Research by Centers

PI: Ye Sun, CPS, ME-EM
Sponsor: National Science Foundation
Award: $499,986 | 5 Years
Awarded: May 2018
Abstract: This Faculty Early Career Development Program (CAREER) award will contribute to the advancement of national prosperity and economic welfare by researching systems that improve access to manufacturing services. Wearable electronics are widely used in health monitoring and wearable computing and there is a compelling need for comfort, biocompatibility, and easy operation. Recent progress in smart fabrics, textiles, and garments and the associated manufacturing technologies provides opportunities for next-generation wearable electronic devices that are fabricated on cloth. Automatic embroidery manufacturing is now an accessible tool for individuals and entrepreneurs. Embroidery offers great potential for electronic design due to its flexibility in transferring a desired pattern to fabric substrates. This project aims to establish a cloud manufacturing framework that integrates electronics and design-to-manufacturing translation in a system that can be used by customers, manufacturers, design experts, and developers to design and produce embroidered wearable electronics. In addition, this project also aims to broaden participation from K-12, undergraduate, and graduate students, to provide rich multidisciplinary classroom and non-classroom experiences for all levels of students, and to inspire student interest in STEM careers.

The research goal of this CAREER project is to establish a cloud-based manufacturing framework for embroidered wearable electronics as an accessible platform technology towards System-on-Cloth. Three tasks are planned to achieve the goal: 1) to understand physical variations of conductive threads and flexible electronics by physical modeling and experimental testing for embroidered electronics; 2) to create Electronic Design Automation (EDA) software that will realize design-to-manufacturing translation from schematics by integrating new Electrical Rule Checking (ERC) and Design Rule Checking (DRC) criteria, optimal stitch generation, and an open library of wearable electronics; and 3) to explore a cloud manufacturing framework with open architecture, open source code, and friendly user-interfaces that is accessible to customers, manufacturers, software developers, as well as electronic design experts. The cloud manufacturing framework will incorporate EDA features, high tractability, reliability, and compatibility for use by a diverse spectrum of individuals and entrepreneurs.

Publication:
Li, Xian and Sun, Ye. "An SSHI Rectifier for Triboelectric Energy Harvesting," IEEE Transactions on Power Electronics, 2019.

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PI: Chee-Wooi Ten, CPS, ECE
Co-PI: Yeonwoo Rho, Math
Sponsor: National Science Foundation
Award: $348,866 | 3 Years
Awarded: August 2017
Abstract: As evidenced by the recent cyberattacks against Ukrainian power grids, attack strategies have advanced and new malware agents will continue to emerge. The current measures to audit the critical cyber assets of the electric power infrastructure do not provide a quantitative guidance that can be used to address security protection improvement. Investing in cybersecurity protection is often limited to compliance enforcement based on reliability standards. Auditors and investors must understand the implications of hypothetical worst case scenarios due to cyberattacks and how they could affect the power grids. This project aims to establish an actuarial framework for strategizing technological improvements of countermeasures against emerging cyberattacks on wide-area power networks. By establishing an actuarial framework to evaluate and manage cyber risks, this project will promote a self-sustaining ecosystem for the energy infrastructure, which will eventually help to improve overall social welfare. The advances in cyber insurance will stimulate actuarial research in handling extreme cyber events. In addition, the research and practice related to cybersecurity and cyber insurance for the critical energy infrastructure will be promoted by educating the next generation of the workforce and disseminating the research results.

The objective of this project is to develop an actuarial framework of risk management for power grid cybersecurity. It involves transformative research on using insurance as a cyber risk management instrument for contemporary power grids. The generation of comprehensive vulnerabilities and reliability-based knowledge from extracted security logs and cyber-induced reliability degradation analysis can enable the establishment of risk portfolios for electric utilities to improve their preparedness in protecting the power infrastructure against cyber threats. The major thrusts of this project are: 1) developing an approach to quantifying cyber risks in power grids and determining how mitigation schemes could affect the cascading consequences to widespread instability; 2) studying comprehensively how hypothesized cyberattack scenarios would impact the grid reliability by performing a probabilistic cyber risk assessment; and 3) using the findings from the first two thrusts to construct actuarial models. Potential cyberattack-induced losses on electric utilities will be assessed, based on which insurance policies will be designed and the associated capital market will be explored.

