Radar Based Collision detection developments on USV ROAZ II

This work presents the integration of obstacle detection and analysis capabilities in a coherent and advanced C&C framework allowing mixed-mode control in unmanned surface systems. The collision avoidance work has been successfully integrated in an operational autonomous surface vehicle and demonstrated in real operational conditions. We present the collision avoidance system, the ROAZ autonomous surface vehicle and the results obtained at sea tests. Limitations of current COTS radar systems are also discussed and further research directions are proposed towards the development and integration of advanced collision avoidance systems taking in account the different requirements in unmanned surface vehicles.

Water jet based autonomous surface vehicle for coastal waters operations

This paper presents the design of low cost, small autonomous surface vehicle for missions in the coastal waters and specifically for the challenging surf zone. The main objective of the vehicle design described in this paper is to address both the capability of operation at sea in relative challenging conditions and maintain a very low set of operational requirements (ease of deployment). This vehicle provides a first step towards being able to perform general purpose missions (such as data gathering or patrolling) and to at least in a relatively short distances to be able to be used in rescue operations (with very low handling requirements) such as carrying support to humans on the water. The USV is based on a commercially available fiber glass hull, it uses a directional waterjet powered by an electrical brushless motor for propulsion, thus without any protruding propeller reducing danger in rescue operations. Its small dimensions (1.5 m length) and weight allow versatility and ease of deployment. The vehicle design is described in this paper both from a hardware and software point of view. A characterization of the vehicle in terms of energy consumption and performance is provided both from test tank and operational scenario tests. An example application in search and rescue is also presented and discussed with the integration of this vehicle in the European ICARUS (7th framework) research project addressing the development and integration of robotic tools for large scale search and rescue operations.

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.