Institute of Computing and Cybersystems (ICC) Blog

Category: Publications

The Lode, Still Going Strong, After Nearly 100 Years

Published
By

by Michigan Tech Lode

What is the Lode, anyway?

The Lode is Michigan Tech’s student newspaper, and we’ve been serving the MTU community since 1921, when we were founded as the Michigan College of Mines Lode.

We currently serve the campus digitally on our website and in print, though COVID-19 safety precautions have momentarily postponed our in-print issues.

Read the Lode!

We publish weekly on Thursday mornings. We feature local, state and national news, arts, cultural events and other happenings around campus, relevant opinion pieces, features on STEM and campus research, sports and more.

Check us out at http://www.mtulode.com.

Continue Reading →

Innovative, Active, Effective. Introducing Sidike Paheding, Applied Computing

Published
By

Be Innovative. Be Active. Be Effective. This is College of Computing Assistant Professor Sidike Paheding’s teaching philosophy.

New to the Department of Applied Computing this fall, Paheding’s teaching interests include digital image processing and machine learning. This academic year he is teaching SAT3812 Cyber Security I.

A member of the Institute of Computing and Cybersystems’s Center for Data Sciences, Paheding’s research seeks to develop novel AI-driven technologies. His primary interests are image/video processing, machine learning, deep learning, computer vision, and remote sensing.

Paheding comes to Michigan Tech from Purdue University Northwest, where he was a visiting assistant professor in the ECE department. Prior to that, he was a postdoctoral research associate and assistant research professor in the Remote Sensing Lab at Saint Louis University from 2017 to 2019.

Paheding is an associate editor of the journals, Signal Image and Video Processing (Springer) and Photogrammetric Engineering and Remote Sensing (ASPRS), and topic editor for Remote Sensing. He completed his Ph.D. in electrical engineering at University of Dayton, Ohio.

Computing is a part of my life.

Sidike Paheding, Assistant Professor, Applied Computing

Active Research

Title: Cybersecurity Modules Aligned with Undergraduate Computer Science and Engineering Curricula
Sponsor: NSF
PI at Michigan Tech
Duration: July 2020 – June 2022
Total Award: $159,417.00

Research Abstract

This project aims to serve the national interest by improving how cybersecurity concepts are taught in undergraduate computing curricula. The need to design and maintain cyber-secure computing systems is increasingly important. As a result, the future technology workforce must be trained to have a security mindset, so that they consider cybersecurity during rather than after system design.

This project aims to achieve this goal by building plug-and-play, hands-on cybersecurity modules for core courses in Computer Engineering, and Computer Science and Engineering. The modules will align with the curricula recommended by the Association for Computing Machinery and will be designed for easy adoption into computing programs nationwide. Modules will be designed for integration into both introductory and advanced courses, thus helping students develop in-depth understanding of cybersecurity as they progress through their computing curriculum. It is expected that the project will encourage more students to pursue careers or higher degrees in the field of cybersecurity.

Recent Publications

Sidike, P., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., … & Fritschi, F. B. (2019). dPEN: deep Progressively Expanded Neural Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery. Remote Sensing of Environment, 221, 756-772. [Impact Factor: 9.085]

Sidike, P., Asari, V. K., & Sagan, V. (2018). Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: A new neural network paradigm for remote sensing image analysis. ISPRS journal of photogrammetry and remote sensing, 146, 161-181. [Impact Factor: 7.319]

Sidike, P., Asari, V. K., & Alam, M. S. (2015). Multiclass object detection with single query in hyperspectral imagery using class-associative spectral fringe-adjusted joint transform correlation. IEEE Transactions on Geoscience and Remote Sensing, 54(2), 1196-1208. [Impact Factor: 5.855]

Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., & Fritschi, F. B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599. [Impact Factor: 9.085]

Continue Reading →

Chee-Wooi Ten Negotiates Two Book Contracts with CRC Press

Published
By

By Karen S. Johnson, Communications Director, Institute of Computing and Cybersystems

Associate Professor Chee-Wooi Ten, Electrical and Computer Engineering, recently finalized contracts to write two books for CRC Press, a major publisher of humanities, social science, and STEM books and textbooks. Ten is a member of the Institute of Computing and Cybersystems’s Center for Cyber-Physical Systems.

