All Michigan Tech researchers are invited to participate in the 2020 Research Development Day at Michigan Tech. The event will be held Thursday, Jan. 9. The content of the 2020 event is new and designed for both new and returning attendees.
Multiple sessions are planned for faculty at all career stages and from all disciplines. Research staff and post-docs from any discipline are also likely to find sessions of interest. We are excited to welcome Jose Fuentes as our keynote speaker.
Fuentes is an experienced faculty researcher at Penn State, with a significant track record of international work and broad research impact. As in previous years, we will end the day with research recognitions, celebrating accomplishments from across the university over the past year, followed by a networking social.
A condensed agenda is found on the reservation form. Your RSVP is requested by Jan. 3 to finalize meal counts and room arrangements. If your schedule does not permit you to attend the full day, the RSVP allows you to sign up for morning, lunch, and/or afternoon sessions.
The RSVP form should take only a minute or two to complete. A reminder and final agenda will be sent in the new year. Please contact email@example.com with any questions.
Nathir Rawashdeh, College of Computing Assistant Professor of Mechatronics, Electrical, and Robotics Engineering Technology, will present a talk this Friday, December 6, from 3:00 to 4:00 p.m., in Rekhi 214. Rawashdeh will present a review of recent advancements in Unmanned Ground Vehicle (UGV) applications, hardware, and software with a focus on vehicle localization and autonomous navigation. Refreshments will be served.
Abstract: Unmanned Ground Vehicles (UGV) are being applied in many scenarios including, indoors, outdoors, and even extraterrestrial. Advancements in hardware and software algorithms reduce their cost and enable the creation of complete UGV platforms designed for custom application development, as well as research into new sensors and algorithms.
Robert Pastel (Computer Science/ICC Center for Human Centered Computing), along with Gary Spikberg (MS Industrial Heritage and Archaeology) and Don Lafreniere (SS/GLRC), presented “A Semiautomated approach to Creating Record Linkages and High Resolution Geocoding Across Historical Datasets” at the annual meeting of the Social Science History Association, which took place November 21-24, 2019, in Chicago, IL.
The Social Science History Association is an interdisciplinary organization that publishes a journal, Social Science History, organizes an annual conference, supports graduate student travel to the conference, and awards book prizes. With scholars from history, economics, sociology, demography, anthropology, and other social sciences, the association brings together scholars in thematic networks where they can explore common questions.
The College of Computing (CC) will present a Friday Seminar Talk on November 15, at 3:00 p.m. in Rekhi 214. Featured this week is Weihua Zhou, assistant professor of Health Informatics and member of the ICC’s Center for Data Sciences. He will present his research titled: “Information retrieval and knowledge discovery from cardiovascular images to improve the treatment of heart failure.” Refreshments will be served.
Abstract: More than 5 million Americans live with heart failure, and the annual new incidence is about 670,000. Once diagnosed, around 50% of patients with heart failure will die within 5 years. Cardiac resynchronization therapy (CRT) is a standard treatment for heart failure. However, based on the current guidelines, 30-40% of patients who have CRT do not benefit from CRT. One of Zhou’s research projects is to improve CRT favorable response by information retrieval and knowledge discovery from clinical records and cardiovascular images. By applying statistical analysis, machine learning, and computer vision to his unique CRT patient database, Zhou has made a number of innovations to select appropriate patients and navigate the real-time surgery. His CRT software toolkit is being validated by 17 hospitals in a large prospective clinical trial.
Kuilin Zhang (CEE/MTTI), a member of the ICC Center for Cyber-Physical Systems (CPS), is the primary investigator on a project that has received a $567,230 contract with the Federal Railroad Administration. This project is entitled, “Developing Safe and Efficient Driving and Routing Strategies at Railroad Grade Crossings Based on Highway-Railway Connectivity.” Pasi Lautala (CEE) is the Co-PI on this potential two-year project.
Zhen Liu, associate professor of civil and environmental engineering and member of the ICC’s Center for Cyber-Physical Systems (CPS), is co-author of the article, “A multivariate freezing-thawing depth prediction model for spring load restriction,” which was published August 6, 2019, in the journal Cold Regions Science and Technology, which is published by Elsevier. Co-authors of the article are Ting Bio and John Bland.
