Memphis, TN, United States

University of Memphis
Memphis, TN, United States

The University of Memphis, also called the U of M, is an American public research university located in the Normal Station neighborhood of Memphis, Tennessee. Founded in 1912, the university has an enrollment of more than 21,000 students. With 25 Chairs of Excellence and five state-approved Centers of Excellence, the school is the flagship institution of the Tennessee Board of Regents system.The University maintains the Center for Earthquake Research and Information , the Cecil C. Humphreys School of Law, the former Lambuth University campus , the Loewenberg School of Nursing, the School of Public Health, the College of Communication and Fine Arts, the FedEx Institute of Technology, the Advanced Distributed Learning Workforce Co-Lab, and the Institute of Egyptian Art and Archaeology. Wikipedia.

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University of Memphis | Date: 2017-04-10

The invention provides compositions featuring chitosan and methods for using such compositions for the local delivery of biologically active agents to an open fracture, complex wound or other site of infection. Advantageously, the degradation and drug elution profiles of the chitosan compositions can be tailored to the needs of particular patients at the point of care (e.g., in a surgical suite, clinic, physicians office, or other clinical setting).

The invention features biodegradable materials, and in vitro and in vivo methods of using such compositions to promote bone and soft tissue growth and healing.

University of Memphis | Date: 2016-11-09

A multilayer printed circuit as well as printed passive and active electronic components using additive printing technology is provided. The fabrication process includes a substrate and a first conductive layer that is printed with conductive ink on the substrate. An insulation layer that has uniform thickness is printed on the first conductive layer and the substrate, less via cavities, test point cavities, and a surface mount component contact point and mounting cavities. The insulation layer is replaceable with resistive layer or semi-conductive layer to fabricate electronic components. The vias are printed with conductive ink inside of the via cavities. Additionally, a second conductive layer is printed on the vias and over the insulation layer. The insulation, resistive, or semi-conducting layer, the vias, and the conductive layers are repeatedly printed in sequence to thus form the multilayer printed circuit.

Bidelman G.M.,University of Memphis | Lee C.-C.,University of Memphis
NeuroImage | Year: 2015

Categorical perception (CP) represents a fundamental process in converting continuous speech acoustics into invariant percepts. Using scalp-recorded event-related brain potentials (ERPs), we investigated how tone-language experience and stimulus context influence the CP for lexical tones-pitch patterns used by a majority of the world's languages to signal word meaning. Stimuli were vowel pairs overlaid with a high-level tone (T1) followed by a pitch continuum spanning between dipping (T3) and rising (T2) contours of the Mandarin tonal space. To vary context, T1 either preceded or followed the critical T2/T3 continuum. Behaviorally, native Chinese showed stronger CP as evident by their steeper, more dichotomous psychometric functions and faster identification of linguistic pitch patterns than native English-speaking controls. Stimulus context produced shifts in both groups' categorical boundary but was more exaggerated in native listeners. Analysis of source activity extracted from primary auditory cortex revealed overall stronger neural encoding of tone in Chinese compared to English, indicating experience-dependent plasticity in cortical pitch processing. More critically, "neurometric" functions derived from multidimensional scaling and clustering of source ERPs established: (i) early auditory cortical activity could accurately predict listeners' psychometric speech identification and contextual shifts in the perceptual boundary; (ii) neurometric profiles were organized more categorically in native speakers. Our data show that tone-language experience refines early auditory cortical brain representations so as to supply more faithful templates to neural mechanisms subserving lexical pitch categorization. We infer that contextual influence on the CP for tones is determined by language experience and the frequency of pitch patterns as they occur in listeners' native lexicon. © 2015 Elsevier Inc.

