Andreas S. Spanias - US grants

Engineering - Electrical (55)

Arizona State University is fully developing and evaluating a web-based undergraduate laboratory tool in the areas of undergraduate digital signal processing(DSP), communications, image processing and controls. We have already developed and successfully tested a prototype laboratory tool (J-DSP) for use in the undergraduate DSP class. This web-based prototype supports capabilities for online signal processing simulations and provides laboratory experiences to distance learning and on-campus undergraduate students. The tool is based on a collection of novel Java applets that support a user-friendly object oriented environment. This exemplary Java software supports a simulation environment that enables students to establish and execute experiments from any computer platform that is equipped with a web browser.

This work provides significant extensions of the laboratory prototype to the other areas (communications, image processing and controls), an assessment and dissemination strategy that includes test sites, and a plan to sustain development, dissemination, and evaluation after the CCLI project. The prototype lab and the proposed extensions represent perhaps the first comprehensive effort to provide on-line lab experiences in distance learning environments. We anticipate that this novel concept can be extended to different types of subjects at different levels of education, e.g., on-line experiments at levels and topics ranging from high-school physics to community college science labs and college-level engineering subjects. The prototype J-DSP along with its proposed extensions to "hot" systems topics such as communications, image processing, and advanced controls will enable new web-course developers to seamlessly integrate online experiments to their web-course content.

Signal Processing for Communications (SP-COM) is an area that focuses on re-search associated with the infrastructure, hardware, and algorithms of future generation digital communications systems. The primary objective of the CRCD/EI project is to provide scientific and investigative experiences to under-graduate students by immersing them into state-of-the-art communications and signal processing research. The methods employed to accomplish this ob-jective use curriculum strategies that include: a) immersing research-oriented modules in four existing junior and senior level classes, b) offering a new senior level undergraduate course entitled "Introduction to signal processing and communications research," c) the integration of senior-level capstone projects in the ongoing research activities of the PIs, d) the institution of summer research freshman and sophomore camps along with an outreach program. The project will impact student learning by instilling the process of scientific inquiry through a continuous research exposure in the undergraduate curriculum. Several SP-COM research topics, such as those dealing with channel equaliza-tion, source and channel coding, are immersed in courses within the framework of this CRCD project. CRCD curriculum courses and modules involve at least one self-contained computer laboratory experience. ASU's Java-DSP (J-DSP) web-based simulation environment is used for these laboratories. Students can access J-DSP on the web, perform computer laboratory exercises, and submit electronic lab reports. Significant pedagogical foundations and strategies for the transition of research to the curriculum are formed with the assistance of in-structional specialists. Dissemination and assessment strategies include: an annual CRCD workshop, a CRCD interactive web site, publications in research and education journals, industrial dissemination through industry partners.

This full-scale EMD collaborative effort involves five universities, namely, Arizona State University (ASU), the University of Washington Bothell (UWB), the University of Texas at Dallas (UTD), the University of Rhode Island (URI), and the University of Central Florida (UCF). The project involves significant educational technology innovations and software extensions that enable the ASU online prototype software Java-DSP (J-DSP; http://jdsp.asu.edu) to be used in undergraduate courses across the five participating universities. Problems that are being addressed include the delivery of technology-enhanced laboratory experiences to undergraduate students using novel Java tools, and the broad assessment of these practices across the participating universities. The project tasks and objectives include: a) software development towards producing a new delivery technology, b) considerable mathematical functionality extensions of J-DSP, c) development of laboratory exercises by all the Co-PIs at the different universities, d) a geographically-diverse assessment that involves the faculty specialists at all five universities, e) a comprehensive pilot test of a new revolutionary multi-site laboratory concept that allows students in the five universities to concurrently run real-time integrated online simulations using the planned connectivity upgrades on J-DSP, and f) dissemination and publication of all results. The educational innovation is enabling distance learners to conduct laboratories over the Internet. The concepts developed in this project are serving as a model for developing and conducting online labs in other science disciplines.

