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Register a new userForecasting Precipitable Water Vapor Using LSTMs
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Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.
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A Low Humidity and Temperature Profiling (LHATPRO) microwave radiometer, manufactured by Radiometer Physics GmbH (RPG), is used to monitor sky conditions over ESOs Paranal observatory in support of VLT science operations. The unit measures several channels across the strong water vapour emission line at 183 GHz, necessary for resolving the low levels of precipitable water vapour (PWV) that are prevalent on Paranal (median ~2.4 mm). The instrument consists of a humidity profiler (183-191 GHz), a temperature profiler (51-58 GHz), and an infrared camera (~10 {mu}m) for cloud detection. We present, for the first time, a statistical analysis of the homogeneity of all-sky PWV using 21 months of periodic (every 6 hours) all-sky scans from the radiometer. These data provide unique insight into the spatial and temporal variation of atmospheric conditions relevant for astronomical observations, particularly in the infrared. We find the PWV over Paranal to be remarkably homogeneous across the sky down to 27.5{deg} elevation with a median variation of 0.32 mm (peak to valley) or 0.07 mm (rms). The homogeneity is a function of the absolute PWV but the relative variation is fairly constant at 10-15% (peak to valley) and 3% (rms). Such variations will not be a significant issue for analysis of astronomical data. Users at ESO can specify PWV - measured at zenith - as an ambient constraint in service mode to enable, for instance, very demanding observations in the infrared that can only be conducted during periods of very good atmospheric transmission and hence low PWV. We conclude that in general it will not be necessary to add another observing constraint for PWV homogeneity to ensure integrity of observations.
We report on the measurements of telluric water vapor made with the instrument FIFI-LS on SOFIA. Since November 2018, FIFI-LS has measured the water vapor overburden with the same measurement setup on each science flight with about 10 data points throughout the flight. This created a large sample of 469 measurements at different locations, flight altitudes and seasons. The paper describes the measurement principle in detail and provides some trend analysis on the 3 parameters. This presents the first systematic analysis with SOFIA based on in situ observations.
The atmospheric water vapor content above the Roque de los Muchachos Observatory (ORM) obtained from Global Positioning Systems (GPS) is presented. GPS measurements have been evaluated by comparison with 940nm-radiometer observations. Statistical analysis of GPS measurements points to ORM as an observing site with suitable conditions for infrared (IR) observations, with a median column of precipitable water vapor (PWV) of 3.8 mm. PWV presents a clear seasonal behavior, being Winter and Spring the best seasons for IR observations. The percentage of nighttime showing PWV values smaller than 3 mm is over 60% in February, March and April. We have also estimated the temporal variability of water vapor content at the ORM. A summary of PWV statistical results at different astronomical sites is presented, recalling that these values are not directly comparable as a result of the differences in the techniques used to recorded the data.
This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state. We propose the use of recurrent neural networks and exploit recent advancements in attention based learning to segment the PCG signal. This allows the network to identify the most salient aspects of the signal and disregard uninformative information. The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings. Furthermore, we empirically analyse different feature combinations including envelop features, wavelet and Mel Frequency Cepstral Coefficients (MFCC), and provide quantitative measurements that explore the importance of different features in the proposed approach. We demonstrate that a recurrent neural network coupled with attention mechanisms can effectively learn from irregular and noisy PCG recordings. Our analysis of different feature combinations shows that MFCC features and their derivatives offer the best performance compared to classical wavelet and envelop features. Heart sound segmentation is a crucial pre-processing step for many diagnostic applications. The proposed method provides a cost effective alternative to labour extensive manual segmentation, and provides a more accurate segmentation than existing methods. As such, it can improve the performance of further analysis including the detection of murmurs and ejection clicks. The proposed method is also applicable for detection and segmentation of other one dimensional biomedical signals.
The Global Navigation Satellite Systems (GNSS) like GPS suffer from accuracy degradation and are almost unavailable in indoor environments. Indoor positioning systems (IPS) based on WiFi signals have been gaining popularity. However, owing to the strong spatial and temporal variations of wireless communication channels in the indoor environment, the achieved accuracy of existing IPS is around several tens of centimeters. We present the detailed design and implementation of a self-adaptive WiFi-based indoor distance estimation system using LSTMs. The system is novel in its method of estimating with high accuracy the distance of an object by overcoming possible causes of channel variations and is self-adaptive to the changing environmental and surrounding conditions. The proposed design has been developed and physically realized over a WiFi network consisting of ESP8266 (NodeMCU) devices. The experiment were conducted in a real indoor environment while changing the surroundings in order to establish the adaptability of the system. We introduce and compare different architectures for this task based on LSTMs, CNNs, and fully connected networks (FCNs). We show that the LSTM based model performs better among all the above-mentioned architectures by achieving an accuracy of 5.85 cm with a confidence interval of 93% on the scale of (4.14 m * 2.86 m). To the best of our knowledge, the proposed method outperforms other methods reported in the literature by a significant margin.
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