scholarly journals Polar maps of C-band backscatter parameters from the Advanced Scatterometer

2021 ◽  
Author(s):  
Jessica Cartwright ◽  
Alexander D. Fraser
Keyword(s):  
Temporal Resolution ◽  
Incidence Angle ◽  
Anisotropy Parameter ◽  
Ice Sheets ◽  
Azimuth Angle ◽  
High Temporal Resolution ◽  
Valuable Data ◽  
Long Time ◽  
Anisotropy Parameters ◽  
Considerable Complexity

Abstract. Maps of backscatter anisotropy parameters from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Advanced Scatterometer (ASCAT), a C-band fan-beam scatterometer, contain unique and valuable data characterising the surface and subsurface of various cryospheric elements, including sea ice and ice sheets. The computational expense and considerable complexity required to produce parameter maps from the raw backscatter data inhibits the wider adoption of ASCAT data. Here, backscatter anisotropy parameter maps gridded at a resolution of 12.5 km per pixel are made available to the community in order to facilitate the exploitation of these parameters for cryospheric applications. These maps have been calculated from the EUMETSAT Level 1B sigma0 product acquired from ASCAT on board MetOp-A, MetOp-B and MetOp-C. The dataset is unique in that it prioritises anisotropy characterisation over temporal resolution, and combines ASCAT data from multiple platforms. The parameterisation chosen assumes a linear falloff of backscatter with incidence angle and a 4th order Fourier series parameterisation of azimuth angle anisotropy. The product (Fraser and Cartwright, 2021) is available at https://doi.org/10.26179/5dd60df7469e2 presented on three time scales depending on orbital platform availability: 5-day (2007 to present – MetOp-A only – suitable for users requiring a long time-series), 2-day (2013 to present – MetOp-A and -B), and 1-day resolution (2019 – present – MetOp -A, -B and -C – suitable for users needing both high temporal resolution and detailed anisitropy characterisation).

scholarly journals Wavelet Average Coefficient: a New Statistical Feature for Capturing the Temporal Pattern of Spike Trains, With Application in Brain Machine Interface

2021 ◽  
Author(s):  
Shixian Wen ◽  
Allen Yin ◽  
Po-He Tseng ◽  
Laurent Itti ◽  
Mikhail Lebedev ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Sliding Window ◽  
Temporal Patterns ◽  
Spike Trains ◽  
Spike Count ◽  
Neurological Injury ◽  
High Temporal Resolution ◽  
Statistical Feature ◽  
Long Time ◽  
Window Approach

Abstract Motor brain machine interfaces (BMI) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders 1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; 2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description - wavelet average coefficients (WAC) - to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

scholarly journals Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shixian Wen ◽  
Allen Yin ◽  
Po-He Tseng ◽  
Laurent Itti ◽  
Mikhail A. Lebedev ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Short Term Memory ◽  
Sliding Window ◽  
Temporal Patterns ◽  
Spike Count ◽  
Neurological Injury ◽  
High Temporal Resolution ◽  
Long Time ◽  
Machine Interface ◽  
Window Approach

AbstractMotor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description—wavelet average coefficients (WAC)—to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

Multidetector-row Computed Tomography in the Assessment of Coronary Artery Disease – New Techniques and Insights

2010 ◽  
Vol 6 (2) ◽  
pp. 43 ◽  
Author(s):  
Andreas H Mahnken ◽  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Data Acquisition ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
Multidetector Row ◽  
Ct Scanners ◽  
Artery Disease ◽  
Cardiac Mdct

Over the last decade, cardiac computed tomography (CT) technology has experienced revolutionary changes and gained broad clinical acceptance in the work-up of patients suffering from coronary artery disease (CAD). Since cardiac multidetector-row CT (MDCT) was introduced in 1998, acquisition time, number of detector rows and spatial and temporal resolution have improved tremendously. Current developments in cardiac CT are focusing on low-dose cardiac scanning at ultra-high temporal resolution. Technically, there are two major approaches to achieving these goals: rapid data acquisition using dual-source CT scanners with high temporal resolution or volumetric data acquisition with 256/320-slice CT scanners. While each approach has specific advantages and disadvantages, both technologies foster the extension of cardiac MDCT beyond morphological imaging towards the functional assessment of CAD. This article examines current trends in the development of cardiac MDCT.

scholarly journals Geological CO2 quantified by high-temporal resolution stable isotope monitoring in a salt mine

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander H. Frank ◽  
Robert van Geldern ◽  
Anssi Myrttinen ◽  
Martin Zimmer ◽  
Johannes A. C. Barth ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Underground Mining ◽  
Stable Carbon Isotope ◽  
Underground Mines ◽  
Background Concentration ◽  
High Temporal Resolution ◽  
Salt Mine ◽  
Gas Migration ◽  
Mining Operations ◽  
Anthropogenic Contributions

AbstractThe relevance of CO2 emissions from geological sources to the atmospheric carbon budget is becoming increasingly recognized. Although geogenic gas migration along faults and in volcanic zones is generally well studied, short-term dynamics of diffusive geogenic CO2 emissions are mostly unknown. While geogenic CO2 is considered a challenging threat for underground mining operations, mines provide an extraordinary opportunity to observe geogenic degassing and dynamics close to its source. Stable carbon isotope monitoring of CO2 allows partitioning geogenic from anthropogenic contributions. High temporal-resolution enables the recognition of temporal and interdependent dynamics, easily missed by discrete sampling. Here, data is presented from an active underground salt mine in central Germany, collected on-site utilizing a field-deployed laser isotope spectrometer. Throughout the 34-day measurement period, total CO2 concentrations varied between 805 ppmV (5th percentile) and 1370 ppmV (95th percentile). With a 400-ppm atmospheric background concentration, an isotope mixing model allows the separation of geogenic (16–27%) from highly dynamic anthropogenic combustion-related contributions (21–54%). The geogenic fraction is inversely correlated to established CO2 concentrations that were driven by anthropogenic CO2 emissions within the mine. The described approach is applicable to other environments, including different types of underground mines, natural caves, and soils.

scholarly journals Revealing the time course of laser evoked potential habituation by high temporal resolution analysis

2021 ◽  
Author(s):  
D. Kersebaum ◽  
S.‐C. Fabig ◽  
M. Sendel ◽  
A. C. Muntean ◽  
R. Baron ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Time Course ◽  
Evoked Potential ◽  
High Temporal Resolution ◽  
Resolution Analysis

scholarly journals Streamlined CTCA During COVID-19 Pandemic Without Beta-Blockade Using High Temporal Resolution Scanners

2021 ◽  
Vol 30 ◽  
pp. S205
Author(s):  
N. Lammoza ◽  
P. Ratnakanthan ◽  
T. Moran ◽  
P. O'Sullivan ◽  
K. O'Donnell ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Beta Blockade ◽  
High Temporal Resolution
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