George Mason University is the largest research university in Virginia and is based in Fairfax County, Virginia, United States, south of and adjacent to the city of Fairfax. Additional campuses are located nearby in Arlington County, Prince William County, and Loudoun County. The university's motto is Freedom and Learning.The university was founded as a branch of the University of Virginia in 1957 and became an independent institution in 1972. Today, Mason is recognized for its strong programs in economics, law, creative writing, computer science, and business. In recent years, George Mason faculty have twice won the Nobel Prize in Economics. The university enrolls 33,917 students, making it the largest university by head count in the Commonwealth of Virginia. Wikipedia.
University of South Florida and George Mason University | Date: 2015-04-21
An implantable magnetic resonant imaging (MRI) safe stylus for biomedical devices is described. In one example, the stylus includes a set of stylus modules. One or more of the stylus modules includes a core rod formed of silicon carbide (SiC) material, a recording array mounted on the core rod, and a stimulation array mounted at a distal end of the core rod. The stylus also includes a hemispherical cap formed of SiC material. In part due to the construction and choice of materials used in the stylus, it does not substantially couple with electromagnetic fields during an MRI, for example. Therefore, the stylus does not produce excessive additional heat. The designs described herein also rely on the high thermal transport but low heat capacity of SiC to provide a thermal pathway which will conduct induced heat throughout the stylus, to dissipate heat more evenly.
University of Maryland University College, United States Of America, George Mason University and George Washington University | Date: 2016-10-19
A nanostructure sensing device comprises a semiconductor nanostructure having an outer surface, and at least one of metal or metal-oxide nanoparticle clusters functionalizing the outer surface of the nanostructure and forming a photoconductive nanostructure/nanocluster hybrid sensor enabling light-assisted sensing of a target analyte.
George Mason University | Date: 2016-10-11
Provided are methods, systems, devices of a security-driven design method. The present methods and systems can enable integration of security requirements in the early stages of design along with other design constrains so that potential attacks during IC development, usage, and retirement would render ineffectual. Example methods and systems can comprise circuits and circuit design using vanishable logic through a novel hybrid design method. An example method or system can comprise vanishable logic based on hardware re-configuration and transformation by employing non-volatile memory cells.
Uhen M.D.,George Mason University
Annual Review of Earth and Planetary Sciences | Year: 2010
Whales are first found in the fossil record approximately 52.5 million years ago (Mya) during the early Eocene in Indo-Pakistan. Our knowledge of early and middle Eocene whales has increased dramatically during the past three decades to the point where hypotheses of whale origins can be supported with a great deal of evidence from paleontology, anatomy, stratigraphy, and molecular biology. Fossils also provide preserved evidence of behavior and habitats, allowing the reconstruction of the modes of life of these semiaquatic animals during their transition from land to sea. Modern whales originated from ancient whales at or near the Eocene/Oligocene boundary, approximately 33.7 Mya. During the Oligocene, ancient whales coexisted with early baleen whales and early toothed whales. By the end of the Miocene, most modern families had originated, and most archaic forms had gone extinct. Whale diversity peaked in the late middle Miocene and fell thereafter toward the Recent, yielding our depauperate modern whale fauna. Copyright © 2010 by Annual Reviews. All rights reserved.
Agency: NSF | Branch: Standard Grant | Program: | Phase: CYBER-PHYSICAL SYSTEMS (CPS) | Award Amount: 399.93K | Year: 2017
The goal of this project is to develop an automated assistive device capable of restoring walking and standing functions in persons with motor impairments. Although research on assistive devices, such as active and passive orthoses and exoskeletons, has been ongoing for several decades, the improvements in mobility have been modest due to a number of limitations. One major challenge has been the limited ability to sense and interpret the state of the human, including volitional motor intent and fatigue. The proposed device will consist of powered electric motors, as well as the power generated by the persons own muscles. This work proposes to develop novel sensors to monitor muscle function, and, muscle fatigue is identified, the system will switch to the electric motors until the muscles recover. Through research on methods of seamless automated control of a hybrid assistive device while minimizing muscle fatigue, this study addresses significant limitations of prior work. The proposed project has the long-term potential to significantly improve walking and quality of life of individuals with spinal cord injuries and stroke. The proposed work will also contribute to new science of cyber-physical systems by integrating wearable image-based biosensing with physical exoskeleton systems through computational algorithms. This project will provide immersive interdisciplinary training for graduate and undergraduate students to integrate computational methods with imaging, robotics, human functional activity and artificial devices for solving challenging public health problems. A strong emphasis will be placed on involving undergraduate students in research as part of structured programs at our institutions. Additionally, students with disabilities will be involved in this research activities by leveraging an ongoing NSF-funded project.
