4 resultados para Signal variability

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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A single raised bog from the eastern Netherlands has been repeatedly analysed and 14C dated over the past few decades. Here we assess the within-site variability of fossil proxy data through comparing the regional
pollen, macrofossils and non-pollen palynomorphs of four of these profiles. High-resolution chronologies were obtained using 14C dating and Bayesian age-depth modelling. Where chronologies of profiles overlap, proxy curves are compared between the profiles using greyscale graphs that visualise chronological uncertainties. Even at this small spatial scale, there is considerable variability of the fossil proxy curves. Implications regarding signal (climate) and noise (internal dynamics) of the different types of fossil proxies are discussed. Single cores are of limited value for reconstructing centennial-scale climate change, and only by combining multiple cores and proxies can we obtain a reliable understanding of past environmental change and possible forcing factors (e.g., solar variability).

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Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

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The emission from young stellar objects (YSOs) in the mid-infrared (mid-IR) is dominated by the inner rim of their circumstellar disks. We present IR data from the Young Stellar Object VARiability (YSOVAR) survey of ~800 objects in the direction of the Lynds 1688 (L1688) star-forming region over four visibility windows spanning 1.6 yr using the Spitzer Space Telescope in its warm mission phase. Among all light curves, 57 sources are cluster members identified based on their spectral energy distribution and X-ray emission. Almost all cluster members show significant variability. The amplitude of the variability is larger in more embedded YSOs. Ten out of 57 cluster members have periodic variations in the light curves with periods typically between three and seven days, but even for those sources, significant variability in addition to the periodic signal can be seen. No period is stable over 1.6 yr. Nonperiodic light curves often still show a preferred timescale of variability that is longer for more embedded sources. About half of all sources exhibit redder colors in a fainter state. This is compatible with time-variable absorption toward the YSO. The other half becomes bluer when fainter. These colors can only be explained with significant changes in the structure of the inner disk. No relation between mid-IR variability and stellar effective temperature or X-ray spectrum is found.

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As part of the Young Stellar Object VARiability (YSOVAR) program, wemonitored NGC 1333 for ∼35 days at 3.6 and 4.5 μm using theSpitzer Space Telescope. We report here on the mid-infrared variabilityof the point sources in the ∼10‧ × ∼20‧ areacentered on 03:29:06, +31:19:30 (J2000). Out of 701 light curves ineither channel, we find 78 variables over the YSOVAR campaign. Abouthalf of the members are variable. The variable fraction for the mostembedded spectral energy distributions (SEDs) (Class I, flat) is higherthan that for less embedded SEDs (Class II), which is in turn higherthan the star-like SEDs (Class III). A few objects have amplitudes(10–90th percentile brightness) in [3.6] or [4.5] > 0.2 mag; amore typical amplitude is 0.1–0.15 mag. The largest color changeis >0.2 mag. There are 24 periodic objects, with 40% of them beingflat SED class. This may mean that the periodic signal is primarily fromthe disk, not the photosphere, in those cases. We find 9 variableslikely to be “dippers,” where texture in the disk occultsthe central star, and 11 likely to be “bursters,” whereaccretion instabilities create brightness bursts. There are 39 objectsthat have significant trends in [3.6]–[4.5] color over thecampaign, about evenly divided between redder-when-fainter (consistentwith extinction variations) and bluer-when-fainter. About a third of the17 Class 0 and/or jet-driving sources from the literature are variableover the YSOVAR campaign, and a larger fraction (∼half) are variablebetween the YSOVAR campaign and the cryogenic-era Spitzer observations(6–7 years), perhaps because it takes time for the envelope torespond to changes in the central source. The NGC 1333 brown dwarfs donot stand out from the stellar light curves in any way except there is amuch larger fraction of periodic objects (∼60% of variable browndwarfs are periodic, compared to ∼30% of the variables overall).