Electrical and Computer Engineering Commons^™

Rewards And Challenges In Adopting Agility In An Academic Department, Massood Towhidnejad, Omar Ochoa, James J. Pembridge, Radu Babiceanu, Carlos Castro

Posters

Introducing agility into department processes may be challenging especially when interfacing with a non-agile environment. While frequent meetings can add more time constraints, the team environment emphasizes more communication, transparency, and accountability in completing the products leading to a higher sense of ownership of the completed work.

Hardware Security For Wireless Communications Systems Using Antenna-Based Radio Frequency Fingerprint Engineering, Noemi Miguelez Gomez

Doctoral Dissertations and Master's Theses

The design and application of novel physical-layer security techniques have been increasing in the last decades as means to enhance the security that more traditional techniques provide to wireless communications systems. Well-known hardware security techniques, such as radio frequency fingerprinting, use unintended manufacturing process variations and unique hardware structures in the semiconductors for applications such as identification and classification of the source of different transmitted signals, and detection of hardware modifications. The uniqueness of the features that two different modules present, even maintaining the same design, can be used for modules characterization at a lower cost and complexity than other …

Advanced Manufacturing And Dielectric Material Characterization Techniques For High-Temperature Mm-Wave Antennas, Seng Loong Yu

Doctoral Dissertations and Master's Theses

As traditional monitoring data sensors in high temperature environments such as gas turbine engines and aerospace applications are being replaced by wireless equivalents in recent years, there is a need for low-cost, low-time, and low-infrastructure alternatives to further push the state of the art. In this work, two aspects of high temperature materials in the above context are explored: (a) the development of advanced manufacturing techniques for high temperature materials that is lowcost, low-time, and low-infrastructure, and (b) the development of material characterization techniques that can be integrated with the manufacturing processes of high temperature materials. Additive manufacturing has been …

Height Information Aided 3d Real-Time Large-Scale Underground User Positioning, Houbing Song, Chengkai Tang, Cunle Zhang, Lingling Zhang, Yi Zhang

Publications

Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large‐scale underground mining environment. To solve this problem, this study proposes large‐scale underground 3D real‐time positioning method with seam height assistance. This method uses the ultrawide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise3D positioning. The simulation results show that the proposed real‐time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large‐scale underground users.

Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa

Beyond: Undergraduate Research Journal

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …

Interactive Planetarium Project, Eric Babcock, Cody Park, Johann Van Hilst

Discovery Day - Prescott

The Interactive Planetarium Project will design and build the software framework for connectivity between the Digistar 6 planetarium projection software and the smartphones of all audience members in the Jim and Linda Lee Planetarium. The goal of this project is to make planetarium shows more participatory, add a feature to our planetarium shows that many other universities do not yet have, and create a framework for future students and faculty to build from. To demonstrate our technology, we will make a real-time competitive trivia game able to support 60 concurrent users (number of expected audience members in the planetarium).

The …

Design, Development, And Calibration Of An Electric Field Probe For Use On Sounding Rockets, Anthony Oreo

Doctoral Dissertations and Master's Theses

Earth’s ionosphere is a dynamic environment that has yet to be fully understood. Interactions of high-energy particles from space with atmospheric plasma and the Earth’s natural magnetic field create many interesting interactions, many of which have direct impacts on the planet and human life. Understanding the dynamics of the upper atmosphere is a compelling endeavor, with much of the research being conducted through high-altitude sounding rockets. These rockets allow for in-situ measurements of the physical parameters of the upper atmosphere which in turn helps in the answering of important questions in space science. In order to quantify the force a …

Effect Of Freestream Noise On Hypersonic Crossflow-Induced Boundary-Layer Transition, Andrew Bustard, Thomas J. Juliano, Harrison B. Yates, Mark Noftz, Joseph Jewell

Publications

Between the windward and leeward rays, boundary-layer transition on cones at angle of attack in hypersonic flow is dominated by the crossflow instability [1, 2], wherein stationary and/or traveling crossflow vortices develop and can breakdown into turbulence [3]. Stationary crossflow modes are caused by surface roughness or any other steady forcing and produce disturbances that are fixed in place on the surface [3–6]. Stationary vortices create a generalized inflection point in the velocity profile that is inviscidly unstable [7]. This distorted mean flow develops secondary instabilities, whose growth and breakdown is one path to turbulence [8–10]. Traveling crossflow modes are …

Airspace Integration Of New Entrants And Safety Risk Management Models, Fadjimata Issoufou Anaroua

Doctoral Dissertations and Master's Theses

In recent years, the demand for airspace access of Unmanned Aerial Systems (UAS) increased significantly and is continuously increasing for different altitude-types UAS. A similar evolution is expected from Commercial Space Operations (CSO) in the next years. These aviation/aerospace systems will need to be seamlessly integrated into the National Airspace System (NAS), at their operational altitude levels, and accounted for from all perspectives, including proactively addressing their safety hazards. This thesis captures the requirements for the new entrants’ integration, and then identifies and analyzes the safety risks added to the NAS operations by its new entrants, the future omnipresent UAS …