The first book is titled, Electric Power Distribution System Engineering, 4th edition. Ten has been teaching EE5250 Distribution Engineering I at Michigan Tech for 10 years.

The second book, Modern Power System Analysis, 3rd Edition, is used to accompany a senior-level power engineering elective. Both books are tentatively scheduled to be published in January 2022.

The new editions continue the work of the late Professor Turan Gönen, a leading expert and popular professor of electrical engineering at California State University, Sacramento. Gönen devoted his life to the writing of four textbooks. One of them, “Electric Power Distribution System Engineering,” published in 2013, is still taught in college classrooms worldwide. Ten notes that it is one of only a few Distribution Engineering textbooks that remains highly regarded by the international research community.

Book contract negotiations were initiated by Nora Konopka, editorial director of engineering at CRC Press/Taylor & Francis. Konopka worked with Ten on a previous book published by the company.

And although Ten did not personally know Prof. Gönen, he has used Gönen’s books in his courses. Ten says he believes Konopka contacted him because she has confidence that he will do an excellent job in carrying on Gönen ‘s work and legacy.

“As a course instructor, especially when you’ve just started, you explore the textbook and master the materials while teaching,” Ten reflects. “Written and revised throughout his long career, the contents of Gönen’s books are enriched from his decades of experience in pedagogy.”

Konopka’s original proposal was for Ten to write four new editions of books by Prof. Gönen. Ten told her, “I cannot do four books, but I can find two other authors who have the expertise to complete those books.”

So, with collaborators at University of Hong Kong and Virginia Tech, all four books will be completed and published. Two of them written by Ten, one each by his collaborators.

“My colleagues on this project are research-active faculty, and I am very proud to have an opportunity to collaborate with them,” Ten says, noting that they represent two of the best engineering programs in the world.

“These books are collaborative, and we will work together to ensure the next editions of these textbooks reflect today’s industrial and academic knowledge and best practices,” Ten says.

But there are challenges associated with this kind of project. Ten explains that the book materials he has inherited, which are in Microsoft Word, must be converted to the typesetting format he prefers, LaTeX. Only then can he begin editing the books. Fortunately, Ten was able to hire a few students; he expects them to complete the conversions by year-end.

“Then, for the next year, I can focus on qualitative development of the content,” Ten predicts. “I plan to ‘test drive’ some of the new content in the power engineering courses I have been teaching.”

Read an obituary of Prof. Turan Gönen here.

CRC Press. is an imprint of Taylor & Francis Group, part of Informa PLC, one of the world’s leading business intelligence and academic publishing businesses. The company publishes more than 2,700 journals and 5,000 new books each year. CRC Press specializes in Science, Technology and Medical books.

Founded in 2015, the Institute of Computing and Cybersystems (ICC) promotes collaborative, cross-disciplinary research and learning experiences in the areas of computing education, cyber-physical systems, cybersecurity, data sciences, human-centered computing, and scalable architectures and systems, for the benefit of Michigan Technological University and society at large.

The ICC creates and supports an arena in which faculty and students work collaboratively across organizational boundaries in an environment that mirrors contemporary technological innovation. The ICC’s 55+ members working in six research centers represent more than 20 academic disciplines at Michigan Tech. Member scientists are collaborating to conduct impactful research, make valuable contributions in the field of computing, and solve problems of critical national importance.

Full Citations

Turan Gönen, Chee-Wooi Ten**, and Ali Mehrizi-Sani, “Electric Power Distribution System Engineering,” 4th Edition CRC, January 2022 (tentatively).

Turan Gönen, Chee-Wooi Ten**, and Yunhe Hou, “Modern Power System Analysis,” 3rd Edition, CRC, January 2022 (tentatively).

Continue Reading →

Paper by Yakov Nekrich Accepted for ACM-SIAM SODA21 Symposium

Published
By

A paper by Associate Professor Yakov Nekrich, Computer Science, has been accepted for the 61st ACM-SIAM Symposium on Discrete Algorithms 2021 (SODA21), which will take place virtually January 10-13, 2021.

