Tracking low-dimensional information in data streams and dynamical systems
SINE Investigator: Mike Wakin Abstract: Recent advances in sensor technology have allowed observation of massive data about complex dynamical systems at an unprecedented scale. Low-dimensional models serve as a useful structure for understanding the information in high-dimensional signals and systems. However, this information often changes over time, and so these models can further be improved by exploiting temporal dynamics. This project is concerned with developing new methods for tracking changing low-dimensional structure in data streams and dynamical systems, particularly in settings where the observations may be missing, incomplete, corrupted, or compressed. This research aims to 1) develop techniques for tracking low-dimensional structure and, in particular, to extend tracking capabilities far beyond conventional signals to much more general data sets with intrinsic low-dimensional structure; 2) develop new tools for tracking low-dimensional structure in systems jointly with estimating the content of time-varying signals and data sets; 3) perform low-dimensional quantitative and qualitative analysis in systems that are too complex and high-dimensional for system identification
Detecting Erosion Events in Earth Dams and Levees
SINE Investigator: Tracy Camp Abstract: Geophysical sensor technologies can be used to understand the structural integrity of Earth Dams and Levees (EDLs). We are part of an interdisciplinary team researching techniques for the advancement of EDL health monitoring and the automatic detection of internal erosion events. We work to develop a pattern recognition process that could be used as a tool for nonintrusive long-term earth dam and levee monitoring. Specifically, we apply various machine-learning techniques to EDL geophysical data to determine how we can best detect internal erosion events, such as anomaly detection. We apply our process to (1) test data collected in am experimental laboratory earth embankment, or “crack box” testbed equipped with geophysical instrumentation, and brought to failure, (2) full-scale test and real-world passive seismic levee data that has been collected from the IJkdijk full-scale test embankment located in Booneschans, Netherlands, and (3) seepage and erosion data from the real-world Colijnsplaat levee in the Netherlands.
Crowdsourcing-Based Spectrum Etiquette Enforcement in Dynamic Spectrum Access
SINE Investigator: DJ Yang Abstract: Due to the openness of wireless medium, it is susceptible to various forms of misuse or abuse. This project develops a crowdsourcing-based framework for etiquette and rule enforcing in dynamic spectrum sharing environments, which engages community users (radios) to detect misuse, and identify and punish unruly devices. Multiple benefits include: 1) larger detection coverage and higher accuracy; 2) no requirement on pervasive dedicated trusted infrastructure or hardware; and 3) stronger deterrence to misbehaviors. The research plan consists of four major components: 1) an optimized crowdsourced passive radio traffic monitoring framework to detect access misbehavior; 2) techniques to identify misbehaving cognitive radio devices using physical layer identification, even when the signal waveform can be adaptively modified; 3) techniques for immediate punishment of spectrum misuse through adaptive friendly jamming which exploits multi-functional re-configurable antennas; and 4) incentive mechanism design via auctions to ensure user participation in each task of crowdsourced etiquette enforcement.
Measure Estimation from Moments: Theory, Algorithms, and Applications
SINE Investigator: Gongguo Tang Abstract: From phase imaging in microscopy to semantic analysis of neural signals, scientific discovery relies on identifying models from massive noisy, incomplete, and corrupted data. One critical challenge for model identification is the nonlinearity inherent in most scientific models and data analysis tasks; however, these nonlinear problems could become linear when "lifted" into higher dimensional spaces. This research develops a unified framework of formulating nonlinear problems as infinite-dimensional linear problems using measure, a concept of modern mathematics that abstracts the notion of volume and mass. This project 1) delineates the class of model identification tasks that can be formulated as measure estimation from moments (ex: include tensor decomposition and completion, non-negative matrix factorization, and solving high-order multi-variate polynomial equations); 2) develops an approach to solve measure estimation using semidefinite programming with guaranteed performance; and 3) provides efficient and scalable algorithms for applications in computational optics and large-scale data analysis.
Subspace Matching and Approximation on the Continuum
SINE Investigator: Mike Wakin Abstract: In many application areas, custom solutions have been proposed for the problem of estimating or extracting information from a signal that is governed by a small number of unknown (continuous-valued) parameters. This research is developing a unified framework for addressing this problem. Under this framework, we consider models where the signals of interest lie within or close to a subspace or a union of subspaces. Rather than attempt to simply discretize parameters for the signal that are continuous-valued, this research involves the development of finite approximations for a locally infinite range of subspaces using efficient local subspace fits whose dimensions match the effective number of local degrees of freedom. We also aim to identify the range of subspaces responsible for generating a signal from a set of discrete observations. This framework will enable the efficient acquisition, processing, and estimation of signals arising from the parameterized subspaces model.
