Na Gong, Ph.D. - US grants

Proposal: 1401507
PI: Khan, Samee U.
Title: GARDE: Design Projects to Enable Veteran Reintegration in an Educational System

Broader Significance & Importance
The GARDE proposal will develop customized solutions for North Dakota State University (NDSU) students (preferably veterans), faculty, and staff with disabilities. Each year, an average of 4 groups of 2-4 students each will work closely with an individual with a disability, through NDSU Disability Services in collaboration with the NDSU Nursing Department, and when applicable, the North Dakota Interagency Program for Assistive Technology and the City of Fargo, to develop practical and applicable solutions to resolve issues that are affecting a specific veteran's academic progress and societal reintegration. In most cases, a group will be entirely composed of NDSU Electrical and Computer Engineering students; however, when appropriate, students from other departments, such as Computer Science and Mechanical Engineering, may also be part of a group.

Technical Description
Customized solutions for specific individuals with disabilities will be developed by groups of undergraduate students following a rigorous design process that includes (i) Requirements Capture; (ii) Analysis; (iii) Design and Testing; and (iv) Validation. Because the developed solutions may also hold value for other persons with disabilities, each group will adhere to the following five design principles: (i) modularity, (ii) adaptability, (iii) cost-effectiveness, (iv) efficiency, and (v) ease of use. This will enable the end-products to be utilized by a larger consumer base. Therefore, provisions have been made to maintain the product at a level that can guarantee longevity.

Intellectual Merit
The developed technologies can be classified as:
-- Projects that address a unique need for a specific student or type of student, such as:
a) Book Reader Application with Voice for Students with Depression and Anxiety Disorders
b) Auditory Enhancement System for Partially Deaf Students
c) Voice Enabled Smart Vending Machine
d) Voice Activated Door Openers for Students with Quadriplegia
e) Custom Computer Keyboard for Veterans with Reduced Motor Skills
-- Generic technologies to support students who are (for personal reasons) not registered
(or do not want to register) with the NDSU Disability Services Department, such as Speech to Overhead Text Display.
-- Projects that employ novel paradigms, such as social networking, smart phone applications, and cloud computing, to build awareness and help connect students with disabilities to other such students, such as:
a) Interactive Academic Supportive System for ADD/ADHD
b) Behavioral Control Training Application for Asperger Cases
c) Route Information System for Visually Impaired Students
d) Automated Cloud-based ADD/ADHD Assisting System

These solutions will reduce dependability and will assist users with life in general, which will help with reintegration into society. Provisions have been made to enable the proposed GARDE program at NDSU to become self-sustaining by: (i) closely working with the City of Fargo to deploy the developed GARDE products to North Dakota elderly care facilities, when appropriate, and (ii) presenting the most promising technologies at the Innovate ND competition, which is a talent and venture capital competition rolled into one.

Broader Impacts
The products and solutions developed will directly benefit disabled veteran students to help them succeed academically and better integrate back into society. Products that can be commercialized will benefit local, regional, state, and national economies. The engineered solutions may also help in developing course content covering design principles.

Mobile devices such as smart-phones and tablets have become the most important medium for delivering Internet traffic, especially multimedia content, to end users. One of the most popular multimedia applications is video streaming. During this process, video decoding has become the dominant energy-intensive application used in mobile devices. In particular, the major signal processing units in video decoders, such as motion estimation and compensation, forward and inverse discrete cosine transform, require a significant amount of calculations and frequent embedded memory accesses. It is understood that embedded SRAM consumes a large amount of power and limits battery life, and this situation is only expected to grow with the emerging popularity of high quality mobile video applications.

This project proposes to address this problem by incorporating advanced data mining techniques particularly suited to mobile video data applications into the hardware design process to yield an intelligent memory having high power efficiency. The PIs will explore and characterize the behaviors of video data and provide a better-informed low power hardware design. The goal is to create new power efficient mobile video memory designs that utilize the identified characteristics extracted by suitable data-mining techniques tailored to video data, which will serve as a core foundation to bring about drastic improvements in energy efficiency. The exploration of these intelligent low power techniques through the interaction of both hardware and software viewpoints will enable a new dimension for power savings. The success of this project will have a huge impact on the mobile computing community, architecture community, and everyday life. This project will also serve as an excellent educational platform to improve the understanding of green computing amongst future computer scientists and computer engineers. The PIs will jointly develop course modules focusing on software/hardware co-design for mobile devices, which can be integrated into a variety of different courses. The PIs will also continue to recruit underrepresented students, such as females and minorities, to participate in this project.