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 150.00K | Year: 2016

Patients with chronic illness require frequent and avoidable hospital visits. This project aims to develop a new class of battery-less, low-cost, disposable, wireless electronic patch sensors to monitor a variety of physiological signals and a custom smartphone app to monitor their health status and to elect to share their anonymized events-of-interest with their community towards a smart and connected community (S&CC). To achieve these aims, the interdisciplinary research team is collaborating with the non-profit McKendree District of the United Methodist Church located in the greater Memphis community to complete this work. This will empower users, permit the community stakeholders to assess population health status, reduce the need for frequent hospital visits, and help identify potential individual and community actions to achieve improvement in health status. The project also involves the training of undergraduate and graduate students in interdisciplinary research activities on emerging technologies, and is expected to impact public and private sector efforts to improve healthcare.

A smart and connected community (S&CC) will utilize distributed sensors and embedded computing to seamlessly generate meaningful interpretations that would be of greater benefit to individuals, the community, and society, in general, through improved health and safety, efficient public infrastructure, and better access to needed services. Although rapidly emerging mobile health technology is already tapping into widely used smartphone infrastructure, data collection using smartphone mobile devices is currently limited by few integrated sensors (e.g., Inertial measurement unit (IMU), camera, optical sensors, temperature sensor, and GPS). There are tremendous opportunities to advance the smart and connected communities by incorporating capabilities from external battery-less sensors into this framework to enable data collection and analysis for broader personal and community gain. Towards this goal, this research will (1) deliver a platform of fully-passive wireless electronic patch sensors for physiological data collection and to incorporate multimodal sensor data, (2) develop an open-source framework for meaningful and reliable Events-of-Interest (EoI) detection using a custom smartphone app for self-monitoring and communal sharing, and (3) deploy the sensors in a Living Lab for a pilot study to collect and classify these data in real-time to generate EoIs for various health conditions, such as arrhythmia, asthma, and sleep disorder.

Agency: NSF | Branch: Continuing grant | Program: | Phase: Combinatorics | Award Amount: 135.00K | Year: 2016

Random geometric graphs can be used to model many large scale networks, such as the Internet and social networks. Such graphs are particularly suited to the modeling of large-scale sensor and transceiver networks, which are becoming more common as electronic devices become smaller and cheaper and are interconnected in very large networks. Modeling the behavior of these networks is becoming more and more important, and the analysis of the behavior of these networks when they become extremely large is becoming increasingly relevant in practical applications. This award supports research on the properties of these large-scale networks, as well as training of graduate students and early-career researchers in the mathematics of random graphs.

The study of random geometric graphs originated with questions about the way fluids seep through porous media. More recently, the study of large-scale electronic and communication networks has prompted many questions about random geometric graphs. The basic model of random geometric graphs was proposed by Gilbert over fifty years ago: take points randomly in the plane according to a Poisson point process of unit intensity, and join two whenever they are within a prescribed distance of each other. The central question concerning this model is: for what values of the prescribed distance do we obtain an infinite connected component? Surprisingly, even after fifty years, only rough upper and lower bounds are known for the critical value of the prescribed distance. Some properties of this Gilbert model are known, but many other questions still remain unanswered. This research project addresses some of these questions, as well as other questions about related models of random graph inspired by both percolation theory and large-scale communication networks.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ENGINEERING EDUCATION | Award Amount: 150.00K | Year: 2016

The current identity of many engineering education programs lacks empathy as a core element. This could be a barrier to entry for women and may also be in conflict with the human-centered values expressed by engineerings professional organizations. To increase the enrollment of women in certain sub-disciplines of engineering, a reformulation of engineering identity to consciously incorporate empathy may be required. This research effort is centered on characterizing the empathetic aspects of this identity within some of the sub-disciplines of engineering and identifying the degree to which a perceived lack of empathy forms a barrier for women pursuing engineering as a field of study. A future effort will formulate methods of transforming faculty and student attitudes to be more empathetic. It is believed that this will lead to the formation of an engineering identity that is more open to the concerns of women and more consistent with the values defined in the professional codes and creeds. The broader impacts are threefold. First, this work could point the way to increasing the representation of women in fields such as electrical, computer, and mechanical engineering. By quantifying the role that empathy plays in the identity of potential engineering students and formulating methods to increase focus on empathetic activities as part of the engineering education process, women may find a greater affinity for disciplines that have suffered from underrepresentation. Second is the potential change for engineering students as a whole. Activities such as working in teams, managing the efforts of others, and identifying with consumers of products are all enhanced through skills in empathy. The final impact is a shift in focus toward more empathetic endeavors bringing greater resonance between the training a student receives and the objectives outlined in the professional societies identity statements. There is an opportunity with this research to empower young engineers to actually do what they are encouraged to do in every graduation speech: make the world better.