Nekrich is sole author of the accepted article, “New Data Structures for Orthogonal Range Reporting and Range Minima Queries.” An extended version of the paper is available for download on ArXiv.

The annual ACM-SIAM Symposium on Discrete Algorithms (SODA) is an academic conference in the fields of algorithm design and discrete mathematics. It is considered among the top conferences for research in algorithms.

Paper Abstract

In this paper we present new data structures for two extensively studied variants of the orthogonal range searching problem.
First, we describe a data structure that supports two-dimensional orthogonal range minima queries in O(n) space and O(logεn) time, where n is the number of points in the data structure and ε is an arbitrarily small positive constant. Previously known linear-space solutions for this problem require O(log1+εn) (Chazelle, 1988) or O(lognloglogn) time (Farzan et al., 2012). A modification of our data structure uses space O(nloglogn) and supports range minima queries in time O(loglogn). Both results can be extended to support three-dimensional five-sided reporting queries.

Next, we turn to the four-dimensional orthogonal range reporting problem and present a data structure that answers queries in optimal O(logn/loglogn+k) time, where k is the number of points in the answer. This is the first data structure that achieves the optimal query time for this problem. Our results are obtained by exploiting the properties of three-dimensional shallow cuttings.

The Society for Industrial and Applied Mathematics (SIAM) is an international community of 14,500+ individual members. Almost 500 academic, manufacturing, research and development, service and consulting organizations, government, and military organizations worldwide are institutional members.

Continue Reading →

What Lies Ahead: Cooperative, Data-Driven Automated Driving

Published
By
Kuilin Zhang

Associate Professor Kuilin Zhang, Civil and Environmental Engineering and affiliated associate professor, Computer Science, was featured in a recent article on Michigan Tech News. The article appears below. Link to the original article here.

By Kelley Christensen, September 28, 2020.

Networked data-driven vehicles can adapt to road hazards at longer range, increasing safety and preventing slowdowns.

Vehicle manufacturers offer smart features such as lane and braking assist to aid drivers in hazardous situations when human reflexes may not be fast enough. But most options only provide immediate benefits to a single vehicle. What if entire groups of vehicles could respond? What if instead of responding solely to the vehicle immediately in front of us, our cars reacted proactively to events happening hundreds of meters ahead?

What if, like a murmuration of starlings, our cars and trucks moved cooperatively on the road in response to each vehicle’s environmental sensors, reacting as a group to lessen traffic jams and protect the humans inside?

This question forms the basis of Kuilin Zhang’s National Science Foundation CAREER Award research. Zhang, an associate professor of civil and environmental engineering at Michigan Technological University, has published “A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions” in the journal Transportation Research Part B: Methodological.

The paper is coauthored with Shuaidong Zhao ’19, now a senior quantitative analyst at National Grid, where he continues to conduct research on the interdependency between smart grid and electric vehicle transportation systems.

Vehicle Platoons Operate in Sync

Creating vehicle systems adept at avoiding traffic accidents is an exercise in proving Newton’s First Law: An object in motion remains so unless acted on by an external force. Without much warning of what’s ahead, car accidents are more likely because drivers don’t have enough time to react. So what stops the car? A collision with another car or obstacle — causing injuries, damage and in the worst case, fatalities.

But cars communicating vehicle-to-vehicle can calculate possible obstacles in the road at increasing distances — and their synchronous reactions can prevent traffic jams and car accidents.

“On the freeway, one bad decision propagates other bad decisions. If we can consider what’s happening 300 meters in front of us, it can really improve road safety. It reduces congestion and accidents.”Kuilin Zhang

Zhang’s research asks how vehicles connect to other vehicles, how those vehicles make decisions together based on data from the driving environment and how to integrate disparate observations into a network.