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PI: Elena Semouchkina, CPS, ECE
Sponsor: National Science Foundation
Award: $337,217 | 3 Years
Awarded: August 2017
Non-Technical Description: Transformation optics (TO) is based on coordinate transformations, which require proper spatial dispersions of the media parameters. Such media force electromagnetic (EM) waves, moving in the original coordinate system, to behave as if they propagate in a transformed coordinate system. Thus TO introduces a new powerful technique for designing advanced EM devices with superior functionalities. Coordinate transformations can be derived for compressing, expanding, bending, or twisting space, enabling designs of invisibility cloaks, field concentrators, perfect lenses, beam shifters, etc., that may bring advances to various areas of human life. Realization of these devices depends on the possibility of creating media with prescribed EM properties, in particular, directional refractive indices to provide wave propagation with superluminal phase velocities and high refractive indices in the normal direction to cause wave movement along curvilinear paths. Originally, artificial metamaterials (MMs) composed of tiny metallic resonators were chosen for building transformation media. However, a number of serious challenges were encountered, such as extremely narrow frequency band of operation and the high losses in metal elements. The proposed approach is to use dielectric photonic crystals to overcome these major limitations of MM media. This project will allow graduate and undergraduate students, especially women in engineering, to participate in theoretical and experimental EM research. Outreach activities include lectures and hands-on projects in several youth programs to K-12 students.

Technical Description: This project will develop a platform for engineering photonic crystal (PhC)-based media that are free from the major limitations of metamaterial media. The project aims to control wave propagation in media along orthogonal crystallographic directions and relies upon self-collimation phenomena at formulating TO-based prescriptions for refractive indices. For realizing directional dispersions of both superluminal and ordinary indices along desired axes of crystals, proper variations of their lattice parameters will be used. Accurate control of index values will be provided by building the media from crystal fragments with optimized dimensions. Microwave experiments using a parallel-plate waveguide chamber will be performed to record wave propagation and to verify computational results. Technologies developed earlier for fabricating low-loss PhCs will help to implement the practical devices. This interdisciplinary research will integrate electromagnetics, physics, optics, and materials science concepts; employ full-wave computational modeling and design; engineer complex materials architectures; and master characterization techniques for complex structures. The project will open up perspectives for TO by developing new approaches for media engineering and by solving fundamental problems, including integration of self-collimation. This research will integrate electromagnetics, physics, optics, and materials science concepts and will advance the potential of PhCs.

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PI: Ye Sun, CPS, ME-EM
Co-PI: Shiyan Hu, ECE
Sponsor: National Science Foundation
Award: $330,504 | 3 Years
Awarded: June 2017
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.
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.
Huang, Hui and Hu, Shiyan and Sun, Ye. "Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition," ACM Transactions on Cyber-Physical Systems, v.3, 2019.
Huang, Hui and Li, Xian and Liu, Si and Hu, Shiyan and Sun, Ye. "TriboMotion: A Self-Powered Triboelectric Motion Sensor in Wearable Internet of Things for Human Activity Recognition and Energy Harvesting," IEEE Internet of Things Journal, v.5, 2018.
Li, Xian and Sun, Ye. "An SSHI Rectifier for Triboelectric Energy Harvesting," IEEE Transactions on Power Electronics, 2019.
Huang, Hui and Hu, Shiyan and Sun, Ye. "ECG Signal Compression for Low-power Sensor Nodes Using Sparse Frequency Spectrum Features," 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018.
Huang, Hui and Hu, Shiyan and Sun, Ye. "A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky Golay Filter for ECG Denoising," Sensors, v.19, 2019.

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PI: Bo Chen, CyberS, CS
Sponsor: National Science Foundation
Award: $249,918 | 3 Years
Awarded: September 2019
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:
[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.
[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

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PI: Bo Chen, CyberS, CS
Sponsor: National Science Foundation
Award: $199,975 | 2 Years
Awarded: July 2019
Abstract: Mainstream mobile computing devices like smart phones and tablets currently rely on remote backups for data recovery upon failures. For example, an iPhone periodically stores a recent snapshot to iCloud, and can get restored if needed. Such a commonly used “off-device” backup mechanism, however, suffers from a fundamental limitation that, the backup in the remote server is not always synchronized with data stored in the local device. Therefore, when a mobile device suffers from a malware attack, it can only be restored to a historical state using the remote backup, rather than the exact state right before the attack occurs. Data are extremely valuable for both organizations and individuals, and thus after the malware attack, it is of paramount importance to restore the data to the exact point (i.e., the corruption point) right before they are corrupted. This, however, is a challenging problem. The project addresses this problem in mobile devices and its outcome could benefit billions of mobile users.

A primary goal of the project is to enable recovery of mobile devices to the corruption point after malware attacks. The malware being considered is the OS-level malware which can compromise the OS and obtain the OS-level privilege. To achieve this goal, the project combines both the traditional off-device data recovery and a novel in-device data recovery. Especially, the following research activities are undertaken: 1) Designing a novel malware detector which runs in flash translation layer (FTL), a firmware layer staying between OS and flash memory hardware. The FTL-based malware detector ensures that data being committed to the remote server will not be tampered with by the OS-level malware. 2) Developing a novel approach which ensures that the OS-level malware is not able to corrupt data changes (i.e., delta) which have not yet been committed to the remote server. This is achieved by hiding the delta in the flash memory using flash storage’s special hardware features, i.e., out-of-place update and strong physical isolation. 3) Developing a user-friendly approach which can allow users to conveniently and efficiently retrieve the delta hidden in the flash memory for data recovery after malware attacks.