This IGERT award at the Arts, Media and Engineering Program at Arizona State University will develop research and training mechanisms for the creation of a new class of media scientists. These scientists will produce new approaches for the integration of computational elements and digital media in the physical human experience. Their work will result in experiential media systems - hybrid physical-digital environments that address significant challenges in key areas of the human condition such as health, education and everyday living.

The knowledge required to create experiential media systems is currently fragmented across engineering, sciences and arts. This IGERT award will train a new generation of hybrid media engineers-scientists-artists who are equipped to transcend this fragmentation. The training will be realized through a large interdisciplinary network combining expertise from twelve contributing disciplines. This network will allow integrated advanced research in sensing, modeling, feedback, experiential construction and learning. The research will result in new knowledge in media systems as well as within each contributing area. It will also result in the development of large-scale applications of societal significance. The graduate training mechanisms are implemented through formally approved concentrations within the graduate degree programs of participating disciplines. They combine discipline specific education in one of the IGERT research areas with interdisciplinary training in media development. The framework of this IGERT allows for methodology found in the sciences to be combined with creativity found in the arts. It will bridge the gap between computation and the physical experience, advance human-centric technologies and produce major advances in education, rehabilitation, communication, and everyday living. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

This collaborative project will assemble a continuous Astronomical Time Scale (ATS) for the Mesozoic Era (65 to 251 million years ago) from orbitally forced paleoclimate cycles recorded in stratigraphic data. The results should improve estimates of rates and timings of a wide range of Earth system processes by at least an order of magnitude. Project activities will be coordinated with more than a dozen international partners and will utilize the expertise of the Cretaceous, Jurassic, and Triassic subcommissions of the International Commission on Stratigraphy. The grand "team" goal is to extend the ATS, now virtually complete for the Cenozoic Era (0 to 65 million years ago), to encompass the past 250 million years. The methods that underpin the Cenozoic ATS will be applied to the Mesozoic. Emphasis will be placed on the goodness of fit of stratigraphic signals to astronomical models, duplication of records from different regions, and high-resolution calibration to geomagnetic polarity signatures and integrated bio- and chemostratigraphy. To aid in the science, a universally accessible signal processing toolbox will be created to modernize and unite leading statistical time series techniques used in cyclostratigraphic research, as well as in many other geoscience fields. Numerical ages from the reconstructed ATS will be inter-calibrated with other dating techniques in conjunction with NSF's EARTHTIME Project. All data and tools will be documented and accessible through multiple NSF public database systems, including GEON, CHRONOS, PaleoStrat, PBDB, and others. This project fulfills an ongoing global community initiative to develop a continuous, astronomical-calibrated, International Geologic Time Scale for the past quarter billion years of Earth history.

The objective of this research is to develop signal processing algorithms for processing data from silicon ion-channel sensors to identify the presence of particular chemicals. Silicon ion-channel sensors are artificial cell membranes containing pore proteins, embedded in a silicon chip that measures current through the pores as the pores interact with target chemicals. This work will apply advanced signal processing techniques such as Hidden Markov, spectral subtraction and adaptive estimation of noise in the engineering of ion channels for explosive detection. The study leverages some existing prototypes to capture experimental data to build signal response databases. The PIs have a strong history of recruiting and mentoring minority and women students. The ion-channel efforts are to be included in undergraduate courses and in distance learning efforts.

Engineering - Other (59)

The project, a collaboration involving Arizona State University (ASU) as the lead institution, Johns Hopkins University (JHU), University of Washington-Bothell (UWB), Prairie View A&M University (PVAMU), and Rose-Hulman Institute of Technology (RHIT), is expanding the use of an award winning software package (J-DSP) and instructional approach into a broad set of new areas including digital signal processing, earth systems and geology, renewable energy systems, arts and media, ion-channel systems, and genomics. Online modules are being designed, deployed, and assessed by a geographically-diverse multidisciplinary team. This educational technology provides free and universally accessible web-based Java software with an intuitive interface that enables instructors to create web-based lectures with synchronized online simulations and animations and to monitor student progress and preferences. It allows students, including distance learners, to conduct online laboratories and collaborate across disciplines, to perform simulations anytime anywhere, and to collaborate online with their colleagues at other universities. The evaluation effort is using self, peer, and instructor assessments to measure the quality of student learning by adapting a set of on-line assessment instruments developed on a previous grant dealing with a set of signal processing courses. The project team is working to disseminate the instructional materials by postings on the project's website and on a discipline-based site (CNX.ORG), by links with the NSDL, by faculty workshops, by conference presentation and journal publications, and by high school and industrial outreach. Broader impacts include an involvement of two MSIs, an outreach effort focused on minorities, multifaceted dissemination involving faculty workshops and web posting on several sites.

