Heuristic solutions to the target identifiability problem in directional sensor networks

Existing algorithms for orienting sensors in directional sensor networks have primarily concerned themselves with the problem of maximizing the number of covered targets, assuming that target identification is a non-issue. Such an assumption however, does not hold true in all situations. In this paper, heuristic algorithms for choosing active sensors and orienting them with the goal of balancing coverage and identifiability are presented. The performance of the algorithms are verified via extensive simulations, and shown to confer increased target identifiability compared to algorithms originally designed to simply maximize the number of targets covered

Wave Energy Amplification in a Metamaterial based Traveling Wave Structure

We consider the interaction between a particle beam and a propagating electromagnetic wave in the presence of a metamaterial. We show that the introduction of a metamaterial gives rise to a novel dispersion curve which determines a unique wave particle relationship, via the frequency dependence of the metamaterial and the novel ability of metamaterials to exhibit simultaneous negative permittivity and permeability. Using a modified form of Madey's theorem we find that the novel dispersion of the metamaterial leads to a amplification of the EM wave power

Noise-sensing energy-harvesting wireless sensor network nodes

Noise pollution is becoming an increasing concern in many urban regions all over the world. An important step in fighting and mitigating noise pollution is its quantification. Wireless sensor networks (WSNs) can potentially help with these efforts, as they enable the simultaneous and continuous gathering of data over wide geographic regions. The need to replace batteries however makes the maintenance of such physically very large networks impractical. As an alternative to batteries, noise-sensing WSNs could also be powered by energy harvesting. While energy-harvesting WSNs have been demonstrated before, utilizing energy harvesting for powering noise-sensing WSNs still pose a significant challenge because of application’s unique requirements, such as a high power consumption profile for extended periods of time. In this thesis, we address four key areas of research necessary on to make energy-harvesting noise-sensing WSNs possible and, more importantly, practical to use in large-scale settings. The first key area that we address is that of new and emerging energy storage technologies, and how current algorithms and infrastructures must be modified to take advantage of them. The second key area is that of currently-accepted technical requirements, and their assessment on whether they would indeed lead to the attainment of long-term goals. The third key area is that of test methodologies for energy-harvesting designs, and how they should be modified to facilitate validation of results between researchers. The final key area is that of techniques and algorithms for future capabilities that energy-harvesting noise-sending WSNs will or can have, and how we should prepare for them, even though they may not yet exist. We provide research to support all four key areas in this work and provide concrete examples for each

An indoor test methodology for solar-powered wireless sensor networks

Repeatable and accurate tests are important when designing hardware and algorithms for solar-powered wireless sensor networks (WSNs). Since no two days are exactly alike with regard to energy harvesting, tests must be carried out indoors. Solar simulators are traditionally used in replicating the effects of sunlight indoors - however, solar simulators are expensive, have lighting elements that have short lifetimes, and are usually not designed to carry out the types of tests that hardware and algorithm designers require. As a result, hardware and algorithm designers use tests that are inaccurate and not repeatable (both for others and also for the designers themselves). In this article we propose an indoor test methodology which does not rely on solar simulators. The test methodology has its basis in astronomy and photovoltaic (PV) cell design. We present a generic design for a test apparatus which can be used in carrying out the test methodology. We also present a specific design which we use in implementing an actual test apparatus. We test the efficacy of our test apparatus and, to demonstrate the usefulness of the test methodology, perform experiments akin to those required in projects involving solar-powered WSNs. Results of the said tests and experiments demonstrate that the test methodology is an invaluable tool for hardware and algorithm designers working with solar-powered WSNs

Radio Spectral Index and Expansion of 3C58

We present new observations of the plerionic supernova remnant 3C58 with the VLA at 74 and 327 MHz. In addition, we re-reduced earlier observations at 1.4 and 4.9 GHz taken in 1973 and 1984. Comparing these various images, we find that: 1. the remnant has a flat and relatively uniform spectral index distribution, 2. any expansion of the remnant with time is significantly less than that expected for uniform, undecelerated expansion since the generally accepted explosion date in 1181 A.D., and 3. there is no evidence for a non-thermal synchrotron emission shell generated by a supernova shock wave, with any such emission having a surface brightness of <1 x 10^(-21) W / (m^2 Hz sr) at 327 MHz.Comment: 18 pages, 7 Figures, Latex, Accepted for publication in the Astrophysical Journa

Radio Images of 3C 58: Expansion and Motion of its Wisp

New 1.4 GHz VLA observations of the pulsar-powered supernova remnant 3C 58 have resulted in the highest-quality radio images of this object to date. The images show filamentary structure over the body of the nebula. The present observations were combined with earlier ones from 1984 and 1991 to investigate the variability of the radio emission on a variety of time-scales. No significant changes are seen over a 110 day interval. In particular, the upper limit on the apparent projected velocity of the wisp is 0.05c. The expansion rate of the radio nebula was determined between 1984 and 2004, and is 0.014+/-0.003%/year, corresponding to a velocity of 630+/-70 km/s along the major axis. If 3C 58 is the remnant of SN 1181, it must have been strongly decelerated, which is unlikely given the absence of emission from the supernova shell. Alternatively, the low expansion speed and a number of other arguments suggest that 3C 58 may be several thousand years old and not be the remnant of SN 1181.Comment: 12 pages; accepted for publication in the Astrophysical Journa

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector, edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses