Portail HAL des publications du LIRMM
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Structural Analysis and Design of Humanoid Arms From Human Arm Reachable Workspace
No full textInternational audienceHaving a workspace (postural-reachable) for the humanoid arm that is similar to a human arm around the upper body is essential for effective operation in human-like environments. However, research in this area is scarce. This letter aims to investigate the optimal structural configuration of a humanoid arm in the workspace around the upper body (WAUB). For this purpose, we initiate by analyzing the reachability data of human arm in the WAUB and subsequently propose a design strategy that incorporates this human data into the humanoid arm design. The proposed strategy consists of two key components: a structural parameter optimization method based on the postural reachability of task points and a task point identification method tailored to the WAUB. The optimization process leverages the covariance matrix adaptation evolutionary strategy (CMA-ES) and integrates collision-free control into each optimization iteration for realistic arm motion. The task point identification involves defining an expected workspace derived from human data and selecting task points through analysis of the hard-to-reach areas. Humanoid upper body models are introduced considering the anthropomorphic contour of human body and humanoid shoulder features. Performance tests conducted on these models assess the effectiveness of optimization. The results can offer practical insights for humanoid arm design
Computing Distances on Graph Associahedra Is Fixed-Parameter Tractable
No full textInternational audienceAn elimination tree of a connected graph G is a rooted tree on the vertices of G obtained by choosing a root v and recursing on the connected components of G-v to obtain the subtrees of v. The graph associahedron of G is a polytope whose vertices correspond to elimination trees of G and whose edges correspond to tree rotations, a natural operation between elimination trees. These objects generalize associahedra, which correspond to the case where G is a path. Ito et al. [ICALP 2023] recently proved that the problem of computing distances on graph associahedra is NP-hard. In this paper we prove that the problem, for a general graph G, is fixed-parameter tractable parameterized by the distance k. Prior to our work, only the case where G is a path was known to be fixed-parameter tractable. To prove our result, we use a novel approach based on a marking scheme that restricts the search to a set of vertices whose size is bounded by a (large) function of k
Twin-Width One
No full textInternational audienceWe investigate the structure of graphs of twin-width at most 1, and obtain the following results: - Graphs of twin-width at most 1 are permutation graphs. In particular they have an intersection model and a linear structure. - There is always a 1-contraction sequence closely following a given permutation diagram. - Based on a recursive decomposition theorem, we obtain a simple algorithm running in linear time that produces a 1-contraction sequence of a graph, or guarantees that it has twin-width more than 1. - We characterise distance-hereditary graphs based on their twin-width and deduce a linear time algorithm to compute optimal sequences on this class of graphs
Étude d’une tenségrité souple pour la conception d’un effecteur à raideur variable d’endoscope chirurgical robotisé
No full textNational audienceLes tenségrités souples sont des assemblages précontraints de barres rigides et de câbles souples. Des mécanismes peuvent être conçus sur la base d’une tenségrité en rendant actif certains de ses câbles. Les mécanismes ainsi obtenus peuvent se reconfigurer et sont à raideur variable, à condition que l’actionnement agisse de manière adéquate sur la précontrainte. De plus, l’utilisation de matériaux souples rend les tenségrités intrinsèquement compliantes, ce qui peut être d’intérêt pour des interactions personne-machine. Dans cet article, un mécanisme basé sur une tenségrité souple est proposé pour une application à l’endoscopie chirurgicale. Il est conçu pour présenter deux espaces de précontrainte pouvant être manipulés indépendamment, l’un par l’actionnement et l’autre passivement. À travers une expression analytique de l’énergie de la tenségrité, son comportement est simulé pour analyser ses performances en termes d’espace de travail et de variation de raideur
Non-Volatile, Double and Triple Node Upsets Tolerant Latch Designs Based on FeFET and CMOS