Title: Biomedical Innovations Using Implementation-Aware
Agile Sensing and Signal Processing
Abstract:
Medical condition diagnosis is heavily based on sensor measurements
and their processing. These measurements correspond to waveforms
that propagate over the complex body environment and are transformed
linearly or nonlinearly according to the characteristic environment properties.
However, the medical community does not fully exploit the potentials of
advanced processing matched to nonlinear structures or modern sensor
technologies such as waveform agility that leads to significant estimation
performance improvements. This research exploits advanced
implementation-aware sensing and processing techniques to improve
medical diagnosis by: (a) efficient processing using compressed sensing
and nonlinear time-varying spectral methods; (b) estimation of environment
descriptors and disease state parameters combined with waveform-agile
sensing; and (c) mapping estimation and waveform-agile sensing algorithms
onto field-programmable gate arrays. This framework brings revolutionary
advances in diagnosing, treating, and tracking disease states that are
otherwise difficult to obtain as advanced processing techniques are either
not available or too costly. The investigators also design on-line software
toolboxes with sensing experiments for use in outreach programs to recruit
and retain freshmen and underrepresented student populations.

The research integrates advanced signal processing, stochastic Bayesian
estimation, and waveform-agile sensing with implementation-aware
algorithms to improve estimation of disease states. The investigators study
time-frequency techniques matched to nonlinear structures to process biomedical
data. Compressive sensing methodologies are developed that reduce the
number of required measurements and allow for alternative computational
algorithms. Mathematical descriptors are designed to model disease states using
time-varying transfer functions. Sequential Bayesian techniques and waveform
adaptation are used to estimate information. Algorithm reconstruction procedures
are developed that trade high performance for reduced implementation cost.
Finally, the investigators design algorithms cognizant of complexity and memory requirements.

IIP 0934418
Arizona State University
Spanias

The Sensor Signal and Information Processing (SenSIP) consortium at the Arizona State University (ASU) is planning to join the Industry/University Cooperative Research Center (I/UCRC) entitled "Net-Centric Software and Systems" which currently is a multi- university Center comprised of the University of North Texas (lead institution), and the University of Texas at Dallas. The mission of SenSIP at ASU is to develop signal and information processing foundations for next-generation integrated multidisciplinary sensing applications in biomedicine, defense, energy, and other systems.

ASU will bring to the existing Center much needed complementary capabilities in the areas of digital signal and image processing, multimedia systems, sensor networks, information theory and wireless communications. The proposed site will enable the creation of new capabilities in sensor signal processing and will bridge the gap between sensor development and large scale sensor deployment; and, is uniquely positioned to promote industry research, education, scholarship that will integrate well with the existing Net-Centric I/UCRC umbrella.

The research at ASU SenSIP will lead to inexpensive, compact, and reusable sensors for industry applications of relevance to medicine, sustainability, and defense. Publications in scientific journals and conferences will be complemented with a strong dissemination effort. The ASU SenSIP site has several established programs for recruitment from underrepresented groups, involvement of undergraduate students in research, and mechanisms to create and package online modules with interdisciplinary research and education content. ASU site also plans to create a workforce in signal processing related areas that support national initiatives in medicine, energy, and defense industries.

The Sensor Signal and Information Processing (SenSIP) consortium at the Arizona State University (ASU) is planning to join the Industry/University Cooperative Research Center (I/UCRC) entitled "Net-Centric Software and Systems" which currently is a multi- university Center comprised of the University of North Texas (lead institution), and the University of Texas at Dallas. The mission of SenSIP at ASU is to develop signal and information processing foundations for next-generation integrated multidisciplinary sensing applications in biomedicine, defense, energy, and other systems.

