On augmented OBDD and performability for sensor networks

The expected hop count (EHC) or performability of a wireless sensor network (WSN) with probabilistic node failures provides the expected number of operational nodes a message traverses from a set of sensors to reach its target station. This paper proposes a novel approach for computing the EHC of a practical communication model for WSN, k-of-all-sources to any-terminal (k-of-S,t). Techniques based on factoring and Boolean techniques solve the EHC when k=1 for |S| greater than/equal to 1 However, they fail to scale with large WSN and are not useful for computing the EHC with k>1. To overcome these problems, we propose an Augmented Ordered Binary Decision Diagram (OBDD-A) approach, which obtains the EHC for all cases of (k-of-S,t). We use randomly generated wireless networks and grid networks having up to 4.6x1020 (s,t)-minpaths to generate results. Results show that OBDD-A can obtain the EHC for networks that are unsolvable with existing approaches

Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1

Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified

Custom Integrated Circuits

Contains reports on ten research projects.Analog Devices, Inc.IBM CorporationNational Science Foundation/Defense Advanced Research Projects Agency Grant MIP 88-14612Analog Devices Career Development Assistant ProfessorshipU.S. Navy - Office of Naval Research Contract N0014-87-K-0825AT&TDigital Equipment CorporationNational Science Foundation Grant MIP 88-5876

Concepts for design of an energy management system incorporating dispersed storage and generation

New forms of generation based on renewable resources must be managed as part of existing power systems in order to be utilized with maximum effectiveness. Many of these generators are by their very nature dispersed or small, so that they will be connected to the distribution part of the power system. This situation poses new questions of control and protection, and the intermittent nature of some of the energy sources poses problems of scheduling and dispatch. Under the assumption that the general objectives of energy management will remain unchanged, the impact of dispersed storage and generation on some of the specific functions of power system control and its hardware are discussed

Study of a navigation and traffic control technique employing satellites. Volume 3 - User hardware Interim report

User hardware configurations and requirements for navigation and air traffic control technique using satellite

Voyager spacecraft phase B, task D. Volume 2 - System description. Book 5 - Final report

Voyager spacecraft design standards, and operational support and mission-dependent equipment requirement

Conceptual mechanization studies for a horizon definition spacecraft communications and data handling subsystem

Conceptual mechanization for horizon definition spacecraft communications and data handling subsyste

A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective

Decision-making based on machine learning systems, especially when this decision-making can affect humanlives, is a subject of maximum interest in the Machine Learning community. It is, therefore, necessary to equipthese systems with a means of estimating uncertainty in the predictions they emit in order to help practition-ers make more informed decisions. In the present work, we introduce the topic of uncertainty estimation, andwe analyze the peculiarities of such estimation when applied to classification systems. We analyze differentmethods that have been designed to provide classification systems based on deep learning with mechanismsfor measuring the uncertainty of their predictions. We will take a look at how this uncertainty can be mod-eled and measured using different approaches, as well as practical considerations of different applications ofuncertainty. Moreover, we review some of the properties that should be borne in mind when developing suchmetrics. All in all, the present survey aims at providing a pragmatic overview of the estimation of uncertaintyin classification systems that can be very useful for both academic research and deep learning practitioners

Modelling information flow for organisations delivering microsystems technology

Motivated by recent growth and applications of microsystems technology (MST), companies within the MST domain are beginning to explore avenues for understanding, maintaining and improving information flow, within their organisations and to/from customers, with a view to enhancing delivery performance. Delivery for organisations is the flow of goods from sellers to buyers and a classic approach to understanding information flow is via the use of modelling techniques. Cont/d

A Bayesian Approach to Sensor Placement and System Health Monitoring

System health monitoring and sensor placement are areas of great technical and scientific interest. Prognostics and health management of a complex system require multiple sensors to extract required information from the sensed environment, because no single sensor can obtain all the required information reliably at all times. The increasing costs of aging systems and infrastructures have become a major concern, and system health monitoring techniques can ensure increased safety and reliability of these systems. Similar concerns also exist for newly designed systems. The main objectives of this research were: (1) to find an effective way for optimal functional sensor placement under uncertainty, and (2) to develop a system health monitoring approach with both prognostic and diagnostic capabilities with limited and uncertain information sensing and monitoring points. This dissertation provides a functional/information --based sensor placement methodology for monitoring the health (state of reliability) of a system and utilizes it in a new system health monitoring approach. The developed sensor placement method is based on Bayesian techniques and is capable of functional sensor placement under uncertainty. It takes into account the uncertainty inherent in characteristics of sensors as well. It uses Bayesian networks for modeling and reasoning the uncertainties as well as for updating the state of knowledge for unknowns of interest and utilizes information metrics for sensor placement based on the amount of information each possible sensor placement scenario provides. A new system health monitoring methodology is also developed which is: (1) capable of assessing current state of a system's health and can predict the remaining life of the system (prognosis), and (2) through appropriate data processing and interpretation can point to elements of the system that have or are likely to cause system failure or degradation (diagnosis). It can also be set up as a dynamic monitoring system such that through consecutive time steps, the system sensors perform observations and send data to the Bayesian network for continuous health assessment. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (upward propagation); how to infer the health of a subsystem based on knowledge of the health of the main system (downward propagation); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (distributed propagation)