Computationally replicating the behaviour of the cerebral cortex to perform the control tasks of daily life in a human being is a challenge today. First, it is necessary to know the structure and connections between the elements of the neural network that perform movement control. Next, a mathematical neural model that adequately resembles biological neurons has to be developed. Finally, a suitable learning model that allows adapting neural network response to changing conditions in the environment is also required. Spiking Neural Networks (SNN) are currently the closest approximation to biological neural networks. SNNs make use of temporal spike trains to deal with inputs and outputs, thus allowing a faster and more complex computation. In this paper, a controller based on an SNN is proposed to perform the control of an anti-lock braking system (ABS) in vehicles. To this end, two neural networks are used to regulate the braking force. The first one is devoted to estimating the optimal slip while the second one is in charge of setting the optimal braking pressure. The latter resembles biological reflex arcs to ensure stability during operation. This neural structure is used to control the fast regulation cycles that occur during ABS operation. Furthermore, an algorithm has been developed to train the network while driving. On-line learning is proposed to update the response of the controller. Hence, to cope with real conditions, a control algorithm based on neural networks that learn by making use of neural plasticity, similar to what occurs in biological systems, has been implemented. Neural connections are modulated using Spike-Timing-Dependent Plasticity (STDP) by means of a supervised learning structure using the slip error as input. Road-type detection has been included in the same neural structure. To validate and to evaluate the performance of the proposed algorithm, simulations as well as experiments in a real vehicle were carried out. The algorithm proved to be able to adapt to changes in adhesion conditions rapidly. This way, the capability of spiking neural networks to perform the full control logic of the ABS has been verified.

As far as we know, currently all the underwater interventions, requiring robotic manipulation, are carried out by using a well-known technology, based on the commercial work class ROVs (Remotely Operated Vehicles). These systems require a lot of support, including a vessel in the surface, where a pilot (i.e. user expert) is able of teleoperating the actions of the ROV by means of an umbilical cable. On the other hand, from the last ten years new research has been developed, promoting in the market the so called Autonomous Underwater Vehicles (AUVs), enabling interventions. Obviously the AUVs have some main advantages over ROVs: no vessel, umbilical or pilots are needed now, but presenting a critical drawback: any kind of potential manipulation skills are impossible. Thus, the present situation becomes in a new vehicle: the Hybrid-ROV (HROV), trying to join the best of both systems, ROVs and AUVs. However, new problems arise concerning the HROV control. Now the system can operate in two different ways: autonomous or teleoperated, and so, a new control approach should be developed. This paper presents a control architecture for an HROV, discussing details of this approach from the human-robot interaction viewpoint.

This article presents research on the subject of autonomous underwater robot manipulation. Ongoing research in underwater robotics intends to increase the autonomy of intervention operations that require physical interaction in order to achieve social benefits in fields such as archaeology or biology that cannot afford the expenses of costly underwater operations using remote operated vehicles. Autonomous grasping is still a very challenging skill, especially in underwater environments, with highly unstructured scenarios, limited availability of sensors and adverse conditions that affect the robot perception and control systems. To tackle these issues, we propose the use of vision and segmentation techniques that aim to improve the specification of grasping operations on underwater primitive shaped objects. Several sources of stereo information are used to gather 3D information in order to obtain a model of the object. Using a RANSAC segmentation algorithm, the model parameters are estimated and a set of feasible grasps are computed. This approach is validated in both simulated and real underwater scenarios.

This paper describes a whole new framework to quickly develop dynamic visual servoing systems embedded in an FPGA. Parallel design of the algorithms increases the precision of this kind of controllers, whereas minimizes the response time. Embedding dynamic visual servoing algorithms converts these systems into real-time systems. Additionally, an optimal control framework to dynamically visual control robot arms is proposed. The architecture re-configurability is tested with the implementation of two dynamic visual servoing controllers. One of the implemented controllers is derived from the proposed control framework and the other is the well-known image transpose Jacobian controller. From these experiments, FPGA resources utilization and timeline of the pipeline show the optimization of the proposed framework.

Today, it is widely accepted that if refactoring is applied in practice, it is mainly interweaved with normal software development - so called "floss refactoring". Unfortunately, the current state-of-the-art is poorly equipped to mine floss refactoring from version histories, mainly because they infer refactorings by comparing two snapshots of a system and making educated guesses about the precise edit operations applied in between. In this paper we propose a solution that reconstructs refactorings not on snapshots of a system but using the actual changes as they are performed in an integrated development environment. We compare our solution against RefFinder and demonstrate that on a small yet representative program (the well-known "Video Rental system") our approach is more accurate in identifying occurrences of the "Move Method" and "Rename Method" refactorings.

