, Ji Huang, Jiaxing Sun, Zhi Wang, Zhijie Xu, Liangti Qu, Yongfeng Lu, Tianhong Cui

Department of Electrical and Computer Engineering: Faculty Publications

Raman spectroscopy plays a crucial role in biochemical analysis. Recently, superhydrophobic surface-enhanced Raman scattering (SERS) substrates have enhanced detection limits by concentrating target molecules into small areas. However, due to the wet transition phenomenon, further reduction of the droplet contact area is prevented, and the detection limit is restricted. This paper proposes a simple method involving femtosecond laser-induced forward transfer for preparing a hybrid superhydrophilic–superhydrophobic SERS (HS-SERS) substrate by introducing a superhydrophilic pattern to promote the target molecules to concentrate on it for ultratrace detection. Furthermore, the HS-SERS substrate is heated to promote a smaller concentrated area. The water vapor …

Advanced Mathematical And Numerical Methods In Control And Optimization For Smart Grids, Zhan Shu, Michael Z.Q. Chen, Qing Hui

Department of Electrical and Computer Engineering: Faculty Publications

While renewable energy, as a part of smart-grid technologies, brings clean energy, it also brings a series of power quality problems. An increasing number of power electronic devices and new smart-grid technologies are used to ensure a safe, reliable, and high-quality operation of the power grid. However, the effectiveness of these control devices and technologies largely depends on the accuracy of the model, the advancement of control methods, and the numerical optimization of the parameters.

This special issue focuses on recent advances in modeling, numerical analysis, control, and optimization of smart grids with some special emphasis on the mathematical problems …

Online Eeg Seizure Detection And Localization, Amirsalar Mansouri, Sanjay P. Singh, Khalid Sayood

Department of Electrical and Computer Engineering: Faculty Publications

Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists-- a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG …

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …

Fqstat: A Parallel Architecture For Very High-Speed Assessment Of Sequencing Quality Metrics, Sree K. Chanumolu, Mustafa Albahrani, Hasan H. Otu

Department of Electrical and Computer Engineering: Faculty Publications

Background: High throughput DNA/RNA sequencing has revolutionized biological and clinical research. Sequencing is widely used, and generates very large amounts of data, mainly due to reduced cost and advanced technologies. Quickly assessing the quality of giga-to-tera base levels of sequencing data has become a routine but important task. Identification and elimination of low-quality sequence data is crucial for reliability of downstream analysis results. There is a need for a high-speed tool that uses optimized parallel programming for batch processing and simply gauges the quality of sequencing data from multiple datasets independent of any other processing steps.

Results: FQStat is a …

Cooperative Learning For The Consensus Of Multi-Agent Systems, Qishuai Liu

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Due to a lot of attention for the multi-agent system in recent years, the consensus algorithm gained immense popularity for building fault-tolerant systems in system and control theory. Generally, the consensus algorithm drives the swarm of agents to work as a coherent group that can reach an agreement regarding a certain quantity of interest, which depends on the state of all agents themselves. The most common consensus algorithm is the average consensus, the final consensus value of which is equal to the average of the initial values. If we want the agents to find the best area of the particular …

Embedded System Design Of Robot Control Architectures For Unmanned Agricultural Ground Vehicles, Ryan Humphrey

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Engineering technology has matured to the extent where accompanying methods for unmanned field management is now becoming a technologically achievable and economically viable solution to agricultural tasks that have been traditionally performed by humans or human operated machines. Additionally, the rapidly increasing world population and the daunting burden it places on farmers in regards to the food production and crop yield demands, only makes such advancements in the agriculture industry all the more imperative. Consequently, the sector is beginning to observe a noticeable shift, where there exist a number of scalable infrastructural changes that are in the process of slowly …

Distributed Edge Bundling For Large Graphs, Yves Tuyishime

School of Computing: Dissertations, Theses, and Student Research

Graphs or networks are widely used to depict the relationships between data entities in diverse scientific and engineering applications. A direct visualization (such as node-link diagram) of a graph with a large number of nodes and edges often incurs visual clutter. To address this issue, researchers have developed edge bundling algorithms that visually merge similar edges into curved bundles and can effectively reveal high-level edge patterns with reduced visual clutter. Although the existing edge bundling algorithms achieve appealing results, they are mostly designed for a single machine, and thereby the size of a graph they can handle is limited by …