During the last decade, the exponential growth of mobile devices and wireless services created a huge demand for radio frequency-based technologies. Meanwhile, the lighting industry has been ...revolutionized due to the popularization of LED light bulbs, which are more economical and efficient. In that context, visible light communication (VLC) is a disruptive technology based on LEDs that offers a free spectrum and high data rate, which can potentially serve as a complementary technology to the current radio frequency standards. In this paper, we present a comprehensive state-of-the-art survey of VLC, as well as the main concepts and challenges related to this emergent area. We overview VLC technology, from its physical aspects and communication architecture to its main applications and research challenges. Finally, we present the main research platforms available today, along with a deep analysis of the system design and future directions in the field.
In this paper, we survey the most recent advances in supervised machine learning (ML) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. ...Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree‐based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high‐frequency financial data.
Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve ...the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.
This paper reports the existence of more than one pseudo-orbit when simulating continuous nonlinear systems using a digital computer in a set-up different from the ones normally seen in the ...literature, that is, in a set-up where the step-size is not varied, the discretization scheme is kept the same as well as the initial conditions. Taking advantage of the roundoff error, a simple but effective method to determine a lower bound error and the critical time for the pseudo-orbits is used and the connection to the maximum (positive) Lyapunov exponent is established considering the bit resolution and the computational platform used for the simulations. To illustrate the effectiveness of the method and problems of using discretization schemes for simulating continuous nonlinear systems in a digital computer, the well-known Lorenz equations, the Rossler hyperchaos system, Mackey–Glass equation and the Sprott A system are used. The method can help the user of such schemes to keep track of the reliability of numerical simulations.
The present study proposes a multi-objective framework for structure selection of nonlinear systems which are represented by polynomial NARX models. This framework integrates the key components of ...Multi-Criteria Decision Making (MCDM) which include preference handling, Multi-Objective Evolutionary Algorithms (MOEAs) and a posteriori selection. To this end, three well-known MOEAs such as NSGA-II, SPEA-II and MOEA/D are thoroughly investigated to determine if there exists any significant difference in their search performance. The sensitivity of all these MOEAs to various qualitative and quantitative parameters, such as the choice of recombination mechanism, crossover and mutation probabilities, is also studied. These issues are critically analyzed considering seven discrete-time and a continuous-time benchmark nonlinear system as well as a practical case study of non-linear wave-force modeling. The results of this investigation demonstrate that MOEAs can be tailored to determine the correct structure of nonlinear systems. Further, it has been established through frequency domain analysis that it is possible to identify multiple valid discrete-time models for continuous-time systems. A rigorous statistical analysis of MOEAs via performance sweet spots in the parameter space convincingly demonstrates that these algorithms are robust over a wide range of control parameters.
•A new method for computing numerical solutions of fractional differential equations in the sense of Riemann-Liouville definition.•Use of Caputo definition in the case of non zero initial ...conditions.•The infinite memory effect of fractional calculus is adequately dealt with.
The simulation of linear and nonlinear fractional-order systems on digital computers is investigated. The Grünwald-Letnikov definition of the fractional-order derivative is analyzed in the light of the initial conditions and as a consequence a new modified scheme for the discretization and simulation of fractional order systems is proposed. For this new scheme, it will be shown a new result where Riemann-Liouville derivative with the lower limit at infinity is related with a Caputo derivative with the lower limit at a finite real value allowing the infinite memory effect of fractional calculus to be adequately dealt with. To illustrate the use of the proposed method, the numerical solution of a linear fractional-order system is compared to the available analytical solution and, in the case of nonlinear fractional systems, the solution is compared to one provided by using the Adams method proposed by Diethelm 1, 2, 3.
Context
The conversion of natural environments into agricultural land has profound effects on the composition of the landscape, often resulting in a mosaic of human-altered and natural habitats. The ...response to these changes may however vary among organisms. Bats are highly vagile, and their requirements often imply the use of distinct habitats, which they select responding to both landscape and local features.
Objectives
We aimed to identify which features influence bat richness and activity within Baixo Vouga Lagunar, a heterogeneous landscape located on the Central-North Portuguese coast, and to investigate if that influence varies across a gradient of focal scales.
Methods
We sampled bats acoustically, while simultaneously sampling insects with light traps. We assessed the relationships between species richness, bat activity, and activity of eco-morphological guilds with landscape and local features, across four scales.
Results
Our results revealed both scale- and guild-dependent responses of bats to landscape and local features. At broader scales we found positive associations between open-space foraging bats and habitat heterogeneity and between edge-space foraging bats and greater edge lengths. Woodland cover and water availability at an intermediate scale and weather conditions and insect abundance at a local scale were the factors that mostly influenced the response variables.
Conclusions
Globally, our results suggest that bats are sensitive to local resource availability and distribution, while simultaneously reacting to landscape features acting at coarser scales. Finally, our results suggest that the responses given by bats are guild-dependent, and some habitats act as keystone structures for bats within this mosaic.
•Poly I:C injection during gestation alters locomotor activity of the adult offspring.•Poly I:C injection during gestation reduces glucose preference of the adult offspring.•Maternal immune ...activation leads to neuroanatomical changes in the adult offspring.
Maternal immune activation (MIA) during pregnancy in rodents increases the risk of the offspring to develop schizophrenia-related behaviors, suggesting a relationship between the immune system and the brain development. Here we tested the hypothesis that MIA induced by the viral mimetic polyinosinic-polycytidylic acid (poly I:C) in early or late gestation of mice leads to behavioral and neuroanatomical disorders in the adulthood. On gestational days (GDs) 9 or 17 pregnant dams were treated with poly I:C or saline via intravenous route and the offspring behaviors were measured during adulthood. Considering the progressive structural neuroanatomical alterations in the brain of individuals with schizophrenia, we used magnetic resonance imaging (MRI) to perform brain morphometric analysis of the offspring aged one year. MIA on GD9 or GD17 led to increased basal locomotor activity, enhanced motor responses to ketamine, a psychotomimetic drug, and reduced time spent in the center of the arena, suggesting an increased anxiety-like behavior. In addition, MIA on GD17 reduced glucose preference in the offspring. None of the treatments altered the relative volume of the lateral ventricles. However, a decrease in brain volume, especially for posterior structures, was observed for one-year-old animals treated with poly I:C compared with control groups. Thus, activation of the maternal immune system at different GDs lead to neuroanatomical and behavioral alterations possibly related to the positive and negative symptoms of schizophrenia. These results provide insights on neuroimmunonological and neurodevelopmental aspects of certain psychopathologies, such as schizophrenia.
The present study proposes a simple grey-box identification approach to model a real DC-DC buck converter operating in continuous conduction mode. The problem associated with the information void in ...the observed dynamical data, which is often obtained over a relatively narrow input range, is alleviated by exploiting the known static behavior of buck converter as a priori knowledge. A simple method is developed based on the concept of term clusters to determine the static response of the candidate models. The error in the static behavior is then directly embedded into the multi-objective framework for structure selection. In essence, the proposed approach casts grey-box identification problem into a multi-objective framework to balance bias-variance dilemma of model building while explicitly integrating a priori knowledge into the structure selection process. The results of the investigation, considering the case of practical buck converter, demonstrate that it is possible to identify parsimonious models which can capture both the dynamic and static behavior of the system over a wide input range.
PDF