The analysis of bug reports is an important subfield within the mining software repositories community. It explores the rich data available in defect tracking systems to uncover interesting and actionable information about the bug triaging process. While bug data is readily accessible from systems like Bugzilla and JIRA, a common database schema and a curated dataset could significantly enhance future research because it allows for easier replication. Consequently, in this paper we propose the Eclipse and Mozilla Defect Tracking Dataset, a representative database of bug data, filtered to contain only genuine defects (i.e., no feature requests) and designed to cover the whole bug-triage life cycle (i.e., store all intermediate actions). We have used this dataset ourselves for predicting bug severity, for studying bug-fixing time and for identifying erroneously assigned components. Sharing these data with the rest of the community will allow for reproducibility, validation and comparison of the results obtained in bug-report analyses and experiments.

This paper presents the progress that has been made recently in the TRITON project. The TRITON project is an on going research project being carried out in Spain which has as principal objective the production of an AUV capable of autonomous underwater interventions. The GRASPER sub-project focuses on developing the necessary manipulation skills. Currently, a lot of research in the underwater robotics context is developing increasing levels of autonomy for all kinds of intervention operations, which always require some kind of physical interaction. However, if autonomous robotic manipulation on land remains a relatively undeveloped field, the situations is at an even more primitive stage in underwater scenarios where currently the systems are tele-operated by an expert user from a surface vessel. Only very few underwater systems have the capacity to carry out manipulation without any kind of umbilical cables teleoperating these actions. In particular, this work introduces a new approach for increasing the autonomy levels of an underwater manipulation system, discussing also preliminary results. In order to test this concept, different objects, without predefined models, are approached and recovered from the bottom in water tank conditions. To achieve this purpose, a scan of the scene is performed using a structured laser beam attached to the forearm of the manipulator. At the same time, a digital video camera is used to capture the scene with the laser beam projected onto the object. The laser stripes are triangulated to obtain a 3D point cloud. Moreover, the underwater robot gripper is provided with strain gauge tactile sensors, which enable the execution of a more reliable grasp. On the other hand, the process is shown inside an underwater simulator previously developed, named UWSim, acting in this case as a virtual representation of the real environment. This virtual representation allows the user to specify the grasp, highlighting how the virtual grasp will be defined for the selected target. The feasibility and reliability of the underwater manipulation system is demonstrated though the experimental results.

This paper presents UWSim: a new software tool for visualization and simulation of underwater robotic missions. The software visualizes an underwater virtual scenario that can be configured using standard modeling software. Controllable underwater vehicles, surface vessels and robotic manipulators, as well as simulated sensors, can be added to the scene and accessed externally through network interfaces. This allows to easily integrate the simulation and visualization tool with existing control architectures, thus allowing hardware-in-the-loop simulations (HIL). UWSim has been successfully used for simulating the logics of underwater intervention missions and for reproducing real missions from the captured logs. The software is offered as open source, thus filling a gap in the underwater robotics community, where commercial simulators oriented to ROV pilot training predominate.

The subject of this paper is the conversion of a given speaker's voice (the source speaker) into another identified voice (the target one). We assume we have at our disposal a large amount of speech samples from source and target voice with at least a part of them being parallel. The proposed system is built on a mapping function between source and target spectral envelopes followed by a frame selection algorithm to produce final spectral envelopes. Converted speech is produced by a basic LP analysis of the source and LP synthesis using the converted spectral envelopes. We compared three types of conversion: without mapping, with mapping and using the excitation of the source speaker and finally with mapping using the excitation of the target. Results show that the combination of mapping and frame selection provide the best results, and underline the interest to work on methods to convert the LP excitation.

We present here our work in automatic parameterization of natural speech by means of a pitch synchronous source-filter decomposition algorithm. The derivative glottal source is modelled using the Liljencrants-Fant (LF) model. The model parameters are obtained simultaneously with the coefficients of an all-pole filter representing the vocal tract response by means of a quadratic programming algorithm. Synthetic data has been created and analyzed in order to show the appropriate function of the estimation method. The parameterization results in high quality synthesized speech for voiced frames. Voice quality extraction is performed on basis to the LF source representation. The inherent modelling of the voice source makes it suitable for voice modification tasks. Work is in progress to add this speech representation to emotional speech synthesis and voice conversion algorithms. 1.

MOTIVATION: DNA sequences are formed by patches or domains of different nucleotide composition. In a few simple sequences, domains can simply be identified by eye; however, most DNA sequences show a complex compositional heterogeneity (fractal structure), which cannot be properly detected by current methods. Recently, a computationally efficient segmentation method to analyse such nonstationary sequence structures, based on the Jensen-Shannon entropic divergence, has been described. Specific algorithms implementing this method are now needed. RESULTS: Here we describe a heuristic segmentation algorithm for DNA sequences, which was implemented on a Windows program (SEGMENT). The program divides a DNA sequence into compositionally homogeneous domains by iterating a local optimization procedure at a given statistical significance. Once a sequence is partitioned into domains, a global measure of sequence compositional complexity (SCC), accounting for both the sizes and compositional biases of all the domains in the sequence, is derived. SEGMENT computes SCC as a function of the significance level, which provides a multiscale view of sequence complexity.