scholarly journals A statistical approach for identifying the ionospheric footprint of magnetospheric boundaries from SuperDARN observations

2008 ◽  
Vol 26 (2) ◽  
pp. 305-314 ◽  
Author(s):  
G. Lointier ◽  
T. Dudok de Wit ◽  
C. Hanuise ◽  
X. Vallières ◽  
J.-P. Villain
Keyword(s):  
Real Time ◽  
Statistical Approach ◽  
Spectral Width ◽  
Threshold Level ◽  
Strongly Correlated ◽  
Spatial Coverage ◽  
First Results ◽  
High Latitude Ionosphere ◽  
Value Decomposition ◽  
Magnetospheric Boundaries

Abstract. Identifying and tracking the projection of magnetospheric regions on the high-latitude ionosphere is of primary importance for studying the Solar Wind-Magnetosphere-Ionosphere system and for space weather applications. By its unique spatial coverage and temporal resolution, the Super Dual Auroral Radar Network (SuperDARN) provides key parameters, such as the Doppler spectral width, which allows the monitoring of the ionospheric footprint of some magnetospheric boundaries in near real-time. In this study, we present the first results of a statistical approach for monitoring these magnetospheric boundaries. The singular value decomposition is used as a data reduction tool to describe the backscattered echoes with a small set of parameters. One of these is strongly correlated with the Doppler spectral width, and can thus be used as a proxy for it. Based on this, we propose a Bayesian classifier for identifying the spectral width boundary, which is classically associated with the Polar Cap boundary. The results are in good agreement with previous studies. Two advantages of the method are: the possibility to apply it in near real-time, and its capacity to select the appropriate threshold level for the boundary detection.

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

scholarly journals A Statistical study of the Doppler spectral width of high-latitude ionospheric F-region echoes recorded with SuperDARN coherent HF radars

2002 ◽  
Vol 20 (11) ◽  
pp. 1769-1781 ◽  
Author(s):  
J.-P. Villain ◽  
R. André ◽  
M. Pinnock ◽  
R. A. Greenwald ◽  
C. Hanuise
Keyword(s):  
High Latitude ◽  
Statistical Study ◽  
Spectral Width ◽  
Auroral Oval ◽  
F Region ◽  
Radar Network ◽  
Period 2 ◽  
Time Period ◽  
High Latitude Ionosphere ◽  
Data Points

Abstract. The HF radars of the Super Dual Auroral Radar Network (SuperDARN) provide measurements of the E × B drift of ionospheric plasma over extended regions of the high-latitude ionosphere. We have conducted a statistical study of the associated Doppler spectral width of ionospheric F-region echoes. The study has been conducted with all available radars from the Northern Hemisphere for 2 specific periods of time. Period 1 corresponds to the winter months of 1994, while period 2 covers October 1996 to March 1997. The distributions of data points and average spectral width are presented as a function of Magnetic Latitude and Magnetic Local Time. The databases are very consistent and exhibit the same features. The most stringent features are: a region of very high spectral width, collocated with the ionospheric LLBL/cusp/mantle region; an oval shaped region of high spectral width, whose equator-ward boundary matches the poleward limit of the Holzworth and Meng auroral oval. A simulation has been conducted to evaluate the geometrical and instrumental effects on the spectral width. It shows that these effects cannot account for the observed spectral features. It is then concluded that these specific spectral width characteristics are the signature of ionospheric/magnetospheric coupling phenomena.Key words. Ionosphere (auroral ionosphere; ionosphere-magnetosphere interactions; ionospheric irregularities)

scholarly journals Multivariate statistical air mass classification for the high-alpine observatory at the Zugspitze Mountain, Germany

2019 ◽  
Vol 19 (19) ◽  
pp. 12477-12494 ◽  
Author(s):  
Armin Sigmund ◽  
Korbinian Freier ◽  
Till Rehm ◽  
Ludwig Ries ◽  
Christian Schunk ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Real Time ◽  
Statistical Approach ◽  
Free Troposphere ◽  
Time Data ◽  
Detection Scheme ◽  
Air Mass ◽  
Thermally Induced ◽  
Air Masses ◽  
Mechanistic Approach

Abstract. To assist atmospheric monitoring at high-alpine sites, a statistical approach for distinguishing between the dominant air masses was developed. This approach was based on a principal component analysis using five gas-phase and two meteorological variables. The analysis focused on the Schneefernerhaus site at Zugspitze Mountain, Germany. The investigated year was divided into 2-month periods, for which the analysis was repeated. Using the 33.3 % and 66.6 % percentiles of the first two principal components, nine air mass regimes were defined. These regimes were interpreted with respect to vertical transport and assigned to the BL (recent contact with the boundary layer), UFT/SIN (undisturbed free troposphere or stratospheric intrusion), and HYBRID (influences of both the boundary layer and the free troposphere or ambiguous) air mass classes. The input data were available for 78 % of the investigated year. BL accounted for 31 % of the cases with similar frequencies in all seasons. UFT/SIN comprised 14 % of the cases but was not found from April to July. HYBRID (55 %) mostly exhibited intermediate characteristics, whereby 17 % of the HYBRID class suggested an influence from the marine boundary layer or the lower free troposphere. The statistical approach was compared to a mechanistic approach using the ceilometer-based mixing layer height from a nearby valley site and a detection scheme for thermally induced mountain winds. Due to data gaps, only 25 % of the cases could be classified using the mechanistic approach. Both approaches agreed well, except in the rare cases of thermally induced uplift. The statistical approach is a promising step towards a real-time classification of air masses. Future work is necessary to assess the uncertainty arising from the standardization of real-time data.

