scholarly journals A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial–Temporal Correlation

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2377
Author(s):  
Mingyue Ding ◽  
Yachao Li ◽  
Yinghui Quan ◽  
Liang Guo ◽  
Mengdao Xing
Keyword(s):  
Texture Component ◽  
Temporal Correlation ◽  
Real Data ◽  
Reconstruction Method ◽  
Spatial Correlations ◽  
Sea Clutter ◽  
Compound Model ◽  
Temporal And Spatial ◽  
Maritime Environment ◽  
Detection And Recognition

The reconstruction of sea clutter plays an important role in target detection and recognition in a maritime environment. Reproducing the temporal and spatial correlations of real data simultaneously is always a problem in the reconstruction of sea clutter due to the complex coupling between them. In this paper, the spatial–temporal correlated proportional method (STCPM), based on a compound model, is proposed to reconstruct K-distributed sea clutter with correlation characteristics obtained from the real data. The texture component with spatial–temporal correlation is generated by the proportional method and the speckle component with temporal correlation is generated by matrix transformation. Compared with previous methods, the biggest innovation of the STCPM is that it can more accurately generate K-distributed sea clutter with both temporal and spatial correlations. The comparison of the reconstructed and real data demonstrates that the method can reproduce the characteristics of real sea clutter well.

Dispersive Fluxes between the Coastal Ocean and a Semienclosed Estuarine Basin

2007 ◽  
Vol 37 (6) ◽  
pp. 1645-1660 ◽  
Author(s):  
Jonathan P. Fram ◽  
Maureen A. Martin ◽  
Mark T. Stacey
Keyword(s):  
San Francisco ◽  
Temporal Correlation ◽  
Coastal Ocean ◽  
Spatial Correlations ◽  
Tidal Pumping ◽  
Spring Runoff ◽  
Temporal And Spatial ◽  
Dispersive Fluxes ◽  
Net Fluxes ◽  
Dispersive Flux

Abstract Scalar exchange between San Francisco Bay and the coastal ocean is examined using shipboard observations made across the Golden Gate Channel. The study consists of experiments during each of the following three “seasons”: winter/spring runoff (March 2002), summer upwelling (July 2003), and autumn relaxation (October 2002). Within each experiment, transects across the channel were repeated approximately every 12 min for 25 h during both spring and neap tides. Velocity was measured from a boat-mounted ADCP. Scalar concentrations were measured at the surface and from a tow-yoed SeaSciences Acrobat. Net salinity exchange rates for each season are quantified with harmonic analysis. Accuracy of the net fluxes is confirmed by comparison with independently measured values. Harmonic results are then decomposed into flux mechanisms using temporal and spatial correlations. In this study, the temporal correlation of cross-sectionally averaged salinity and velocity (tidal pumping flux) is the largest part of the dispersive flux of salinity into the bay. From the tidal pumping flux portion of the dispersive flux, it is shown that there is less exchange than was found in earlier studies. Furthermore, tidal pumping flux scales strongly with freshwater flow resulting from the density-driven movement of a tidally trapped eddy and stratification-induced increases in ebb–flood frictional phasing. Complex bathymetry makes salinity exchange scale differently with flow than would be expected from simple tidal asymmetry and gravitational circulation models.

scholarly journals Refractive Two-View Reconstruction for Underwater 3D Vision

2019 ◽  
Vol 128 (5) ◽  
pp. 1101-1117 ◽  
Author(s):  
François Chadebecq ◽  
Francisco Vasconcelos ◽  
René Lacher ◽  
Efthymios Maneas ◽  
Adrien Desjardins ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Real Data ◽  
Reconstruction Method ◽  
Underwater Imaging ◽  
Shape Estimation ◽  
Camera Model ◽  
Projection Model ◽  
3D Vision ◽  
Object Distance ◽  
View Reconstruction