This research infrastructure will enable North Dakota State University (NDSU) to pursue a wide area of research topics: System-on-Chip (SoC) integrated silicon-based CMOS/Silicon-Germanium (SiGe) Radio Frequency (RF) and millimeter-wave Integrated Circuits (ICs) for high data rate (Gb/s) wireless body/personal area network (WBAN, WPAN) communication involving humans and computers, communication radar and sensors for detection applications ranging from hand-held scanners for bio-medical imaging to portable/wearable weapon scanners for military applications, asynchronous logic circuits for ultra-low power computer chips, extreme environment ICs for use in outer space and high temperature power electronic applications, side-channel attack resistant ICs, RF nanotechnology, and developing sub-millimeter wave and Terahertz frequency ICs. These research topics are very crucial and play a significant role in bolstering the leading position of the United States in research and development related to wireless communication, information technology, and IC design, which will strengthen our country's defense and security.

State-of-the-Art research instrumentation is essential. This instrumentation will enable precise characterization of devices and circuits to understand their behavior in order to be able to successfully design new classes of ultra-low-power BAN sensors ICs that are adaptable to dynamic changes based on body movements or environmental changes for Cyber Physical Systems (CPS) applications. This will further significantly aid faculty and students to conduct fundamental research, which will directly benefit the research community from the tests and analysis enabled by the probe station equipment.

In addition to facilitating cutting-edge research, this probe station will provide a variety of undergraduate and graduate students the ability to be trained on state-of-the-art industry-standard equipment, affording them a tremendous competitive advantage in the job market. The system offer broad opportunities for general Electromagnetics, Computer Communication Networks, Biomedical Engineering, and Material Characterization Research.

Mobile devices, such as smart phones, are being increasingly utilized for watching videos, since they can be conveniently used for this purpose anywhere anytime, such as commuting on a subway or train, sitting in a waiting room, or lounging at home. Due to the large data size and intensive computation, video processing requires frequent memory access that consumes a large amount of power, limiting battery life and frustrating mobile users. On one hand, memory designers are focusing on hardware-level power-optimization techniques without considering how hardware performance influences viewers' actual experience. On the other hand, the human visual system is limited in its ability to detect subtle degradations in image quality; for example, under conditions of high ambient illumination, such as outdoors in direct sunlight, the veiling luminance (i.e., glare) on the screen of a mobile device can effectively mask imperfections in the image, so that under these circumstances a video can be rendered in lower than full quality without the viewer being able to detect any difference. This isolation between hardware design and viewer experience significantly increases hardware implementation overhead due to overly pessimistic design margins. This project integrates viewer-awareness and hardware adaptation to achieve power optimization without degrading video quality, as perceived by users. The results of this project will impact both basic research on hardware design and human vision, and provide critical viewer awareness data from human subjects, which can be used to engineer better video rendering for increased battery life on mobile devices. The project will directly involve undergraduate and graduate students, including females and Native Americans, in interdisciplinary research.

Developing a viewer-aware mobile video-memory solution has proven to be a very challenging problem due to (i) complex existing viewer-experience models; (ii) memory modules without runtime adaptation; and (iii) the difficulty of viewer-experience analysis for hardware designers. This project addresses the problem by (i) focusing on the most influential viewing-context factor impacting viewer experience - ambient luminance; (ii) proposing novel methodologies for adaptive hardware design; and (iii) integrating a unique combination of expertise from the investigators, ranging from psychology to Integrated Circuit design and embedded systems. Specifically, this project will (i) experimentally and mathematically connect viewer experience, ambient illuminance, and memory performance; (ii) develop energy-quality adaptive hardware that can adjust memory usage based on ambient luminance so as to reduce power usage without impacting viewer experience; and (iii) design a mobile video system to fully evaluate the effectiveness of the developed methodologies.

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.

This project aims to serve the national interest by developing, implementing, and researching the effectiveness of a program for undergraduate pre-service teachers (PSTs) to integrate computer science learning in elementary education. Many states in the U.S. have required that all public schools offer computer science courses or instruction. However, computer science is currently not offered as an independent course in most U.S. elementary schools. Hence, there is a critical need to integrate computer science into K-6 subject content courses to ensure every student has access to computer science learning. A systematic educational program is necessary for elementary PSTs to learn computer science knowledge and, more importantly, strategies for integrating it into K-6 subjects. However, it is challenging to place such an intensive endeavor into a single course in teacher education programs, so there is a shortage of PSTs who can integrate computer science learning in K-6 subject classes. The Technological Pedagogical Content Computational Thinking (TPC2T) model, a model for the infusion of computational thinking into another subject, will be used. In this Engaged Student Learning Level-1 project, 175 elementary PSTs from 5 cohorts will participate in a series of sessions and student teaching over three semesters to learn and apply computer science across all K-6 subjects. This project will also generate an empirically tested PST educational framework and a computer science integration teaching model for classroom implementation.

