相关论文: FlakeOut: A Geometric Approach to Remove Wind-Blow…

The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and…

It was recently demonstrated that laser filamentation was able to generate an optically transparent channel through cloud and fog for free-space optical communications applications. However, no quantitative measurement of the interaction…

We have developed a new method that uses wavelet analysis to remove interference fringe patterns from images. This method is particularly useful for flat fields in the common case where fringes vary between the calibration and object data.…

The detection reliability of weak signals is a critical issue in many astronomical contexts and may have severe consequences for determining number counts and luminosity functions, but also for optimising the use of telescope time in…

The presence of undesirable dominating signals in geophysical experimental data is a challenge in many subfields. One remarkable example is surface gravimetry, where frequencies from Earth tides correspond to time-series fluctuations up to…

Machine learning is a data-driven field, and the quality of the underlying datasets plays a crucial role in learning success. However, high performance on held-out test data does not necessarily indicate that a model generalizes or learns…

Many different methods exist for reducing data obtained when an astronomical source is studied with a two-channel polarimeter, such as a Wollaston prism system. This paper presents a rigorous method of reducing the data from raw aperture…

This paper describes the data preprocessing and reduction methods together with SLODAR analysis and wind profiling techniques for GeMS: the Gemini MCAO System. The wavefront gradient measurements of the five GeMS's Shack-Hartmann sensors,…

We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learning approaches. Our filtering technique is interesting for decision-making pipelines deploying…

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is…

Methodical aerial population surveys monitoring critically endangered species in Canadian North Atlantic waters are instrumental in influencing government policies both in economic and conservational efforts. The primary factor hindering…

Differentiating between real transit events and false positive signals in photometric time series data is a bottleneck in the identification of transiting exoplanets, particularly long-period planets. This differentiation typically requires…

In this contribution we evaluate the impact of filtering techniques in enhancing the accuracy of forecasts of optical turbulence and atmospheric parameters critical for ground-based telescopes. These techniques make use of the data…

In this paper we discuss the issue of loosing telemetry (TM) data due to different reasons (e.g. spacecraft-ground transmissions) while performing a full-sky survey with space-borne instrumentation. This is a particularly important issue…

Controlled feature selection aims to discover the features a response depends on while limiting the false discovery rate (FDR) to a predefined level. Recently, multiple deep-learning-based methods have been proposed to perform controlled…

This work has been accepted by IEEE TGRS for publication. The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information,…

A physical (e.g. astrophysical, geophysical, meteorological etc.) data may appear as an output of an experiment or it may contain some sociological, economic or biological information. Whatever be the source of a time series data some…

Avoiding aliasing in time-resolved flow data obtained through high fidelity simulations while keeping the computational and storage costs at acceptable levels is often a challenge. Well-established solutions such as increasing the sampling…

Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace.…

Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection…