Also In This Section
  • Categories

  • Recent News

  • Tag: FY20

    Heterogeneous Multisensor Buried Target Detection Using Spatiotemporal Feature Learning

    Researchers:

    Timothy Havens, PI, William and Gloria Jackson Associate Professor of Computer Systems

    Timothy Schulz, Co-PI, University Professor, Electrical and Computer Engineering

    Sponsor: U.S. Army Research Office

    Amount of Support: $285,900 (for the first year out of a potential 3-year project totaling $983,124)

    Abstract: This project will investigate theory and algorithms for multisensor buried target detection that achieve high probability of detection and classification with low false-alarm-rate. The primary sensors of interest are multisensor FLGPR (i.e., FLGPR plus other sensor modalities, such as thermal video or LIDAR) and acoustic/seismic systems, although our methods will be applicable to other modalities as well.


    Advanced Signal Processing and Detection Algorithms for Handheld Explosive Hazard Detection

    Researchers:

    Joseph Burns, PI, Senior Research Scientist, Michigan Tech Research Institute (MTRI)

    Timothy Havens, Co-PI, William and Gloria Jackson Associate Professor of Computer Systems, and Director, Institute of Computing and Cybersystems

    Brian Thelen, Co-PI

    Mark Stuff, Co-PI

    Joel LeBlanc, Co-PI

    Adam Webb, Co-PI

    Sponsor: U.S. Army

    Amount of Support: $1,238,255

    Abstract: The project investigates theory and algorithms for multi sensor buried target detection that achieve high probability of detection and classification with low false-alarm rate. The primary sensors of interest are handheld GPR and electromagnetic induction sensors.


    Adaptive Memory Resource Management in a Data Center -A Transfer Learning Approach

    Digital illustration of Cloud computing devices

    Researcher: Steven Carr, PI

    Sponsor: National Science Foundation, CSR: Small: Collaborative Research

    Amount of Support: $112,000

    Duration of Support: 5 years

    Abstract: Cloud computing has become a dominant scalable computing platform for both online services and conventional data-intensive computing (examples include Amazon’s EC2, Microsoft’s Azure, IBM’s SmartCloud, etc.). Cloud computing data centers share computing resources among a large set of users, providing a cost effective means to allow users access to computational power and data storage not practical for an individual. A data center often has to over-commit its resources to meet Quality of Service contracts. The data center software needs to effectively manage its resources to meet the demands of users submitting a variety of applications, without any prior knowledge of these applications.

    This work is focused on the issue of management of memory resources in a data center. Recent progress in transfer learning methods inspires this work in the creation of dynamic models to predict the cache and memory requirements of an application. The project has four main tasks: (i) an investigation into how recent advancements in transfer learning can help solve data center resource management problems, (ii) development of a dynamic cache predictor using on-the-fly virtual machine measurements, (iii) creation of a dynamic memory predictor using runtime characteristics of a virtual machine, and (iv) development of a unified resource management scheme creating a set of heuristics that dynamically adjust cache and memory allocation to fulfill Quality of Service goals. In tasks (i)-(iii), transfer learning methods are employed and explored to facilitate the transfer of knowledge and models to new system environments and applications based on extensive training on existing systems and benchmark applications. The prediction models and management scheme will be evaluated on common benchmarks including SPEC WEB and CloudSuite 2.0. The results of this research will have broad impact on the design and implementation of cloud computing data centers. The results will help improve resource utilization, boost system throughput, and improve predication performance in a cloud computing virtualization system. Additionally, the methods designed and knowledge they impart will advance understanding in both systems research and machine learning.

    Link to additional info here.


    A Controls Approach to Improve How Society Interacts with Electricity

    Researchers:

    Laura Brown, PI, Associate Professor, Computer Science

    Wayne Weaver, Dave House Associate Professor, Mechanical Engineering-Engineering Mechanics

    Chee-Wooi Ten, Associate Professor, Electrical and Computer Engineering

    Sponsor: National Science Foundation: Collaborative Research: CRISP Type 2: Revolution through Evolution

    Amount of Support: $699,796

    Duration of Support: 4 years

    Abstract: This CRISP project addresses the challenges associated with the rapid evolution of the electricity grid to a highly distributed infrastructure. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels for delivering electricity to customers, to distribution microgrids, entities that actively manage local production, storage and use of electricity, with participation from individual customers. Distribution microgrids combine the advantages of the traditional electricity grid with the advantages of emerging distributed technologies, including the ability to produce and use power locally in the event of grid outages. The project will result in a unified model that incorporates key aspects of power generation and delivery, information flow, market design and human behavior. The model predictions can be used by policymakers to guide a transition to clean energy via distribution microgrids. The expectation is to enable at least 50% of electric power to come from renewable resources. This cannot be done with either the traditional grid, due to its limited capacity to accommodate intermittent renewable power sources, or with fully decentralized approaches, which would not be affordable for most utility customers.