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PI: Yu Cai, CyberS, CMH
Co-PI: Tim A. Van Wagner, Guy C. Hembroff, Bo Chen, CyberS, CMH, CS
Sponsor: Department of Defense
Award: $87,895 | 1 Years
Awarded: May 2019

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PI: Yu Cai, CyberS, CMH
Co-PI: Tim A. Van Wagner, Guy C. Hembroff, Bo Chen, CyberS, CMH, CS
Sponsor: Department of Defense
Award: $82,416 | 1 Years
Awarded: May 2019

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PI: Guy C. Hembroff, CyberS, CMH
Co-PI: Richard J. Berkey, PHC, PHC
Sponsor: Munson Medical Center
Award: $17,500 | 1 Years
Awarded: November 2019 

PI: Jeremy P. Bos, DataS, ECE
Sponsor: Department of Defense
Award: $459,894 | 3 Years
Awarded: April 2017
Abstract: This project will explore the nature of imaging in conditions characterized by extreme anisoplanatism.  Under these conditions each point in an image may be affected by a locally unique blurring kernel implying a violation of the linear shift invariance. Bos and his students will use a combination analysis and extensive experimental data to develop new models and new understanding of this phenomenon. Bos has also proposed using angular diversity as a means of mitigating the effects of extreme anisoplanatism on imaging and beam control problems.

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PI: Timothy C. Havens, DataS, Computing
Sponsor: Signature Research Inc.
Award: $120,000 | 1.5 Years
Awarded: December 2019
Abstract: This project contributes to an effort to develop a methodology that predicts the impact to human vision due to the existence of atmospheric particles. Due to the variability of atmospheric conditions and particulate matter (dust, ice, etc.) extensive field test campaigns to characterize the impacts to human vision are impractical. As a result, a model-based approach must be developed in order to evaluate all possible conditions in a virtual environment. It is envisioned that this approach will incorporate both human in-the-loop evaluations as well as generation of machine learning algorithms to serve as an in-situ human observer.

Signature Research, Inc. provides solutions to DoD and the Intelligence Community, specializing in Signature Phenomenology, Analysis, and Modeling of items of military interest covering the breadth of the electromagnetic spectrum. Signature Research, Inc. engineers and scientists have developed methodologies, tools and products to help visualize and interpret electromagnetic signatures, and Signature Research, Inc. staff are recognized experts within the various communities in which they work. SGR’s corporate headquarters is located in Calumet, Michigan, with a second operating location in Navarre, Florida near Eglin Air Force Base and Hurlburt Field.

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PI: Jeremy P. Bos, DataS, ECE
Co-PI: Darrell L. Robinette, ME-EM
Sponsor: Department of Defense (FPT-University of Michigan)
Award: $100,000 | 1.5 Years
Awarded: May 2019
Abstract: Autonomous navigation by robots in challenging terrain and varying environmental conditions remains a difficult and open research problem. The goal of this project is a robust motion planning system for ground robots. Most autonomous robot systems are bespoke; hand-tuned and optimized for a specific platform and set of operating conditions. By robust, we mean to say that the system can be easily adapted to perform on a variety of platforms and over a range of conditions. Implicit here is that the autonomous navigation system must apply a certain level of self-supervised tuning of system parameters that react to changes in platform (i.e. loss of functionality) or environmental conditions (i.e. change in terrain due to weather).

The aim of this research project is two-fold:

1. The development of a robust motion planner capable of plotting a feasible trajectory through challenging non-planar terrain between arbitrary start and goal positions in a predefined map.
2. Assuming an autonomous robot system equipped with a camera and LiDAR system what is the effect on performance (either in terms of completion rate or overall path length) of a reduction in sensor resolution. Accordingly, understanding the minimum resolution required to complete a task (in this case traversing a known trajectory) we also better understand the necessary level of redundancy.