Zhang and Zhao created a data-driven, optimization-based control model for a “platoon” of automated vehicles driving cooperatively under uncertain traffic conditions. Their model, based on the concept of forecasting the forecasts of others, uses streaming data from the modeled vehicles to predict the driving states (accelerating, decelerating or stopped) of preceding platoon vehicles. The predictions are integrated into real-time, machine-learning controllers that provide onboard sensed data. For these automated vehicles, data from controllers across the platoon become resources for cooperative decision-making. 

CAREER Award 

Kuilin Zhang won an NSF CAREER Award in 2019 for research on connected, autonomous vehicles and predictive modeling. 

Proving-Grounds Ready

The next phase of Zhang’s CAREER Award-supported research is to test the model’s simulations using actual connected, autonomous vehicles. Among the locations well-suited to this kind of testing is Michigan Tech’s Keweenaw Research Center, a proving ground for autonomous vehicles, with expertise in unpredictable environments.

Ground truthing the model will enable data-driven, predictive controllers to consider all kinds of hazards vehicles might encounter while driving and create a safer, more certain future for everyone sharing the road.

Tomorrow Needs Mobility

Michigan Technological University is a public research university, home to more than 7,000 students from 54 countries. Founded in 1885, the University offers more than 120 undergraduate and graduate degree programs in science and technology, engineering, forestry, business and economics, health professions, humanities, mathematics, and social sciences. Our campus in Michigan’s Upper Peninsula overlooks the Keweenaw Waterway and is just a few miles from Lake Superior.

Kuilin Zhang

About the Researcher: Kuilin Zhang

  • Data-driven optimization and control models for connected and automated vehicles (CAVs)
  • Big traffic data analytics using machine learning
  • Mobile and crowd sensing of dynamic traffic systems
  • Dynamic network equilibrium and optimization
  • Modeling and simulation of large-scale complex systems
  • Freight logistics and supply chain systems
  • Impact of plug-in electric vehicles to smart grid and transportation network systems
  • Interdependency and resiliency of large-scale networked infrastructure systems
  • Vehicular Ad-hoc Networks (VANETs)
  • Smart Cities
  • Cyber-Physical Systems
Continue Reading →

Bo Chen’s Research on COVID-19 Prevention Method to be Published in IEEE IoT Magazine

Published
By

A paper authored by Michigan Tech Assistant Professor Bo Chen, Computer Science, and Data Science master’s student Shashank Reddy Danda, has been accepted for publication in the IEEE Internet of Things Magazine special issue on Smart IoT Solutions for Combating COVID-19 Pandemic. The special issue will be published in September 2020.

The paper focuses on Chen’s research of COVID-19 prevention through the leveraging of computing technology. The project is currently supported by a Michigan Tech College of Computing seed grant, and external funding for further development is being pursued.

Chen is a member of the ICC’s Center for Cybersecurity.

Download a preprint of the paper here.

Abstract:
Recently, the impact of coronavirus has been witnessed by almost every country around the world. To mitigate spreading of coronavirus, a fundamental strategy would be reducing the chance of healthy people from being exposed to it. Having observed the fact that most viruses come from coughing/sneezing/runny nose of infected people, in this work we propose to detect such symptom events via mobile devices (e.g., smartphones, smart watches, and other IoT devices) possessed by most people in modern world and, to instantly broadcast locations where the symptoms have been observed to other people. This would be able to significantly reduce risk that healthy people get exposed to the viruses. The mobile devices today are usually equipped with various sensors including microphone, accelerometer, and GPS, as well as network connection (4G, LTE, Wi-Fi), which makes our proposal feasible. Further experimental evaluation shows that coronavirus-like symptoms (coughing/sneezing/runny nose) can be detected with an accuracy around 90%; in addition, the dry cough (more likely happening to COVID-19 patients) and wet cough can also be differentiated with a high accuracy.

Bo Chen is an assistant professor in the Department of Computer Science. His areas of expertise include mobile device security, cloud computing security, named data networking security, big data security, and blockchain.

Shashank Reddy Danda is an MS student in Data Science. He is currently working as a research assistant in MTU Security and Privacy (SnP) Lab under the supervision of Dr. Bo Chen.