AbstractRecovering 3D geometry from cameras in underwater applications involves the Refractive Structure-from-Motion problem where the non-linear distortion of light induced by a change of medium density invalidates the single viewpoint assumption. The pinhole-plus-distortion camera projection model suffers from a systematic geometric bias since refractive distortion depends on object distance. This leads to inaccurate camera pose and 3D shape estimation. To account for refraction, it is possible to use the axial camera model or to explicitly consider one or multiple parallel refractive interfaces whose orientations and positions with respect to the camera can be calibrated. Although it has been demonstrated that the refractive camera model is well-suited for underwater imaging, Refractive Structure-from-Motion remains particularly difficult to use in practice when considering the seldom studied case of a camera with a flat refractive interface. Our method applies to the case of underwater imaging systems whose entrance lens is in direct contact with the external medium. By adopting the refractive camera model, we provide a succinct derivation and expression for the refractive fundamental matrix and use this as the basis for a novel two-view reconstruction method for underwater imaging. For validation we use synthetic data to show the numerical properties of our method and we provide results on real data to demonstrate its practical application within laboratory settings and for medical applications in fluid-immersed endoscopy. We demonstrate our approach outperforms classic two-view Structure-from-Motion method relying on the pinhole-plus-distortion camera model.

scholarly journals Integration Capability Evaluation of Wind and Photovoltaic Generation in Power Systems Based on Temporal and Spatial Correlations

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 171 ◽  
Author(s):  
Hua Zhou ◽  
Huahua Wu ◽  
Chengjin Ye ◽  
Shijie Xiao ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Power Systems ◽  
Wind Power ◽  
Energy Generation ◽  
Copula Model ◽  
Spatial Correlations ◽  
Temporal And Spatial ◽  
Renewable Energy Generation ◽  
Pv Power Generation

With the rapid growth of renewable energy generation, it has become essential to give a comprehensive evaluation of renewable energy integration capability in power systems to reduce renewable generation curtailment. Existing research has not considered the correlations between wind power and photovoltaic (PV) power. In this paper, temporal and spatial correlations among different renewable generations are utilized to evaluate the integration capability of power systems based on the copula model. Firstly, the temporal and spatial correlation between wind and PV power generation is analyzed. Secondly, the temporal and spatial distribution model of both wind and PV power generation output is formulated based on the copula model. Thirdly, aggregated generation output scenarios of wind and PV power are generated. Fourthly, wind and PV power scenarios are utilized in an optimal power flow calculation model of power systems. Lastly, the integration capacity of wind power and PV power is shown to be able to be evaluated by satisfying the reliability of power system operation. Simulation results of a modified IEEE RTS-24 bus system indicate that the integration capability of renewable energy generation in power systems can be comprehensively evaluated based on the temporal and spatial correlations of renewable energy generation.

scholarly journals SCIAMACHY WFM-DOAS XCO<sub>2</sub>: comparison with CarbonTracker XCO<sub>2</sub> focusing on aerosols and thin clouds

2012 ◽  
Vol 5 (2) ◽  
pp. 2887-2931 ◽  
Author(s):  
J. Heymann ◽  
O. Schneising ◽  
M. Reuter ◽  
M. Buchwitz ◽  
V. V. Rozanov ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Temporal Correlation ◽  
Anthropogenic Activity ◽  
Data Sets ◽  
Spatial Correlations ◽  
Data Set ◽  
Cloud Data ◽  
Temporal Correlations ◽  
Retrieval Errors ◽  
The Difference

Abstract. Carbon dioxide (CO2) is the most important greenhouse gas whose atmospheric loading has been significantly increased by anthropogenic activity leading to global warming. Accurate measurements and models are needed in order to reliably predict our future climate. This, however, has challenging requirements. Errors in measurements and models need to be identified and minimised. In this context, we present a comparison between satellite-derived column-averaged dry air mole fractions of CO2, denoted XCO2, retrieved from SCIAMACHY/ENVISAT using the WFM-DOAS algorithm, and output from NOAA's global CO2 modelling and assimilation system CarbonTracker. We investigate to what extent differences between these two data sets are influenced by systematic retrieval errors due to aerosols and unaccounted clouds. We analyse seven years of SCIAMACHY WFM-DOAS version 2.1 retrievals (WFMDv2.1) using the latest version of CarbonTracker (version 2010). We investigate to what extent the difference between SCIAMACHY and CarbonTracker XCO2 are temporally and spatially correlated with global aerosol and cloud data sets. For this purpose, we use a global aerosol data set generated within the European GEMS project, which is based on assimilated MODIS satellite data. For clouds, we use a data set derived from CALIOP/CALIPSO. We find significant correlations of the SCIAMACHY minus CarbonTracker XCO2 difference with thin clouds over the Southern Hemisphere. The maximum temporal correlation we find for Darwin, Australia (r2 = 54%). Large temporal correlations with thin clouds are also observed over other regions of the Southern Hemisphere (e.g. 43% for South America and 31% for South Africa). Over the Northern Hemisphere the temporal correlations are typically much lower. An exception is India, where large temporal correlations with clouds and aerosols have also been found. For all other regions the temporal correlations with aerosol are typically low. For the spatial correlations the picture is less clear. They are typically low for both aerosols and clouds, but dependent on region and season, they may exceed 30% (the maximum value of 46% has been found for Darwin during September to November). Overall we find that the presence of thin clouds can potentially explain a significant fraction of the difference between SCIAMACHY WFMDv2.1 XCO2 and CarbonTracker over the Southern Hemisphere. Aerosols appear to be less of a problem. Our study indicates that the quality of the satellite derived XCO2 will significantly benefit from a reduction of scattering related retrieval errors at least for the Southern Hemisphere.