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PI: Timothy C. Havens, DataS, Computing
Co-PI: Andrew Barnard, GLRC, ME-EM
Sponsor: Department of Defense
Award: $96,643 | 1 Years
Awarded: February 2019

PI: Benjamin Ong, DataS, Math
Sponsor: National Science Foundation
Award: $36,636 | 1 Years
Awarded: October 2019
Abstract: Computational simulations are a key part of scientific research for government, industry, and academia, complementing laboratory experimentation and theory.  However changes in computer architectures are leading to future supercomputers that will have billions of processors, as opposed to millions today. Further, each individual processor will be no faster than individual processors today.  Thus, these next generation machines will no longer automatically provide a speedup to existing computational simulations, and new mathematical algorithms must be developed and deployed that can utilize this unprecedented number of processors. One such class of mathematical algorithms, parallel-in-time methods, is the subject of this workshop.  In particular, parallel-in-time methods add a new dimension (time) of parallelism and thus allow existing computer models to be extended to next generation supercomputers. The range of potential applications for parallel-in-time to dramatically speed-up is vast, e.g., computational molecular dynamics (e.g., protein and DNA folding), computational biology (e.g., heart modeling), computational fluid dynamics (e.g., combustion, climate, and weather), and machine learning.

The primary focus of the proposed parallel-in-time workshop is to educate and inspire researchers and students in new and innovative numerical techniques for the parallel-in-time solution of large-scale evolution problems on modern supercomputing architectures, and to stimulate further studies in their analysis and applications. This workshop aligns with the National Strategic Computing Initiative (NSCI) objective: “increase coherence between technology for modeling/simulation and data analytics”.  The conference will feature ten lectures by Professor Gander, an expert in parallel time integration. Using appropriate mathematical methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretizations, integration techniques, and convergence analyses of these iterative methods, conference participants will be exposed to the numerical analysis of parallel-in-time methodologies and their implementations. The proposed topics include multiple shooting type methods, waveform relaxation methods, time-multigrid methods, and direct time-parallel methods. These lectures will be accessible to a wide audience from a broad range of disciplines, including mathematics, computer science and engineering.

Learn more about the CBMS Conference.

PI: Benjamin Ong, DataS, Math
Sponsor: National Science Foundation
Award: $25,185 | 1 Years
Awarded: August 2019
Abstract: Computer models and simulations play a central role in the study of complex systems in engineering, life sciences, medicine, chemistry, and physics. Utilizing modern supercomputers to run models and simulations allows for experimentation in virtual laboratories, thus saving both time and resources. Although the next generation of supercomputers will contain an unprecedented number of processors, this will not automatically increase the speed of running simulations. New mathematical algorithms are needed that can fully harness the processing potential of these new systems. Parallel-in-time methods, the subject of this workshop, are timely and necessary, as they extend existing computer models to these next generation machines by adding a new dimension of scalability. Thus, the use of parallel-in-time methods will provide dramatically faster simulations in many important areas, such as biomedical applications (e.g., heart modeling), computational fluid dynamics (e.g., aerodynamics and weather prediction), and machine learning. Computational and applied mathematics plays a foundational role in this projected advancement.

The primary focus of the proposed parallel-in-time workshop is to disseminate cutting-edge research and facilitate scientific discussions on the field of parallel time integration methods. This workshop aligns with the National Strategic Computing Initiative (NSCI) objective: “increase coherence between technology for modeling/simulation and data analytics”. The need for parallel time integration is being driven by microprocessor trends, where future speedups for computational simulations will come through using increasing numbers of cores and not through faster clock speeds. Thus as spatial parallelism techniques saturate, parallelization in the time direction offers the best avenue for leveraging next generation supercomputers with billions of processors. Regarding the mathematical treatment of parallel time integrators, one must use advanced methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretization and integration, convergence analyses of iterative methods, and the development and implementation of new parallel algorithms. Thus, the workshop will bring together an interdisciplinary group of experts spanning these areas.

Learn more about the 9th Workshop[ on Parallel-in-Time Integration.

PI: Weihua Zhou, DataS, CMH
Sponsor: Department of Health and Human Services (FPT-Tulane University)
Award: $24,497 | 1 Years
Awarded: November 2019

PI: Keith D. Vertanen, HCC, CS
Co-PI: Scott A. Kuhl, HCC, CS
Sponsor: National Science Foundation
Award: $499,552 | 3 Years
Awarded: August 2019
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.

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PI: Kevin M. Trewartha, HCC, CLS
Co-PI: Shane T. Mueller, HCC, CLS
Sponsor: Department of Health and Human Services
Award: $455,884 | 3 Years
Awarded: June 2018
Abstract: Alzheimer's  disease  is  a  debilitating  condition  that  has  no  cure  and  is  the  sixth  leading  cause  of  death  in  the  United  States.  It  is  critical  for  research  to  identify  alternative  behavioral  markers  that  can  help  with  early diagnosis so that physicians can maximize the benefits of available treatment options that slow the  progression  of  the  disease.  In  the  proposed  research  we  aim  to  identify  subtle  behavioral  changes  in  the  form  of  impairments  in  motor  skill  learning  that  could  improve  the  reliability  of  existing  methods  for  distinguishing healthy aging from early stages of Alzheimer?s disease.    