ASU requests funding for becoming a third site of the NSF Center for Net-Centric Software and Systems under the leadership of Professor Andreas Spanias. ASU will bring to the existing Center much needed complementary capabilities in the areas of digital signal and image processing, multimedia systems, sensor networks, information theory and wireless communications. The proposed site will enable the creation of new capabilities in sensor signal processing and will bridge the gap between sensor development and large scale sensor deployment; and, is uniquely positioned to promote industry research, education, scholarship that will integrate well with the existing Net-Centric I/UCRC umbrella.

The proposed work will advance the development of signal processing and communication technologies for sensing systems. The proposed research activities at this site are expected to lead to inexpensive, compact, and reusable sensors for industry applications of relevance to medicine, sustainability, entertainment, and defense. The proposed site has several dissemination programs and established structures for recruiting students from underrepresented groups, involving undergraduate students in research, and mechanisms to create and package online modules with interdisciplinary research and education content.

The proposed research targets fundamental work in sensor networks. Specifically the work addresses the central problem that distributed inference systems need to be robust to a wide variety of sensing, and channel impairments. The effort seeks to design a suite of robust, distributed detection and estimation algorithms for sensor networks and Net-Centric applications. A comprehensive approach to robust distributed inference for a wide range of communication channels and topologies, with minimal assumptions about the sensing noise, is proposed.

The outcomes of the proposed work have the potential to greatly impact many Net-Centric applications including environmental, military and health care that require aggregation of data from sensors which are linked only through wireless means. The work is supported by the Industry Advisory Board as well as individual industry members of the center and has the potential to extend the center?s portfolio while potentially attracting new members. The PI has collaborative TUES Phase 3 software project with HBCU colleges that will be tapped on for recruitment of minority students for training in the Ph.D. program associated with this I/UCRC FRP.

Objective:

The objective of this project is to design and test nonlinear, robust, and power-aware consensus estimation algorithms for fully distributed cyber-physical systems (CPS). Systems that use a large number of networked sensors with limited transmission range due to power limitations are considered. Such systems have a large number of transmitters that are subject to non-Gaussian impulsive noise and network interference. Robust decentralized distributed sensing will be considered with joint design of the sensing and communication systems with low-power constraints.

Intellectual Merit:

The intellectual merit is in the novel design of distributed nonlinear consensus algorithms to solve parameter estimation problems for CPS applications. Proposed convergence analysis, and variance of the estimators in the presence of these nonlinearities go beyond existing analyses of linear approaches. The research will provide power-aware consensus estimation algorithms with minimal assumptions about the sensing and channel noise through judicious design of nonlinearities. Algorithms will be tested with photo-voltaic array data and a testbed will be developed to address and characterize real-time estimation.

Broader Impact:

The broader impacts are on alternative energy, environmental, infrastructure monitoring, and STEM education. The PIs will design educational software modules that use sensing as a paradigm to illustrate basic concepts in probability and communications at the undergraduate level. Dissemination and education efforts will include computer and mobile-based applications using the award-winning JAVA-DSP software packages. The investigators will also actively recruit students from under-represented minorities to work on the project.

GOALI: Intelligent Networked Solar Panel Array Management This three year GOALI proposal addresses several new signal processing, power, modeling, and control methods for optimizing photovoltaic (PV) arrays and inverters through Smart Monitoring Devices (SMD). The objectives for this GOALI project have been designed jointly by our faculty and industry partners and include: a) studying how the individual PV monitoring devices can improve solar panel array operation and efficiency, b) examining communication and networking methodologies for data flow through the system, and c) investigating optimization methodologies for the overall improvement of PV array and inverter performance. Based on these objectives our short term goals are: a) to develop intelligent, interactive PV monitoring technologies, b) to develop switching strategies for PV modules, c) to optimize PV array performance, d) to provide fault tolerant capabilities, e) to establish communications and networking among SMDs, servers and inverters, f) to provide anti-shading strategies and reduce mismatch, and g) to establish these innovations along with a tech transfer and IP roadmap for the GOALI project. The long term goal is to develop smart PV technologies that will help define new standards and protocols for PV array communication and control.

