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Djamal Merad

Publications and source records attributed to Djamal Merad.

2 recordsLinked to original sources

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 solution for marine biodiversity monitoring. The primary objective of this work is to enable efficient and cost-effective data muling from underwater sensors by investigating the use of optical wireless communications to transmit 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 photo-multiplier. As a main contribution, we develop a comprehensive Monte Carlo-based ray-tracing algorithm to characterize this complex channel. This framework rigorously incorporates the impact of air bubbles modeled through the Mie scattering theory, a realistic sea surface representation derived from the JONSWAP spectrum, and an analytical derivation of the channel loss resulting from UAV instability under wind-induced perturbations. Furthermore, we conduct a comprehensive analysis of the W2A channel, examining the influence of key parameters such as wind speed, transmitter configurations, and receiver characteristics. The end-to-end performance evaluation demonstrates the practical feasibility of the proposed approach, achieving a bit-error rate of $10^{-3}$ at a data rate of 1 Mbps for a transmitter depth of 47 m and wind speeds up to 13 m/s.

eess.SP

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.

cs.CV