Portail HAL des publications du LIRMM
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Insights into the in-vivo physiological energetics of juvenile Atlantic bluefin tuna Thunnus thynnus
No full textInternational audienceThe Atlantic bluefin tuna (ABFT) is an extremely valuable pelagic marine fish, with interestingphysiological adaptations to a migratory lifestyle of ceaseless swimming, notably partial endothermy and obligatory ram ventilation. Very little is known, however, about ABFT physiological energetics. In 2023, ABFT were bred for the first time in captivity, offering unprecedented access to live age 0+ juveniles. We used swimming respirometry to evaluate standard and routine metabolic rates (SMR and RMR) and aerobic metabolic scope (AS) in juveniles (mass ~550g, forklength ~30cm) at 19 °C. We performed respirometry over a range of swimming speeds but tuna lost equilibrium and became agitated below 1.4 bodylengths per second (BL s), refused to swim faster than 2.2 BL s, and had their highest metabolic rates when agitated. Tuna SMR was about 1.5 times those of other Mediterranean fishes at similar sizes and temperatures whereas their AS was two to threefold higher. Video analysis of tuna in their rearing tank revealed they were actually cruising spontaneously at speeds between 2.5 and 3.5 BL s. Extrapolation of respirometry data to 3 BL s indicated an RMR about fourfold higher than direct measures, by tank respirometry, in other Mediterranean species. These preliminary observations would confirm longstanding predictions about the comparative energetics of ABFT but more work is clearly needed. Analysis of videos of tuna swimming in the tunnel may provide insights into their poor performance and permit further estimates of RMR at cruising speeds in the tank
Owl-Vision: Augmentation of Visual Field by Virtual Amplification of Head Rotation
No full textInternational audienceThe human visual field is limited due to the eyes being located on the front of the face. Although head rotation can expand the visual field, it is also limited by the biomechanical constraints of the neck. Due to these constraints we need to turn our bodies when we want to see beyond 180 degrees in either direction. To address this issue, we have developed a sensory augmentation system that expands the visual field by intervening in the visuo-motor transformation between our vision and head rotation. The ’Owl-Vision’ system virtually amplifies the rotation angle relative to the actual head rotation, enabling 180-degree vision in either direction (360 degrees in total) controlled by a natural user head movement of 90 degrees in either direction. The system was implemented using a 360-degree camera mounted on a head-mounted display and in virtual environments. The usability of the Owl-Vision system was evaluated and found to be good and similar to our normal (not-amplified) vision system. This system provides a ’natural’ and effective method of sensory augmentation of the visual field
A mathematical characterization of the convergence domain for Direct Visual Servoing
No full textInternational audienceDirect Visual Servoing (DVS) is a technique that controls the robot motion by using the pixel intensities captured by a camera. DVS demonstrates high accuracy at convergence, prompting the development of various methods aimed at expanding its convergence domain.In this paper, we propose a mathematical characterization of the DVS convergence domain with closed-form expressions for the controlled degrees of freedom. From these expressions, we concluded that the extent of the convergence domain is related to the presence of isotropic or defocus blur, a phenomenon that had only been observed previously as a trend in empirical experiments
A CMOS-compatible oscillation-based VO2 Ising machine solver
No full textInternational audiencePhase-encoded oscillating neural networks offer compelling advantages over metal-oxide-semiconductor-based technology for tackling complex optimization problems, with promising potential for ultralow power consumption and exceptionally rapid computational performance. In this work, we investigate the ability of these networks to solve optimization problems belonging to the nondeterministic polynomial time complexity class using nanoscale vanadium-dioxide-based oscillators integrated onto a Silicon platform. Specifically, we demonstrate how the dynamic behavior of coupled vanadium dioxide devices can effectively solve combinatorial optimization problems, including Graph Coloring, Max-cut, and Max-3SAT problems. The electrical mappings of these problems are derived from the equivalent Ising Hamiltonian formulation to design circuits with up to nine crossbar vanadium dioxide oscillators. Using sub-harmonic injection locking techniques, we binarize the solution space provided by the oscillators and demonstrate that graphs with high connection density (η > 0.4) converge more easily towards the optimal solution due to the small spectral radius of the problem’s equivalent adjacency matrix. Our findings indicate that these systems achieve stability within 25 oscillation cycles and exhibit power efficiency and potential for scaling that surpasses available commercial options and other technologies under study. These results pave the way for accelerated parallel computing enabled by large-scale networks of interconnected oscillators
IDLD: Interlocked Dual-Circle Latch Design with Low Cost and Triple-Node-Upset-Recovery for Aerospace Applications
