Computer Engineering Commons^™

Digital Platform To Aid Youth Substance Abuse Prevention, Bingxuan Li

Discovery Undergraduate Interdisciplinary Research Internship

Through research and interviews, I discovered that a significant portion of students in Africa become drug addicts and drop out of school. The solution is to prevent youth substance abuse before it happens, so that more students in Africa may continue their education. With the strong motivation of expanding African student involvement in higher education, I participated DURI program to increase higher education rates in the Democratic Republic of the Congo, Africa. The local government is establishing rehabilitation centers to monitor at-risk students and prevent youth substance abuse, but due to extremely limited resources, it is critical to evaluate the …

Development Of Directed Randomization For Discussing A Minimal Security Architecture, Henrique Oyama, Dominic Messina, Keshav Kasturi Rangan, Akkarakaran Francis Leonard, Kip Nieman, Helen Durand, Katie Tyrrell, Katrina Hinzman, Michael Williamson

Chemical Engineering and Materials Science Faculty Research Publications

Strategies for mitigating the impacts of cyberattacks on control systems using a control-oriented perspective have become of greater interest in recent years. Our group has contributed to this trend by developing several methods for detecting cyberattacks on process sensors, actuators, or both sensors and actuators simultaneously using an advanced optimization-based control strategy known as Lyapunov-based economic model predictive control (LEMPC). However, each technique comes with benefits and limitations, both with respect to one another and with respect to traditional information technology and computer science-type approaches to cybersecurity. An important question to ask, therefore, is what the goal should be of …

A Robust Platform For Mobile Robotics Teaching And Developing Using Arduino’S Integrated Development Environment (Ide) For Programming The Arduino Mega 2560, Sajjad Alhassan

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

In light of the rapid pace at which development happens with modern technology, mobile robots play an important role in our daily lives. This is due to their great importance in facilitating the affairs of life in various economic, commercial, industrial, scientific, and many other fields. In this research and project, we have restructured the microcontroller and system for one of the mobile robots (CEENBOT) that was designed by the University of Nebraska and replaced it with an Arduino Mega 2560.

The purpose of using the Arduino Mega 2560 robot is to provide alternative programming for the CEENBOT platform to …

Effective Short Text Classification Via The Fusion Of Hybrid Features For Iot Social Data, Xiong Luo, Zhijian Yu, Zhigang Zhao, Wenbing Zhao, Jenq-Haur Wang

Electrical and Computer Engineering Faculty Publications

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive …

Low Power Multi-Channel Interface For Charge Based Tactile Sensors, Samuel Hansen

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

Analog front end electronics are designed in 65 nm CMOS technology to process charge pulses arriving from a tactile sensor array. This is accomplished through the use of charge sensitive amplifiers and discrete time filters with tunable clock signals located in each of the analog front ends. Sensors were emulated using Gaussian pulses during simulation. The digital side of the system uses SAR (successive approximation register) ADCs for sampling of the processed sensor signals.

Adviser: Sina Balkır

A Stacking-Based Misbehavior Detection System In Vehicular Communication Networks, Troy Green

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

Over the past few decades communication systems for vehicles have continued to advance. Communications between these vehicles can be classified into safety related and non safety related messages. An example of a safety related message would be one vehicle warning others of an icy road it encountered, where a non safety related communication would be a passenger streaming a movie. In either case it's important to secure the communications so that the system continues to behave as expected. In this thesis we propose a Misbehavior Detection System (MDS), which is a system that monitors messages sent between vehicles, and detects …

A Low-Power, Low-Area 10-Bit Sar Adc With Length-Based Capacitive Dac, Zhili Pan

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

A 2.5 V single-ended 10-bit successive-approximation-register analog-to-digital converter (SAR ADC) based on the TSMC 65 nm CMOS process is designed with the goal of achieving low power consumption (33.63 pJ/sample) and small area (2874 µm^2 ). It utilizes a novel length-based capacitive digital-to-analog converter (CDAC) layout to achieve low total capacitance for power efficiency, and a custom static asynchronous logic to free the dependence on a high-frequency external clock source. Two test chips have been designed and the problems found through testing the first chip are analyzed. Multiple improved versions of the ADC with minor variations are implemented on the …

An Overview Of Bidirectional Electric Vehicles Charging System As A Vehicle To Anything (V2x) Under Cyber–Physical Power System (Cpps), Onur Elma, Umit Cali, Murat Kuzlu

Engineering Technology Faculty Publications

Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much …

A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on …

Computer Engineering Education, Marilyn Wolf

CSE Conference and Workshop Papers

Computer engineering is a rapidly evolving discipline. How should we teach it to our students?

