THE IDENTIFICATION OF COMPLEX SPATIOTEMPORAL PATTERNS USING COUPLED MAP LATTICE MODELS

2008 ◽  
Vol 18 (04) ◽  
pp. 997-1013 ◽  
Author(s):  
Y. PAN ◽  
S. A. BILLINGS
Keyword(s):  
Spatial Data ◽  
Bayesian Learning ◽  
Learning Algorithm ◽  
Lattice Models ◽  
Sampling Procedure ◽  
Coupled Map Lattice ◽  
Spatiotemporal Patterns ◽  
Data Sampling ◽  
Wide Range ◽  
Correlation Analysis Method

Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation-based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models.

scholarly journals A Simultaneous Sparse Learning Algorithm of Structured Approximation with Transformation Analysis Embedded in Bayesian Framework

2021 ◽  
Author(s):  
Guisheng Wang
Keyword(s):  
Bayesian Learning ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Bayesian Framework ◽  
Sparse Approximation ◽  
Sparse Learning ◽  
Sparse Signals ◽  
Sparse Models ◽  
Wide Range ◽  
Feasible Solutions

<div>Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. In this study, a simultaneous Bayesian framework was extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation (SSL-SA) is proposed with transformation analysis which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting were embedded in the framework to achieve speedy convergence as well as high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis were embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared to conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyse the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denosing.</div>

scholarly journals A Simultaneous Sparse Learning Algorithm of Structured Approximation with Transformation Analysis Embedded in Bayesian Framework

2021 ◽  
Author(s):  
Guisheng Wang
Keyword(s):  
Bayesian Learning ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Bayesian Framework ◽  
Sparse Approximation ◽  
Sparse Learning ◽  
Sparse Signals ◽  
Sparse Models ◽  
Wide Range ◽  
Feasible Solutions

<div>Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. In this study, a simultaneous Bayesian framework was extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation (SSL-SA) is proposed with transformation analysis which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting were embedded in the framework to achieve speedy convergence as well as high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis were embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared to conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyse the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denosing.</div>

Unified approach to spatial description of objects terrain departmental registries / cadastres as a perspective basis of the state system territory mapping

2019 ◽  
Vol 942 (12) ◽  
pp. 41-49
Author(s):  
A.M. Portnov
Keyword(s):  
Spatial Data ◽  
Spatial Description ◽  
Unified Approach ◽  
Technological Basis ◽  
The Public ◽  
Wide Range ◽  
The Russian Federation ◽  
Map Data ◽  
Consistency Verification ◽  
Thematic Data

Using unified principles of formation and maintenance of register/cadaster with information about spatial data of landscape objects as the informational and technological basis for updating the public topographic maps and modernization of state cartographic system is proposed. The problems of informational relevancy of unified electronical cartographic basis and capacity of its renovation in case of public cadaster map data. The need to modernize the system of classification and coding of cartographic information, the use of unified standards for the coordinate description of register objects for their topological consistency, verification and updating is emphasized. Implementing such solutions is determined by economical expediency as well as necessity of providing a variety of real thematic data for wide range of consumers in the field of urban planning, territories development and completing the tasks of Governmental program “Digital economy of the Russian Federation”.

Turbulent three-dimensional dielectric electrohydrodynamic convection between two plates

2012 ◽  
Vol 696 ◽  
pp. 228-262 ◽  
Author(s):  
A. Kourmatzis ◽  
J. S. Shrimpton
Keyword(s):  
Spatial Data ◽  
Three Dimensional ◽  
Reynolds Numbers ◽  
Two Dimensions ◽  
Three Dimensions ◽  
Energy Budgets ◽  
Turbulent Regime ◽  
Scalar Flux ◽  
Wide Range ◽  
Scalar Variance

