Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming

The development of better monitoring technologies, the early combat of outbreaks, massive mortality, and promoting sustainability are challenges that the aquaculture industry still faces, and the development of solutions for this is an open problem. In this paper, focusing our attention on monitoring technologies as a promising solution to these issues, we report a Gaussian distribution model for detecting dangerous operating conditions in industrial fish farming. This approach allows us to indicate through a 2D image visualization when fish production is under normal, warning, or dangerous operating conditions. Furthermore, our proposed method has promising possibilities for application in the most varied fields of science, given that the mathematical procedure described allows us to discover the fundamental statistical structure of physical, chemical, and biological systems governed by laws of a probabilistic nature.

Noise Reduction in Swallowing Muscle Activity Measurement Based on Mixture Gaussian Distribution Model

For the noninvasive measurement of swallowing muscle activity, surface electromyograms and swallowing sounds are used. The electromyogram electrodes can be placed appropriately only by experts with specialized knowledge about the location of the swallowing muscle group. Therefore, these sensors have not been used for measurements in food development, for which there were no experts. In order to develop a simple swallowing muscle measurement method for food development, we proposed a sensor sheet consisting of multiple electromyogram electrodes and observed that different swallowing muscle activities could be measured depending on the type of food. In this work, we study a calculation method for the elimination of noise, which is inevitable in electromyograms, from the sensor sheet measurement results and prove that the method improves the performance of the swallowing muscle activity measurements.