Abstract: Road damages induced by heavily loaded truck traffic during the spring thaw are a major road distress in cold regions. To minimize these damages, Spring Load Restriction (SLR) is widely applied in the U.S., Canada, and other countries during the early thawing season by controlling the movement of freight-carrying trucks and heavy equipment travel until the thawing ends. Most SLR policies rely on the Freezing Depth (FD) and Thawing Depth (TD), especially the latter one. Therefore, accurate predictions of FD and TD are important to prevent both the extensive damage to the pavement due to the late placement or early removal of SLR and the economic loss of road users due to an unnecessarily long SLR period. Here, we propose a new multivariate model for predicting FD and TD in support of SLR decision-making. The model gives a curving surface of FD and TD in a 3-dimensional space, instead of 2-dimensional in traditional methods, by considering both the freezing and thawing indices in the entire freeze-thaw cycle. For model evaluations, yearly field data measured at five typical sites from 104 sites in Michigan were adopted. The evaluation results showed that the proposed model is accurate in predicting FD and TD for most sites. Compared to the previous TD predictions in the existing study, the TD predictions with the proposed model have been significantly improved. In addition, this study provides field data that have not been reported earlier in the literature and that can be used for validating other prediction models. The reported work is ready for practice for roadways in cold regions to support SLR decision-making.
Bo Chen, assistant professor of computer science and member of the Institute of Computing and Cybersystems Center for Cybersecurity, is the principal investigator on a project that has received a $249,918 research and development grant from the National Science Foundation. The project is entitled, “SaTC: CORE: Small: Collaborative: Hardware-Assisted Plausibly Deniable System for Mobile Devices.” This is a potential three-year project.
Abstract: Mobile computing devices typically use encryption to protect sensitive information. However, traditional encryption systems used in mobile devices cannot defend against an active attacker who can force the mobile device owner to disclose the key used for decrypting the sensitive information. This is particularly of concern to dissident users who are targets of nation states. An example of this would be a human rights worker collecting evidence of untoward activities in a region of oppression or conflict and storing the same in an encrypted form on the mobile device, and then being coerced to disclose the decryption key by an official. Plausibly Deniable Encryption (PDE) has been proposed to defend against such adversaries who can coerce users into revealing the encrypted sensitive content. However, existing techniques suffer from several problems when used in flash-memory-based mobile devices, such as weak deniability because of the way read/write/erase operations are handled at the operating systems level and at the flash translation layer, various types of side channel attacks, and computation and power limitations of mobile devices. This project investigates a unique opportunity to develop an efficient (low-overhead) and effective (high-deniability) hardware-assisted PDE scheme on mainstream mobile devices that is robust against a multi snapshot adversary. The project includes significant curriculum development activities and outreach activities to K-12 students.
This project fundamentally advances the mobile PDE systems by leveraging existing hardware features such as flash translation layer (FTL) firmware and TrustZone to achieve a high deniability with a low overhead. Specifically, this project develops a PDE system with capabilities to: 1) defend against snapshot attacks using raw flash memory on mobile devices; and 2) eliminate side-channel attacks that compromise deniability; 3) be scalable to deploy on mainstream mobile devices; and 4) efficiently provide usable functions like fast mode switching. This project also develops novel teaching material on PDE and cybersecurity for K-12 students and the Regional Cybersecurity Education Collaboration (RCEC), a new educational partnership on cybersecurity in Michigan.
Publications related to this research:
[DSN ’18] Bing Chang, Fengwei Zhang, Bo Chen, Yingjiu Li, Wen Tao Zhu, Yangguang Tian, Zhan Wang, and Albert Ching. MobiCeal: Towards Secure and Practical Plausibly Deniable Encryption on Mobile Devices. The 48th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN ’18), June 2018 (Acceptance rate: 28%)
[Cybersecurity ’18] Qionglu Zhang, Shijie Jia, Bing Chang, Bo Chen. Ensuring Data Confidentiality via Plausibly Deniable Encryption and Secure Deletion – A Survey. Cybersecurity (2018) 1: 1.
[ComSec ’18 ] Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen Tao Zhu, Yingjiu Li, and Zhan Wang. User-Friendly Deniable Storage for Mobile Devices. Elsevier Computers & Security, vol. 72, pp. 163-174, January 2018
[CCS ’17] Shijie Jia, Luning Xia, Bo Chen, and Peng Liu. DEFTL: Implementing Plausibly Deniable Encryption in Flash Translation Layer. 2017 ACM Conference on Computer and Communications Security (CCS ’17), Dallas, Texas, USA, Oct 30 – Nov 3, 2017 (Acceptance rate: 18%)
[ACSAC ’15] Bing Chang, Zhan Wang, Bo Chen, and Fengwei Zhang. MobiPluto: File System Friendly Deniable Storage for Mobile Devices. 2015 Annual Computer Security Applications Conference (ACSAC ’15), Los Angeles, California, USA, December 2015 (Acceptance rate: 24.4%)
[ISC ’14] Xingjie Yu, Bo Chen, Zhan Wang, Bing Chang, Wen Tao Zhu, and Jiwu Jing. MobiHydra: Pragmatic and Multi-Level Plausibly Deniable Encryption Storage for Mobile Devices. The 17th Information Security Conference (ISC ’14), Hong Kong, China, Oct. 2014
Soner Onder, professor of computer science, was recently awarded $246,329 for the first year of a four-year NSF grant for his project, “SHF: Medium: Collaborative Research: Statically Controlled Asynchronous Lane Execution (SCALE).” The project is in collaboration with Prof. David Whalley of Florida State University. Michigan Tech is the lead institution in the project, it is expected to total $1.2 million, with Michigan Tech receiving $600,000.