This research will be performed using two instruments administered over a two-year period. The first is a quantitative survey instrument that will measure: 1) empathy of the participant, 2) perceived empathy of engineering and non-engineering disciplines, 3) likelihood of choosing an engineering or non-engineering discipline for study, and 4) perceived empathy of the current faculty and students in the major of study. Survey results will be solicited from a pool of 4000 participants taken from STEM and non-STEM majors. Based on an analysis of the results of this survey, a qualitative focus group study will be performed using a semi-structured protocol. This qualitative component will further explore the empathetic factors related to choice of major among students. The study will be administered to groups of only men, only women, and mixed groups. Groups will be composed of 5-10 volunteers at three separate universities. The proposed research is significant because it seeks to assess, for the first time, empathy among students and faculty and to attempt to relate this to the participation of women in engineering programs. This study will contribute to a greater understanding of the role empathy plays in the diversity of engineering programs.

Agency: NSF | Branch: Continuing grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 136.38K | Year: 2016

This is a Faculty Early Career Development Program (CAREER) proposal responsive to Program Solicitation NSF 15-555. The CAREER program is a National Science Foundation-wide activity that offers the most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research. This project will investigate whether six urban middle schools are implementing highly effective science, technology, engineering and mathematics (STEM) programs based on factors identified through relevant research and national reports on what constitutes exemplary practices in 21st century-focused schools. The project will make this determination through the use of a STEM level of readiness rubric developed through a previous award that will be further revised through this study. The rubric will document the participating schools level of readiness at the principal, teacher, and student levels using 15 criteria that include a combination of essential supports, core elements, attributes, and characteristics about STEM through: (1) school leadership as the driver of change in education; (2) professional capacity among teachers and staff in all academic areas; (3) student-centered learning climate reflective of high-quality teaching and learning practices; and (4) investment of resources (e.g. staffing, time, space, materials and supplies, partnerships) that support exemplary school-based programs.

The project will use surveys, focus groups, and face-to-face interviews to collect data from 18 principals; classroom observations and a survey to collect data from 380 teachers, and a survey to collect data from 3700 students. These data collections, augmented by other intermittent research activities, will provide insights about extant programs in participating schools regarding effective school leadership, state-of-the art teaching and learning practices, and the impact on students interest, motivation, and self-efficacy about STEM education. The primary outcome from this project will be a field-tested jointly refined STEM level of readiness rubric based on input from principals, teachers, and students with guidance from the projects advisory board and the Center for Research in Educational Policy at the University of Memphis. The rubric will be instrumental in informing district-level education stakeholders and university-partner decision-makers choices about where and when to invest resources to further support the development of higher quality STEM programs and schools. It will also be useful in identifying ways to improve students overall perceptions about future courses of study and careers and the development of professional development modules for teacher training. Beyond these key school district-level outcomes, results will be used to enhance teacher preparation efforts through further refinement of methods courses and the STEM Teacher Leadership Certificate Program at the University.

Agency: NSF | Branch: Standard Grant | Program: | Phase: SPECIAL PROJECTS - CISE | Award Amount: 500.00K | Year: 2016

The goal of this project is to support the evaluation, experimentation, and further development of the Named Data Networking (NDN) architecture through building the core NDN infrastructure as a community resource, serving to advance research in the Information-Centric Networking (ICN) paradigm.