    This project addresses many socio-technological gaps necessary to translate from research discovery to commercial applications. To date, there is no theoretical framework to ensure system stability as renewable energy routed through power electronics replaces traditional rotating machinery. To achieve an optimal mix of storage performance and information bandwidth and to design nonlinear controllers, we will use Hamiltonian Surface Shaping Power Flow Control theory. We will study methods to detect malicious tampering with information flows. The complex interaction of intermittent resources, human behavior and market structures will be modeled in an agent-based simulation. System inputs will be provided by utility and meteorological data, and by behavioral models that incorporate information obtained by surveys, interviews and metering data. Emergent system dynamics will be abstracted and studied using dynamical complex network theory, to explore stability limits as a function of human behavior and market design. Finally, the effect of enhanced controllability of distribution systems on the robustness of large energy-information-social networks will be analyzed using interdependent Markov-chain models. Graduate students involved in this program will be exposed to a unique combination of skills from engineering, data analysis and social sciences; such cross-disciplinary training will prepare them for leadership roles in the emerging energy economy of tomorrow.


    The ITSEED: Active Learning Laboratory Experiments for IT Security Education

    Researchers

    Xinli Wang, PI

    Guy C. Hembroff, Associate Professor, College of Computing

    Sponsor: National Science Foundation

    Amount of Support: $199,934

    Duration of Support: 4 years

    The goal of this research is to enhance the security component in undergraduate IT education to meet the strong demand for security professionals in IT fields.

    It has been widely admitted by researchers, educators and students that the benefits of hands-on lab experiments are threefold in IT security education:

    • They expose students to the real-world challenges of computer and network security.
    • They help students consolidate and gain in-depth understanding of the knowledge presented in class lectures.
    • These hands-on activities help students to be better prepared for their careers in industry.

    This project will develop a collection of instructional hands-on laboratories for undergraduate IT security education to achieve the following objectives:

    • To provide students with an active-learning environment by challenging them with real-world problems in the field of IT security.
    • To provide students with the opportunity to learn from experiences with advanced technologies and well developed tools which will make them better prepared for their careers in industry.
    • To help instructors prepare and deliver security courses more effectively and efficiently by making the lab experiments publicly accessible through the Internet.

    This project is a collaborative effort between Michigan Technological University and the University of Washington Tacoma.

    Visit the National Science Foundation page for this research.

    Publications and Presentations

    Teaching Offensive Security in a Virtual Environment. A Tutorial Presentation at the Seventeenth Annual CCSC Northwestern Regional Conference in Seattle, WA, USA, October 9-10, 2015 and published on the Journal of Computing Sciences in Colleges, October 2015.

    Hands-on Exercises for IT Security Education. A paper presented and published on the Proceedings of the 16th Annual Conference on Information Technology Education, in Chicago, IL, USA, September 30 – October 3, 2015.

    Certification with Multiple Signatures. A paper presented and published on the Proceedings of the 4th Annual ACM Conference on Research in Information Technology, in Chicago, IL, USA, September 30 – October 3, 2015.

    Domain Based Certification and Revocation. A paper presented and published on the Proceedings of the 2015 International Conference on Security and Management (SAM/15), in Las Vegas, NV, US, July 27 – 30, s015.

    ITSEED: hands-on labs for IT security education. A workshop at the ACM SIGCSE 2014 in Atlanta, GA, US, March 5-8, 2014.

    ITSEED: Development of Instructional Laboratories for IT Security Education. A presentation at the “2013 USENIX Summit for Education in System Administration”, Washington, D.C., US, November 5, 2013.

    Administrative Evaluation of Intrusion Detection System. A paper presented on the ACM SIGITE/RiIT 2013, in Orlando, Florida, US, October 10-12, 2013.


