1,041 research outputs found
Analog Signal Buffering and Reconstruction
Wireless sensor networks (WSNs) are capable of a myriad of tasks, from monitoring critical infrastructure such as bridges to monitoring a person\u27s vital signs in biomedical applications. However, their deployment is impractical for many applications due to their limited power budget. Sleep states are one method used to conserve power in resource-constrained systems, but they necessitate a wake-up circuit for detecting unpredictable events. In conventional wake-up-based systems, all information preceding a wake-up event will be forfeited. To avoid this data loss, it is necessary to include a buffer that can record prelude information without sacrificing the power savings garnered by the active use of sleep states.;Unfortunately, traditional memory buffer systems utilize digital electronics which are costly in terms of power. Instead of operating in the target signal\u27s native analog environment, a digital buffer must first expend a great deal of energy to convert the signal into a digital signal. This issue is further compounded by the use of traditional Nyquist sampling which does not adapt to the characteristics of a dynamically changing signal. These characteristics reveal why a digital buffer is not an appropriate choice for a WSN or other resource-constrained system.;This thesis documents the development of an analog pre-processing block that buffers an incoming signal using a new method of sampling. This method requires sampling only local maxima and minima (both amplitude and time), effectively approximating the instantaneous Nyquist rate throughout a time-varying signal. The use of this sampling method along with ultra-low-power analog electronics enables the entire system to operate in the muW power levels. In addition to these power saving techniques, a reconfigurable architecture will be explored as infrastructure for this system. This reconfigurable architecture will also be leveraged to explore wake-up circuits that can be used in parallel with the buffer system
Data Conversion Within Energy Constrained Environments
Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings
Some Aspects of Measurement Error in Linear Regression of Astronomical Data
I describe a Bayesian method to account for measurement errors in linear
regression of astronomical data. The method allows for heteroscedastic and
possibly correlated measurement errors, and intrinsic scatter in the regression
relationship. The method is based on deriving a likelihood function for the
measured data, and I focus on the case when the intrinsic distribution of the
independent variables can be approximated using a mixture of Gaussians. I
generalize the method to incorporate multiple independent variables,
non-detections, and selection effects (e.g., Malmquist bias). A Gibbs sampler
is described for simulating random draws from the probability distribution of
the parameters, given the observed data. I use simulation to compare the method
with other common estimators. The simulations illustrate that the Gaussian
mixture model outperforms other common estimators and can effectively give
constraints on the regression parameters, even when the measurement errors
dominate the observed scatter, source detection fraction is low, or the
intrinsic distribution of the independent variables is not a mixture of
Gaussians. I conclude by using this method to fit the X-ray spectral slope as a
function of Eddington ratio using a sample of 39 z < 0.8 radio-quiet quasars. I
confirm the correlation seen by other authors between the radio-quiet quasar
X-ray spectral slope and the Eddington ratio, where the X-ray spectral slope
softens as the Eddington ratio increases.Comment: 39 pages, 11 figures, 1 table, accepted by ApJ. IDL routines
(linmix_err.pro) for performing the Markov Chain Monte Carlo are available at
the IDL astronomy user's library, http://idlastro.gsfc.nasa.gov/homepage.htm
The Cross-Wavelet Transform and Analysis of Quasiperiodic Behavior in the Pearson-Readhead VLBI Survey Sources
We introduce an algorithm for applying a cross-wavelet transform to analysis
of quasiperiodic variations in a time-series, and introduce significance tests
for the technique. We apply a continuous wavelet transform and the
cross-wavelet algorithm to the Pearson-Readhead VLBI survey sources using data
obtained from the University of Michigan 26-m parabloid at observing
frequencies of 14.5, 8.0, and 4.8 GHz. Thirty of the sixty-two sources were
chosen to have sufficient data for analysis, having at least 100 data points
for a given time-series. Of these thirty sources, a little more than half
exhibited evidence for quasiperiodic behavior in at least one observing
frequency, with a mean characteristic period of 2.4 yr and standard deviation
of 1.3 yr. We find that out of the thirty sources, there were about four time
scales for every ten time series, and about half of those sources showing
quasiperiodic behavior repeated the behavior in at least one other observing
frequency.Comment: Revised version, accepted by ApJ. 17 pages, 13 figures, color figures
included as gifs, seperate from the text. The addition of statistical
significance tests has resulted in modifying the technique and results, but
the broad conclusion remain the same. A high resolution version may be found
at http://www.astro.lsa.umich.edu/obs/radiotel/prcwdata.htm
Dust SEDs in the era of Herschel and Planck: a Hierarchical Bayesian fitting technique
We present a hierarchical Bayesian method for fitting infrared spectral
energy distributions (SEDs) of dust emission to observed fluxes. Under the
standard assumption of optically thin single temperature (T) sources the dust
SED as represented by a power--law modified black body is subject to a strong
degeneracy between T and the spectral index beta. The traditional
non-hierarchical approaches, typically based on chi-square minimization, are
severely limited by this degeneracy, as it produces an artificial
anti-correlation between T and beta even with modest levels of observational
noise. The hierarchical Bayesian method rigorously and self-consistently treats
measurement uncertainties, including calibration and noise, resulting in more
precise SED fits. As a result, the Bayesian fits do not produce any spurious
anti-correlations between the SED parameters due to measurement uncertainty. We
demonstrate that the Bayesian method is substantially more accurate than the
chi-square fit in recovering the SED parameters, as well as the correlations
between them. As an illustration, we apply our method to Herschel and sub
millimeter ground-based observations of the star-forming Bok globule CB244.