Intellectual Merit: Scientific problems that the proposal addresses revolve around information extraction and processing from PV arrays and inverter units that are intended for utility scale power production. The PV data and information processing algorithms derived for these applications will impact many areas in solar array power production and distribution. More specifically they will result in designing and deploying effective and robust PV arrays that operate in near optimum conditions and are robust to faults, noise and weather changes.

Broader Impact: The proposed work will advance the development of PV and inverter technologies. Our research will lead to inexpensive, smart, and robust PV units for utility scale applications. As a whole, our research will reduce the cost of energy by optimizing PV array and inverter operation. In the proposal, we describe a process to create compelling realizations of mobile iJDSP for dissemination and outreach of this PV monitoring research.

The central idea in this project is to motivate students to pursue studies in STEM areas by creating and disseminating scalable modules that demonstrate in a compelling manner how math and engineering theory enables modern applications such as those embedded in wireless devices. The goal is to motivate graduates to create high-tech products, enter the high-tech workforce, and become innovators. This project will promote a transformative STEM education agenda by developing and disseminating innovative and scalable content for several courses. These innovative products will promote a positive attitude change towards learning STEM concepts by continuously fusing theory with high tech applications. Objectives include developing a diverse community of users, innovative products with mobile video-streamed content, software training modules for skill building, e-books, and training workshops. The PIs will create and disseminate products on multidisciplinary STEM applications in areas including engineering, arts and media, and earth systems.

The innovative educational modules will have comprehensive multidisciplinary content to address diverse audiences. Modules will be packaged for mobile delivery and will be multipurpose, multidisciplinary, and will: a) motivate students to learn theory through compelling applications, b) engage students in implementation for skill building and workforce creation purposes, and c) immerse diverse audiences and stakeholders in hands-on workshops for outreach, retention, and recruitment purposes. This project is based on collaboration between ASU and Clarkson University and engages faculty from Johns Hopkins University, Phoenix College, St. Lawrence University, Prairie View A&M University, and Corona del Sol high school. The project will use a mixed-method assessment process (qualitative and quantitative data collection) to build an understanding of the impact of the use of the modules, apps, and other tools on student learning gains, from the individual concept level to more general knowledge about scientific and engineering habits of mind and research practice. Assessments will be done through electronic web tools, pre- and post- quizzes, presentations, one-to-one interviews, and ordinary in-class testing.

Arizona State University (ASU) and Tecnologico de Monterrey (ITESM) have recently signed a memorandum of understanding (MOU) and held two administrative meetings at ASU to explore collaborative research and education endeavors. This NSF award provides support to organize two workshops whose themes will be on Sensor Signal and Information Processing Systems. This program will positively impact US-Mexico research and education initiatives and will engage a number of qualified students and faculty from two large institutions in the area of sensors and signal processing. The program will engage underrepresented groups within STEM disciplines such as electrical engineering, with broader impacts on improving the engineering workforce base, with commensurate economic benefits to the two countries. The program will disseminate successful academy-industry collaborative models such as the I/UCRC in Mexico. Several application aspects of sensors will be addressed in industrial areas including localization, mobility with extensions to manufacturing, health, and other systems of common interest.

The workshops which will engage USA and Mexico researchers, faculty and students and are titled:

-Workshop 1: Sensors and Signal Processing for Localization and Security Applications;
-Workshop 2: Academy-Industry Collaborations in Sensors and Signal Processing.

The two workshops will be organized by the PI who will lead an organizing committee of faculty and industry partners. The first workshop will be in Tempe, Arizona (December 2015) and the second one in will be held in Monterrey, Mexico (May 2016). The objectives of the workshops are to: a) initiate collaborative structures between faculty and industry researchers in the US and Mexico through research and panel sessions, b) develop STEM foundations in the areas of sensor systems through organized tutorials for students and researchers that will support future workforce development efforts, and c) initiate industry-university relations
in Mexico using US industry academia-research models such as the I/UCRC.

With this award, the Arizona State University IUCRC site of the Net-centric and Cloud Software and Systems (NCSS) I/UCRC will transition to a Phase 2 Site. The NCSS involves University of North Texas (Lead Site), Southern Methodist University, University of Texas at Dallas, Missouri University of S&T and Arizona State University. The site has already established the necessary intellectual base and marketing infrastructure to attract and retain industry partners for Phase 2, and has an excellent track record of industry membership and engagement within the NCSS. Networked sensing systems are important in homeland security, Net Centric defense systems, industry applications, health monitoring and other areas. The Phase 2 center will enable the creation of new capabilities in sensor signal processing and will bridge the gap between sensor development and large scale sensor deployment. The center is uniquely positioned to promote fundamental research, education, scholarship, and industry activities in an innovative manner that will continue integrating well in the existing Net-Centric umbrella.

With the NCSS, this Site focuses on research areas such as digital signal and image processing, sensor networks, information technologies and algorithm and cloud software development for wireless communications. Specific areas of planned research advances and contributions include: a) Advances in radar direction of arrival estimation using virtual sensor arrays, b) development of networked sensor monitoring algorithms and cloud software which predict process failures, c) fault detection in sensors monitoring networked PV arrays, d) networked imaging sensors for activity detection, and e) net centric approaches with camera arrays for image understanding.

The aim of this collaborative project is to increase the efficiency of utility scale solar arrays using sensors, machine learning and signal processing methods to detect faults and optimize power. New cyber-computing strategies, that rely on sensor data and imaging methods to predict solar panel shading, are used to improve efficiency. A programmable 18kW testbed that consists of 104 panels equipped with sensors, actuators and cameras is used to validate all theoretical results and test new approaches for using solar analytics to optimize power generation. Machine learning and dynamic image modeling algorithms are used to control each individual panel and change connection topologies to optimize power for different cloud, load, and fault conditions.

Outcomes of the CPS project include advances in: a) cloud movement modeling and shading prediction using computer vision algorithms, b) PV fault detection and optimization methods that will switch array topologies dynamically while limiting PV inverter transients, d) experimental (testbed) validation of all array monitoring methods, and e) secure wireless sensor and data fusion. Theoretical and experimental research which enables real-time analytics and remote connection topology control may influence PV array standards and smart grid initiatives. The project tasks also include: education activities, outreach at high schools, and engagement with several organizations including minority and HBCU institutions to enhance diversity.

This three year REU site will recruit and train nine undergraduate students each summer and engage them in research endeavors on the design of sensors including student training in mathematical methods for extracting information from sensor systems. The investigators along with a team of faculty advisors will supervise a series of multidisciplinary projects in the design of integrated sensor systems. In addition to the planned projects, the faculty leaders of this program will organize a series of industry collaborative training activities for the students. This REU features multidisciplinary synergies across different research labs that provide access to unique sensor and algorithm technology. The program also includes crosscutting modules, and workshops in public speaking, policy, standards, ethics, patents, SBIR planning, and outreach. Annual REU workshops will train students to communicate with stakeholders who can help establish new standards. An evaluation unit will assess REU goals annually using feedback from academia, industry and stakeholders. The program engages minority colleges to broaden participation and enhance recruitment.

The REU will address STEM problems associated with sensor applications in internet of things, health monitoring and security. Specific objectives of the REU site are to: a) introduce students to general research practices by immersing them in government and industry research activities, b) engage students in integrated design of sensor devices and relevant information processing methods, c) motivate students to pursue research careers, d) provide cross cutting skills in presentation, developing patents, entrepreneurship, and building awareness on social implications, policies and standards. The REU projects are designed to introduce students to an array of sensor device design technologies that emphasize low power circuits, flexible electronics, MEMS, and embedded systems. During the same period, projects will train REU students to interpret data from sensors by studying and programming machine learning algorithms, sensor fusion methods, and techniques to interpret big data sets.

