University of North Carolina at Charlotte

About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.

A survey of energy harvesting sources for embedded systems

Abstract: Historically, batteries have been the source of energy for most mobile, embedded and remote system applications. Now, with ubiquitous computing requirements in the fields of embedded systems, wireless sensor networks and low- power electronics such as MEMS devices, an alternative source of energy is required. Also with the limited capacity of finite power sources and the need for supplying energy for a lifetime of a system, there is a requirement for self- powered devices. The process of extracting energy from the surrounding environment is termed as energy harvesting. Energy harvesting, which originated from the windmill and water wheel, is widely being considered as a low- maintenance solution for a wide variety of applications. There are various forms of energy that can be scavenged, like thermal, mechanical, solar, acoustic, wind, and wave. This paper serves as a survey for identifying the sources of energy harvesting based on various technical papers available in the public domain.

Mindfulness meditation and substance use in an incarcerated population

Abstract: Despite the availability of various substance abuse treatments, alcohol and drug misuse and related negative consequences remain prevalent. Vipassana meditation (VM), a Buddhist mindfulness-based practice, provides an alternative for individuals who do not wish to attend or have not succeeded with traditional addiction treatments. In this study, the authors evaluated the effectiveness of a VM course on substance use and psychosocial outcomes in an incarcerated population. Results indicate that after release from jail, participants in the VM course, as compared with those in a treatment-as-usual control condition, showed significant reductions in alcohol, marijuana, and crack cocaine use. VM participants showed decreases in alcohol-related problems and psychiatric symptoms as well as increases in positive psychosocial outcomes. The utility of mindfulness-based treatments for substance use is discussed.

Performance of multipath routing for on-demand protocols in mobile ad hoc networks

Abstract: Mobile ad hoc networks are characterized by multi-hop wireless links, absence of any cellular infrastructure, and frequent host mobility. Design of efficient routing protocols in such networks is a challenging issue. As class of routing protocols called on-demandprotocols hs recently found attention because of their low routing overhead. The on-demand protocols depend on query floods to discover routes whenever a new route is needed. Such floods take up a substantial portion of network bandwidth. We focus on a particular on-demand protocol, called Dynamic Source Routing, and show how intelligent use of multipath techniques can reduce the frequency of query floods. We develop an analytic modeling framework to determine the relative frequency of query floods for various techniques. Our modeling effort shows that while multipath routing is significantly better than single path routing, the performance advantage is small beyond a few paths and for long paths lengths. It also shows that providing all intermediate nodes in the primary (shortest) route with alternative paths has a significantly better performance than providing only the source with alternate paths. We perform some simulation experiments which validate these findings.

Functional-Coefficient Regression Models for Nonlinear Time Series

Abstract: Functional-co efficient Regression Models for Nonlinear Time Series ZONGWU C A I Department of Mathematics University of North Carolina Charlotte, NC 28223, USA J I A N Q I N G FAN* Department of Statistics University of California Los Angeles, CA 90095, USA QlWEI Yao' Institute of Mathematics and Statistics University of Kent at Canterbury Canterbury, Kent CT2 7NF, U K Abstract We apply the local linear regression technique for estimation of functional-coefficient regres­ sion models for time series data The models include threshold autoregressive models (Tong 1990) and functional-coefficient autoregressive models (Chen and Tsay 1993) as special cases but with the added advantages such as depicting finer structure of the underlying dynamics and better post-sample forecasting performance We have also proposed a new bootstrap test for the goodness of fit of models and a bandwidth selector based on newly defined cross-validatory estimation for the expected forecasting errors The proposed methodology is data-analytic and is of appreciable flexibility to analyze complex and multivariate nonlinear structures without suffering from the curse of dimensionality The asymptotic properties of the proposed esti­ mators are investigated under the a-mixing condition Both simulated and real data examples are used for illustration Keywords: a-mixing; Asymptotic normality; Bootstrap; Forecasting; Goodness-of-fit test; Local linear regression; Nonlinear time series; Varying-coefficient models *Partially supported by N S F Grant DMS-9803200 and NSA 96-1-0015 +Partially supported by E P S R C Grant L16358 and B B S R C / E P S R C Grant 96/MMI09785

Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities

Abstract: With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. However, these implicitly ignore the fundamental role of nonlinear agglomeration integral to the life history of cities. As such, per capita indicators conflate general nonlinear effects, common to all cities, with local dynamics, specific to each city, failing to provide direct measures of the impact of local events and policy. Agglomeration nonlinearities are explicitly manifested by the superlinear power law scaling of most urban socioeconomic indicators with population size, all with similar exponents (*1.15). As a result larger cities are disproportionally the centers of innovation, wealth and crime, all to approximately the same degree. We use these general urban laws to develop new urban metrics that disentangle dynamics at different scales and provide true measures of local urban performance. New rankings of cities and a novel and simpler perspective on urban systems emerge. We find that local urban dynamics display long-term memory, so cities under or outperforming their size expectation maintain such (dis)advantage for decades. Spatiotemporal correlation analyses reveal a novel functional taxonomy of U.S. metropolitan areas that is generally not organized geographically but based instead on common local economic models, innovation strategies and patterns of crime.