Detection of Hidden Structures in Nonstationary Spike Trains

2011 ◽  
Vol 23 (5) ◽  
pp. 1205-1233 ◽  
Author(s):  
Ken Takiyama ◽  
Masato Okada
Keyword(s):  
Data Analysis ◽  
Markov Models ◽  
Temporal Correlation ◽  
Real Data ◽  
State Transitions ◽  
Model Parameters ◽  
Temporal Area ◽  
Firing Rates ◽  
Observation Process ◽  
Bayes Algorithm

We propose an algorithm for simultaneously estimating state transitions among neural states and nonstationary firing rates using a switching state-space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only discontinuous changes in mean firing rates but also discontinuous changes in the temporal profiles of firing rates (e.g., temporal correlation). We construct estimation and learning algorithms for a nongaussian SSSM, whose nongaussian property is caused by binary spike events. Local variational methods can transform the binary observation process into a quadratic form. The transformed observation process enables us to construct a variational Bayes algorithm that can determine the number of neural states based on automatic relevance determination. Additionally, our algorithm can estimate model parameters from single-trial data using a priori knowledge about state transitions and firing rates. Synthetic data analysis reveals that our algorithm has higher performance for estimating nonstationary firing rates than previous methods. The analysis also confirms that our algorithm can detect state transitions on the basis of discontinuous changes in temporal correlation, which are transitions that previous hidden Markov models could not detect. We also analyze neural data recorded from the medial temporal area. The statistically detected neural states probably coincide with transient and sustained states that have been detected heuristically. Estimated parameters suggest that our algorithm detects the state transitions on the basis of discontinuous changes in the temporal correlation of firing rates. These results suggest that our algorithm is advantageous in real-data analysis.

scholarly journals Embracing the uncertainty: the evolution of SOFI into a diverse family of fluctuation-based super-resolution microscopy methods

2021 ◽  
Author(s):  
Monika Pawlowska ◽  
Ron Tenne ◽  
Bohnishikha Ghosh ◽  
Adrian Makowski ◽  
Radek Lapkiewicz
Keyword(s):  
3D Imaging ◽  
Deep Neural Networks ◽  
Fluorescent Proteins ◽  
Super Resolution ◽  
Significant Progress ◽  
Spatial Correlations ◽  
Order Of Magnitude ◽  
Temporal And Spatial ◽  
Microscopy Techniques ◽  
Super Resolution Microscopy

Abstract Super-resolution microscopy techniques have pushed the limits of resolution in optical imaging by more than an order of magnitude. However, these methods often require long acquisition times as well as complex setups and sample preparation protocols. Super-resolution Optical Fluctuation Imaging (SOFI) emerged over ten years ago as an approach that exploits temporal and spatial correlations within the acquired images to obtain increased resolution with less strict requirements. This review follows the progress of SOFI from its first demonstration to the development of a branch of methods that treat fluctuations as a source of contrast, rather than noise. Among others, we highlight the implementation of SOFI with standard fluorescent proteins as well as the microscope modification that facilitate 3D imaging and the application of modern cameras. Going beyond the classical framework of SOFI, we explore different innovative concepts from deep neural networks all the way to a quantum analogue of SOFI, antibunching microscopy. While SOFI has not reached the same level of ubiquity as other super-resolution methods, our overview finds significant progress and substantial potential for the concept of leveraging fluorescence fluctuations to obtain super-resolved images.

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