This virtual roundtable on computer engineering education was conducted in summer 2022 over a combination of email and virtual meetings. The panel considered what topics are of importance to the computer engineering curriculum, what distinguishes computer engineering from related disciplines, and how computer engineering concepts should be taught.

Energy Efficient Routing Technique In Underwater Wireless Sensor Networks, Yashwanth N Dr.

Technical Collection

Wireless sensor networks can improve the efficiency for collecting information in underwater environments. In the past, researchers collected data under the sea by the way of both sending oceanographic equipments into water and recovering it at the end of mission. The whole process often took long time so real-time services cannot be supported. Wireless sensor networks can detect events and transmit the collected data to a data center immediately. However, current terrestrial routing protocols are not suitable in the underwater sensor networks. Due to unique characteristics in the water, the transmission performance and energy of the routing protocols are inefficient. …

A Smart Parallel Gripper Industrial Automation System For Measurement Of Gripped Work Piece Thickness, Erik Kocher, Chukwuemeka George Ochieze, Ahmat Oumar, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh

Michigan Tech Publications

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project is performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and ladder programming of the smart parallel gripper system to measure the width of components grasped with the gripper. In addition, details of the system’s components, operation, more advanced uses are discussed. On the automation line, this smart gripper can be used to measure the thickness of work pieces while handling them and classifying these as either acceptable, too large …

Gesture Controlled Collaborative Robot Arm And Lab Kit, Abel A. Reyes, Skylar Reinhardt, Tony Wise, Nathir Rawashdeh, Sidike Paheding

Michigan Tech Publications

In this paper, a mechatronics system was designed and implemented to include the subjects of artificial intelligence, control algorithms, robot servo motor control, and human-machine interface (HMI). The goal was to create an inexpensive, multi-functional robotics lab kit to promote students’ interest in STEM fields including computing and mechtronics. Industrial robotic systems have become vastly popular in manufacturing and other industries, and the demand for individuals with related skills is rapidly increasing. Robots can complete jobs that are dangerous, dull, or dirty for humans to perform. Recently, more and more collaborative robotic systems have been developed and implemented in the …

Mechatronics Bachelor Curriculum Development In Light Of Industry 4.0 Technology Needs: Contrasting Us And German University Curricula, Paniz Hazaveh, Aleksandr Sergeyev, Nathir Rawashdeh

Michigan Tech Publications

This study compares Mechatronics bachelor curricula at universities in the United States of America and German universities. Mechatronics education is relatively new in the United States, but has been common in Germany for over a decade. With the multidisciplinary nature of technologies required by the 4’th industrial revolution, a.k.a. Industry 4.0, composing an appropriate Mechatronics curriculum becomes a challenge and an opportunity. This paper studies how Mechatronics education can address the future needs of industry, while building on a specific university’s strengths and industry links. We have also analyzed the new undergraduate Mechatronics program at Michigan Technological University (MTU) and …

Event-Triggered Optimal Adaptive Control Of Partially Unknown Linear Continuous-Time Systems With State Delay, Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan

Publications

This paper proposes an event-triggered optimal adaptive output feedback control design approach by utilizing integral reinforcement learning (IRL) for linear time-invariant systems with state delay and uncertain internal dynamics. In the proposed approach, the general optimal control problem is formulated into the game-theoretic framework by treating the event-triggering threshold and the optimal control policy as players. A cost function is defined and a value functional, which includes the delayed system output, is considered. First, by using the value functional and applying stationarity conditions using the Hamiltonian function, the output game delay algebraic Riccati equation (OGDARE) and optimal control policy are …

Operation Of A Controllable Force-Sensing Industrial Pneumatic Parallel Gripper System, Brian Piechocki, Chelsey Spitzner, Namratha Karanam, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh

Michigan Tech Publications

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project was performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and application of a force-programmable and sensing pneumatic parallel gripper system. Force sensing is a critical part of many systems in modern automation systems. Applications such as prosthetics, robotic surgery, or basic manufacturing systems may rely on the ability to properly read and control forces applied to an object. This work evaluates the basic operation of the pneumatic force-sensing gripper …

An Industrial Pneumatic And Servo Four-Axis Robotic Gripper System: Description And Unitronics Ladder Logic Programming, Zongguang Liu, Chrispin Johnston, Aleksi Leino, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh

Michigan Tech Publications

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project is performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and ladder programming of a four-axis robot enclosed in a cage with one side guarded by an optical fence. The robot has pneumatically actuated X-Y linear motion and a pneumatic gripper. Furthermore, the Z-axis motion and gripper wrist rotation are controlled by servo motors. A human machine interface (HMI) is also present, and it allows for easy manipulation and programming …

Tutorial: Knowledge-Infused Learning For Autonomous Driving (Kl4ad), Ruwan Wickramarachchi, Cory Henson, Sebastian Monka, Daria Stepanova, Amit Sheth