Machine Learning For Unmanned Aerial System (Uas) Networking, Jian Wang

Doctoral Dissertations and Master's Theses

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex …

Improvement On Pdp Evaluation Performance Based On Neural Networks And Sgdk-Means Algorithm, Fan Deng, Houbing Song, Zhenhua Yu, Liyong Zhang, Xi Song, Min Zhang, Zhenyu Zhang, Yu Mei

Publications

With the purpose of improving the PDP (policy decision point) evaluation performance, a novel and efficient evaluation engine, namely XDNNEngine, based on neural networks and an SGDK-means (stochastic gradient descent K-means) algorithm is proposed. We divide a policy set into different clusters, distinguish different rules based on their own features and label them for the training of neural networks by using the K-means algorithm and an asynchronous SGDK-means algorithm. Then, we utilize neural networks to search for the applicable rule. A quantitative neural network is introduced to reduce a server’s computational cost. By simulating the arrival of requests, XDNNEngine is …

Zero-Bias Deep Neural Network For Quickest Rf Signal Surveillance, Yongxin Liu, Jian Wang, Dahai Liu, Houbing Song, Yingjie Chen, Shuteng Niu

Publications

The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open nature also brings obstacles to surveillance. For alleviation, a surveillance oracle, or a cognitive communication entity needs to identify and confirm the appearance of known or unknown signal sources in real-time. In this paper, we provide a deep learning framework for RF signal surveillance. Specifically, we jointly integrate the Deep Neural Networks (DNNs) and Quickest Detection (QD) to form a sequential signal surveillance scheme. We first analyze the latent space characteristic …

Rf Fingerprinting Unmanned Aerial Vehicles, Norah Ondus

Doctoral Dissertations and Master's Theses

As unmanned aerial vehicles (UAVs) continue to become more readily available, their use in civil, military, and commercial applications is growing significantly. From aerial surveillance to search-and-rescue to package delivery the use cases of UAVs are accelerating. This accelerating popularity gives rise to numerous attack possibilities for example impersonation attacks in drone-based delivery, in a UAV swarm, etc. In order to ensure drone security, in this project we propose an authentication system based on RF fingerprinting. Specifically, we extract and use the device-specific hardware impairments embedded in the transmitted RF signal to separate the identity of each UAV. To achieve …

Federated Variational Learning For Anomaly Detection In Multivariate Time Series, Kai Zhang, Houbing Song, Yushan Jiang, Lee Seversky, Chengtao M. Xu, Dahai Liu

Publications

Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework …

Learning To Detect: A Data-Driven Approach For Network Intrusion Detection, Zachary Tauscher, Yushan Jiang, Kai Zhang, Jian Wang, Houbing Song

Publications

With massive data being generated daily and the ever-increasing interconnectivity of the world’s Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of …

Multimedia Networks And Communications, Houbing Song

Publications

Sight and sound is a type of correspondence that joins distinctive substance structures like content, sound, pictures, liveliness, or video into a solitary show, rather than customary broad communications, like written word or sound chronicles. Famous instances of interactive media incorporate video web recordings, sound slideshows and animated videos. Multimedia can be recorded for playback on PCs, workstations, cell phones, and other electronic gadgets, either on request or progressively (streaming). In the early long stretches of sight and sound, the expression "rich media" was inseparable from intuitive mixed media. Over the long run. Improved degrees of intuitiveness are made conceivable …

Learning-To-Dispatch: Reinforcement Learning Based Flight Planning Under Emergency, Kai Zhang, Yupeng Yang, Chengtao Xu, Dahai Liu, Houbing Song

Publications

The effectiveness of resource allocation under emergencies especially hurricane disasters is crucial. However, most researchers focus on emergency resource allocation in a ground transportation system. In this paper, we propose Learning-to- Dispatch (L2D), a reinforcement learning (RL) based air route dispatching system, that aims to add additional flights for hurricane evacuation while minimizing the airspace’s complexity and air traffic controller’s workload. Given a bipartite graph with weights that are learned from the historical flight data using RL in consideration of short- and long-term gains, we formulate the flight dispatch as an online maximum weight matching problem. Different from the conventional …

Real-Time Machine Learning For Quickest Detection, Yongxin Liu

Doctoral Dissertations and Master's Theses

Safety-critical Cyber-Physical Systems (CPS) require real-time machine learning for control and decision making. One promising solution is to use deep learning to discover useful patterns for event detection from heterogeneous data. However, deep learning algorithms encounter challenges in CPS with assurability requirements: 1) Decision explainability, 2) Real-time and quickest event detection, and 3) Time-eficient incremental learning.

