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Dust is one of main aerosol types in the atmosphere and plays an important role in modulating climate via a number of complex processes. A 3D multi-year monthly mean climatology of Saharan dust advection over Europe is presented under cloud-free conditions, using an area-optimized pure dust CALIPSO product. The product has been developed by applying EARLINET-measured dust lidar ratios and depolarization-based dust discrimination methods. This approach results in a very good agreement when comparing CALIPSO AOD with AERONET retrievals over Europe/North Africa and with the MODIS/Aqua product over the Mediterranean. The processing of such purely observational data can provide seasonal patterns of pure Saharan dust transportation towards Europe and the Atlantic Ocean. The physical and optical properties of the dust layers are identified for several areas near the Saharan sources, over the Mediterranean and over continental Europe.

In this paper, we present an iterative Kalman smoother (IKS) for robust 3D localization and mapping, using visual and inertial measurements. Contrary to extended Kalman filter (EKF) methods, smoothing increases the convergence rate of critical parameters (e.g., IMU’s velocity and camera’s clock drift), improves the positioning accuracy during challenging conditions (e.g., scarcity of visual features), and allows the immediate processing of visual observations. As opposed to existing smoothing approaches to VINS, based on the inverse filter (INVF), the proposed IKS exhibits superior numerical properties, allowing efficient implementations on mobile devices. Furthermore, we propose a classification of visual observations, for smoothing algorithms applied to VINS, based on their: (i) Track length, allowing their efficient processing as multi-state constraints, when possible and (ii) First observation, allowing their optional re-processing. Finally, we demonstrate the robustness of the proposed approach, over challenging indoor VINS scenarios, including, system (re)-initialization, and scarcity of visual observations.

This paper addresses the problem of estimating the intrinsic parameters of the 3D Velodyne lidar while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to address this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal observability conditions, under which, it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes are used to assess the validity of our approach.

Nowadays, the use of Social Networking Sites (SNS) is becoming indispensable. On SNS people provide authentic information about their travels, gained through personal experience and when they interact over a long period of time they trust the opinions of their ‘friends’ or ‘followers’ and take them into consideration when making a purchase decision. The paper aims at investigating attitudes of MBA students towards SNS use for travel activities. It takes into consideration four popular SNS, namely Facebook, Twitter, LinkedIn and Google +. Fishbein model was used for the evaluation of students’ attitudes. The findings reveal that MBA students use SNS for getting socialized with other travelers and companies and informed about tourism destinations/companies/travel. Facebook is the most popular Social Networking Site for travel activities except from the professional reasons (e.g., search for job in the tourism sector), in which LinkedIn comes first.

Fuzzy Cognitive Networks (FCN) stem from Fuzzy Cognitive Maps (FCM), initially introduced by Kosko to model complex behavioral systems in various scientific areas. This chapter presents basic definitions related to FCM and the traditional way of their operation. It starts with a brief bibliographical introduction, presenting various extensions of the initial model and areas of application. Since their convergence is a crucial point for the development of Fuzzy Cognitive Networks, particular emphasis is given to the subject of their convergence to equilibrium points and some noticeable peculiarities that may appear. Then, the scope of Part II of this book is analyzed and the organization of the relevant chapters is presented.

The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) using F-RHONNs under the presence of parameter and dynamic uncertainties, is considered in this chapter. The method is based on the new NF dynamical systems definition introduced in Chap. 2, which uses the concept of adaptive fuzzy systems (AFS) operating in conjunction with recurrent high order neural networks. Since the plant is considered unknown, we first propose the calculation of fuzzy output centers by systems data or linguistic information and in the sequel the fuzzy rules are approximated by appropriate HONNs. Thus, the identification scheme leads up to fuzzy subsystems approximated by recurrent high order neural networks, which however takes into account the centers of the fuzzy output partitions (F-RHONNs). Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONNs are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, or at least uniform ultimate boundedness of all signals in the closed-loop. The control signal is constructed to be valid for both square and nonsquare systems by using a pseudo-inversion. The existence of the control signal is always assured by employing the method of parameter hopping instead of the conventional projection method.

In this chapter, we present the framework of operation of FCN, which is based on the adaptive estimation algorithms developed in the previous chapter and on the proposal of a fuzzy rule-based mechanism for storing acquired knowledge during its operation and training. Moreover, selected applications are presented, which demonstrate the applicability of the FCN framework both in conventional benchmark control problems and in real life applications. Both parameter adaptation algorithms, the linear and the bilinear one, are tested, emphasizing their distinct characteristics and advantages. The applications include the control of an inverted pendulum, the control of a hydroelectric power plant, and the coordination of different renewable power sources in order to yield an overall optimal power production and consumption in a smart grid application.

In this chapter, we present a study for the existence of equilibrium points of FCNs equipped with continuous differentiable sigmoid functions that have contractive or at least nonexpansive properties. The study is done by using an appropriately defined contraction mapping theorem and the nonexpansive mapping theorem. It is proved that, when the weight interconnections fulfill certain conditions, related to the size of the FCN and the inclination of the sigmoid functions, the concept values will converge to a unique solution regardless of their initial states, or in some cases a solution exists that may not necessarily be unique. Otherwise the existence or the uniqueness of equilibria may or may not exist, it may depend on the initial states, but it cannot be assured. In case the FCN has also input nodes (that is nodes that influence but are not influenced by other nodes), the unique equilibrium does not depend solely on the weight set, as in the case of FCNs with no input nodes; it depends also on the values of the input nodes. Numerical examples explore the results and a thorough discussion interprets them.

In this chapter, we analyze the identification problem, which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. As identification models, we use fuzzy-recurrent high-order neural networks (F-RHONNs). This model exploits the use of high-order networks (HONN), which are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. It combines HONN with an underlying fuzzy model of Mamdani type assuming a standard defuzzification procedure such as centroid of area or weighted average. . Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. In the proposed method, there are two core ideas : (1) Several high-order neural networks are specialized to work around fuzzy centers, separating in this way the system in simpler (NF) subsystems with better approximation abilities and (2) the use of a novel method called switching parameter hopping to replace the commonly used \(\sigma \)-modification for the robustness of our system in order to restrict the weights and avoid drifting of their values to infinity.