Multiwavelength fiber laser based on bidirectional lyot filter in conjunction with intensity dependent loss mechanism

We experimentally demonstrate a multiwavelength fiber laser (MWFL) based on bidirectional Lyot filter. A semiconductor optical amplifier (SOA) is used as the gain medium, while its combination with polarization controllers (PCs) and polarization beam combiner (PBC) induces intensity dependent loss (IDL) mechanism. The IDL mechanism acts as an intensity equalizer to flatten the multiwavelength spectrum, which can be obtained at a certain polarization state. Using different ratio of optical splitter has affected to multiwavelength flatness degradation. Subsequently, when we removed a polarizer in the setup, the extinction ratio (ER) is decreased. Ultimately, with two segments of polarization maintaining fiber (PMF), two channel spacings can be achieved due to splicing shift of 0° and 90°

A Novel Artificial Organic Controller with Hermite Optical Flow Feedback for Mobile Robot Navigation

This chapter describes a novel nature-inspired and intelligent control system for mobile robot navigation using a fuzzy-molecular inference (FMI) system as the control strategy and a single vision-based sensor device, that is, image acquisition system, as feedback. In particular, FMI system is proposed as a hybrid fuzzy inference system with an artificial hydrocarbon network structure as defuzzifier that deals with uncertainty in motion feedback, improving robot navigation in dynamic environments. Additionally, the robotics system uses processed information from an image acquisition device using a real-time Hermite optical flow approach. This organic and nature-inspired control strategy was compared with a conventional controller and validated in an educational robot platform, providing excellent results when navigating in dynamic environments with a single-constrained perception device

Challenges and Issues on Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

International audienceInspiration in nature has been widely explored, from macro to micro-scale. When looking into chemical phenomena, stability and organization are two properties that emerge. Recently, artificial hydrocarbon networks (AHN), a supervised learning method inspired in the inner structures and mechanisms of chemical compounds, have been proposed as a data-driven approach in artificial intelligence. AHN have been successfully applied in data-driven approaches, such as: regression and classification models, control systems, signal processing, and robotics. To do so, molecules –the basic units of information in AHN– play an important role in the stability, organization and interpretability of this method. Interpretability, saving computing resources, and predictability have been handled by AHN, as any other machine learning model. This short paper aims to highlight the challenges, issues and trends of artificial hydrocarbon networks as a data-driven method. Throughout this document, it presents a description of the main insights of AHN and the efforts to tackle interpretability and training acceleration. Potential applications and future trends on AHN are also discussed