Publications

Autonomous Driving (AD) is considered as a testbed for tackling many hard AI problems. Despite the recent advancements in the field, AD is still far from achieving full autonomy due to core technical problems inherent in AD. The emerging field of neuro-symbolic AI and the methods for knowledge-infused learning are showing exciting ways of leveraging external knowledge within machine/deep learning solutions, with the potential benefits for interpretability, explainability, robustness, and transferability. In this tutorial, we will examine the use of knowledge-infused learning for three core state-of-the-art technical achievements within the AD domain. With a collaborative team from both academia and …

Virtual Sensor Middleware: Managing Iot Data For The Fog-Cloud Platform, Fadi Almahamid, Hanan Lutfiyya, Katarina Grolinger

Electrical and Computer Engineering Publications

This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent an IoT application and distributed sensor data processing across multiple fog nodes. Furthermore, the virtual sensor deals with the heterogeneous nature of IoT devices and the various communication protocols using different adapters to communicate with the IoT devices and the underlying protocol. VSM uses the publish-subscribe design pattern …

Updated Perspectives On The Role Of Biomechanics In Copd: Considerations For The Clinician, Jennifer M. Yentes, Wai-Yan Liu, Kuan Zhang, Eric J. Markvicka, Stephen I. Rennard

Department of Electrical and Computer Engineering: Faculty Publications

Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline …

Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Metaversekg: Knowledge Graph For Engineering And Design Application In Industrial Metaverse, Utkarshani Jaimini, Tongtao Zhang, Georgia Olympia Brikis

Publications

While the term Metaverse was first coined by the author Neal Stephenson in 1992 in his science fiction novel “Snow Crash”, today the vision of an integrated virtual world is becoming a reality across different sectors. Applications in gaming and consumer products are gaining traction, industrial metaverse applications are, still in their early stages of development with one of the challenges being interoperability across various metaverse development platforms and existing software tools. In this work we propose the use of a knowledge graph based semantic data exchange layer, the Metaverse Knowledge Graph, to enable seamless transfer of information across platforms. …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation And Classification Utilizing Small Datasets, Amr Yousef, Jeff Flora, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients …

Sers Spectroscopy With Machine Learning To Analyze Human Plasma Derived Sevs For Coronary Artery Disease Diagnosis And Prognosis, Xi Huang, Bo Liu, Shenghan Guo, Weihong Guo, Ke Liao, Guoku Hu, Wen Shi, Mitchell Kuss, Michael J. Duryee, Daniel R. Anderson, Yongfeng Lu, Bin Duan

Department of Electrical and Computer Engineering: Faculty Publications

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then …

Sers Spectroscopy With Machine Learning To Analyze Human Plasma Derived Sevs For Coronary Artery Disease Diagnosis And Prognosis, Xi Huang, Bo Liu, Shenghan Guo, Weihong Guo, Ke Liao, Guoku Hu, Wen Shi, Mitchell Kuss, Michael J. Duryee, Daniel R. Anderson, Yongfeng Lu, Bin Duan

Department of Electrical and Computer Engineering: Faculty Publications

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then …

Towards Efficient Scoring Of Student-Generated Long-Form Analogies In Stem, Thilini Wijesiriwardene, Ruwan Wickramarachchi, Valerie L. Shalin, Amit P. Sheth

Publications

Switching from an analogy pedagogy based on comprehension to analogy pedagogy based on production raises an impractical manual analogy scoring problem. Conventional symbol-matching approaches to computational analogy evaluation focus on positive cases, and challenge computational feasibility. This work presents the Discriminative Analogy Features (DAF) pipeline to identify the discriminative features of strong and weak long-form text analogies. We introduce four feature categories (semantic, syntactic, sentiment, and statistical) used with supervised vector-based learning methods to discriminate between strong and weak analogies. Using a modestly sized vector of engineered features with SVM attains a 0.67 macro F1 score. While a semantic feature …

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.

Parasol: Efficient Parallel Synthesis Of Large Model Spaces, Clay Stevens, Hamid Bagheri

CSE Conference and Workshop Papers

Formal analysis is an invaluable tool for software engineers, yet state-of-the-art formal analysis techniques suffer from well-known limitations in terms of scalability. In particular, some software design domains—such as tradeoff analysis and security analysis—require systematic exploration of potentially huge model spaces, which further exacerbates the problem. Despite this present and urgent challenge, few techniques exist to support the systematic exploration of large model spaces. This paper introduces Parasol, an approach and accompanying tool suite, to improve the scalability of large-scale formal model space exploration. Parasol presents a novel parallel model space synthesis approach, backed with unsupervised learning to automatically derive …