Intelligent planning and the design of a new risk-based,intelligent flight plan

A safe flight starts with effective performance of the pre-flight flight planning and briefing task. However, several problems related to the execution of this task can be identified. Potentially, the introduction of an improved flight plan provides an opportunity to improve the quality and availability of information provided to Flight Crew, thereby enhancing the quality of crew briefings. The proposed risk-based, intelligent flight plan is designed from the perspective of the current operational concept (e.g. fixed routes and ATC managerial role for separation), and associated airline Flight Planning and Dispatch functions. In this case, the focus is sharing information across specific airline stakeholders (e.g. Flight Operations Management and Safety functions) and Maintenance, to support a safe and efficient flight operation. Overall, the introduction of this new flight plan will result in the definition of new operational and organisational processes, along with a new way of performing the pre-flight, planning and briefing task. It is anticipated that this will impact positively on the operational and safety outcome of the flight.     

Symbolic hierarchical analysis in currency markets: An application to contagion in currency crises

In this paper we introduce a new method to describe dynamical patterns of the real exchange rate co-movements time series and to analyze contagion in currency crisis. The method combines the tools of symbolic time series analysis with the nearest neighbor single linkage clustering algorithm. Data symbolization allows us obtaining a metric distance between two different time series that is used to construct an ultrametric distance. By analyzing the data of various countries, we derive a hierarchical organization, constructing minimal-spanning and hierarchical trees. From these trees we detect different clusters of countries according to their proximity. We show that this methodology permits us to construct a structural and dynamic topology that is useful to study interdependence and contagion effects among financial time series.     

Two-space variability compensation technique for speaker verification in short length and reverberant environments

The performance of state-of-the-art speaker verification in uncontrolled environment is affected by different variabilities. Short duration variability is very common in these scenarios and causes the speaker verification performance to decrease quickly while the duration of verification utterances decreases. Linear discriminant analysis (LDA) is the most common session variability compensation algorithm, nevertheless it presents some shortcomings when trained with insufficient data. In this paper we introduce two methods for session variability compensation to deal with short-length utterances on i-vector space. The first method proposes to incorporate the short duration variability information in the within-class variance estimation process. The second proposes to compensate the session and short duration variabilities in two different spaces with LDA algorithms (2S-LDA). First, we analyzed the behavior of the within and between class scatters in the first proposed method. Then, both proposed methods are evaluated on telephone session from NIST SRE-08 for different duration of the evaluation utterances: full (average 2.5 min), 20, 15, 10 and 5 s. The 2S-LDA method obtains good results on different short-length utterances conditions in the evaluations, with a EER relative average improvement of 1.58%, compared to the best baseline (WCCN[LDA]). Finally, we applied the 2S-LDA method in speaker verification under reverberant environment, using different reverberant conditions from Reverb challenge 2013, obtaining an improvement of 8.96 and 23% under matched and mismatched reverberant conditions, respectively.     

Optimal memoryless control in Gaussian noise: A simple counterexample

In this paper, we investigate control strategies for a scalar, one-step delay system in discrete-time, i.e., the state of the system is the input delayed by one time unit. In contrast with classical approaches, here the control action must be a memoryless function of the output of the plant, which comprises the current state corrupted by measurement noise. We adopt a first order state-space representation for the delay system, where the initial state is a Gaussian random variable. In addition, we assume that the measurement noise is drawn from a white and Gaussian process with zero mean and constant variance. Performance evaluation is carried out via a finite-time quadratic cost that combines the second moment of the control signal, and the second moment of the difference between the initial state and the state at the final time. We show that if the time-horizon is one or two then the optimal control is a linear function of the plant’s output, while for a sufficiently large horizon a control taking on only two values will outperform the optimal affine solution. This paper complements the well-known counterexample by Hans Witsenhausen, which showed that the solution to a linear, quadratic and Gaussian optimal control paradigm might be nonlinear. Witsenhausen’s counterexample considered an optimization horizon with two time-steps (two stage control). In contrast with Witsenhausen’s work, the solution to our counterexample is linear for one and two stages but it becomes nonlinear as the number of stages is increased. The fact that our paradigm leads to nonlinear solutions, in the multi-stage case, could not be predicted from prior results. In contrast to prior work, the validity of our counterexample is based on analytical proof methods. Our proof technique rests on a simple nonlinear strategy that is useful in its own right, since it outperforms any affine solution.     

State of the art of frameworks and middleware for facilitating mobile and ubiquitous learning development

The emergence of mobile and ubiquitous technologies as important tools to complement formal learning has been accompanied by a growing interest in their educational benefits and applications. Mobile devices can be used to promote learning anywhere and anytime, to foster social learning and knowledge sharing, or to visualize augmented reality applications for learning purposes. However, the development of these applications is difficult for many researchers because it requires understanding many different protocols; dealing with distributed schemas, processes, platforms, and services; learning new programming languages; and interacting with different hardware sensors and drivers. For that reason, the use of frameworks and middleware that encapsulate part of this complexity appears to be fundamental to the further development of mobile learning projects. This study analyzes the state of the art of frameworks and middleware devoted to simplifying the development of mobile and ubiquitous learning applications. The results can be useful to many researchers involved in the development of projects using these technologies by providing an overview of the features implemented in each of these frameworks.     

Transforming smoothers for PDE constrained optimization problems

Transforming smoothers are known as a successful approach to the multigrid treatment of saddlepoint problems resulting from variational problems. In this paper we analyze similar multigrid methods in the context of the practically important class of optimization problems with partial differential equation constraint, which lead to a different kind of saddle point problems. We prove level independent convergence properties for the resulting multigrid methods and demonstrate this also in numerical investigations for a relevant model problem.