Mobile Social Group Monitoring
SINE Investigator: Qi Han Abstract: Identifying communities is an important research topic in traditional as well as online social networks. A community is a densely connected group of nodes/users such that connections between communities are sparse. This project researches community detection in proximity-based mobile social networks based on their spatio-temporal contact profile, recognition of groups with similar activities such as queuing, differentiation of co-located proximity groups.
Identification of Large Scale Dynamical Systems
SINE Investigators: Tyrone Vincent and Mike Wakin Abstract: Spatially distributed systems are complex due to the large number of observable variables and the possible ways in which these variables can interact. A typical example of this is thermal dynamics of large building, where temperature, energy usage, and occupation vary both spatially and temporally, with interactions among variables at different locations. In this project, we utilized directed graphs to represent these systems, with nodes representing variables, and edges representing interactions. We examined methods for efficient methods for identifying the correct topology of these graphs from data.
Unleashing Spectrum Effectively and Willingly: Optimization and Incentives
SINE Investigator: DJ Yang Abstract: An intriguing fact emerges during the study of spectrum usage. On one hand, the proliferation of wireless devices, e.g. smartphones, laptops, and tablets, and bandwidth-hungry applications has resulted in the problem of spectrum scarcity. On the other hand, a recent report by FCC reveals that the licensed users are extremely underutilizing the allocated spectrum. To remove the barriers to efficient spectrum utilization, this project aims to 1) design effective spectrum allocation algorithms to allow as many secondary users (SUs) to coexist with the primary user (PU) as possible while taking into account the interference generated by both SUs and PU; and 2) develop incentive mechanisms for enticing spectrum licensees to share or license their under-utilized spectrum for better utilization.
Extracting and Modeling Mobility From Cellular Phone/Social Media Communication
SINE Investigator: Tracy Camp Abstract: The proliferation of mobile devices (such as cellular phones and tablets) brought a relatively easy way to record location information from a large number of individuals during extended periods of time. This invaluable data is shedding new light on human mobility research, and is being applied in areas with high social impact, such as urban planning, emergency response, and epidemic simulation. This research studies how mobile phone and social media communication can be used to collect location information from mobile devices and, consequently, their users. The ultimate goal is to use cellular phone data and/or social media communication in order to develop a realistic large-scale mobile model.
Enabling Automated Oil and Gas Processes Using Unmanned Mobile Robots
SINE Investigator: Qi Han Abstract: The main objective of this project is to explore, develop, and demonstrate a robot prototype system for an unmanned process, or process section, to improve safety and increase reliability and to be operated in remote and harsh environments. The presence of toxic gasses (such as H2S) and very high outdoor temperatures in the Middle East region poses great risks to staff on the site of oil and gas facilities, so the clear benefits of using robotic technology for the relevant tasks such as inspection, operations and maintenance of oil and gas facilities are reliability, robustness and flexibility. Such a system will have to perform a number of different tasks, some of which are guided by a remotely located operator or operation team, and some of which are performed automatically by the robot without human intervention.
SINE Investigator: Mike Wakin Abstract: There is a great need to inspire K-12 students--especially female and underrepresented minority students--to pursue education and careers in Science, Technology, Engineering, and Math (STEM) subject areas. As a means of outreach to K-12 students, we are involved in creating and disseminating short educational modules that can be delivered by K-12 teachers to educate and inspire students about topics broadly related to signal and information processing. These modules are developed at CSM and delivered to local students as part of CSM's Discover STEM summer camp for middle school students and CSM's Discovering Technology program for elementary school students. After development and evaluation, these modules are submitted to the TeachEngineering digital library where they are made freely available for K-12 teachers to access.
C-START: Colorado - STrategic Approach to Rally Teachers
SINE Investigator: Tracy Camp Abstract: When compared to national numbers, it is clear that Colorado is in desperate need of more teachers who have the ability to offer rigorous, engaging CS courses. C-START aims to improve the skills of existing CS teachers, share best practices in CS pedagogy, and positively change the diversity of students in existing and new CS courses. C-START will create high-quality professional development (PD) opportunities and empower (and support) 50+ high school teachers in Colorado to teach a progression of CS courses, from introductory through AP CS. These opportunities will leverage successful elements of several existing efforts for K-12 computer science PD that have been recently developed across the country, as well as best practices in broadening participation in computing that have been developed by Alliances and other BPC projects. The effectiveness of C-START's approach will be measured by changes in expected outcomes.