scholarly journals Collection of Garbage using Internet of Things

2019 ◽  
Vol 8 (6) ◽  
pp. 3481-3483
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Threshold Level ◽  
Pond Water ◽  
Conveyor Belt ◽  
Human Intervention ◽  
Practical Application ◽  
Electric Oven ◽  
Plastic Wastes ◽  
Opening And Closing

Due to rise in population, the waste disposed by human has become enormous. This paper deals with a real time practical application of designing and building a prototype for an automatic opening and closing of dustbin on the detection of the human intervention who wish to throw out their trash. In this system the level of garbage in the bin can be known by the use of sensors. Each dustbin has a unique ID. If the garbage in the bin reaches the threshold level, the garbage collectors are given information based on which they can collect the garbage. In case the dustbins reach threshold level, user will not be able to access the bin. In order to avoid the decaying smell around the bin the harmless chemical sprinklers are used. Further, the garbage is segregated into bio degradable and non-biodegradable wet and dry waste using a conveyor belt. Internally electric oven burns the dry waste and the ashes are used for certain applications such as in cleaning the pond and in preventing the growth of algae in the pond water. The wet wastes are made to decompose and it acts as a fertilizer to the fields. The plastic wastes collected are used in building plastic tar roads

Machine learning approaches for improving near-real-time IMERG rainfall estimates by integrating Cloud Properties from NOAA CDR PATMOSX

2021 ◽  
Author(s):  
Zhi Zhang ◽  
Dagang Wang ◽  
Jianxiu Qiu ◽  
Jinxin Zhu ◽  
Tingli Wang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
River Basin ◽  
Central China ◽  
Support Vector ◽  
Learning Approaches ◽  
Satellite Precipitation ◽  
Cloud Properties ◽  
Spatial Coverage ◽  
Extreme Gradient Boosting

AbstractThe Global Precipitation Measurement (GPM) mission provides satellite precipitation products with an unprecedented spatio-temporal resolution and spatial coverage. However, its near-real-time (NRT) product still suffers from low accuracy. This study aims to improve the early run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) by using four machine learning approaches, i.e., support vector machine (SVM), random forest (RF), artificial neural network (ANN), and Extreme Gradient Boosting (XGB). The cloud properties are selected as the predictors in addition to the original IMERG in these approaches. All the four approaches show similar improvement, with 53%-60% reduction of root-mean-square error (RMSE) compared with the original IMERG in a humid area, i.e., the Dongjiang River Basin (DJR) in southeastern China. The improvements are even greater in a semi-arid area, i.e., the Fenhe River Basin (FHR) in central China, the RMSE reduction ranges from 63%-66%. The products generated by the machine learning methods performs similarly to or even outperform than the final run of IMERG. Feature importance analysis, a technique to evaluate input features based on how useful they are in predicting a target variable, indicates that the cloud height and the brightness temperature are the most useful information in improving satellite precipitation products, followed by the atmospheric reflectivity and the surface temperature. This study shows that a more accurate NRT precipitation product can be produced by combining machine learning approaches and cloud information, which is of importance for hydrological applications that requires NRT precipitation information including flood monitoring.

A Real-time Synchrophasor Data Compression Method Using Singular Value Decomposition

2021 ◽  
pp. 1-1
Author(s):  
Reza Pourramezan ◽  
Reza Hassani ◽  
Houshang Karimi ◽  
Mario Paolone ◽  
Jean Mahseredjian
Keyword(s):  
Singular Value Decomposition ◽  
Data Compression ◽  
Real Time ◽  
Singular Value ◽  
Compression Method ◽  
Value Decomposition

scholarly journals Secure Exchange of Medical Data Using a Novel Real-Time Biometric-Based Protection and Recognition Method

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2013
Author(s):  
Shams Ud Din ◽  
Zahoor Jan ◽  
Muhammad Sajjad ◽  
Maqbool Hussain ◽  
Rahman Ali ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Real Time ◽  
Signal To Noise Ratio ◽  
Security And Privacy ◽  
The Internet ◽  
Healthcare Organizations ◽  
Health Records ◽  
Clinical Notes ◽  
Electronic Health ◽  
Value Decomposition

Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies.

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