Gregory J. Pottie - US grants

The primary goal of this project is to address three transition points critical to increasing the number of CSEMS students qualified and prepared to enter the high technology workforce. These three transition points present barriers that impact the recruitment and retention of incoming freshmen, rising juniors, and community college transfers. The project plan strengthens existing efforts and renews collaborations among the Henry Samueli School of Engineering and Applied Science faculty, the Center for Excellence in Engineering and Diversity, industry, and alumni in an effort to effectively develop, graduate, and place CSEM students into career positions or graduate school. At the end of this multi- phased, four-year project the governance team expects to positively impact 80 economically disadvantaged and underrepresented engineering, computer science and mathematics students.

Along with new recruitment programs, the CSEMS Project team includes faculty in key retention and student support components and institutionalizes promising pilot programs. The UCLA CSEMS Project team includes four faculty members and the Director for the Center for Excellence in Engineering and Diversity.

The Energy-Aware Articulation in Sensor Networks program will develop and demonstrate a fundamental, new distributed sensing capability that combine intelligent nodes with articulated electromagnetic communication systems, articulated sensors, and articulated energy collection devices. This program directly addresses primary, long-standing problems in distributed wireless sensors with a comprehensive, new approach.

Distributed, networked, wireless sensor systems are being developed and deployed for diverse applications ranging from physical security to environmental monitoring. In the past, conventional systems have been deployed in a static fashion with their critical components (communication, sensing, and energy collection) being fixed to the sensor node package. This limits the antenna structure choices and sensor systems to only general purpose, fixed systems that may operate at far from an optimal configuration with severe limitations on antenna gain and link energy requirements as well as sensor event detection and identification capability.

The Energy-Aware Articulation in Sensor Networks program will develop the principles for a direct solution to these system limitations through a convergence of actuator principles, energy-aware articulated networking, articulated distributed sensing, and distributed computing fundamentals. This will be based on distributed sensor node systems in an open, shared testbed that include nodes carrying articulated antenna structures (for optimized, high gain communication), articulated sensors (for optimized and cooperative event detection), and articulated solar energy collection (for extending operating life.) The combination of these capabilities promises to impact nearly all the system characteristics from deployment through detection capability.

Over the past decade, wireless sensor networks have evolved from primarily unidirectional star networks into a wide variety of forms, with large-scale research programs and significant commercialization activity. These systems will increasingly connect the physical world to wide-area networks for monitoring and control purposes including homeland defense and industrial applications. Yet relatively little is known on the fundamental limits to how efficiently a network can glean useful information from the environment to process it and relay it to the larger public network. Research is being conducted on fundamental limits in four areas: the rate distortion region for correlated sources, capacity for distributed nodes, packet delay across networks, and the dependability of heterogeneous wireless networks. These four topics collectively address many of the important parameters in sensor network design and will allow practical algorithms to be tested against fundamental limits.

Environmental monitoring has traditionally been limited by sensors that are very constrained in their information-gathering capacities and that acquire data only at a relatively small number of distinct, widely-separated locations. This severly limits the ability to characterize an environment, to then model it, and to subsequently provide accurate information for policy decisions relating to its management. The Networked Infomechanical Systems (NIMS) program introduces a revolutionary new class of mobile distributed sensors that are networked both physically by infrastructure and electronically to enable monitoring and interaction with the full three-dimensional environments in which biological and other systems exist. NIMS sensors measure changes in the environment, autonomously move to locations of particular interest, collaborate to exploit diverse sensing modalities (imagery, acoustic, chemical and meteorological, etc.) in the detection and characterization of events, and learn from the collective experience of the network.

With members from the University of California, Los Angeles, Merced, and Riverside, and the University of Southern California, the NIMS team brings information technology experts in computer science, information theory, robotics, signal processing, and networked embedded computing together with biologists and public health specialists. The initial demonstration applications of NIMS will focus on 1) monitoring of global change indicators in a mountain riparian stream canyon and 2) detection of biological pathogens in coastal waters and urban rivers. However, the techniques being developed apply across an extremely wide range of environments and applications and have the potental to broadly impact science, policy, and national security. The NIMS program also enables an engaging, explorative Grade 6-8 science instruction program with both hands-on science and remote network access to NIMS.