PI: Shane T. Mueller, HCC, CLS
Sponsor: Department of Defense (FPT-Florida Institute for Human and Machine Cognition)
Award: $354,823 | 4 Years
Awarded: June 2017
Abstract: Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine’s current inability to explain their decisions and actions to human users (Figure 1). The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI—especially explainable machine learning—will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.

The Explainable AI (XAI) program aims to create a suite of machine learning techniques that 1) produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and  2) Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

Learn more about DARPA XAI.

PI: Keith D. Vertanen, HCC, CS
Sponsor: National Science Foundation
Award: $225,663 | 3 Years
Awarded: August 2019
Abstract: Smartphones are an essential part of our everyday lives. But for people with visual impairments, basic tasks like composing text messages or browsing the web can be prohibitively slow and difficult. The goal of this project is to develop accessible text entry methods that will enable people with visual impairments to enter text at rates comparable to sighted people. This project will design new algorithms and feedback methods for today’s standard text entry approaches of tapping on individual keys, gesturing across keys, or dictating via speech. The project aims to:  1) help users avoid errors by enabling more accurate input via audio and tactile feedback, 2) help users find errors by providing audio and visual annotation of uncertain portions of the text, and 3) help users correct errors by combining the probabilistic information from the original input, the correction, and approximate information about an error’s location. Improving text entry methods for people who are blind or have low vision will enable them to use their mobile devices more effectively for work and leisure. Thus, this project represents an important step to achieving equity for people with visual impairments.

This project will contribute novel interface designs to the accessibility and human-computer interaction literature. It will advance the state-of-the-art in mobile device accessibility by: 1) studying text entry accessibility for low vision in addition to blind people, 2) studying and developing accessible gesture typing input methods, and 3) studying and developing accessible speech input methods.  This project will produce design guidelines, feedback methods, input techniques, recognition algorithms, user study results, and software prototypes that will guide improvements to research and commercial input systems for users who are blind or low-vision. Further, the project’s work on the error correction and revision process will improve the usability and performance of touchscreen and speech input methods for everyone.

PI: Keith D. Vertanen, HCC, CS
Sponsor: National Science Foundation
Award: $194,541 | 5 Years
Awarded: March 2018
Abstract: Face-to-face conversation is an important way in which people communicate with each other, but unfortunately there are millions who suffer from disorders that impede normal conversation. This project will explore new real-time communication solutions for people who face speaking challenges, including those with physical or cognitive disabilities, for example by exploiting implicit and explicit contextual input obtained from a person's conversation partner. The goal is to develop technology that improves upon the Augmentative and Alternative Communication (AAC) devices currently available to help people speak faster and more fluidly. The project will expand the resources for research into conversational interactive systems, the deliverables to include a probabilistic text entry toolkit, AAC user interfaces, and an augmented reality conversation assistant. Project outcomes will include flexible, robust, and data-driven methods that extend to new use scenarios. To enhance its broader impact, the project will educate the public about AAC via outreach events and by the online community the work will create. The PI will assemble teams of undergraduates to develop the project's software, and he will host a summer youth program on the technology behind text messaging, offering scholarships for women, students with disabilities, and students from underrepresented groups. Funded first-year research opportunities will further help retain undergraduates, particularly women, in computing.

This project will explore the design space of conversational interactive systems, by investigating both systems that improve communication for non-speaking individuals who use AAC devices and systems that enhance communication for speaking individuals who face other conversation-related challenges. Context-sensitive prediction algorithms that use: 1) speech recognition on the conversation partner's turns; 2) the identity of the partner as determined by speaker identification; 3) dialogue state information; and 4) suggestions made by a partner on a mobile device will be considered. User studies will investigate the effectiveness and user acceptance of partner-based predictions. New methodologies will be created for evaluating context-sensitive AAC interfaces. The impact of training AAC language models on data from existing corpora, from simulated AAC users, and from actual AAC users will be compared. This research will expand our knowledge about how to leverage conversational context in augmented reality, and it will curate a public test set contributed by AAC users.

Publication: Adhikary, Jiban and Watling, Robbie and Fletcher, Crystal and Stanage, Alex and Vertanen, Keith. "Investigating Speech Recognition for Improving Predictive AAC," Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies, 2019.

