Boundary Estimation Algorithm Research Articles

Switching boundary estimation and adaptive sliding mode control for the dynamical systems with discontinuity due to the actuators.

Due to the discontinuous physical property of the control actuators, the state space of such a dynamical system is divided into many subdomains. For each subdomain, the flow of such a system is governed by the corresponding subsystem. The state boundary between the adjacent subdomains is called the physical switching boundary. The controller is designed to switch when the subsystem of such a discontinuous dynamical system is switched in order to have the optimum control performance. Since the ambiguity and uncertainty of modeling, the mathematical expressions for describing the discontinuous physical properties of the control actuators may not be accurate. Since the nominal switching boundary where the controller really switches is not exactly the corresponding physical switching boundary, the mismatch between the subsystem and the corresponding controller will occur and it may seriously affect the control performance. Therefore, a boundary estimation algorithm is proposed to estimate the physical switching boundaries based on the model reference control and error backpropagation. The simulation results show that the adaptive sliding mode control with the boundary estimation algorithm has superior control performance and strong robustness to deal with the internal uncertainty, the external interference, and the boundary ambiguity.

Multi-Unmanned-Aerial-Vehicle Wildfire Boundary Estimation Using a Semantic Segmentation Neural Network

This paper presents a system to command and control a team of fixed-wing unmanned aerial vehicles (UAVs) to sense dynamic wildfire boundaries. UAV team task and trajectory planning strategies enable the team to rapidly find, rally around, and map the wildfire boundaries. A novel boundary estimation algorithm generates two-dimensional concave polygonal estimates of multiple dynamic boundaries given sparse observation data. The algorithm was tested with simulated wildfire scenario binary fire or free-point observations collected by the UAV team. First, all gathered observations are used to classify groups of points into clusters belonging to individual wildfires; then, spatiotemporal information from wildfire observations is encoded as an image with observation age represented as pixel brightness. A neural network performs semantic segmentation on each image and outputs a predicted binary image of the wildfire. This image is decoded back into a point set that feeds into a boundary estimation algorithm (Polylidar) to extract a concave boundary. Benchmarks for planner and boundary estimation times and accuracy comparisons are provided. Our boundary estimation algorithm and supporting multiagent planning strategies were used to win the 2019 U.S. Air Force Research Laboratory’s Swarm and Search Wildfire challenge using the aerospace multiagent simulation environment.

English

In thisage of digital technology, almost all processing is done using computer systems. The world around us is inherently analog and three dimensional. Interpreting this 3D data involves vast amounts of processing. Edge Detection & Boundary estimation is a method which enables us to obtain vital information about the actual physical 3D world from a 2D image. A 2D image is converted to grayscaleformat and fed to the Edge Detection and Boundary Estimation algorithm. This algorithm detects the regions with changes in light intensity and marks those regions. Since the changes in light intensity are due to the variations of physical parameters, we are, in effect, obtaining information about the 3D world. The counting algorithm smoothens the edges and counts the number of places at which there are physical parameter variations.

Distributed Boundary Estimation for Spectrum Sensing in Cognitive Radio Networks

In a cognitive radio network, a primary user (PU) shares its spectrum with secondary users (SUs) temporally and spatially, while allowing for some interference. We consider the problem of estimating the no-talk region of the PU, i.e., the region outside which SUs may utilize the PU's spectrum regardless of whether the PU is transmitting or not. We propose a distributed boundary estimation algorithm that allows SUs to estimate the boundary of the no-talk region collaboratively through message passing between SUs, and analyze the trade-offs between estimation error, communication cost, setup complexity, throughput and robustness. Simulations suggest that our proposed scheme has better estimation performance and communication cost trade-off compared to several other alternative benchmark methods, and is more robust to SU sensing errors, except when compared to the least squares support vector machine approach, which however incurs a much higher communication cost.

A flexible software design to determine the plasma boundary in Damavand tokamak

A plasma boundary reconstruction code has been designed by using current filament method to calculate the magnetic flux and consequently plasma boundary in Damavand tokamak. Hence, a computer-based code "The Plasma Boundary Reconstruction Code in Tokamak (PBRCT)" was developed to make a graphical user interface and to speed up the plasma boundary estimation algorithm. All required tools as the plasma boundary and magnetic surface display (MSD), error display, primary conditions and modeling panel as well as a search motor to determine a good position and number of the current filaments to find a precise model have been considered. The core is a 3000 lines Matlab code and the graphical user interface is 10,000 lines in C# language.

Dynamic boundary estimation of human heart within a complete cardiac cycle using electrical impedance tomography

This paper presents an EKF based boundary estimation algorithm to estimate the shape and size of human heart ventricle during a complete cardiac cycle. First-order kinematic model is used as a state evolution model. The boundary of the heart is expressed as coefficients of truncated Fourier series and the conductivity distribution inside the thorax region is assumed to be known a priori. The proposed method is tested with the use of a realistic chest shape FEM mesh.

A general technique for automatic left ventricle boundary validation: relation between gray scale cardioangiograms and observed boundary errors.

This article presents an automatic left ventricle boundary validation technique using the gray scale cardioangiograms, the observed boundary errors, and the left ventricle boundaries from any source that needs to be validated. This validation technique is based on the gray scale information near the boundary of the left ventricle in the cardioangiograms. Using a mutually exclusive window of fixed size, which is centered on the left ventricle boundary vertex and along the left ventricle contour, we compute a difference in contrast value for areas of the window both inside and outside the left ventricle region. These contrast values then are regressed against the observed boundary errors. The observed boundary errors are computed using the polyline distance measure, by comparing two sets of boundaries: boundaries estimated from any boundary estimation algorithm, and the original ground truth boundaries as traced by the cardiologist. We performed our experiments on a database of 245 patient studies, each having two frames: end-diastole (ED) and end-systole (ES). The mean boundary error before running the validation system was 4.4 mm. Using our boundary validation system, by rejecting 5% to 10% of the patient studies, the validation system results in an error of 4.0 mm for the cross-validation case and 3.85 mm for the ideal case. We show the reliability curves of our validation system by computing the probability of false alarm, probability of mis-detection, and mean predicted errors when a total of n patients are rejected from the database of 245 studies.

Implementation, Interpretation, and Analysis of a Suboptimal Boundary Finding Algorithm

This paper presents a suboptimal boundary estimation algorithm for noisy images which is based upon an optimal maximum likelihood problem formulation. Both the maximum likelihood formulation and the resulting algorithm are described in detail, and computational results are given. In addition, the potential power of the likelihood formulation is demonstrated through the presentation of three simple but insightful analyses of algorithm performance. These analyses are based on a technique we have developed for comparing the accuracies of different boundary finding algorithms. This technique also helps in understanding the interplay of object shape and data models in the relative performances of boundary finders. Some of the algorithm design considerations resulting from the use of our analysis technique are new and, at first, surprising. Our technique appears to be the only one developed for comparing the accuracies of different boundary finding algorithms.

An application of dynamic programming to sequential boundary estimation

Making use of a Markovian boundary generation model, a dynamic programming algorithm is presented for estimating short boundary segments. Certain choices for the transition probabilities associated with the underlying Markov process are presented, and a method is described for analyzing the performance of the dynamic programming algorithm when each of these is used. Finally the short segment estimator is incorporated into a complete boundary estimation algorithm and examples of its performance are given.