AbstractThe fundamental mechanisms responsible for the creation of electrohydrodynamically driven roll structures in free electroconvection between two plates are analysed with reference to traditional Rayleigh–Bénard convection (RBC). Previously available knowledge limited to two dimensions is extended to three-dimensions, and a wide range of electric Reynolds numbers is analysed, extending into a fully inherently three-dimensional turbulent regime. Results reveal that structures appearing in three-dimensional electrohydrodynamics (EHD) are similar to those observed for RBC, and while two-dimensional EHD results bear some similarities with the three-dimensional results there are distinct differences. Analysis of two-point correlations and integral length scales show that full three-dimensional electroconvection is more chaotic than in two dimensions and this is also noted by qualitatively observing the roll structures that arise for both low (${\mathit{Re}}_{E} = 1$) and high electric Reynolds numbers (up to ${\mathit{Re}}_{E} = 120$). Furthermore, calculations of mean profiles and second-order moments along with energy budgets and spectra have examined the validity of neglecting the fluctuating electric field ${ E}_{i}^{\ensuremath{\prime} } $ in the Reynolds-averaged EHD equations and provide insight into the generation and transport mechanisms of turbulent EHD. Spectral and spatial data clearly indicate how fluctuating energy is transferred from electrical to hydrodynamic forms, on moving through the domain away from the charging electrode. It is shown that ${ E}_{i}^{\ensuremath{\prime} } $ is not negligible close to the walls and terms acting as sources and sinks in the turbulent kinetic energy, turbulent scalar flux and turbulent scalar variance equations are examined. Profiles of hydrodynamic terms in the budgets resemble those in the literature for RBC; however there are terms specific to EHD that are significant, indicating that the transfer of energy in EHD is also attributed to further electrodynamic terms and a strong coupling exists between the charge flux and variance, due to the ionic drift term.

Sparse Bayesian learning algorithm for separable dictionaries

2021 ◽  
Vol 111 ◽  
pp. 102990
Author(s):  
Andra Băltoiu ◽  
Bogdan Dumitrescu
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Sparse Bayesian Learning

scholarly journals Real-time gaze estimation via pupil center tracking

2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Natural Environments ◽  
Gaze Estimation ◽  
Head Pose ◽  
Data Set ◽  
Gaze Tracking ◽  
Illumination Changes ◽  
Wide Range ◽  
Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

scholarly journals A Survey on Spatio-temporal Data Analytics Systems

2022 ◽  
Author(s):  
Md Mahbub Alam ◽  
Luis Torgo ◽  
Albert Bifet
Keyword(s):  
Programming Languages ◽  
Spatial Data ◽  
Data Analytics ◽  
Spatial Databases ◽  
Location Based Services ◽  
Temporal Data ◽  
Wide Range ◽  
Data Volume ◽  
Spatio Temporal ◽  
Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

scholarly journals A MODULAR MOBILE MAPPING PLATFORM FOR COMPLEX INDOOR AND OUTDOOR ENVIRONMENTS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 243-250
Author(s):  
I. Kalisperakis ◽  
T. Mandilaras ◽  
A. El Saer ◽  
P. Stamatopoulou ◽  
C. Stentoumis ◽  
...  
Keyword(s):  
Spatial Data ◽  
Modular Design ◽  
Physical World ◽  
Remote Access ◽  
Mobile Mapping ◽  
Motion Trajectories ◽  
Multiple Sensors ◽  
Wide Range ◽  
The Individual ◽  
Indoor And Outdoor Environments

Abstract. In this work we present the development of a prototype, mobile mapping platform with modular design and architecture that can be suitably modified to address effectively both outdoors and indoors environments. Our system is built on the Robotics Operation System (ROS) and utilizes multiple sensors to capture images, pointclouds and 3D motion trajectories. These include synchronized cameras with wide angle lenses, a lidar sensor, a GPS/IMU unit and a tracking optical sensor. We report on the individual components of the platform, it’s architecture, the integration and the calibration of its components, the fusion of all recorded data and provide initial 3D reconstruction results. The processing algorithms are based on existing implementations of SLAM (Simultaneous Localisation and Mapping) methods combined with SfM (Structure-from-Motion) for optimal estimations of orientations and 3D pointclouds. The scope of this work, which is part of an ongoing H2020 program, is to digitize the physical world, collect relevant spatial data and make digital copies available to experts and public for covering a wide range of needs; remote access and viewing, process, design, use in VR etc.

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