    Optimal Joint Spectrum Allocation and Scheduling for Cognitive Radio Networks

    Researcher: Xiaohua Xu, PI, Affiliate ICC Member

    Sponsor: National Science Foundation

    Amount of Support: $244,808

    Duration of Support: 2 Years

    Abstract: Cognitive Radio Network is considered as a promising paradigm for the future networks. To significantly improve spectrum utilization, we conduct optimal or near-optimal joint spectrum allocation and scheduling in cognitive radio networks. We address critical and practical challenges for spectrum allocation and scheduling in cognitive radio networks, in particular multi-hop cognitive radio networks, such as dynamic traffic demands and pattern, unpredictable primary user activity, wireless interference, and coexistence. We develop creative models and algorithms in the framework of restless multi-armed bandit where the problem for spectrum allocation and scheduling in cognitive radio networks is formulated as a partially observable Markov decision process. The proposed methodology is novel in that it intelligently combines the networked multi-armed bandit modeling, graph theory, and communication scheduling theories. The developed algorithms, models, and protocols significantly improve spectrum utilization in future wireless communication systems and advance the fundamental knowledge and understanding of cognitive radio networks. The proposed algorithms, protocols, and models enable future wireless systems to design, deploy, and operate much more efficiently than today’s systems, which will result in significant economical, societal, and public safety impacts

    Objectives: The objective of this project is to significantly improve spectrum utilization through conducting optimal or near-optimal joint spectrum allocation and scheduling in cognitive radio networks. The PIs address critical and practical challenges for spectrum allocation and scheduling in cognitive radio networks, in particular multi-hop cognitive radio networks, such as dynamic traffic demands and pattern, unpredictable primary user activity, wireless interference, and coexistence. A test-bed will be set up to extensively evaluate the designed algorithms and protocols.

    Broader Impacts: This project significantly improves the design, deployment, and operation of future wireless communication systems. The proposed algorithms, protocols, and models enable future wireless systems to share spectrum much more efficiently than today’s systems, which will result in significant economical, societal, and public safety impacts. In addition, the proposed research is integrated into education and training for both undergraduate and graduate students. This project also significantly broadens the participation of underrepresented minority groups, e.g., the Native Americans in South Dakota.

    Publications
    Wang, Lixin and Xu, Xiaohua. “Approximation Algorithms for Maximum Weight Independent Set of Links Under the SINR Model,” Ad-hoc \& sensor wireless networks, v.17, 2013, p. 293–311.

    Xu, Xiaohua and Li, Xiang-Yang and Song, Min. “Efficient aggregation scheduling in multihop wireless sensor networks with sinr constraints,” Mobile Computing, IEEE Transactions on, v.12, 2013, p. 2518–252.

    More details


    The Ontology of Inter-Vehicle Networking with Spatio-Temporal Correlation and Spectrum Cognition

    Researcher: Min Song, Professor, Electrical and Computer Engineering

    Sponsor: National Science Foundation: NeTS: Small: Collaborative Research

    Amount of Support: $221,797

    Duration of Support: 3 years

    This project investigates fundamental understanding and challenges of inter-vehicle networking, including theoretical foundation and constraints in practice that enable such networks to achieve their performance limits. This is a collaborative research project with Professor Wenye Wang at North Carolina State University.

    Summary: Vehicle networks have been playing an increasingly important role in promoting mobile applications, driving safety, network economy, and people’s daily life. It is predicted that there will be over 50 million self-driving cars on the road by 2035; the sheer number and density of vehicles have provided non-negligible resources for computing and communication in vehicular environments. In addition, vehicular communications are also driven by the demands and enforcement of intelligent transportation system (ITS) and standardization activities on DSRC and IEEE 802.11p/WAVE. Many applications, either time-sensitive or delay-tolerant have been proposed and explored, such as cooperative traffic monitoring and control, and recently extended for blind crossing, prevention of collision, real-time detour routes computation, and many others as defined by Car2Car Communication Consortium (C2CCC). Along with the popularity of smart mobile devices, there is also an explosion of mobile applications in various categories, including terrestrial navigation, mobile games, and social networking, through Apple’s App store, Google Play, and Windows phonestore etc. Each aforementioned application seemingly is well-suited for either vehicle-to-vehicle (V2V) ad hoc networks or vehicle-to-infrastructure (V2I) communications. Therefore, vehicular networks have been playing an increasingly important role in promoting mobile applications, driving safety, network economy, and people’s daily life.

    In this project, a systematic investigation of vehicular networking properties, which is so called ontology of inter-vehicle communications, will be carried out to acquire in-depth scientific understanding and engineering guidelines that are critical to achieving theoretical performance limits and desirable services. This research includes four key innovative contributions: (i) the discovery of inter-vehicle networks composition by using spectrum cognition in finite and large-scale of V2V and V2I networks, (ii) the space and time domains correlations of vehicles on the move, and development of a set of dissemination strategies using a new constrained mobility model, (iii) detection and identification algorithms to achieve fast neighbor discovery using reinforcement learning, and case-based reasoning scheme; and (iv) theoretical limits of the coverage of messages by following the message trajectory in vehicle networks and schemes to achieve the maximum message coverage in both V2V and V2I networks. The results will advance the knowledge of opportunistic communications and facilitate engineering practice for much-needed applications in vehicular environments.