This project includes the development of wearable ultrasound imaging sensors and real-time image analysis algorithms that can provide direct measurement of the function and status of the underlying muscles. This will allow development of dynamic control allocation algorithms that utilize this information to distribute control between actuation and stimulation. This approach for closed-loop control based on muscle-specific feedback represents a paradigm shift from conventional lower extremity exoskeletons that rely only on joint kinematics for feedback. As a testbed for this new approach, the team will utilize a hybrid exoskeleton that combines active joint actuators with functional electrical stimulation of a persons own muscles. Repetitive electrical stimulation leads to the rapid onset of muscle fatigue that limits the utility of these hybrid systems and potentially increases risk of injury. The goals of the project are: develop novel ultrasound sensing technology and image analysis algorithms for real-time sensing of muscle function and fatigue; investigate closed-loop control allocation algorithms utilizing measured muscle contraction rates to minimize fatigue; integrate sensing and control methods into a closed loop hybrid exoskeleton system and evaluate on patients with spinal cord injury. The proposed approach will lead to innovative CPS science by (1) integrating a human-in-the-loop physical exoskeleton system with novel image-based real-time robust sensing of complex time-varying physical phenomena, such as dynamic neuromuscular activity and fatigue, and (2) developing novel computational models to interpret such phenomena and effectively adapt control strategies. This research will enable practical wearable image-based biosensing, with broader applications in healthcare. This framework can be widely applicable in a number of medical CPS problems that involve a human in the loop, including upper and lower extremity prostheses and exoskeletons, rehabilitation and surgical robots. The new control allocation algorithms relying on sensor measurements could have broader applicability in fault-tolerant and redundant actuator systems, and reliable fault-tolerant control of unmanned aerial vehicles.
Agency: NSF | Branch: Standard Grant | Program: | Phase: S-STEM:SCHLR SCI TECH ENG&MATH | Award Amount: 643.92K | Year: 2016
The Rural and Diverse Student Scholars (RADSS) Program at George Mason University will offer scholarships and research experiences to rural and diverse undergraduates, who are academically talented and have demonstrated financial need. Students will major in science degrees such as astronomy, biology, chemistry, environmental science, mathematics, geology, and physics at George Mason University. The specific objectives of RADSS are to: (1) strategically attract talented rural and diverse students to major in College of Science (COS) degrees, (2) promote retention of talented rural and diverse students in COS majors, and (3) directly support undergraduate scholarly activity by these underrepresented students in the College of Science. The RADSS Program will recruit and target high school students from rural parts of Virginia who express interest in STEM, and will work with target schools to increase awareness of science, technology, and/or math degree options.
RADSS will build capacity at Mason to form partnerships with regional laboratories and industry, for students to gain research experiences and mentorship experiences. Rural student access to research opportunities has the potential to be a gateway for them to pursue a science, technology, or math degree. The inclusion of undergraduate research and S-STEM Scholar support by Learning Assistants is an innovative design for a two-cohort model. The undergraduate research component has been shown to be effective for supporting student scientific identity formation and motivates students to complete STEM courses and degrees. The synergistic effects of these components and the effectiveness of an interdisciplinary faculty team will be investigated. The evaluation of the RADSS S-STEM program will benefit our understanding of how to recruit and support rural undergraduates in science, technology, and math. This program will be supported by the STEM Accelerator Program in the College of Science (COS), the COS Deans Office, the Admissions Office, and the Students as Scholars Program in the Provosts Office at Mason. By working to increase the number of rural and diverse students in STEM at Mason, the RADSS program will benefit NSFs mission to increase participation of those typically underrepresented in science and technology.
Agency: NSF | Branch: Standard Grant | Program: | Phase: MAJOR RESEARCH INSTRUMENTATION | Award Amount: 1.65M | Year: 2016
This award provides support to George Mason University for the acquisition of a high performance 3 Tesla (3T), whole body Magnetic Resonance Imaging (MRI) scanner to support innovative, transformative research into brain and body. The new 3T MRI system, along with sophisticated coils and software, will be the centerpiece of an Interdisciplinary Multimodal Imaging Center (IMIC). MRI allows detailed, noninvasive imaging of brain and body anatomy and connectivity; function via blood oxygen level dependent (BOLD) responses; and metabolism via magnetic resonance spectroscopy. GMU scientists, representing more than eight disciplines across five colleges, will benefit from this award by collaborating to conduct cross-cutting interdisciplinary research on groundbreaking associations between brain and body. The 3T MRI scanner will also enable researchers at GMU to conduct innovative training of undergraduates, graduate students, and junior scientists in brain-body science approaches. This group of researchers all take advantage of our location in the Northern VA/Washington DC region to study sample diverse in age, disability status, ethnicity, and socioeconomic status.
One central theme to the research of GMU investigators is to understand the interrelations of brain and body functions from a biological, psychological, and social perspective, and the alterations of those relationships in the presence of acute and chronic stress, trauma, and pain. Examples of the interdisciplinary research programs that will significantly benefit from this instrumentation include: those examining interactions between brain and peripheral nervous systems following stress and trauma; central and peripheral pain perception; neural, gene, and protein networks and social ties; neurodevelopment and head impacts; and sensorimotor integration and control. These and the other MRI research programs at GMU will be greatly advanced by the acquisition of a Siemens MAGNETOM Prisma 3T scanner, which offers industry-leading gradient performance, parallel imaging capabilities, and a supportive and innovative sequence development community. To address the big data challenges posed by the research outlined in this proposal, a data analytics core will be established, comprised of computer scientists, engineers, and biostaticians, to facilitate data handling and storage, and to develop new techniques for the analysis and integration of MRI and related biological, physiological, and behavioral data.