IEEE Internet of Things Magazine (IEEE IoTM) is a publication of the IEEE Internet of Things Initiative, a Multi-Society Technical Group.

Continue Reading →

Tim Havens, Tony Pinar Co-Authors of Article in IEEE Trans. Fuzzy Systems

Published
By

An article by Anthony Pinar (DataS/ECE) and Timothy Havens (DataS/CC), in collaboration with University of Missouri researchers Muhammad Islam, Derek Anderson, Grant Scott, and Jim Keller, all of University of Missouri, has been published in the July 2020 issue of the journal IEEE Transactions on Fuzzy Systems.

The article is titled, “Enabling explainable fusion in deep learning with fuzzy integral neural networks.” Link to the article here.

Tony Pinar

Tim Havens

Abstract:
Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multilayer network, referred to hereafter as ChIMP.

We also put forth an improved ChIMP (iChIMP) that leads to a stochastic-gradient-descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables explainable artificial intelligence (XAI). Synthetic validation experiments are provided, and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy, and our previously established XAI indices shed light on the quality of our data, model, and its decisions.

Citation
M. Islam, D. T. Anderson, A. J. Pinar, T. C. Havens, G. Scott and J. M. Keller, “Enabling Explainable Fusion in Deep Learning With Fuzzy Integral Neural Networks,” in IEEE Transactions on Fuzzy Systems, vol. 28, no. 7, pp. 1291-1300, July 2020, doi: 10.1109/TFUZZ.2019.2917124.

Continue Reading →

ICC Releases FY19 Annual Report

Published
By

The Institute of Computing and Cybersystems has released its FY 19 Annual Report, which can be viewed and downloaded on the ICC website.

We had a strong year in 2018-19,” says Timothy Havens, director of the ICC and associate dean for research, College of Computing.

“In FY20, new awards and research expenditures were even stronger, and I look forward to sharing more accomplishments with you in the coming months.”

Tim Havens, ICC Director
Continue Reading →

Nathir Rawashdeh Publishes Paper in BioSciences Journal

Published
By

A paper co-authored by Assistant Professor Nathir Rawashdeh (DataS, Applied Computing) on Skin Cancer Image Feature Extraction, has been published this month in the EurAsian Journal of BioSciences.

View the open access article, “Visual feature extraction from dermoscopic colour images for classification of melanocytic skin lesions,” here.

Additional authors are Walid Al-Zyoud, Athar Abu Helou, and Eslam AlQasem, all with the Department of Biomedical Engineering, German Jordanian University, Amman, Jordan.

Citation: Al-Zyoud, Walid et al. “Visual feature extraction from dermoscopic colour images for classification of melanocytic skin lesions”. Eurasian Journal of Biosciences, vol. 14, no. 1, 2020, pp. 1299-1307.

Rawashdeh’s interests include unmanned ground vehicles, electromobility, robotics, image analysis, and color science. He is a senior member of the IEEE.

Continue Reading →

Sergeyev, Students Earn ASEE Conference Awards

Published
By

Professor Aleksandr Segeyev (DataS), Applied Computing, and a group of Michigan Tech students presented two papers at the 2020 American Society for Engineering Education (ASEE) Gulf-Southwest Annual conference, which was conducted online April 23-24, 2020. Both papers received conference awards.

Faculty Paper Award

“Pioneering Approach for Offering the Convergence MS Degree in Mechatronics and Associate Graduate Certificate”
by Sergeyev, Professor and Associate Chair John Irwin (MMET), and Dean Adrienne Minerick (CC).

Student Paper Award

“Efficient Way of Converting outdated Allen Bradley PLC-5 System into Modern ControlLogix 5000 suit”, by Spencer Thompson (pictured), Larry Stambeck, Andy Posa, Sergeyev, and Lecturer Paniz Hazaveh, Applied Computing.

Sergeyev is director of the Michigan Tech Mechatronics Graduate Program and FANUC Certified Industrial Robotics Training Center.

Founded in 1893, the American Society for Engineering Education is a nonprofit organization of individuals and institutions committed to furthering education in engineering and engineering technology.

Continue Reading →