Cooperative Beamforming for Efficient and Secure Wireless Communication
SINE Investigator: Tracy Camp Abstract: This project introduces cooperative beamforming (CB), a novel technique that enables secure, high throughput, and power efficient wireless network communications. CB consists of two stages: 1) the sources share their data with neighboring nodes via low-power communications, and 2) the cooperative nodes apply a weight to the signal received during first stage, and transmit. The weights are such that a specific objective criterion (e.g., signal to interference at the destination) is maximized. In CB, although each node uses low power, all nodes together can deliver high power to a faraway destination. This increase in power offsets power reduction due to propagation attenuation. CB can be viewed as an alternative to multihop transmission and, unlike multihop transmission, does not deplete the power resources of other nodes. Since CB can achieve long distance communication, new paths can be found to improve the overall network performance. Also, CB improves network security by avoiding eavesdroppers; unlike traditional cryptographic-based protocols that operate at higher layers and are sensitive to the broadcast nature of the transmission medium, CB improves security at the physical layer. CB will be implemented on a hardware network testbed to demonstrate how the developed techniques can revolutionize wireless communications.
New Theory and Algorithms for Scalable Data Fusion
SINE Investigator: Mike Wakin Abstract: Recent developments in sensor technology, signal processing, and wireless communications have enabled the conception and deployment of large-scale networked sensing systems comprising coordinated stationary and mobile platforms carrying sensors of diverse modalities. The promise of systems lies in their ability to intelligently make decisions by integrating information from massive amounts of sensor data. However, this great promise is offset by a number of critical challenges, which include growing volumes of sensor data, increasingly diverse data, diverse and changing operating conditions, novel information appearing, and increasing mobility of sensor platforms. This project aims to develop a principled theory of data fusion and decision making that provides predictable, optimal performance for a range of different problems through the effective utilization of the available network of resources.
Building Energy Monitoring and Control using Wireless Sensor Networks
SINE Investigator: Qi Han Abstract: This research developed network and systems-level software to support distributed monitoring and control in wireless sensor networks (WSN). Previous WSN architectures have focused on centralized systems, with less emphasis on peer to peer information sharing.
This research developed a complete WSN architecture, better optimized for distributed monitoring and control using peer to peer communication. Central to our approach is a novel multicast implementation for IPv6 WSNs; Using multicast communication allows sensor nodes to efficiently share data in a distributed fashion while the use of standard IPv6 communication greatly improves interoperability. Additionally, by embracing WSN-based monitoring and developing improved non-intrusive load monitoring techniques for buildings, we are able to provide a more detailed analysis of building energy consumption: distinguishing between smaller appliances. Preliminary studies have demonstrated a 7%-14% reduction in energy consumption using a distributed WSN-based control system.
Cyber-Enabled Efficient Energy Management of Structures
SINE Investigator: Tyrone Vincent Abstract: This research aims to develop methods for the control of energy flow in buildings enabled by cyber infrastructure. Electrical and mechanical engineers alongside computer scientists are advancing the state of the art in simulation, design, specification and control of buildings with multiple forms of energy systems, including generation and storage. A significant novelty of this project lies in a fundamental view of a building as a set of overlapping, interacting networks. These networks include the thermal network of the physical building, the energy distribution network, the sensing and control network, as well as the human network, which in the past have been considered only separately. The advent of maturing distributed and renewable energy sources for on-site cooling, heating, and power production and the concomitant developments in the areas of cyberphysical and microgrid systems present an enormous opportunity to substantially increase energy efficiency and reduce energy-related emissions in the commercial building energy sector. Additionally, this work trains students with backgrounds in the unique blend of engineering and computer science that is needed for the study of cyber-enabled energy efficient management of structures, as well as planned interactions at the undergraduate and K-12 level.
Quality-aware Sensor Data Collection
SINE Investigator: Qi Han Abstract: As sensors become smaller, cheaper and more configurable, large sensor array systems become more feasible. Besides the technological aspects of sensor design, a critical factor enabling future sensor-driven applications will be the availability of an integrated infrastructure taking care of the onus of data management.
In this project, we investigate some of the issues that such an infrastructure must address. Unlike conventional distributed database systems, a sensor data architecture must handle extremely high data generation rates from a large number of small autonomous components. Due to severe bandwidth and energy constraints of battery-operated wireless sensors, all this data cannot be streamed into the query processing site. Thus, sensing data architectures must become quality-aware, regulating the quality of data at all levels of the distributed system, and supporting user applications' quality requirements in the most efficient manner possible.
Dynamic Context Monitoring and Modeling for QoS-aware Applications
SINE Investigator: Qi Han Abstract: This project aims to provide a context information collection service for context aware applications. The complexity of providing the context information service arises from (i) dynamically changing status of information sources; (ii) diverse user requirements in terms of Quality of Service (QoS: such as response timeliness or reliability etc.) and Quality of Data (QoD, such as data accuracy or freshness); and (iii) constantly changing system conditions.
