End-to-End Optical Propagation Modeling for Water-to-Air Channels under Sea Surface and UAV Effects

Underwater observatories have recently emerged as an efficient mean of marine biodiversity monitoring. In order to conduct data muling from the underwater sensors in an efficient and cost-effective way, we consider the use of optical wireless communications to transmit the data from the underwater sensors to an aerial node close to the water surface, such as an unmanned aerial vehicle (UAV). More specifically, we utilize a direct water-to-air (W2A) optical communication link between the sensor node equipped with an LED emitter and the UAV equipped with an ultra-sensitive receiver, i.e., a silicon photomultiplier (SiPM). To characterize this particularly complex communication channel, we introduce a ray-tracing algorithm based on the Monte Carlo method, incorporating the impact of bubbles modeled through the Mie scattering theory and a realistic sea surface representation derived from the JONSWAP spectrum. Additionally, we incorporate into this model the channel losses resulting from UAV instability under windy weather conditions. Furthermore, we conduct a comprehensive analysis of the wireless channel, examining the influence of key parameters such as wind speed, transmitter configurations, and receiver characteristics. Finally, we evaluate the end-to-end performance of the system by analyzing the average bit-error rate at varying depths and data rates, providing valuable insights into the feasibility and efficiency of the proposed approach.

Gaining Explainability from a CNN for Stereotype Detection Based on Mice Stopping Behavior

Understanding the behavior of laboratory animals is a key to find answers about diseases and neurodevelopmental disorders that also affects humans. One behavior of interest is the stopping, as it correlates with exploration, feeding and sleeping habits of individuals. To improve comprehension of animal's behavior, we focus on identifying trait revealing age/sex of mice through the series of stopping spots of each individual. We track 4 mice using LiveMouseTracker (LMT) system during 3 days. Then, we build a stack of 2D histograms of the stop positions. This stack of histograms passes through a shallow CNN architecture to classify mice in terms of age and sex. We observe that female mice show more recognizable behavioral patterns, reaching a classification accuracy of more than 90%, while males, which do not present as many distinguishable patterns, reach an accuracy of 62.5%. To gain explainability from the model, we look at the activation function of the convolutional layers and found that some regions of the cage are preferentially explored by females. Males, especially juveniles, present behavior patterns that oscillate between juvenile female and adult male.