A Novel Feature Selection Approach Based on FODPSO and SVM

A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.IEEE Geoscience and Remote Sensing SocietyRitrýnt tímaritPeer Reviewe

Additive decomposability of functions over abelian groups

Abelian groups are classified by the existence of certain additive decompositions of group-valued functions of several variables with arity gap 2.Comment: 17 page

The ARCANE Project: how an ecological dynamics framework can enhance performance assessment and prediction in football

This paper discusses how an ecological dynamics framework can be implemented to interpret data, design practice tasks and interpret athletic performance in collective sports, exemplified here by research ideas within the Augmented peRCeption ANalysis framEwork for Football (ARCANE) project promoting an augmented perception of football teams for scientists and practitioners. An ecological dynamics rationale can provide an interpretation of athletes’ positional and physiological data during performance, using new methods to assess athletes’ behaviours in real-time and, to some extent, predict health and performance outcomes. The proposed approach signals practical applications for coaches, sports analysts, exercise physiologists and practitioners through merging a large volume of data into a smaller set of variables, resulting in a deeper analysis than typical measures of performance outcomes of competitive games

On the effect of variable identification on the essential arity of functions

We show that every function of several variables on a finite set of k elements with n>k essential variables has a variable identification minor with at least n-k essential variables. This is a generalization of a theorem of Salomaa on the essential variables of Boolean functions. We also strengthen Salomaa's theorem by characterizing all the Boolean functions f having a variable identification minor that has just one essential variable less than f.Comment: 10 page

Fusing sonars and LRF data to perform SLAM in reduced visibility scenarios

Simultaneous Localization and Mapping (SLAM) approaches have evolved considerably in recent years. However, there are many situations which are not easily handled, such as the case of smoky, dusty, or foggy environments where commonly used range sensors for SLAM are highly disturbed by noise induced in the measurement process by particles of smoke, dust or steam. This work presents a sensor fusion method for range sensing in Simultaneous Localization and Mapping (SLAM) under reduced visibility conditions. The proposed method uses the complementary characteristics between a Laser Range Finder (LRF) and an array of sonars in order to ultimately map smoky environments. The method was validated through experiments in a smoky indoor scenario, and results showed that it is able to adequately cope with induced disturbances, thus decreasing the impact of smoke particles in the mapping task

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery

In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble human reasoning have been proposed as alternatives. Fuzzy C-means is the most common fuzzy clustering technique, but its concept is based on a local search mechanism and its convergence rate is rather slow, especially considering high-dimensional problems (e.g., in processing of hyperspectral images). Here, in order to address those shortcomings of hard approaches, a new approach is proposed, i.e., fuzzy C-means which is optimized by fractional order Darwinian particle swarm optimization. In addition, to speed up the clustering process, the histogram of image intensities is used during the clustering process instead of the raw image data. Furthermore, the proposed clustering approach is combined with support vector machine classification to accurately classify hyperspectral images. The new classification framework is applied on two well-known hyperspectral data sets; Indian Pines and Salinas. Experimental results confirm that the proposed swarm-based clustering approach can group hyperspectral images accurately in a time-efficient manner compared to other existing clustering techniques.PostPrin

Hybrid adaptive control of a dragonfly model

Dragonflies show unique and superior flight performances than most of other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper, it is presented an adaptive scheme controlling a nonlinear model inspired in a dragonfly-like robot. It is proposed a hybrid adaptive (HA) law for adjusting the parameters analyzing the tracking error. At the current stage of the project it is considered essential the development of computational simulation models based in the dynamics to test whether strategies or algorithms of control, parts of the system (such as different wing configurations, tail) as well as the complete system. The performance analysis proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence

Application of fractional algorithms in the control of a robotic bird

In this paper, it is studied the dynamics of the robotic bird in terms of time response and robustness. It is analyzed the wing angle of attack and the velocity of the bird, the tail influence, the gliding flight and the flapping flight. The results are positive for the construction of flying robots. The development of computational simulation based on the dynamic of the robotic bird should allow testing strategies and different algorithms of control such as integer and fractional controllers