This project develops a testbed for solar energy grids that brings together several labs, centers, and researchers to address
? Elevating power efficiency in solar farms,
? Automatic fault detection,
? Maximizing solar power output,
? Optimizing inverter performance,
? Providing secure communication for analytics, and m
? Making provisions for Smart Grid.
Solar energy research strives to solve challenging problems in increased photovoltaic (PV) efficiency, grid management, power storage, intelligent inverters, and various problems of economic nature, such as financing and manufacturing. The instrument is intended to enable several innovations and elevate the state of the art of solar technologies. It enables research on integrating sensors and machine learning to elevate considerably the output power and robustness of solar arrays. The seamless integration of several software and hardware components is designed to enhance all aspects of solar array monitoring and control.

'A key component of the instrument, an Intelligent Monitoring and Control Device (IMCD) consists of sensors, actuators, a processor/controller chip, a secure radio, embedded machine learning software, and signal processing and authentication algorithms. The overarching long term goal is to Miniaturize IMCDs with the goal to embed it in photovoltaic (PV) modules, to enable the building of a new generation of smart programmable PV modules. This platform integrates a new intelligent monitoring and control instrument suite and enabling researchers to obtain, process, and utilize real-time PV and environmental data in order to:
? Develop, integrate and test detection and classification algorithms for PV faults;
? Track cloud movement and predict panel shading, and hence optimize further the output of PV arrays;
? Use secure networked connectivity and protocols to protect data and avoid MRI instrument hacking;
? Integrate all the acquired data using fusion algorithms and enable appropriate control of the panels;
? Predict and eliminate inverter transients caused by faults and dynamic shading conditions;
? Provide continuous analytics and create a mobile dashboard to monitor and control the array;
? Use data and design ground breaking optimization algorithms to improve the PV array power output;
? Elevate overall efficiency of solar farms in terms of power output by more than 16%;
? Create the foundations for designing a new generation of solar panel technology.

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.

In order to open up the economy in light of the reality of COVID-19, a suite of solutions are needed to minimize the spread of COVID-19 which include providing tools for businesses to minimize the risk for their employees and customers. It is important to detect transmission hotspots where the contact between infected and uninfected persons is higher than average. This project will provide information to assess precisely the size, density and locations of COVID-19 hotspots and enable issuing well-informed advisories based on data-driven continuous risk assessment. Every step will be taken to ensure privacy and network security and specific algorithms will be developed for secure access and information transfer. The project will access databases at CDC, Johns Hopkins and the WHO, and create a comprehensive website to disseminate real-time localized COVID-19 hotspot data, while maintaining privacy. The project will create new algorithms and embed them in iOS and Android apps that will continuously interact with databases. The software for mobile devices as well as central hubs will be made publicly available through APIs for use by the broader community.

The project will use advanced consensus-based methods for estimating network area/size, node locations and node counts in a network based on minimal transmit-receive data. The proposed methods will lead to significant improvements compared to existing algorithms. The project will design consensus-based algorithms to estimate (a) the center, radius, and consequently, the size of the network, and (b) the number of users in the network. Localization algorithms will be designed that work with noisy and incomplete data. The proposed work is different from the contact-tracing technology used by Google and Apple which is limited to newer devices. The proposed algorithms and software will advance the state of the art while retaining compatibility with emerging and existing mobile technology. The project will help reduce COVID-19 infections and save lives. The research will also have applicability to other fields such as the E911 system, indoor user tracking, infrastructure-free implementations applicable to robotics, autonomous systems and vehicle fleets, and location-aware patient care and other mobile health applications. The developed algorithms can be used in other emergency situations, such as locating clusters of sheltering groups in the case of earthquakes and tsunamis, to assist first responders in finding survivors after an event, and for detection of transmission nodes in the case of future pandemics or future waves of COVID-19. Outreach activities will be integrated with the research and include the creation of software and web content for dissemination.

