Spatiotemporal characterization of geophysical signal detection capabilities of GRACE‐FO

The increasing interest in the radar detection of low-elevation and small-size targets in complicated ground environments (such as urban, suburban, and mixed country areas) calls for a precise quantification of the radar detection capabilities in those areas. Hence, a set of procedures is devised and tested, both theoretically and experimentally, using a commercial X-band radar, to (i) calibrate the radar sensor (with an online evaluation of its losses) using standard scatterers, (ii) measure the multipath effect and compensate for it, and (iii) create “true radar cross section” maps of the area of interest for both point and distributed clutter. The above methods and the related field results are aimed at future qualification procedures and practical usage of small, cheap, and easily moveable radars for the detection of low-observable air targets, such as unmanned air vehicles/systems (UAV/UAS), in difficult ground areas. A significant set of experimental results as discussed in the paper confirms the great relevance of multipath in ground-based radar detection, with the need for correcting measures.

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Towards Thermal Characterization of Pico-Liter Volumes Using the 3Omega Method

Volume 8A: Heat Transfer and Thermal Engineering ◽

10.1115/imece2013-66428 ◽

2013 ◽

Cited By ~ 1

Author(s):

Shilpi Roy ◽

Takashi Kodama ◽

Srilakshmi Lingamneni ◽

Matthew A. Panzer ◽

Mehdi Asheghi ◽

...

Keyword(s):

Silicone Oil ◽

Thermal Characterization ◽

Sample Volume ◽

Temperature Dependent ◽

Buffer Solution ◽

Bulk Fluid ◽

Detection Capabilities ◽

Uncertainty Analyses ◽

Thermal Phenomena

The continued efforts in the biological community to optimize methodologies such as PCR and to characterize biological reactions and processes are motivating reductions in sample volume. There is a growing need for the detection of thermal phenomena in these small volumes, such as the heat released by recombination and the effective conductivities and capacities in extremely small fluidic regions. While past work has focused largely on heat transport in essentially bulk fluid volumes, there is a need to scale these techniques to the much smaller volumes of interest for biological and biomedical research.” This work applies the 3ω measurement technique to μL volumes by using heaters with dimensions of 200–700μm in lengths and 2–5μm in widths. We investigate fluid samples of DI water, silicone oil, and a salt buffer solution to experimentally determine their temperature-dependent thermal properties from 25°C to 80°C. Validation is achieved through comparison of these values of thermal conductivity κ and volumetric heat capacity Cν to literature. The work also demonstrates the device capability to conduct temperature-dependent measurements down to pL droplet volumes by conducting a volume analysis given the dimensions of heaters used, independent of droplet boundary conditions. Sensitivity and uncertainty analyses based on these heater dimensions and surrounding material stack show the detection capabilities of these heaters, as they are optimally designed to maximize signal while accommodating the size restrictions of small volume droplets.

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Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability

10.5194/acp-2017-954 ◽

2017 ◽

Author(s):

Benjamin Brown-Steiner ◽

Noelle E. Selin ◽

Ronald G. Prinn ◽

Erwan Monier ◽

Simone Tilmes ◽

...

Keyword(s):

United States ◽

Air Quality ◽

Signal Detection ◽

Surface Ozone ◽

Quality Data ◽

Spatial Averaging ◽

Ozone Data ◽

Detection Capabilities ◽

The Mean ◽

Temporal And Spatial

Abstract. The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical and meteorological variabilities that exist both in space and in time. This can present difficulties for both policy-makers and researchers as they attempt to identify the influence or signal of climate trends (e.g. any pauses in warming trends), the impact of enacted emission reductions policies (e.g. United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g. summertime ozone over the Northeastern United States). Here we examine the scale-dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the Continental US. We recommend temporal averaging of at least 10–15 years combined with regional spatial averaging over several hundred kilometer spatial scales. These results are consistent between simulated and observed data, and within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data, and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

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TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys

Astronomy and Astrophysics ◽

10.1051/0004-6361/201730671 ◽

2018 ◽

Vol 619 ◽

pp. A86 ◽

Cited By ~ 2

Author(s):

D. del Ser ◽

O. Fors ◽

J. Núñez

Keyword(s):

Signal Detection ◽

Detection Rate ◽

Detection Efficiency ◽

Signal Reconstruction ◽

Random Noise ◽

Light Curves ◽

Periodic Signal ◽

Systematic Effects ◽

Planetary Transits

Context. There have been many efforts to correct systematic effects in astronomical light curves to improve the detection and characterization of planetary transits and astrophysical variability. Algorithms such as the trend filtering algorithm (TFA) use simultaneously-observed stars to measure and remove systematic effects, and binning is used to reduce high-frequency random noise. Aims. We present TFAW, a wavelet-based modified version of TFA. First, TFAW aims to increase the periodic signal detection and second, to return a detrended and denoised signal without modifying its intrinsic characteristics. Methods. We modified TFA’s frequency analysis step adding a stationary wavelet transform filter to perform an initial noise and outlier removal and increase the detection of variable signals. A wavelet-based filter was added to TFA’s signal reconstruction to perform an adaptive characterization of the noise- and trend-free signal and the underlying noise contribution at each iteration while preserving astrophysical signals. We carried out tests over simulated sinusoidal and transit-like signals to assess the effectiveness of the method and applied TFAW to real light curves from TFRM. We also studied TFAW’s application to simulated multiperiodic signals. Results. TFAW improves the signal detection rate by increasing the signal detection efficiency (SDE) up to a factor ∼2.5× for low S/R light curves. For simulated transits, the transit detection rate improves by a factor ∼2 − 5× in the low-S/R regime compared to TFA. TFAW signal approximation performs up to a factor ∼2× better than bin averaging for planetary transits. The standard deviations of simulated and real TFAW light curves are ∼40% better compared to TFA. TFAW yields better MCMC posterior distributions and returns lower uncertainties, less biased transit parameters and narrower (by approximately ten times) credibility intervals for simulated transits. TFAW is also able to improve the characterization of multiperiodic signals. We present a newly-discovered variable star from TFRM.

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Characterization of tetrodes coated with Au nanoparticles (AuNPs) and PEDOT and their application to thalamic neural signal detection in vivo

Brain Stimulation ◽

10.1016/j.brs.2018.12.452 ◽

2019 ◽

Vol 12 (2) ◽

pp. 447-448

Author(s):

D. Lee ◽

Y. Lee ◽

E. Chhetri ◽

Y. Park

Keyword(s):

Signal Detection ◽

Au Nanoparticles ◽

Neural Signal

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