This source is a small, nearby molecular cloud containing a single low-mass
protostar and a starless core. We find that T and beta are weakly positively
correlated -- in contradiction with the chi-square fits, which indicate a
T-beta anti-correlation from the same data-set. Additionally, in comparison to
the chi-square fits the Bayesian SED parameter estimates exhibit a reduced
range in values.Comment: 20 pages, 9 figures, ApJ format, revised version matches ApJ-accepted
versio
Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey II: Multiwavelength Classification
We describe the application of the `shapelet' linear decomposition of galaxy
images to multi-wavelength morphological classification using the
and -band images of 1519 galaxies from the Sloan Digital Sky Survey. We
utilize elliptical shapelets to remove to first-order the effect of inclination
on morphology. After decomposing the galaxies we perform a principal component
analysis on the shapelet coefficients to reduce the dimensionality of the
spectral morphological parameter space. We give a description of each of the
first ten principal component's contribution to a galaxy's spectral morphology.
We find that galaxies of different broad Hubble type separate cleanly in the
principal component space. We apply a mixture of Gaussians model to the
2-dimensional space spanned by the first two principal components and use the
results as a basis for classification. Using the mixture model, we separate
galaxies into three classes and give a description of each class's physical and
morphological properties. We find that the two dominant mixture model classes
correspond to early and late type galaxies, respectively. The third class has,
on average, a blue, extended core surrounded by a faint red halo, and typically
exhibits some asymmetry. We compare our method to a simple cut on color
and find the shapelet method to be superior in separating galaxies.
Furthermore, we find evidence that the decision boundary may not be
optimal for separation between early and late type galaxies, and suggest that
the optimal cut may be .Comment: 42 pages, 18 figs, revised version in press at AJ. Some modification
to the technique, more discussion, addition/deletion/modification of several
figures, color figures have been added. A high resolution version may be
obtained at
http://bllac.as.arizona.edu/~bkelly/shapelets/shapelets_ugriz.ps.g
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Dust Spectral Energy Distributions in the Era of Herschel and Planck: A Hierarchical Bayesian-Fitting Technique
We present a hierarchical Bayesian method for fitting infrared spectral energy distributions (SEDs) of dust emission to observed fluxes. Under the standard assumption of optically thin single temperature (T) sources, the dust SED as represented by a power-law-modified blackbody is subject to a strong degeneracy between T and the spectral index . The traditional non-hierarchical approaches, typically based on minimization, are severely limited by this degeneracy, as it produces an artificial anti-correlation between T and even with modest levels of observational noise. The hierarchical Bayesian method rigorously and self-consistently treats measurement uncertainties, including calibration and noise, resulting in more precise SED fits. As a result, the Bayesian fits do not produce any spurious anti-correlations between the SED parameters due to measurement uncertainty. We demonstrate that the Bayesian method is substantially more accurate than the fit in recovering the SED parameters, as well as the correlations between them. As an illustration, we apply our method to Herschel and submillimeter ground-based observations of the star-forming Bok globule CB244. This source is a small, nearby molecular cloud containing a single low-mass protostar and a starless core. We find that T and are weakly positively correlated—in contradiction with the fits, which indicate a T- anti-correlation from the same data set. Additionally, in comparison to the fits the Bayesian SED parameter estimates exhibit a reduced range in values.Astronom
Review of UK microgeneration. Part 1 : policy and behavioural aspects
A critical review of the literature relating to government policy and behavioural aspects relevant to the uptake and application of microgeneration in the UK is presented. Given the current policy context aspiring to zero-carbon new homes by 2016 and a variety of minimum standards and financial policy instruments supporting microgeneration in existing dwellings, it appears that this class of technologies could make a significant contribution to UK energy supply and low-carbon buildings in the future. Indeed, achievement of a reduction in greenhouse gas emissions by 80% (the UK government's 2050 target) for the residential sector may entail substantial deployment of microgeneration. Realisation of the large potential market for microgeneration relies on a variety of inter-related factors such as microeconomics, behavioural aspects, the structure of supporting policy instruments and well-informed technology development. This article explores these issues in terms of current and proposed policy instruments in the UK. Behavioural aspects associated with both initial uptake of the technology and after purchase are also considered
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VarSight: prioritizing clinically reported variants with binary classification algorithms.
BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets
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