    Intellectual Merit: The intellectual merit of the project centers on the development of theoretical and practical foundations for services using inter-vehicle networks. The project starts from the formation of such opportunistic networks, and then moves on to the coverage of messages, with respect to Euclidean distance and time to stop. Given that an inter-network is in present, the project further studies how resilient such a network under network dynamics, including vehicular movements, message dissemination, and routing schemes. The broader impacts of the proposed research are timely yet long-term, from fully realistic setting of channel modeling, to much-needed applications in vehicular environments, and to transforming performance analysis and protocol design for distributed, dynamic, and mobile systems. Therefore, the proposed research outcome will advance knowledge and understanding not only in the field of vehicular networks, but also mobile ad-hoc networks, cognitive radio networks, wireless sensor networks, and future 5G networks.

    More details


    Developing Hands-on Cybersecurity Curriculum with Real-world Case Analysis

    Researcher: Yu Cai, Associate Professor, College of Computing

    Sponsor: National Security Agency

    Amount of Support: $149,184

    Duration of Support: 1 year

    Abstract: Recent high-profile cyber breaches indicate that cyber attacks are becoming more common, sophisticated and damaging. People with cybersecurity skills are in great demand as the threat environment increasingly becomes more complex and challenging. The need to have well-trained and well-prepared cybersecurity workforce is a pressing issue. The goal of this project is to develop a hands-on cybersecurity curriculum with real-world case analysis. The proposed curriculum includes six cybersecurity related courses: 1. Cyber Ethics; 2. Cyber Security I; 3. Scripting for Automation and Security; 4. Wireless System Administration; 5. Cyber Security II; 6. Digital Forensics. This curriculum is designed for CS and IT students who are interested in cybersecurity.


    Under-Ice Mobile Networking: Exploratory Study of Network Cognition and Mobility Control

    Researchers:

    Min Song, Professor, Electrical and Computer Engineering

    Zhaohui Wang, Assistant Professor, Electrical and Computer Engineering

    Sponsor: National Science Foundation: EAGER: NeTS

    Amount of Support: $299,716

    Duration of Support: 3 years

    Abstract: Autonomous underwater vehicles (AUVs) with acoustic communication capabilities are the platform of choice for under-ice exploration. Different from commonly studied open-water environment, the sound speed in the under-ice environment exhibits an increasing trend with water depth, which renders sound propagation shadowing and multiple reflections by the ice cover. Such acoustic environment characteristics have to be judiciously accounted in under-ice acoustic communication systems, which otherwise could lead to severe communication disconnection as observed in field experiments. This project focuses on an under-ice AUV network that migrates as a swarm for water sampling in an unknown ice-covered region, and develops algorithms for AUVs to learn the under-ice acoustic environment and adapt AUV mobility to the characteristics of the acoustic environment and the water sample field to achieve optimal under-ice mission performance while maintaining desired acoustic connectivity. This project will expand the frontier of under-ice exploration by autonomous vehicles. Given the vital role of ice-covered regions in many underpinning factors of modern society, such as economic growth and scientific research, this project will yield significant socio-economic impacts. In addition, the project will support two Ph.D. dissertations, and involve junior researchers in both algorithm development and field experiments.

    This project will innovate over two interrelated domains: under-ice acoustic environment and network cognition, and adaptive AUV mobility control. Specifically, a recursive algorithm will be developed to estimate the environment parameters pertaining to acoustic propagation, as well as the network state (including AUV positions and velocities), leveraging the acoustic measurements obtained during packet transmissions within the AUV network. The estimated parameters will characterize under-ice acoustic field for AUV mobility control. Moreover, an adaptive algorithm will be designed to adjust the mobility of AUVs to the acoustic field and the water sample field, with a goal of minimizing the sample field estimation error while ensuring desired acoustic connectivity among the AUVs. The developed algorithms will be evaluated via simulations and offline experiment data processing. Within an about 10-month ice-cover period of local lakes in this project, extensive under-ice experiments will be conducted under a wide range of geometric and environment conditions. This project will develop and showcase fundamental and crosscutting techniques for under-ice AUV mobile networking, underlying the synergy of environment cognition, statistical signal processing, and wireless mobile networking.