Named Data Networking (NDN) is a new Internet architecture that replaces todays architectural focus on ``where, i.e., the addresses and hosts of Internet Protocol (IP), with ``what, i.e., the content that users and applications care about. This fundamental shift brings profound impacts on enhancing Internet security, enabling mobility support, scaling content distribution, and facilitating new application development. NDN has attracted researchers from around the world, both in academia and industry, to explore all aspects of its design, implementation, and applications. It is a very prominent realization of the vision for Information-Centric Networking (ICN), around which a growing research community has formed over the past several years.

A full exploration and examination of future Internet architecture designs like NDN, and ICN more broadly, require working prototypes, evaluation tools, and experimentation platforms, which are the core infrastructure that this project aims to develop. More specifically, building upon the existing NDN research, this project will develop for the research community more robust, extensible, and well-documented implementations of the (i) NDN software forwarder providing core network functionality, (ii) libraries of essential features to support application development, (iii) a routing protocol to connect NDN nodes, (iv) NDN simulator and emulator packages, lab testbed, and global testbed for realistic evaluations and experimentation, and (v) demonstration kits, tools, documentations, and tutorials.

Broader Impacts: As the first comprehensive infrastructure to support NDN and ICN research, it will make significant impacts in several ways. First, by making NDN systems available on multiple platforms and accessible to all interested researchers and students, this infrastructure will enable new research opportunities and help grow the research community. Second, through venues such as academic conferences, community meetings, and the open-source development approach, the research community will be involved in both the development and the use of the infrastructure, contributing to and benefiting from the success of the project. Finally, the development and the use of the infrastructure provide a great education opportunity to train graduate and undergraduate students in thinking forward while experimenting with a running system.

Agency: NSF | Branch: Standard Grant | Program: | Phase: INFORMATION TECHNOLOGY RESEARC | Award Amount: 4.00M | Year: 2016

This project addresses a rapidly growing opportunity: the ability of the research community to use high-frequency mobile sensor data. Mobile sensors (embedded in phones, vehicles, wearables, and the environment) continuously capture data in great detail, and have the potential to address problems in a range of scientific and engineering domains. This effort focuses upon a specific case -- health data -- that builds upon several capabilities developed in National Institutes of Health (NIH) sponsored projects for assembling and analyzing health data collected through mobile sensors and apps. Improvements to the usefulness of extremely noisy, distributed data can serve many communities, and the components are extensible outside the human health domain.

Mobile sensors present a distinct set of data challenges: the data quantity and quality fluctuate, and uncertainty can be high. Establishing provenance on such noisy data is a challenge, and there are limitations on access to data from human subjects. This project addresses several of the distinctive challenges associated with mobile sensor data. Variability is addressed by providing detailed annotation with metadata (such as provenance and quality), and by providing facilities for context-specific reasoning about the metadata. The system captures provenance metadata along with data in a stream, and propagates this information alongside derived data from one stage to the next. This creates cyberinfrastructure that makes it possible to replay mobile device data with different configurations, to comparatively benchmark two algorithms or to diagnose erroneous output. The project builds upon the capabilities and success of the NIH-funded Center of Excellence in Mobile Sensor Data to Knowledge (MD2K), which provides an open-source cyberinfrastructure enabling the collection, curation, analysis, visualization, and interpretation of high-frequency mobile sensor data. Conducting research with mobile sensor data collected by others continues to be challenging; this project develops a companion open-source provenance cyberinfrastructure, facilitating the sharing of the mobile sensor data itself. Results include metadata standards, interfaces, and runtime support for annotating data streams with the source (sensor, location, sampling rate, continuous or episodic), semantics of output (number, probability, class), provenance (features, rules for decision), and validation (specificity, sensitivity, benchmark used). The infrastructure accommodates a wide variety of data types and enables data discovery, analytics, visualization, integration, and validation by third party researchers. The project improves the ability of the wider scientific and engineering community to use mobile sensing systems and metadata, and it also has immediate, tangible societal benefits in health and wellness.

This award by the Advanced Cyberinfrastructure Division is jointly supported by the NSF Directorate for Computer & Information Science & Engineering (Division of Computer and Network Systems, and Division of Information and Intelligent Systems).

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