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 IRES project brings together advances in sensor devices with machine learning and digital signal processing (DSP) algorithms in an international research endeavor that promises to elevate precision in mobile and wearable technologies. Arizona State University (ASU) faculty and students will collaborate with the Dublin City University (DCU) Insight Center for Data Analytics, which has a synergistic relationship with ASU in several areas including sensors, analytics, machine learning (ML), wearables and Internet of Things (IoT). ASU brings expertise in flexible sensors, chemical and biosensors, statistical signal processing, bio-informatics, and machine learning. DCU brings expertise in data analytics, human activity monitoring, environmental monitoring, artificial intelligence, sensor analytics and big data analysis. IRES participating students will spend an immersive six-week summer program at the DCU Insight Center to actively improve their ML and sensor research skills; participants will produce and evaluate sensor analytics, and create algorithms and software for IoT, wearables and mobile health monitoring. Programs and workshops will be established to train IRES participants to skillfully and effectively present their research in international settings. Weekly presentations at the international site and guidance by international mentors will enrich the cohort’s professional experience. Embedding students in the DCU Insight center funded by European Union (EU) and Irish Science Foundation (ISF) grants will provide knowledge on EU and international research practices, ethics, standards and policies.

The goals of this project are to: a) advance the science of integrated design of sensors and machine learning algorithms, b) train and enable a diverse cohort of students to make research contributions in integrated sensing and ML for IoT systems, c) gain knowledge on international policies/standards of deploying AI, big data systems, and sensors, and d) provide experiences that broaden understanding of global practices and career options. This project is motivated by the fact that inexpensive sensors are required for IoT, mobile health and wearable systems; to achieve the requisite precision, sensor design must be accompanied by corrective ML and SP algorithms. IRES research therefore focuses on the overlap of new sensor device design and novel ML algorithm development. In terms of ML, one of the key objectives is to develop compact, low power algorithms adequate for integration with sensors and mobile devices. IRES project areas include flexible sensors, sensor information management, efficient deep learning, and big data analysis. Example research project applications include biomarker detection, big data processing, gait detection, and deep neural nets for sensor and IoT systems. Work will be disseminated via collaborative publications and presentations in international conferences and refereed journals. Industry engagement at the ASU SenSIP and DCU Insight centers will provide ongoing valuable feedback, and annual external evaluation will assess progress and outcomes across all IRES activities.

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 introducing undergraduate students to Quantum Computing (QC), establishing foundations and software tools for workforce development in QC, and implementing these tools in Electrical Engineering and other STEM courses at the undergraduate level. The project plans to develop online content, modules and interactive software to introduce undergraduate students to Quantum computing with emphasis on quantum machine learning (QML). The project team will engage undergraduate students in Electrical and Computer Engineering as well as students from other STEM areas. The project will also include workforce-focused research experiences for undergraduate (REU) students during the summers. The team will also include a research experience for STEM teachers during the summer. The content developed for quantum information systems will be adapted for different groups, including undergraduate students, REU students, and for high school outreach. Grant activities and objectives also include developing a diverse community of users, innovative video-streamed content, interactive software for skill building, and summer training workshops. The materials created will: a) impact several STEM disciplines, b) engage and energize undergraduate students, c) create impactful quantum information science awareness and introductory skills, and d) establish modules and tools for workforce development in quantum information systems. <br/><br/>This project is motivated by the national need to develop a workforce in quantum computing with emphasis on QML. The project team will develop and thoroughly assess several QML products for undergraduate courses and training, including widely accessible online materials and interactive software. Materials and modules developed will support senior level elective courses in signal processing and machine learning. The project will introduce undergraduate students to Quantum computing and QML, using application-driven materials and interactive software. Assessment will be handled by the College Research and Evaluation Services Team (CREST). Finally, the project seeks to broaden participation through several strategies including collaborations with minority student chapters and minority serving institutions, and the leveraging of international university collaborations for global dissemination. Specific products include interactive analysis and visualization software tools for quantum machine learning and quantum Fourier transforms. These tools will enable students to understand and experiment with quantum parameters and assess their effect in compelling applications such as voice recognition. The assessment team will evaluate all the modules, their ability to engage students, capabilities in broadening participation, and the overall effectiveness of QML materials and interactive software in workforce development. The project will use a mixed-method assessment process (qualitative and quantitative data collection) to build an understanding of the impact of the use of the quantum computing tools on student learning gains. Assessments will be done through electronic web tools, pre- and post-quizzes, presentations, one-to-one interviews, and ordinary in-class testing. 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.<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.