This project aims 1) to improve PSTs’ interest, self-efficacy, and knowledge of computer science integration in K-6 subjects by the end of their teacher preparation program, and 2) to advance the TPC2T model and make it a comprehensive model for K-6 computer science integrated teaching. To address these goals, the project team will conduct a mixed-method triangulation design research based on Bandura’s self-efficacy theory and Lent, Brown, and Hackett’s social cognitive career theory. The research findings and educational materials will be disseminated via the public project site, conference presentations, peer-reviewed journal publications, in-person and online workshops, and events hosted by NSF-funded centers, such as the STEM Learning and Research Center. This project is expected to benefit society by developing the teacher workforce in computer science K-6 education. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. The Robert Noyce Teacher Scholarship (Noyce) Program is providing co-funding for this IUSE: EHR project to support the project's pre-service teacher preparation goals, which are well-aligned with Noyce Program goals.

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.

Differential privacy (DP) has been widely accepted as the de facto technique for protecting data privacy. Despite the decade-long research efforts on DP, there still exists a critical research problem that has been largely overlooked, that is all existing DP studies are grounded on the hypothesis that software can easily and faithfully sample and add noises from a probability distribution. However, this hypothesis is being constantly challenged by recent findings about its privacy violation and by the growing demand of privacy protection in low-end devices that may lack high-level software libraries. Hence, this project's innovative research angle is to realize DP mechanisms directly on embedded memories, which are ubiquitous in modern electronic devices. On the technical front, the developed innovation has the following merits. (1) It frees host devices from dedicated software and accomplishes the vision of "privacy by design"; (2) It concurrently improves manifold system performance such as power efficiency, privacy, and chip overhead; (3) The developed technique is primitive, generic, and scalable to every electronic device.<br/><br/>This project will also create profound impact on our society, economy, and workforce development. Specifically, the developed technique will be transformative to numerous sensitive applications (e.g., surveillance and sensing) and critical infrastructures (e.g., Internet of Things devices). It will potentially increase the U.S. chip vendors' revenue and competitiveness by adding privacy-preserving functionality to their chips, protect taxpayers' and enterprises' sensitive data, safeguard national security, and help the U.S. out-compete global competitors in cybersecurity. Moreover, this project will help PIs to update existing curricula, engage students – with priority to female or/and first-generation college students – for research training, and promote community outreach through existing successful programs such as the NSF-funded RET site in Mobile, Alabama.<br/><br/>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.

Using Artificial Intelligence (AI) currently requires access to the internet and very large and complex remote computers for making decisions and predictions. This causes long delays and privacy and security concerns. The latest techniques in AI, known as “Edge AI”, avoid these problems by collecting and analyzing data directly on cameras, smart phones, and wearable devices. However, Edge AI is still in its infancy and there are several important technical problems that need to be solved. This Research Infrastructure Improvement Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC) award is a collaboration between six universities (including two minority-serving institutions) and several private-sector partners in Alabama, Arkansas, and North Dakota. As a test of the project's new technology, the project team will build a smart wearable device to predict the onset of diabetes by monitoring a patient's own breath without the need for a doctor to interpret the results. It will provide research training opportunities for advanced college students and will also train high-school teachers in lessons to educate their own students in the principles of Edge AI to seed the future US workforce in these essential concepts for tomorrow’s world.<br/><br/>The goal of this RII Track-2 FEC award is to develop integrated research infrastructure and workforce in Edge AI. Fundamental contributions and technical innovations to be developed by the team include: (i) light-weight AI-empowered reasoning and machine learning algorithms for edge platforms; (ii) a new Application-Specific Integrated Circuits (ASIC) design methodology to enable AI ASICs with ultra-low power, reconfigurability, and short development cycles; (iii) a sensor device platform for Edge AI based on novel functionalized nano-scaled sensing materials with nano-3D printing techniques; and (iv) an Edge AI device platform exploiting the previous advances to meet the requirements of different use cases. Based on the developed infrastructure, targeting the use case of diabetes care, the team will design, prototype, and test a low-cost smart wearable device for personalized diabetes management. The developed wearable diabetes device will enable significant cost reduction and high power efficiency compared to existing techniques. The leading institution is the University of South Alabama; the collaborating institutions are North Dakota State University, the University of Arkansas, the University of North Dakota, Alabama A&M University, and Nueta Hidatsa Sahnish College. The team will work closely with multiple industry partners to adopt and adapt the developed Edge AI infrastructure in different use cases. Research outcomes of this project will accelerate the development of Edge AI and will increase the competitiveness of the United States in AI. Also, this project will integrate research, education, and workforce development in order to provide effective training at multiple levels. The project will develop an Education-to-Workforce Pipeline from high school to undergraduate, graduate, Post-Doctoral training, junior faculty, and industry practitioners.<br/><br/>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.