Computational Methods for Sparse Solution of Linear Inverse Problems

The goal of the sparse approximation problem is to approximate a target signal using a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees available. Many fundamental questions in electrical engineering, statistics, and applied mathematics can be posed as sparse approximation problems, making these algorithms versatile and relevant to a plethora of applications

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Autonomous flight and remote site landing guidance research for helicopters

Automated low-altitude flight and landing in remote areas within a civilian environment are investigated, where initial cost, ongoing maintenance costs, and system productivity are important considerations. An approach has been taken which has: (1) utilized those technologies developed for military applications which are directly transferable to a civilian mission; (2) exploited and developed technology areas where new methods or concepts are required; and (3) undertaken research with the potential to lead to innovative methods or concepts required to achieve a manual and fully automatic remote area low-altitude and landing capability. The project has resulted in a definition of system operational concept that includes a sensor subsystem, a sensor fusion/feature extraction capability, and a guidance and control law concept. These subsystem concepts have been developed to sufficient depth to enable further exploration within the NASA simulation environment, and to support programs leading to the flight test

Measuring Shadows: FPGA-based image sensor control systems for next-generation NASA missions

Astronomy and astrophysics are fields of constant growth and exploration, and discoveries are being made every day. Behind each discovery, however, is the equipment and engineering that makes that science possible. The science and engineering go hand in hand in two ways: advances in engineering make new scientific discoveries possible, and new scientific questions create the need for more advanced engineering. The work that led to this thesis is an example of the latter statement. The big-picture goal is to support the development of next-generation detectors such as the Quanta Image Sensor (QIS), a gigapixel-scaleble Complementary Metal-Oxide Semiconductor (CMOS) photon-number resolving image sensor. This thesis focuses on one crucial part of the development process: the characterisation of the QIS. In order to advance the NASA Technology Readiness Level (TRL) from three to four, the detector needs to undergo extensive laboratory and telescope environment testing. The testing framework is being run by an FPGA hardware design that includes a processor, and this set of hardware and software is responsible for operating the detector, managing experiment parameters, running experiments, and collecting resultant data and passing it to a host PC. The majority of the work of this portion of the project revolved around creating, improving, and testing the framework to allow for fully functional and automated detector characterisation. Test systems already exist for the QIS in a room temperature environment, as well as for current-generation image sensors in cryogenic vacuum environments. However, there is no existing test system that allows the QIS to be tested in a cryo-vac environment. This thesis details a functional system that fills that niche. The system is built to be modular and extensible so that it can be expanded upon to characterise other types of detectors in the future as well. For now, however, the system shines as the only one that allows the QIS to be tested in an environment that simulates its behaviour in outer space

Spartan Daily, September 14, 1983

Volume 81, Issue 11https://scholarworks.sjsu.edu/spartandaily/7060/thumbnail.jp

Learning about a Moving Target in Resource Management: Optimal Bayesian Disease Control

Resource managers must often make difficult choices in the face of imperfectly observed and dynamically changing systems (e.g., livestock, fisheries, water, and invasive species). A rich set of techniques exists for identifying optimal choices when that uncertainty is assumed to be understood and irreducible. Standard optimization approaches, however, cannot address situations in which reducible uncertainty applies to either system behavior or environmental states. The adaptive management literature overcomes this limitation with tools for optimal learning, but has been limited to highly simplified models with state and action spaces that are discrete and small. We overcome this problem by using a recently developed extension of the Partially Observable Markov Decision Process (POMDP) framework to allow for learning about a continuous state. We illustrate this methodology by exploring optimal control of bovine tuberculosis in New Zealand cattle. Disease testing—the control variable—serves to identify herds for treatment and provides information on prevalence, which is both imperfectly observed and subject to change due to controllable and uncontrollable factors. We find substantial efficiency losses from both ignoring learning (standard stochastic optimization) and from simplifying system dynamics (to facilitate a typical, simple learning model), though the latter effect dominates in our setting. We also find that under an adaptive management approach, simplifying dynamics can lead to a belief trap in which information gathering ceases, beliefs become increasingly inaccurate, and losses abound