This project addressed the tradeoffs between QoS, QoD, and resource consumption by exploiting the tolerance of applications to quality violations. To ensure that applications receive the information at the desired levels of quality while ensuring effective utilization of underlying resources, we have focused on designing adaptive and cost-effective algorithms for the representation, collection and maintenance of the dynamic context information in heterogeneous distributed systems. In addition to these algorithmic efforts, we have designed a middleware framework supporting context awareness.
Monitoring of Dynamic Amorphous Phenomena
SINE Investigator: Qi Han Abstract: Applications using wireless sensor networks for critical areas such as environmental monitoring and emergency response highlight the urgent need for more powerful tools for tracking amorphous and dynamic events. Current efforts in event detection and tracking have mostly assumed that either events remain distinct, or if they do cross that they were identified prior and nothing new has formed. This project addresses the research challenges in designing and implementing a system that is capable of tracking events with or without well-defined shapes and identities in the presence of stringent energy constraints and unpredictable network failures posed by WSNs. Specific research objectives include: design and evaluation of algorithms that 1) detect and track any types of events; 2) form and reform communication structures around events of interest; and development of an integrated system that provides interfaces to high level application tasks to execute on each identified event.
Process Control for Low-Cost Electrochromic Film on Plastic
SINE Investigator: Tyrone Vincent Abstract: Electrochromic windows have the ability to darken or lighten in response to an electric signal, and if broadly used in buildings they would have the potential to greatly cut back cooling costs by reducing the amount of radiant energy entering the building. Unfortunately, with current manufacturing processes, electrochromic windows remain too expensive for widespread use. With support from the Department of Energy, ITN Energy Systems of Littleton CO is developing a low cost manufacturing process based on a wide-web, continuous processing sputtering systems. CSM is working with ITN Energy Systems on the control systems for this process, as repeatable processes with good cross-web uniformity are key to high yields, and thus low cost manufacturing.
A Heterogeneous Networking Test Bed
SINE Investigator: Qi Han Abstract: Numerous interesting applications have been enabled by embedded sensing technologies and significant research progress on wireless sensor networks. To further ensure a wider adoption of this emerging technology, seamless integration of wireless sensor networks with other existing networks such as WiFi and the Internet is a must. In order to address challenges that arise from such an integrated infrastructure, this project builds HeteroNet, a heterogeneous networking infrastructure, by augmenting an existing flat and homogeneous sensor network test bed. HeteroNet integrates resource constrained sensor nodes and more powerful sensing devices, stationary nodes, mobile nodes, and resource sufficient servers. These nodes communicate in wireless or wired fashion. This test bed establishes an experimental infrastructure to serve as a platform for development, testing, validation, and evaluation of our current research on middleware services for emerging applications on hybrid networks.
Subsurface Contaminant Monitoring and Prediction using Wireless Sensor Networks
SINE Investigator: Qi Han Abstract: Release of chemicals or biological agents in the subsurface often results in plumes migrating in the medium, posing risk to human and ecological environments. Current underground contaminant plume monitoring technologies are inefficient, expensive and ineffective. Wireless sensor technologies have the potential to dramatically improve this process.
A closed-loop system integrating wireless sensor network based monitoring with numerical models for plume tracking is being developed, in which sensor data continuously calibrates and validates the system identification and prediction models, while the output from these models direct the sensor network operation to optimize constraints such as accuracy and power consumption. Algorithms and protocols being developed support the formation, usage, adaptation and maintenance of dynamic subsets of collaborating sensors, named Virtual Sensor Networks (VSNs). VSN protocols for collaboration among groups of sensors will greatly ease the task of deploying sensor networks.
Leveraging Low-Dimensional Structure for Time Series Analysis and Prediction
SINE Investigator: Mike Wakin Abstract: Predicting the behavior of complex systems is central to many tasks of great scientific and national importance, including arenas such as meteorology, financial markets and global conflict. Modern science is ingrained with the premise that repeated observations of a dynamic phenomenon can help in understanding its driving mechanisms and predicting its future behavior. The investigators study methods for improving our ability to characterize and predict such systems even when they are very large (i.e., with many interacting factors) or appear highly unordered (i.e., chaotic systems). This research leverages new mathematical results that enable analysts to efficiently capture the simple structure that is often present even in systems that appear very complex. These results lead to improvements and performance guarantees for heuristic prediction methods based on artificial neural networks, which are often used in practice but can sometimes fail inexplicably. This research improved upon and make performance guarantees for reservoir computing methods, where randomly-connected neural networks have been identified as effective mechanisms for predicting chaotic time series.