Abstract: Enabling better performing systems benefits applications that span those running on mobile devices to large data applications running on data centers. The efficiency of most applications is still primarily affected by single thread performance. Instruction-level parallelism (ILP) speeds up programs by executing instructions of the program in parallel, with ‘superscalar’ processors achieving maximum performance. At the same time, energy efficiency is a key criteria to keep in mind as such speedup happens, with these two being conflicting criteria in system design. This project develops a Statically Controlled Asynchronous Lane Execution (SCALE) approach that has the potential to meet or exceed the performance of a traditional superscalar processor while approaching the energy efficiency of a very long instruction word (VLIW) processor. As implied by its name, the SCALE approach has the ability to scale to different types and levels of parallelism. The toolset and designs developed in this project will be available as open-source and will also have an impact on both education and research. The SCALE architectural and compiler techniques will be included in undergraduate and graduate curricula.
The SCALE approach supports separate asynchronous execution lanes where dependencies between instructions in different lanes are statically identified by the compiler to provide inter-lane synchronization. Providing distinct lanes of instructions allows the compiler to generate code for different modes of execution to adapt to the type of parallelism that is available at each point within an application. These execution modes include explicit packaging of parallel instructions, parallel and pipelined execution of loop iterations, single program multiple data (SPMD) execution, and independent multi-threading.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.
Keith Vertanen, assistant professor of computer science (HCC), and Scott Kuhl (HCC), associate professor of computer science, are principal investigators of a recently funded three-year National Science Foundation grant for their project, “CHS: Small: Rich Surface Interaction for Augmented Environments.” The expected funding over three years is $499,552.00.
Abstract: Virtual Reality (VR) and Augmented Reality (AR) head-mounted displays are increasingly being used in different computing related activities such as data visualization, education, and training. Currently, VR and AR devices lack efficient and ergonomic ways to perform common desktop interactions such as pointing-and-clicking and entering text. The goal of this project is to transform flat, everyday surfaces into a rich interactive surface. For example, a desk or a wall could be transformed into a virtual keyboard. Flat surfaces afford not only haptic feedback, but also provide ergonomic advantages by providing a place to rest your arms. This project will develop a system where microphones are placed on surfaces to enable the sensing of when and where a tap has occurred. Further, the system aims to differentiate different types of touch interactions such as tapping with a fingernail, tapping with a finger pad, or making short swipe gestures.
This project will investigate different machine learning algorithms for producing a continuous coordinate for taps on a surface along with associated error bars. Using the confidence of sensed taps, the project will investigate ways to intelligently inform aspects of the user interface, e.g. guiding the autocorrection algorithm of a virtual keyboard decoder. Initially, the project will investigate sensing via an array of surface-mounted microphones and design “surface algorithms” to determine and compare the location accuracy of the finger taps on the virtual keyboard. These algorithms will experiment with different models including existing time-of-flight model, a new model based on Gaussian Process Regression, and a baseline of classification using support vector machines. For all models, the project will investigate the impact of the amount of training data from other users, and varying the amount of adaptation data from the target user. The project will compare surface microphones with approaches utilizing cameras and wrist-based inertial sensors. The project will generate human-factors results on the accuracy, user preference, and ergonomics of interacting midair versus on a rigid surface. By examining different sensors, input surfaces, and interface designs, the project will map the design space for future AR and VR interactive systems. The project will disseminate software and data allowing others to outfit tables or walls with microphones to enable rich interactive experiences.
Research conducted by Michigan Tech doctoral candidate James Bialas and faculty members Thomas Oommen (DataS/GMES/CEE) and Timothy Havens (DataS/CS) made news in the Michigan Ag Connection, August 7, 2019. The item is a re-posting of the Michigan Tech Unscripted article, “Found in Translation, which was posted on the Michigan Tech website July 12, 2019.