To address these obstacles, I developed a real-time Machine Learning Framework for Quickest Detection (MLQD). To be specific, I first propose the zero-bias neural network, which removes decision bias and preferabilities from regular neural networks and provides an interpretable decision process. Second, I discover …

Class-Incremental Learning For Wireless Device Identification In Iot, Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, Houbing Song

Publications

Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Noncryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not …

Thruster Communication For Subsurface Environments; Turning Waste Noise Into Useful Data, Stephen Cronin

Doctoral Dissertations and Master's Theses

Acoustic communication serves as one of the primary means of wirelessly communicating underwater. Whereas much of the developments in the field of wireless communication have focused on radio frequency technology, water highly absorbs radio waves rendering the link not feasible for most all subsurface operations. While acoustic links have enabled new capabilities for systems operating in this challenging environment, it has yet to reach the commodity availability of radio systems, meaning that an entire class of small, low-cost systems have been unable to make use of these links. The systems in question are primarily autonomous underwater vehicles (AUVs), as they …

A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami

Doctoral Dissertations and Master's Theses

Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable …

Communication Aware Uav Swarm Surveillance Based On Hierarchical Architecture, Chengtao Xu, Kai Zhang, Yushan Jiang, Shuteng Niu, Thomas Yang, Houbing Song

Publications

Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of …

Zero-Bias Deep Learning Enabled Quick And Reliable Abnormality Detection In Iot, Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, Houbing Song

Publications

Abnormality detection is essential to the performance of safety-critical and latency-constrained systems. However, as systems are becoming increasingly complicated with a large quantity of heterogeneous data, conventional statistical change point detection methods are becoming less effective and efficient. Although Deep Learning (DL) and Deep Neural Networks (DNNs) are increasingly employed to handle heterogeneous data, they still lack theoretic assurable performance and explainability. This paper integrates zero-bias DNN and Quickest Event Detection algorithms to provide a holistic framework for quick and reliable detection of both abnormalities and time-dependent abnormal events in Internet of Things (IoT).We first use the zero bias dense …

Data-Efficient Machine Learning With Focus On Transfer Learning, Shuteng Niu

Doctoral Dissertations and Master's Theses

Machine learning (ML) has attracted a significant amount of attention from the artificial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain discrepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) …

Differential Privacy For Industrial Internet Of Things: Opportunities, Applications And Challenges, Bin Jiang, Houbing Song, Jianqiang Li, Guanghui Yue

Publications

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential …

Testing And Validation Framework For Autonomous Aerial Vehicles, Mustafa I. Akbas

Journal of Aviation/Aerospace Education & Research

Autonomous aerial vehicles (AAV) have the potential to have market disruptions for various industries such as ground delivery and aerial transportation. Hence, the USAF has called for increased level of autonomy. There has been a significant progress in artificial intelligence engines, complex and non-deterministic system components, which are at the core of the autonomous aerial platforms. Traditional testing and validation methods fall short of satisfying the requirement of testing such complex systems. Therefore, to achieve highly or fully autonomous capabilities, a major leap forward in the validation is required. The key challenges are the localization of problems, development of object …

Scenario-Based Development And Verification Of Domain-Specific Languages, Bharvi Chhaya

Doctoral Dissertations and Master's Theses

The use of domain-specific languages (DSLs) has increased manifold for problem solving in specific domain areas as they allow for a wider variety of expressions within their domain. Modeling using DSLs has shown high increases in productivity after accounting for the time and cost expended in developing them, making them a suitable target for improvement in order to reap higher rewards. The currently used approach for domain modeling involves the creation of an ontology which is then used to describe the domain model. This ontology encapsulates all domain knowledge and can be cumbersome to create, requiring external sources of information …

Zero-Bias Deep Learning For Accurate Identification Of Internet Of Things (Iot) Devices, Yongxin Liu, Houbing Song, Thomas Yang, Jian Wang, Jianqiang Li, Shuteng Niu, Zhong Ming

Publications

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework …

Performance Testing Of Aero-Naut Camfolding Propellers, Or D. Dantsker, Robert W. Deters, Marco Caccamo, Michael S. Selig

Publications

The increase in popularity of unmanned aerial vehicles (UAVs) has been driven by their use in civilian, education, government, and military applications. However, limited on-board energy storage significantly limits flight time and ultimately usability. The propulsion system plays a critical part in the overall energy consumption of the UAV; therefore, it is necessary to determine the most optimal combination of possible propulsion system components for a given mission profile, i.e. propellers, motors, and electronic speed controllers (ESC). Hundreds of options are available for the different components with little performance specifications available for most of them. By examining a variety of …

Tech Comm Eagle Eye-Tracking Control System, Gabiella Lail, Robert Shaw, Hunter Smatla, Matthew Gary

Discovery Day - Prescott

Mobile eye-tracking systems provide usability research support as well as access to a wide range of robotics and technical communication research opportunities. Optical tracking systems are often prohibitively expensive and do not provide the mobility or flexibility needed for a variety of research application possibilities. Our team proposes building a simple mobile eye-tracking system to be used in-house at ERAU for heat-mapping, robotics, control systems, and various technical communication applications in a structured research environment. The mobile eyetracking system will become part of a larger research and hands-on technical communication usability lab and research center. The research team intends to …