Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Guaranteed parameter estimation in nonlinear dynamic systems using improved bounding techniques

Abstract: This paper is concerned with guaranteed parameter estimation in nonlinear dynamic systems in a context of bounded measurement error. The problem consists of finding-or approximating as closely as possible-the set of all possible parameter values such that the predicted outputs match the corresponding measurements within prescribed error bounds. An exhaustive search procedure is applied, whereby the parameter set is successively partitioned into smaller boxes and exclusion tests are performed to eliminate some of these boxes, until a prespecified threshold on the approximation level is met. Exclusion tests rely on the ability to bound the solution set of the dynamic system for a given parameter subset and the tightness of these bounds is therefore paramount. Equally important is the time required to compute the bounds, thereby defining a trade-off. It is the objective of this paper to investigate this trade-off by comparing various bounding techniques based on interval arithmetic, Taylor model arithmetic and ellipsoidal calculus. When applied to a simple case study, ellipsoidal and Taylor model approaches are found to reduce the number of iterations significantly compared to interval analysis, yet the overall computational time is only reduced for tight approximation levels due to the computational overhead.

Dynamic views within gaming environments

Abstract: Systems and methods for providing dynamic views for video game programs are provided herein. Exemplary methods for providing a dynamic view of a gaming environment may include continually calculating a bounding area of a gaming environment, the bounding area being defined by locations of a plurality of players relative to one another within the gaming environment, the bounding area changing as the plurality of players move relative to one another, as well as continually generating a top down view of the bounding area of the gaming environment, the top down view being dynamically altered as the bounding area is dynamically adjusted.

Vehicle License Plate Detection Using Deep Learning

Abstract: This study focuses on using a Deep Convolutional Network trained with data from license plates to automatically categorize and geolocate vehicles (DCNN). Toll collection, accident reconstruction, and the identification of suspicious vehicles are just some real-world applications that use license plate readers. The study recommended using a vehicle classifier based on deep learning to pinpoint the location of license plates and license numbers simultaneously. Bounding quadrilaterals are provided by the classifier instead of bounding rectangles, which provides a more accurate indication for vehicle registration estimation to license plate localization. This task was accomplished using the Python programming language and various deep learning libraries. Since the training of the proposed DCNN model began with a weight that had already undergone a certain number of iterations in a model without a classification head, the total number of training iterations will be close to 10,000 when taking into account the transfer learning component of DCNN. Because of transfer learning, the DCNN model could begin at a good place, making it simpler to enhance functional heads at once. According to the study's characterization of the task at hand-vehicle number estimation as well as license plate segmentation and vehicle-the DCNN achieved 98.8% accuracy in classification.

A Dual Observer Method for Global Stabilization of Nonlinear Systems with Limited and Uncertain Information

Abstract: This paper considers a general setting for the stabilization of a planar nonlinear system given only the measurement of the output state. Additionally, we assume that a certain amount of uncertainties is inherent in the system under consideration, where we only need to know the bounding function of the nonlinear terms. Under this setting we consider a class of systems whose nonlinear bounding functions are polynomially bounded in the unmeasurable state, with orders both greater than and less than one. The primary novelty of this method is the utilization of a dual observer approach, estimating lower-order states and higher-order states in parallel.