A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

This paper proposed a hybrid vision-based robot control strategy for micro ground robots by mediating two vision models from mixed categories: a bio-inspired collision avoidance model and a segmentation based target following model. The implemented model coordination strategy is described as a probabilistic model using finite state machine (FSM) that allows the robot to switch behaviours adapting to the acquired visual information. Experiments demonstrated the stability and convergence of the embedded hybrid system by real robots, including the studying of collective behaviour by a swarm of such robots with environment mediation. This research enables micro robots to run visual models with more complexity. Moreover, it showed the possibility to realize aggregation behaviour on micro robots by utilizing vision as the only sensing modality from non-omnidirectional cameras

A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios

This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the locust's visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles

User-Centered Virtual Reality for Promoting Relaxation: An Innovative Approach

[EN] Virtual reality has been used effectively to promote relaxation and reduce stress. It is possible to find two main approaches to achieve such aims across the literature. The first one is focused on genetic environments filled with relaxing "narratives" to induce control over one's own body and physiological response, while the second one engages the user in virtual reality-mediated activities to empower his/her own abilities to regulate emotion. The scope of the present contribution is to extend the discourse on VR use to promote relaxation, by proposing a third approach. This would be based on VR with personalized content, based on user research to identify important life events. As a second step, distinctive features of such events may be rendered with symbols, activities or other virtual environments contents. According to literature, it is possible that such an approach would obtain more sophisticated and long-lasting relaxation in users. 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International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

The role of neural synchrony and rate in high-dimensional input systems. The Antennal Lobe: a case study

International audienceDealing with high-throughput information systems is becoming an everyday problem in many fields of science, as technological advances improve our ability to gather data. In particular, the information encoding problem in highdimensional spaces is a crucial aspect to consider. In fact, biological systems are known to be very efficient at encoding and processing high-dimensional information. Here we propose a biologically-based solution that mimics the neural processing performed by the Antennal Lobe of insects. Based on our understanding of this system, our model exploits plausible neural mechanisms to transform the massive and high-dimensional spatial and temporal input of the olfactory receptor neurons into a neural population encoding based on synchrony and frequency, consistent with known physiology. We demonstrate the capabilities of our Antennal Lobe model in the context of a classification task of different olfactory stimuli of varying concentrations. We show that the generated neural representation conveys both the identity and the concentration of each stimuli

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A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidanc