Automatic Bluefin Tuna Sizing with a Combined Acoustic and Optical Sensor.

A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor's field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes' ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna's Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.

Clustering of electrocardiograph signals in computer-aided Holter analysis.

A number of methods for temporal alignment, feature extraction and clustering of electrocardiographic signals are proposed. The ultimate aim of the paper is to find a method to automatically reduce the quantity of beats to examine in a long-term electrocardiographic signal, known as Holter signal, without loss of valuable information for the diagnosis. These signals include thousands of beats and therefore visual inspection is difficult and cumbersome. All the elements involved in each stage will be described and a thorough experimental study will be presented. The electrocardiograph signals used in the experiments belong to the well-known MIT database, where many different waveforms can be found. Based on the results of the experiments, a complete process is proposed to obtain the significant beats present within a signal, with a reasonable computational cost. Hence, cardiologists will only have to examine a small but fully representative subset of beats, making this method a very useful tool for medical decision support systems.