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PI: Robert Pastel, HCC, CS
Sponsor: National Science Foundation (FPT-Northern Arizona University)
Award: $116,561 | 5 Years
Awarded: February 2019

PI: Elizabeth S. Veinott, HCC, CLS
Co-PI: Shane T. Mueller, HCC, CLS
Sponsor: Office of the Director of National Intelligence (FPT-Charles River Analytics Inc)
Award: $40,451 | 1 Years
Awarded: May 2019

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Co-PI: Robert Pastel, HCC, CS
Sponsor: National Science Foundation (FPT-Northern Arizona University)
Award: $20,577 | 2 Years
Awarded: November 2019
Abstract: Flooding is the most damaging natural hazard in the U.S. and around the world, and most flood damage occurs in cities. Yet the ability to know when flooding is happening and communicate that risk to the public and first responders is limited. At the same time there is a surge in digitally connected technologies, many at the fingertips of the general public (e.g., smartphones). The need is for new flood information that can be generated from primary observations that are collected in exactly the right places and times to be coupled with the ability to more effectively communicate this risk to communities. This project will develop the Integrated Flood Stage Observation Network (IFSON), a system that can take in crowd-sourced information on flooding (from cameras, a smartphone app, and social media), intelligently assess flood risk (using machine learning), and communicate those risks in real time. IFSON will be scalable to any community or city and will provide a backbone for new crowd-sourced technologies.

This project will i) integrate several new technologies (each that directly engages with different communities) to provide new insights into and communication capacity around urban flooding hazards, ii) connect a range of communities to each other in near-realtime (from the general public to first responders to infrastructure managers) and develop flood sensing and avoidance capacities that can be used anywhere in the U.S. or even internationally, iii) develop new insights into how urban morphology contributes to flood risk, and iv) leverage prior funding by connecting practitioners from existing sustainability research networks and sending data to CUAHSI and eRams. Additionally, this research will develop outreach activities that will educate the public and practitioners on how flooding hazards occur, their impacts, and how to mitigate risks. The research will directly empower and engage local citizens in flood event reporting and response, and explores a concrete model for what it would mean to have a "smart and connected community" for minimizing flood risk. Although driven by a number of novel technologies and techniques, the central focus of this work is on the interface of community with technology and, in particular, how modern network technologies can engage and bring together ordinary citizens, city planners, first responders, and other local stakeholders within a shared, collaboratively constructed information space; a broad range of educational and outreach opportunities are included to engage stakeholders and amplify project impact. In addition to training students through research positions, the project will create a summer Research Experience for Undergraduates (REU) program. It will also connect with national, state, and local societies across a number of disciplines. For example, the project will work with the City of Phoenix during their Monsoon Preparedness day to educate first responders on how to use project results. Interdisciplinary course modules that show how to engage various communities (including the public, first responders, and infrastructure managers) in mitigating flood risk will be developed and disseminated. Additionally, infrastructure managers will be recruited to participate in workshops on how project data will reveal new insights into the condition of infrastructure and what strategies can be employed to reduce hazards.

Publication: Lowry, Christopher S. and Fienen, Michael N. and Hall, Damon M. and Stepenuck, Kristine F.. "Growing Pains of Crowdsourced Stream Stage Monitoring Using Mobile Phones: The Development of CrowdHydrology," Frontiers in Earth Science, v.7, 2019.

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PI: Soner Onder, SAS, CS
Sponsor: National Science Foundation
Award: $560,000 | 5 Years
Awarded: July 2015
Abstract: It has become increasingly difficult to improve the performance of processors so that they can meet the demands of existing and emerging workloads. Recent emphasis has been towards enhancing the performance through the use of multi-core processors and Graphics Processing Units. However, these processors remain difficult to program and inflexible to adapt to dynamic changes in the available parallelism in a given program. Although the computer architecture and programming language community continues to innovate and make important gains towards better programmability and better designs, it remains that parallel programming is inherently costly and error prone, and automatic parallelization of programs is not always feasible or effective. The intellectual merits of this project are the development of a new program execution paradigm and the establishment of critical compiler and micro-architecture mechanisms so that one can design processors that can be easily programmed using existing programming languages and at the same time surpass the performance of existing parallel computers. The project's broader significance and importance are wide-spread: the deployment of such processors will push the limits of computation in every field of science and commerce.

The execution paradigm under consideration is a previously unexplored execution model, the demand-driven execution of imperative programs (DDE). The DDE paradigm rests on a solid theoretical framework and promises to efficiently deliver very high-levels of fine-grain parallelism. This parallelism is extracted from a program written in an imperative language such as C, and it is realized by means of an effective compiler-architecture collaboration mechanism using a common, single-assignment form for the program representation. DDE processors can extract instruction-level parallelism much more efficiently than existing superscalar processors as the paradigm does not require dynamic dependency checking. Such processors can fetch, buffer, and execute many more instructions in parallel than current superscalar processors. Owing to its dependence-driven instruction fetching and execution, the paradigm leads to extremely scalable designs, as the communication is naturally localized and synchronization is inherent in the model. Conventional thread-level parallelism (TLP) is orthogonal to DDE, and thus DDE designs can exploit both ILP and TLP. DDE architectures thus represent promising building blocks for extreme-scale machines.

