An extension to the filtered-x LMS algorithm with logarithmic transformation

© 2015 IEEE. Active control of impulsive noise has been of increasing interest due to high impact of such noise on humans. The algorithm with logarithmic transformation, developed by Wu, et al. has been found particularly interesting. In this paper this idea is continued, and an extension to this algorithm is proposed to improve its convergence properties and allow for successful control if the noise has also another type of noise together with the impulses. A number of simulations are performed to validate the algorithm and compare it with algorithms leading in the literature. Additionally to simulated benchmark impulsive noises, real recordings are considered, which bring another insight into efficiency of the algorithms

The K-filter: a new architecture to model and design non-linear systems from Kolmogorov's theorem

A new architecture to model and design nonlinear transfer functions is presented using a new formulation for nonlinear systems. This approach follows the guidelines of the mapping theorem due to A. Kolmogorov and it is based on the direct Fourier transform of the transfer function. The resulting scheme is formed by two stages; the first stage contains phase modulators, which, based on random sampling concepts reported by I. Bilinskis, are duplicated with a small perturbation in the modulation factor. This stage depends on the number of diversity data and it is independent of the function. The second step reduces to Volterra systems and a direct combiner of the new diversity kernels. The reported architecture and design seem to be able to cope with both linear and nonlinear filtering problems, which can be considered as a formal framework for generalised signal processing.Peer ReviewedPostprint (published version

CVD-MET: an image difference metric designed for analysis of color vision deficiency aids

Color vision deficiency (CVD) has gained in relevance in the last decade, with a surge of proposals for aid systems that aim to improve the color discrimination capabilities of CVD subjects. This paper focuses on the proposal of a new metric called CVD-MET, that can evaluate the efficiency and naturalness of these systems through a set of images using a simulation of the subject’s vision. In the simulation, the effect of chromatic adaptation is introduced via CIECAM02, which is relevant for the evaluation of passive aids (color filters). To demonstrate the potential of the CVD-MET, an evaluation of a representative set of passive and active aids is carried out both with conventional image quality metrics and with CVD-MET. The results suggest that the active aids (recoloration algorithms) are in general more efficient and produce more natural images, although the changes that are introduced do not shift the CVD’s perception of the scene towards the normal observer’s perception.Junta de Andalucia A-TIC-050-UGR18Spanish Government FIS2017-89258-PMinisterio de Ciencia, Innovación y Universidades RTI2018-094738-B-I0

Navigace mobilních robotů v neznámém prostředí s využitím měření vzdáleností

The ability of a robot to navigate itself in the environment is a crucial step towards its autonomy. Navigation as a subtask of the development of autonomous robots is the subject of this thesis, focusing on the development of a method for simultaneous localization an mapping (SLAM) of mobile robots in six degrees of freedom (DOF). As a part of this research, a platform for 3D range data acquisition based on a continuously inclined laser rangefinder was developed. This platform is presented, evaluating the measurements and also presenting the robotic equipment on which the platform can be fitted. The localization and mapping task is equal to the registration of multiple 3D images into a common frame of reference. For this purpose, a method based on the Iterative Closest Point (ICP) algorithm was developed. First, the originally implemented SLAM method is presented, focusing on the time-wise performance and the registration quality issues introduced by the implemented algorithms. In order to accelerate and improve the quality of the time-demanding 6DOF image registration, an extended method was developed. The major extension is the introduction of a factorized registration, extracting 2D representations of vertical objects called leveled maps from the 3D point sets, ensuring these representations are 3DOF invariant. The extracted representations are registered in 3DOF using ICP algorithm, allowing pre-alignment of the 3D data for the subsequent robust 6DOF ICP based registration. The extended method is presented, showing all important modifications to the original method. The developed registration method was evaluated using real 3D data acquired in different indoor environments, examining the benefits of the factorization and other extensions as well as the performance of the original ICP based method. The factorization gives promising results compared to a single phase 6DOF registration in vertically structured environments. Also, the disadvantages of the method are discussed, proposing possible solutions. Finally, the future prospects of the research are presented.Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.

Active disturbance cancellation in nonlinear dynamical systems using neural networks

A proposal for the use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee convergence of the algorithm and provide insight into the necessary reduction in learning gain as a function of the system parameters. Effectiveness of the algorithm is evaluated through mathematical analysis, simulation studies, and experimental application of the technique on an acoustic duct laboratory model

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

The Implementation And Analysis Of Wide Dynamic Range Imaging Methods

This dissertation presents the implementation and analysis of wide dynamic range (WDR) imaging methods. It covers the study of wide dynamic range imaging, its applications and techniques

Semi-Active Vibration Control Based on Switched Piezoelectric Transducers

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