    Publications:

    W. Sun, and Z.-H. Wang. “Modeling and Prediction of Large-Scale Temporal Variation in Underwater Acoustic Channels,” Proc. of MTS/IEEE OCEANS Conference, 2016.

    W. Sun, C. Wang, Z.-H. Wang, and M. Song. “Experimental Comparison Between Under-Ice and Open-Water Acoustic Channels,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2015.

    Z.-H. Wang, C. Wang, and W. Sun. “Adaptive Transmission Scheduling in Time-Varying Underwater Acoustic Channels,” Proc. of MTS/IEEE OCEANS Conference, 2015.

    C. Wang, and Z.-H. Wang. “Signal Alignment for Secure Underwater Coordinated Multipoint Transmissions,” IEEE Transactions on Signal Processing, 2016.

    X. Kuai, S. Zhou, Z.-H. Wang. And E. Cheng. “Receiver design for spread-spectrum communications with a small spread in underwater clustered multipath channels,” Journal of Acoustical Society of America, 2017.

    C. Wang, and Z.-H. Wang. “Signal Alignment for Secure Underwater Coordinated Multipoint Transmissions,” IEEE Transactions on Signal Processing, 2016.

    L. Wei, Y. Tang, Y. Cao, Z.-H. Wang, and M. Gerla. “A Simulation Platform for Software-Defined Underwater Wireless Networks,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2017.

    W. Sun, and Z.-H. Wang. “Modeling and Prediction of Large-Scale Temporal Variation in Underwater Acoustic Channels,” Proc. of the MTS/IEEE OCEANS Conference, 2016.

    W. Sun, C. Wang, Z.-H. Wang, and M. Song. “Experimental Comparison Between Under-Ice and Open-Water Acoustic Channels,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2015.

    W. Sun, C. Wang, Z.-H. Wang, and M. Song. “Estimation of the Under-Ice Acoustic Field in AUV Communication Networks,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2017.

    More info


    Understanding and Mitigating Triboelectric Artifacts in Wearable Electronics by Synergic Approaches

    Researchers:

    Ye Sun, Assistant Professor, Mechanical Engineering—Engineering Mechanics

    Shiyan Hu, Adjunct Professor, Electrical and Computer Engineering

    Sponsor: National Science Foundation

    Amount of Support: $330,504

    Duration of Support: 3 years

    Abstract: Electrophysiological measurement is a well-accepted tool and standard for health monitoring and well-being management. A great number of electrophysiological measurement devices have been developed including clinical equipment, research products, and consumer electronics. However, until now, it is still challenging to secure long-term stable and accurate signal acquisition, especially in wearable condition, not only for medical application in hospital settings, but also for daily well-being management. Motion-induced artifacts widely exist in electrophysiological recording regardless of electrodes (wet, dry, or noncontact). These artifacts are one of the major impediments against the acceptance of wearable devices and capacitive electrodes in clinical diagnosis. This project is to provide new strategies to mitigate motion-induced artifacts in wearable electronics and design accurate wearable electronics for daily monitoring and disease diagnosis. The PIs will disseminate the research products to both students and the research community. New course materials will be developed for undergraduate and graduate education. Undergraduate and graduate students involved in the research program will obtain diverse knowledge in hardware design and data analytics. For K-12 students, the PIs will provide an integrated research and educational experience through the programs of Engineering Exploration Day for Girls and the Summer Youth Program at Michigan Technological University. A research demo and hands-on experience for triboelectric generation in textile materials will be developed and provided to K-12 students.

    The research goal of this proposal is to understand the fundamental mechanism of triboelectric artifacts in wearable devices and provide synergistic solutions to mitigating the artifacts. Three approaches are proposed to achieve the goal: 1) understanding the mechanism of triboelectric charge generation in wearable condition by physical modeling and experimental validation; 2) guided by the understanding, developing tribomaterial-based sensors to manipulate triboelectric charges for artifact removal; 3) leveraging the proposed new tribomaterial-based sensors and statistical data analytics for true electrophysiological signal estimation. If successful, the synergic knowledge produced by the project will not only help improve the traditional bioinstrumentation in the medical society, but also benefit industrial community of consumer wearable electronics.

    Publications:
    Li, Xian and Sun, Ye. “WearETE: A Scalable Wearable E-Textile Triboelectric Energy Harvesting System for Human Motion Scavenging,” Sensors, v.17, 2017. doi:10.3390/s17112649

    Huang, Hui and Hu, Shiyan and Sun, Ye. “Energy-efficient ECG compression in wearable body sensor network by leveraging empirical mode decomposition,” 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018. doi:10.1109/bhi.2018.8333391

    More info