Publications:

Jin, Zhaoxiang and Onder, Soner. "A two-phase recovery mechanism.," The 32nd ACM International Conference on Supercomputing., 2018.
Jin, Zhaoxiang and Onder, Soner. "Dynamic Memory Dependence Predication," ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), 2018, p. 235.
M. Stokes, R. Baird, Z. Jin, D. Whalley, S. Onder. "Decoupling Address Generation from Loads and Stores to Improve Data Access Energy Efficiency," ACM Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES), 2018.
M. Stokes, R. Baird, Z. Jin, D. Whalley, S. Onder. "Improving Energy Efficiency by Memoizing Data Access Information," ACM/IEEE International Symposium on Low Power Electronics and Design, 2019.
Zhaoxiang Jin and Soner Önder. "A two-phase recovery mechanism," International Conference on Supercomputing (ICS '18), 2018.
Stokes, Michael and Baird, Ryan and Jin, Zhaoxiang and Whalley, David and Onder, Soner. "Decoupling Address Generation from Loads and Stores to Improve Data Access Energy Efficiency," Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, 2018.
Zhaoxiang Jin and Soner Önder. "Dynamic memory dependence predication," The 45th Annual International Symposium on Computer Architecture (ISCA '18), 2018.
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PI: Zhenlin Wang, SAS, CS
Sponsor: National Science Foundation
Award: $390,876 | 4 Years
Awarded: August 2016
Abstract: Caches, such as distributed in-memory cache for key-value store, often play a key role in overall system performance. Miss ratio curves (MRCs) that relate cache miss ratio to cache size are an effective tool for cache management. This project develops a new cache locality theory that can significantly reduce the time and space overhead of MRC construction and thus makes it suitable for online profiling. The research will influence system design in both software and hardware, as nearly every system involves multiple types of cache. The results can thus benefit a wide range of systems from personal desktops to large scale data centers. We will integrate our results into existing open source infrastructure for the industry to adopt. In addition, this project will offer new course materials that motivate core computer science research and practice.

The project investigates a new cache locality theory, applies it to several caching or memory management systems, and examines the impact of different online random sampling techniques. The theory introduces a concept of average eviction time that facilitates modeling data movement in cache. The new model constructs MRCs with data reuse distribution that can be effectively sampled. This model yields a constant space overhead and linear time complexity. The research is focused on theoretical properties and limitations of this model when compared with other recent MRC models. With this lightweight model, the project seeks to guide hardware cache partitioning, improve memory demand prediction and management in a virtualized system, and optimize key-value memory cache allocation.

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PI: Soner Onder, SAS, CS
Sponsor: National Science Foundation
Award: $246,329 | 4 Years
Awarded: September 2019
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.

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PI: Soner Onder, SAS, CS
Sponsor: National Science Foundation
Award: $230,744 | 3 Years
Awarded: August 2019
Abstract: Instruction-level parallelism (ILP) in computing allows different machine-level instructions within an application to execute in parallel within a micro-processor. Exploitation of ILP has provided significant performance benefits in computing, but there has been little improvement in ILP in recent years. This project proposes a new approach called "eager execution" that could significantly increase ILP. The success of many applications depends on how efficiently they can be executed. The proposed eager execution technique will benefit applications that span those running on mobile devices to large data applications running on the ever-growing number of data centers. Enabling better systems at all scales will further enable the ubiquitous computing that continues to pervade lives.

The project's approach includes the following advantages: (1) immediately-dependent consumer instructions can be more quickly delivered to functional units for execution; (2) the execution of instructions whose source register values have not changed since its last execution can be detected and redundant computation can be avoided; (3) the dependency between a producer/consumer pair of instructions can sometimes be collapsed so they can be simultaneously dispatched for execution; (4) consumer instructions from multiple paths may be speculatively executed and their results can be naturally retained in the paradigm to avoid re-execution after a branch misprediction; and (5) critical instructions can be eagerly executed to improve performance, which include loads to prefetch cache lines and pre-computation of branch results to avoid branch misprediction delays.