No full textInternational audienceModern powerful CMOS chips are usually highly integrated and implemented with aggressively shrunk technology nodes. In radiation environment, under charge-sharing mechanism, one particle striking can simultaneously impact multiple nodes causing double-node-upsets (DNUs) and triple-node-upsets (TNUs). In this paper, we propose an Interlocked Dual-circle Latch Design, namely IDLD, with low cost and TNU recovery for aerospace applications. IDLD consists of four transmission gates and twelve 2-input Celements (CEs) implemented in 22nm CMOS process. Simulation results demonstrate the complete TNU recovery as well as costeffectiveness for the proposed IDLD latch
Tendon-Driven vs Rod-Driven Continuum Robots: A Bench Test Evaluation
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Detecting local variations across metazoan communities in back-reef depressions of Reunion Island (Mascarene Archipelago) through environmental DNA survey
No full textInternational audienceThe back-reef depressions, or lagoons, of Reunion Island (western Indian Ocean) host a high abundance of organisms living amongst the coral reefs and are critical sites for artisanal fishing, tourism, and shoreline stability for the island. Over time, increasing degradation of Reunionese reefs has been observed due to overexploitation, beach erosion and eutrophication. Efforts to mitigate the impact of these pressures on aquatic organisms include biodiversity surveys primarily performed through visual censuses that can be logistically complex and may unintentionally overlook organisms. Surveys integrating environmental DNA (eDNA) collections have provided rapid biodiversity assessments, while helping to circumvent some limitations of visual surveys. The present study describes the results of an exploratory eDNA survey, which aims to characterize metazoan communities of four Reunionese lagoons located along the west coast of the island. As eDNA surveys first require deliberate study design and optimization for each new context, we sought to establish a modernized workflow implementing specialized equipment to collect and preserve samples to facilitate future studies in these lagoons. During the austral summer of 2023, samples were pumped directly from surface and bottom depths at each site through self-preserving filters which were then processed for DNA metabarcoding using regions of the 12S ribosomal RNA (12S), small ribosomal subunit 18S (18S) and Cytochrome Oxidase I (COI) genes. The survey detected high species richness that varied by site, and in a single collection period, recovered the presence of 60 teleost families and numerous invertebrate taxa, including members of the coral faunal community that are less studied in Reunion. Distinct biological communities were observed at each site, and within a single lagoon, suggesting that these differences are due to site-specific factors (e.g., environmental variables, geographic distance, etc.). Although continued protocol optimization is needed, the present findings demonstrate the successful application of an eDNA-based survey for biodiversity assessment within Reunionese lagoons
Revolutionizing Plant Pathogen Conservation: The Past, Present, and Future of AI in Preserving Natural Ecosystems
No full textInternational audienceTraditionally, plant pathologists have emphasized controlling crop pathogens, neglecting the importance of conserving their diversity in natural ecosystems. Native plant pathogens thriving in natural environments significantly contribute to ecosystem structure, stability, nutrient cycling, and productivity. The coevolution of wild crop progenitors with native pathogens yields a diverse array of disease resistance factors, serving as a critical resource for farmers and breeders in developing disease-resistant cultivars. Moreover, native plant pathogens hold promise as valuable research tools, model systems for scientists, and potential sources of novel drugs, pesticides, bio-control agents, and biotechnological innovations (Ingram 1999, Ingram 2022)Artificial Intelligence (AI) technology, specifically Deep Learning (DL), is revolutionizing agricultural sustainability by advancing plant disease identification (Ayoub Shaikh et al. 2022, Wongchai et al. 2022). DL extends beyond single-crop disease identification, encompassing multiple crops and diseases (Lee et al. 2020). Utilizing computer vision and Internet of Things (IoT), AI enhances the ability to recognize and categorize plant diseases across diverse agricultural landscapes (Sinha and Dhanalakshmi 2022). The data used is crowdsourced from images captured through cameras or smartphones.However, to comprehensively monitor and understand plant diseases on a larger scale for diversity study, AI practitioners should integrate broader factors into their predictive modeling such as weather patterns, geographical variations, environmental conditions, and real-world challenges. Overcoming real-world challenges involves addressing previously unseen diseases, modeling disease distribution across extensive geographical areas, and managing domain adaptation, which arise from differences in data distributions between the source and target domains.An innovative plant disease identification framework has been established, benchmarked on the largest plant disease dataset (Mohanty et al. 2016). This framework specifically focuses on addressing the crucial challenges in this field. The reliability of DL models was thoroughly analyzed and evaluated through machine vision interpretation, and cases lacking labeled data were explored (Chai et al. 2023). This presentation will not only highlight the significant advancements achieved but also outline future plans to new ecological studies of plant diseases identification as indispensable elements in the broader landscape of global environmental change research
Predicting suicidal ideation from irregular and incomplete time series of questionnaires in a smartphone-based suicide prevention platform: a pilot study
No full textInternational audienceOver 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Selfassessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application