Ekaitz Zulueta

Selection of Lexical Units for Continuous Speech Recognition of basque

The selection of appropriate Lexical Units (LUs) is an important issue in the development of Continuous Speech Recognition (CSR) systems. Words have been used classically as the recognition unit in most of them. However, proposals of non-word units are beginning to arise. Basque is an agglutinative language with some structure inside words, for which non-word morpheme like units could be an appropriate choice. In this work a statistical analysis of units obtained after morphological segmentation has been carried out. This analysis shows a potential gain of confusion rates in CSR systems, due to the growth of the set of acoustically similar and short morphemes. Thus, several proposals of Lexical Units are analysed to deal with the problem. Measures of Phonetic Perplexity and Speech Recognition rates have been computed using different sets of units and, based on these measures, a set of alternative non-word units have been selected.

IMPROVING THE CONTROL OF SINGLE ROBOT HOSE TRANSPORT

Single robot hose transport is a limit case of linked multicomponent robotic systems, where one robot moves the tip of a hose to a desired position. The interaction between the passive, flexible hose and the robot introduces highly nonlinear effects in the systems dynamics, requiring innovative control design approaches, such as reinforcement learning. This article improves previous approaches to this problem by introducing a novel reinforcement learning algorithm (TRQ-learning) and a new system state definition for the autonomous derivation of the hose–robot control algorithm. Computational experiments based on accurate geometrically exact dynamic splines hose dynamics simulations show the improvement obtained.

Linked multicomponent robotic systems: basic assessment of linking element dynamical effect

The Linked Multicomponent Robotic Systems are characterized by the existence of a non-rigid linking element This linking element can produce many dynamical effects that introduce perturbations of the basic system behavior, different from uncoupled systems We show through a simulation of a distributed control of a hose tranportation system, that even a minimal dynamical feature of the hose (elastic forces oppossing stretching) can produce significant behavior perturbations.

A Remote Laboratory for a Basic Course on Control Engineering

The current work presents a remote laboratory for a basic course in control engineering over which several experiments may be performed. The proposed experiments have been carefully selected in order to illustrate the maximum number of concepts learnt in the classroom over a unique plant, a Ball & Hoop system. In this work, Labview has been used to acquire and handle process data whereas OPC technology is used to connect remote servers with web-integrated front-end applications. This choice has been made on the basis that these tools do not require very advanced skills and may be a reasonable approach for a wide range of simple remote laboratories.

HYBRID MODELING OF OPEN LOOP DC-DC CONVERTERS

Power electronic converters always have been circuits of difficult modelling because differential equations that describe them have discontinuities. Although this situation has been improved since the appearance of the Hybrid Systems theory, able to jointly describe both continuous and discrete behaviors exhibited by some physical systems, nowadays it is possible to obtain very precise models which help us in the study and design of such circuits. An excellent option for the discrete part model (reactive system) is to use statecharts, since this powerful language has recently been implemented and named Stateflow as a part of the Simulink toolbox of Matlab. So, today, the complete modeling of some hybrid systems within Matlab environment is possible. In this work the open loop hybrid modeling and simulation of the wellknown dc-dc converters named buck and boost, using Matlab-Simulink-Stateflow, is presented.

Empirical study of Q-learning based elemental hose transport control

Non-rigid physical elements attached to robotic systems introduce non-linear dynamics that requires innovative control approaches. This paper describes some of our results applying Q-Learning to learn the control commands to solve a hose transportation problem. The learning process is developed in a simulated environment. Computationally expensive but dynamically accurate Geometrically Exact Dynamic Splines (GEDS) have been used to model the hose to be transported by a single robot, showing the difficulties of controlling flexible elastic passive linking elements.

Lattice independent component analysis for mobile robot localization

This paper introduces an approach to appearance based mobile robot localization using Lattice Independent Component Analysis (LICA) The Endmember Induction Heuristic Algorithm (EIHA) is used to select a set of Strong Lattice Independent (SLI) vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data Selected endmembers are used to compute the linear unmixing of the robots acquired images The resulting mixing coefficients are used as feature vectors for view recognition through classification We show on a sample path experiment that our approach can recognise the localization of the robot and we compare the results with the Independent Component Analysis (ICA).

Economical implementation of control loops for multi-robot systems

In spite of the multiple advantages that multi-robot systems offer, to turn them into a realistic option and to get their proliferation, they must be economically attractive. Multi-robot systems are composed of several robots that generally are similar, so if an economic optimization is done in one of them, such optimization can be replicated in each member. In this paper we deal with the economic optimization of each control loops of the subsystems that each robot must control individually. As the subsystems can be complex, we propose to use a Predictive Control modeled by Time Delayed Neural Networks and implemented using very low cost Field Programmable Gate Arrays.

Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction

This paper shows experimental results on electromyography (EMG)-based system control applied to motorized orthoses. Biceps and triceps EMG signals are captured through two biometrical sensors, which are then filtered and processed by an acquisition system. Finally an output/control signal is produced and sent to the actuators, which will then perform the actual movement, using algorithms based on autoregressive (AR) models and neural networks, among others. The research goal is to predict the desired movement of the lower arm through the analysis of EMG signals, so that the movement can be reproduced by an arm orthosis, powered by two linear actuators. In this experiment, best accuracy has achieved values up to 91%, using a fourth-order AR-model and 100ms block length.

Training Multiagent Systems by Q-Learning: Approaches and Empirical Results

Multiagent systems are increasingly present in computational environments. However, the problem of agent design or control is an open research field. Reinforcement learning approaches offer solutions that allow autonomous learning with minimal supervision. The Q‐learning algorithm is a model‐free reinforcement learning solution that has proven its usefulness in single‐agent domains; however, it suffers from dimensionality curse when applied to multiagent systems. In this article, we discuss two approaches, namely TRQ‐learning and distributed Q‐learning, that overcome the limitations of Q‐learning offering feasible solutions. We test these approaches in two separate domains. The first is the control of a hose by a team of robots. The second is the trash disposal problem. Computational results show the effectiveness of Q‐learning solutions to multiagent systems’ control.