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PI: Daniel R. Fuhrmann, SAS, CMH
Co-PI: Saeid Nooshabadi, ECE
Sponsor: Ford Motor Co.
Award: $202,567 | 2 Years
Awarded: January 2019

PI: Jianhui Yue, SAS, CS
Sponsor: National Science Foundation
Award: $192,716 | 3 Years
Awarded: July 2017
Abstract: Emerging nonvolatile memory (NVM) technologies, such as PCM, STT-RAM, and memristors, provide not only byte-addressability, low-latency reads and writes comparable to DRAM, but also persistent writes and potentially large storage capacity like an SSD. These advantages make NVM likely to be next-generation fast persistent storage for massive data, referred to as in-memory storage. Yet, NVM-based storage has two challenges: (1) Memory cells have limited write endurance (i.e., the total number of program/erase cycles per cell); (2) NVM has to remain in a consistent state in the event of a system crash or power loss. The goal of this project is to develop an efficient in-memory storage framework that addresses these two challenges. This project will take a holistic approach, spanning from low-level architecture design to high-level OS management, to optimize the reliability, performance, and manageability of in-memory storage. The technical approach will involve understanding the implication and impact of the write endurance issue when cutting-edge NVM is adopted into storage systems. The improved understanding will motivate and aid the design of cost-effective methods to improve the life-time of in-memory storage and to achieve efficient and reliable consistence maintenance.

Publications:
Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Optimizing DRAM Cache by a Trade-off between Hit Rate and Hit Latency,” IEEE Transactions on Emerging Topics in Computing, 2018.
Chenlei Tang, Jiguang Wan, Yifeng Zhu, Zhiyuan Liu, Peng Xu, Fei Wu and Changsheng Xie. “RAFS: A RAID-Aware File System to Reduce Parity Update Overhead for SSD RAID,” Design Automation Test In Europe Conference (DATE) 2019, 2019.
Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Trade-off between Hit Rate and Hit Latency for Optimizing DRAM Cache,” IEEE Transactions on Emerging Topics in Computing, 2018.

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PI: Soner Onder, SAS, CS
Sponsor: National Science Foundation
Award: $15,876 | 5 Years
Awarded: August 2016
Abstract: It has become increasingly difficult to improve the performance of processors so that they can meet the demands of existing and emerging workloads. Recent emphasis has been towards enhancing the performance through the use of multi-core processors and Graphics Processing Units. However, these processors remain difficult to program and inflexible to adapt to dynamic changes in the available parallelism in a given program. Although the computer architecture and programming language community continues to innovate and make important gains towards better programmability and better designs, it remains that parallel programming is inherently costly and error prone, and automatic parallelization of programs is not always feasible or effective. The intellectual merits of this project are the development of a new program execution paradigm and the establishment of critical compiler and micro-architecture mechanisms so that one can design processors that can be easily programmed using existing programming languages and at the same time surpass the performance of existing parallel computers. The project's broader significance and importance are wide-spread: the deployment of such processors will push the limits of computation in every field of science and commerce.

The execution paradigm under consideration is a previously unexplored execution model, the demand-driven execution of imperative programs (DDE). The DDE paradigm rests on a solid theoretical framework and promises to efficiently deliver very high-levels of fine-grain parallelism. This parallelism is extracted from a program written in an imperative language such as C, and it is realized by means of an effective compiler-architecture collaboration mechanism using a common, single-assignment form for the program representation. DDE processors can extract instruction-level parallelism much more efficiently than existing superscalar processors as the paradigm does not require dynamic dependency checking. Such processors can fetch, buffer, and execute many more instructions in parallel than current superscalar processors. Owing to its dependence-driven instruction fetching and execution, the paradigm leads to extremely scalable designs, as the communication is naturally localized and synchronization is inherent in the model. Conventional thread-level parallelism (TLP) is orthogonal to DDE, and thus DDE designs can exploit both ILP and TLP. DDE architectures thus represent promising building blocks for extreme-scale machines.

Publications:
Jin, Zhaoxiang and Onder, Soner. "A two-phase recovery mechanism.," The 32nd ACM International Conference on Supercomputing., 2018.
Jin, Zhaoxiang and Onder, Soner. "Dynamic Memory Dependence Predication," ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), 2018, p. 235.
M. Stokes, R. Baird, Z. Jin, D. Whalley, S. Onder. "Decoupling Address Generation from Loads and Stores to Improve Data Access Energy Efficiency," ACM Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES), 2018.
M. Stokes, R. Baird, Z. Jin, D. Whalley, S. Onder. "Improving Energy Efficiency by Memoizing Data Access Information," ACM/IEEE International Symposium on Low Power Electronics and Design, 2019.
Zhaoxiang Jin and Soner Önder. "A two-phase recovery mechanism," International Conference on Supercomputing (ICS '18), 2018.
Stokes, Michael and Baird, Ryan and Jin, Zhaoxiang and Whalley, David and Onder, Soner. "Decoupling Address Generation from Loads and Stores to Improve Data Access Energy Efficiency," Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, 2018.
Zhaoxiang Jin and Soner Önder. "Dynamic memory dependence predication," The 45th Annual International Symposium on Computer Architecture (ISCA '18), 2018. 

Learn more.