This award provides support for 15 undergraduate students to engage in a summer research experience at the University of California, Los Angeles Center for Embedded Networked Sensing (CENS), an NSF-funded Science and Technology Center. The students will have a unique opportunity at the Center to experience a systems approach to undergraduate research training. Students will spend ten weeks on site and will have the opportunity to have access to various activities and events through the academic year through participation in the CENS Alumni Network program. This program will serve to encourage undergraduates to pursue advanced degrees in engineering and thus impact the nations need to increase the number of U.S. citizens and permanent residents engaged in research-related careers. It is also anticipated that this program will have a Broader Impact on the nations need to have broader participation in the research enterprise of citizens and permanent residents from demographic groups traditionally underrepresented in science and engineering.

Wireless sensor networks with their ability of in situ and dense spatiotemporal sampling of physical phenomena have rapidly emerged as a valuable new class of instrument for the basic sciences. Like any instrument, however, sensor networks suffer from impairments that adversely impact the integrity of the sensed information about the physical phenomena. The causes of reduced integrity in sensor networks are many, and include various internal and external uncertainties in the sensing, processing, and communication elements of the system. Examples include calibration errors, faults, sensing channel degradation and obstructions, bio-fouling etc. This project is comprehensively addressing the integrity problem by studying its causes, understanding fundamental limits, developing algorithms and system support, and validating the approach in real-life. The two key elements of the overall approach are autonomous detection of integrity compromise, and resilient estimation and aggregation. Underlying these two are novel statistical and signal-processing techniques that exploit learned models of the physical phenomenon, the measurement process, and the faults which result in corrupted or missed data. In addition, the project is investigating approaches for guiding remediation of the causes of integrity problems, and for integrity-driven deployment of sensor network to achieve desired resilience. The research would result in a thorough understanding of integrity failure in sensor networks, and result in a toolkit of design tools and run-time software for ensuring high-integrity operation. These would be validated via terrestrial, under-soil, and aquatic ecological observation application, and also disseminated for broader use via the project web site at http://nesl.ee.ucla.edu/projects/integrity.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Obtaining physiological/behavioral data from human subjects in their natural environments is essential to conducting ecologically valid social and behavioral research. While several body area wireless sensor network (BAWSN) systems exist today for physiological data collection, their use has been restricted to controlled settings (laboratories, driving/flying scenarios, etc.); significant noise, motion artifacts, and existence of other uncontrollable confounding factors are the often cited reasons for not using physiological measurements from natural environments. In order to provide scientifically valid data from natural environments, a BAWSN system must meet several unique requirements (1) Stringent data quality without sensing redundancy, (2) Personalization to account for wide between person differences in physiological measurements, and (3) Real-time inferencing to allow for subject confirmation and timely intervention.

Intellectual Merit: In this project, a multidisciplinary team of researchers spanning various computing disciplines and behavioral sciences are developing a general purpose framework called FieldStream that will make it possible for BAWSN systems to provide long term unattended collection of objective, continuous, and reliable physiological/behavioral data from natural environments that can be used for conducting population based scientific studies. FieldStream is being incorporated in two real-life projects ? NIH sponsored AutoSense at Memphis and NSF sponsored Urban Sensing at UCLA, to help validate the assumptions, establish the feasibility of developed solutions, and to uncover new requirements.

Broader Impact: By making it possible to obtain scientifically valid objective data from the field, FieldStream promises to help solve several behavioral problems of critical importance to human society that have remained unanswered for lack of such data.

Pervasive computing, such as sensors in smartphones, buildings, automobiles and cities, result in increased sharing of sensor data, whether initiated by users or by other authorities such as service providers, government entities, interest groups, and individuals. Embedded in this data is information which others, even using sophisticated data mining algorithms, can fuse to construct a virtual biography of our activities, revealing private behaviors and lifestyle patterns. Researchers in this project are devising computational methods to let users exercise privacy control over their personal sensory data that is shared.