This MRI award is supported by the Directorate for Social, Behavioral and Economic Sciences (SBE) Division of Behavioral and Cognitive Sciences (BCS), The Directorate for Engineering (ENG) and the ENG Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET).
Agency: NSF | Branch: Standard Grant | Program: | Phase: Algorithms in the Field | Award Amount: 507.85K | Year: 2016
In the current data-centered era, there are many highly diverse data sources that provide information about movement on transportation networks. Examples include GPS trajectories, social media data, and traffic flow measurements. Much of this movement data is challenging to utilize due to the inherent uncertainty caused by infrequent sampling and sparse coverage. The goal of this project is to develop a unified framework that uses as many available data sources as possible to extract meaningful traffic and movement information automatically from the data. Probabilistic network movement models will be developed that capture movement probabilities and traffic volume on a network over time. The results will impact a range of applications that rely on capturing population movements, such as urban planning, geomarketing, traffic management, and emergency management. Educational activities will be integrated throughout the project. Students will be closely involved in research and practical implementations, and will be trained in spatio-temporal data management, algorithms development, and (trajectory) data analysis. The combination of such skills is increasingly important in spatial data science. Topics involved in this project will enrich the course material and curriculum development at both institutions.
The objective of this project is to create a unified framework for aggregating and analyzing diverse and uncertain movement data on road networks, with the aim to provide tools for querying and predicting traffic volume and movement. Probabilistic movement models on the network will be developed that can handle heterogeneous data sources, including GPS trajectories, geo-tagged social media data, bike-share data, public transport data, and traffic volume data. The diversity and spatio-temporal uncertainty of this data will be addressed with a Bayesian traffic pattern learning approach that first trains the movement models with the more certain data, which in turn will be used to fill gaps in the more uncertain data. The project will advance the state-of-the-art in theoretical communities (computational geometry, data mining) as well as in applied communities (spatial databases, location science). The results of the research will available on the project website (movementanalytics.org), and will be disseminated in prestigious venues through presentations and demonstrations at conferences, and through publications in journals.
Agency: National Aeronautics and Space Administration | Branch: | Program: STTR | Phase: Phase II | Award Amount: 749.75K | Year: 2017
Extravehicular Mobility Units (EVU) are the necessary to perform elaborate, dynamic tasks in the biologically harsh conditions of space and they have stringent requirements on physical and chemical nature of the equipment/components/processes, to ensure safety and health of the individual require proper functioning of its life-support systems. Monitoring the Portable Life Support System (PLSS) of the EVU in real time ensures the safety of the astronaut and success of the mission. In Phase I, N5 Sensors has demonstrated and manufactured an ultra-small form factor, highly reliable, rugged, low-power sensor architecture for carbon dioxide (CO2) and ammonia (NH3) that is ideally suited for monitoring trace chemicals in spacesuite environment in presence of humidity and oxygen. N5 will perform additional design refinements in Phase II and implement on-chip components for enhanced analytical and operational reliability. Additionally, a complete detector system will be designed, integrated with various electronic components and tested to determine system level performance and reliability. Subsequent design refinements will be done.
Agency: NSF | Branch: Standard Grant | Program: | Phase: Smart and Connected Health | Award Amount: 891.13K | Year: 2016
The automobile presents a great opportunity for healthcare monitoring. For one, most Americans engage in daily driving, and patients time spent in vehicles is a missed opportunity to monitor their condition and general wellbeing. The goal of this project is to develop and evaluate technology for automatic in-vehicle monitoring of early symptoms of medical conditions and disrupted medications of patients, and to provide preventive care. Specifically, in this project we will focus on Attention-Deficit/Hyperactivity disorder (ADHD) in teenagers and young adults, a prevalent chronic medical condition which when uncontrolled has the potential for known negative health and quality of life consequences. The approach of using driving behavior to monitor ADHD symptoms could be applied to many other medical conditions (such as diabetes, failing eyesight, intoxication, fatigue or heart attacks) thereby transforming medical management into real-time sensing and management. Identification of all these conditions from driving behavior and alerting the proper agent could transform how we think about health monitoring and result in saved lives and reduced injuries.
The main goal of this project is to leverage the large amounts of health data that can be collected while driving via machine learning, in order to detect subtle changes in behavior due to out-of-control ADHD symptoms that can, for example, indicate the onset of episodes of inattention before they happen. Via lab-based driving simulator as well as on-road studies, the research team will investigate the individualized behaviors and patterns in vehicle control behaviors that are characteristic of ADHD patients under various states of medication usage. The team will develop a machine learning framework based on case-based and context-based reasoning to match the current driving behavior of the patient with previously recorded driving behavior corresponding to different ADHD symptoms. The key machine learning challenge is to define appropriate similarity measures to compare driving behavior that take into account the key distinctive features of ADHD driving behavior identified during our study. The team will evaluate the accuracy with which the proposed approach can identify and distinguish between different out-of-control ADHD symptoms, which are the implications for long-term handling of ADHD patients, via driving simulator experiments as well as using instrumented cars with real patients.