The goal of this study is to generate high-resolution sea floor maps using a Side-Scan Sonar(SSS). This is achieved by explicitly taking into account the SSS operation as follows. First, the raw sensor data is corrected by means of a physics-based SSS model. Second, the data is projected to the sea-floor. The errors involved in this projection are thoroughfully analysed. Third, a probabilistic SSS model is defined and used to estimate the probability of each sea-floor region to be observed. This probabilistic information is then used to weight the contribution of each SSS measurement to the map. Because of these models, arbitrary map resolutions can be achieved, even beyond the sensor resolution. Finally, a geometric map building method is presented and combined with the probabilistic approach. The resulting map is composed of two layers. The echo intensity layer holds the most likely echo intensities at each point in the sea-floor. The probabilistic layer contains information about how confident can the user or the higher control layers be about the echo intensity layer data. Experimental results have been conducted in a large subsea region.

Objective: This paper presents a fast approach to detect QRS complexes based on a simple analysis of the temporal ECG structure. Methods: The ECG is processed through several steps involving noise removal, feature detection and feature analysis. The obtained feature set, which holds most of the ECG information while requiring low data storage, constitutes a lossy compressed version of the ECG. Results: The experiments, performed using 12 different ECG databases, emphasize the advantages of our proposal. For example, 130 minute ECG recordings are processed in average in 0.77 s. Also, sensitivities and positive predictions surpass 99.9% in some databases and a global data saving of 90.35% is achieved. Conclusion and significance: When compared to other approaches, this study offers a parameterless and computationally efficient alternative for QRS complex detection and lossy ECG compression. Moreover, some of the presented techniques are general enough to be used by other ECG analysis tools. Finally, the documented source code corresponding to this study is publicly available.

Controlling and quantifying the presence of Posidonia Oceanica (P.O.) in the Mediterranean sea is crucial for the conservation of these endemic ecosystems and to underscore the negative impact of many anthropogenic activities. These activities, which include uncontrolled leisure anchoring or illegal drag fishing, directly affect the tourism and fishing industries. Nowadays, the control and quantification of P.O. is done by divers, in a slow and imprecise process achieved in successive missions of a duration limited by the capacity of the oxygen scuba tanks. This paper proposes the application of robotic and computer vision technologies to upgrade the current P.O. control methods, building large scale coverage maps using the imagery provided by an autonomous underwater vehicle endowed with a bottom-looking camera. The process includes four main steps: 1) training a classifier based on two different Gabor filter image patch descriptors and a support vector machine; 2) detecting P.O. autonomously, both on-line and off-line, in each individual image; 3) color photo-mosaicking the area explored by the vehicle to obtain a global view of the meadow structure; these mosaics are extremely useful to analyze the structure and extension of the meadow and to calculate some of the biological descriptors needed to diagnose its state; and 4) building a binary coverage map in which the classification results of areas with image overlap are fused according to four different strategies. The experiments, performed in coastal areas of Mallorca and Girona, evaluate and compare the proposed descriptors and fusion techniques, showing, in some cases, accuracies and precisions above 90% in the detection of different patterns of P.O., from video sequences at different locations, in different seasons and with different environmental conditions.

This paper presents a novel approach to mobile robot localization using sonar sensors. This approach is based on the use of particle filters. Each particle is augmented with local environment information which is updated during the mission execution. An experimental characterization of the sonar sensors used is provided in the paper. A probabilistic measurement model that takes into account the sonar uncertainties is defined according to the experimental characterization. The experimental results quantitatively evaluate the presented approach and provide a comparison with other localization strategies based on both the sonar and the laser. Some qualitative results are also provided for visual inspection.

This paper proposes a method to perform on-line multi-class segmentation of Side-Scan Sonar acoustic images, thus being able to build a semantic map of the sea bottom usable to search loop candidates in a SLAM context. The proposal follows three main steps. First, the sonar data is pre-processed by means of acoustics based models. Second, the data is segmented thanks to a lightweight Convolutional Neural Network which is fed with acoustic swaths gathered within a temporal window. Third, the segmented swaths are fused into a consistent segmented image. The experiments, performed with real data gathered in coastal areas of Mallorca (Spain), explore all the possible configurations and show the validity of our proposal both in terms of segmentation quality, with per-class precisions and recalls surpassing the 90%, and in terms of computational speed, requiring less than a 7% of CPU time on a standard laptop computer. The fully documented source code, and some trained models and datasets are provided as part of this study.