Intellectual Merit: The project is developing a user-configurable cryptographically-secure ?privacy shield? to run on smartphones and act upon sensor information flowing to other users, apps, and services. To make privacy understandable, the user is presented with a higher level abstraction for expressing privacy and sharing in terms of rich inferences and contexts drawn from sensor measurements. The user can designate some inferences and contexts as private. To provide privacy while ensuring the quality of service provided by the recipients of the sensory information, the system also incorporates algorithms which, over time, learn a personalized model of the privacy risk from sharing an inference. The theoretical concepts and the system realization are being validated via user studies in mobile health and personal sensing.

Broader Impacts: By providing better understanding of the behavioral privacy problem and risks inherent in sharing seemingly innocuous data, results from this project will lead to a more educated and informed citizenry, regulators, and policy makers, and provide effective tools for privacy management to those who share sensory information.

The research objectives are to advance the state of the art in methods of wireless health. Projects span the range of end-to-end systems including robust inference using body networks of sensors, real-time and energy efficient analysis of sensor data using smartphones, development of effective interfaces and other feedback mechanisms, and construction of readily searchable large-scale databases of time series data. Collectively, the projects enable quantitative determination of patient state and compliance with therapy, and thus facilitate large-scale clinical studies that evaluate the effectiveness of particular therapies. Projects related to inference of human motion and provision of appropriate feedback also have application to sports training and the learning of other physical skills.

The broader impacts include training of undergraduate researchers by graduate mentors and their inspiration to pursue graduate studies. The summer research is designed to develop materials that refresh capstone design courses and produce hands-on activities that are added to lecture courses. The research of the graduate mentors, the summer research, and the capstone design activities are all closely related in a cycle that advances research and training in each phase.

Additional information is provided at the project web site (http://www.wirelesshealth.ucla.edu/).

High-speed wireless communication has attracted extensive attention in the past decades because of dramatic changes in the ways people create and share information. The expected data rate to satisfy the needs of the customers is projected to be 100 Gbit/s by 2020. However, the speed of today's wireless communication systems is limited by the narrow bandwidth of existing transmitters and receivers and the heavy use of the electromagnetic spectrum up to 60 GHz. This trend has been forcing researchers to push the carrier frequencies of transceivers to higher frequencies to allow operation at frequency bands which have not been allocated to any specific active service yet. In order to increase the channel capacity, researchers have been trying to operate at terahertz frequencies, which cover the frequency range of 0.3-10 THz in the electromagnetic spectrum. While communication systems with such high carrier frequencies have been hard to develop so far, a data link operating at terahertz frequencies with data rates exceeding 100 Gbit/s over 10-20 meters distance is believed to be viable now thanks to recent advancements in terahertz photonics and electronics. The research component of this program plans to develop such a high data rate wireless communication system at terahertz carrier frequencies for indoor communication applications. A system with this capability can be very suitable for the next generation indoor wireless communication systems such as wireless local area networks and wireless personal area networks, kiosk downloads, wireless backhauling, data center connection, and real-time data analysis for wearable devices. Additionally, this program plans to integrate research with education and outreach activities by developing new courses, recruitment and involvement of undergraduate students, and organizing high-school seminars.

During this program a directional terahertz transceiver system based on an adaptive two-dimensional array of plasmonic photomixers operating under novel communication protocols is designed, fabricated, and experimentally demonstrated. The proposed work presents an entirely new perspective on the potential use of terahertz bandwidth for increasing wireless communication data rates to levels that could not be envisioned before due to low output power and limited adaptability of existing terahertz transmitters. This limitation can be tackled through use of plasmonic photomixer arrays that offer significantly higher terahertz radiation powers and higher level of transmitter adaptability compared to other terahertz transmitter candidates. Additionally, the adaptive nature of the proposed terahertz transceiver system allows compensation for accidental misalignments and shadowing effects through beam-forming and non-line-of-sight communication links. While theoretical investigations provide a deep understanding of fundamental physical limitations of terahertz transceiver systems based on plasmonic photomixer arrays and achievable wireless communication data rates, the experimental effort provides valuable data to develop accurate channel models at terahertz frequencies in order to utilize appropriate communication modalities, equalization techniques, and adaptive signal tracking systems to mitigate signal fading in indoor terahertz communication systems.