Sampled data provided by a process are usually corrupted by various noises. Although data are required to be denoised before applying any further processing, the corrupting noises could encode a part of desired inform...
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Sampled data provided by a process are usually corrupted by various noises. Although data are required to be denoised before applying any further processing, the corrupting noises could encode a part of desired information. A partial denoising is suitable in this case, as part of pre-processing procedure. Another requirement frequently used in data pre-processing is to compress the useful information in a smaller number of data. Partial denoising and compression are gathered in the concept of "signal compaction". The paper introduces an original method for providing compacted preliminary data, relied on the concept of "verisimilitude" and requiring no synchronization signal accompanying the acquired data.
Our research group, interested in outdoor scenes, has developed a methodology to register a CAD model of a city, with images taken with video cameras installed in a car, while driving city streets. So, we can merge vi...
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In computer vision, texture plays an important role. In this work we propose five human perceptual texture features heuristically extracted. Since a modeling can not be obtained from these features, we use a discrimin...
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Studying the impact of climate change on wintertime polar stratosphere is of particular relevance not only for climate knowledge but also for tropospheric projections. Machine learning provides a way to extract inform...
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This work presents a simulation study of an event-based selective control strategy for a raceway reactor. The control system aims are to maintain simultaneously a pH and dissolved oxygen within specific limits. In the...
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Magnetic measurement is a typical inverse problem in Biomedical field. In this kind of problem we always need to locate the positions and moments of one or more magnetic dipoles. Although using the traditional methods...
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Magnetic measurement is a typical inverse problem in Biomedical field. In this kind of problem we always need to locate the positions and moments of one or more magnetic dipoles. Although using the traditional methods to solve this kind of inverse problem has all kinds of shortcomings, BPNN (Back Propagation Neural Networks) method can be used to solve this typical inverse problem fast enough for real time measurement. In the traditional BPNN method, gradient descent search method is performed for error propagation. In this paper the authors propose a new algorithm that Newton method is performed for error propagation. For the cost function is highly nonconvex in the magnetic measurement problem, the new kind of BPNN can get convergent results quickly and precisely. A simulation result for this method is also presented.
In this paper we address the question of "Why is it that a mobile robot, programmed in a certain way and placed in some environment to execute a program, behaves in the way it does?". We present three real w...
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This paper presents two modern modulation techniques applied to three phase inverters from a hardware implementation point of view. The considered techniques are the sinusoidal pulse width modulation with zero sequenc...
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control-flow dependence has always been posited as a substantial dilemma against program acceleration. With the availability of instruction-level parallel architectures, ifconversion optimization has become pivotal fo...
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