No full textInternational audienceNon-volatile memories (NVMs) are widely used in energy-harvesting Internet of Things (IoTs) systems, and ferroelectric field effect transistor (FeFET) devices present opportunities for NVM designs due to such important features as low power consumption, fast access speed and compatibility with the CMOS technology. However, rising soft error rates caused by radiation pose a severe reliability challenge, and state-of-the-art radiation hardening by design (RHBD) methods are urgently needed. This paper presents a non-volatile double-node-upset (DNU) tolerant latch (NVDTL) and its enhanced version, i.e., a non-volatile triple-node-upset (TNU) tolerant latch (NVTTL), which do not require auxiliary control signals to provide the non-volatility feature. The NVDTL utilizes two parallel single-node-upset (SNU) recovery units. Each unit integrates multiple 2-input C-elements and input-split inverters with embedded FeFETs to form interlocked feedback loops. Moreover, NVTTL enhances its robustness reliability through a two-stage error-blocking mechanism in the output module to provide TNU tolerance. Simulation results based on Cadence Virtuoso have validated the proposed latches' fault-tolerance and non-volatility and also show that the comprehensive delay-power-area product is reduced by approximately 77.21% for NVDTL and 61.67% for NVTTL, and the power-delay product is reduced by 23.45% and 11.15%, respectively, compared to the existing latches with the same level of radiation hardness
Fine-Tuning Detection Criteria for Enhancing Anomaly Detection in Time Series
No full textInternational audienceAnomaly detection is the problem of identifying observations that do not conform to the typical ones in a time series. Detection methods implicitly define detection criteria, such as deviation measures, filter thresholds, and candidate anomaly selection strategies. Choosing inappropriate criteria results in inaccurate outputs, generating spurious alerts or missing events. Adjusting these criteria is essential for monitoring systems. To address this challenge, this paper explores the fine-tuning of deviation measures, filter thresholds, and candidate selection strategies. Experimental results show that the proper choice of criteria significantly improves anomaly detection performance, often with greater impact than changing the detection methods
Scalable and accurate online multivariate anomaly detection
No full textInternational audienceThe continuous monitoring of dynamic processes generates vast amounts of streaming multivariate time series data. Detecting anomalies within them is crucial for real-time identification of significant events, such as environmental phenomena, security breaches, or system failures, which can critically impact sensitive applications. Despite significant advances in univariate time series anomaly detection, scalable and efficient solutions for online detection in multivariate streams remain underexplored. This challenge becomes increasingly prominent with the growing volume and complexity of multivariate time series data in streaming scenarios. In this paper, we provide the first structured survey primarily focused on scalable and online anomaly detection techniques for multivariate time series, offering a comprehensive taxonomy. Additionally, we introduce the Online Distributed Outlier Detection (2OD) methodology, a novel well-defined and repeatable process designed to benchmark the online and distributed execution of anomaly detection methods. Experimental results with both synthetic and real-world datasets, covering up to hundreds of millions of observations, demonstrate that a distributed approach can enable centralized algorithms to achieve significant computational efficiency gains, averaging tens and reaching up to hundreds in speedup, without compromising detection accuracy
Words avoiding the morphic images of most of their factors
No full textInternational audienceWe say that a finite factor of a word is \emph{imaged} if there exists a non-erasing morphism , distinct from the identity, such that contains . We show that every infinite word contains an imaged factor of length at least 6 and that 6 is best possible. We show that every infinite binary word contains at least 36 distinct imaged factors and that 36 is best possible
Overview of PlantCLEF 2025: Multi-Species Plant Identification in Vegetation Quadrat Images ⋆ Notebook for the LifeCLEF Lab at CLEF 2025
No full textInternational audienceQuadrat images are essential for ecological studies, as they enable standardized sampling, the assessment of plant biodiversity, long-term monitoring, and large-scale field campaigns. These images typically cover an area of fifty centimetres or one square meter, and botanists carefully identify all the species present. Integrating AI could help specialists accelerate their inventories and expand the spatial coverage of ecological studies. To assess progress in this area, the PlantCLEF 2025 challenge relies on a new test set of 2,105 high-resolution multi-label images annotated by experts and covering around 400 species. It also provides a large training set of 1.4 million individual plant images, along with vision transformer models pre-trained on this data. The task is formulated as a (weakly labelled) multi-label classification problem, where the goal is to predict all species present in a quadrat image using single-label training data. This paper provides a detailed description of the data, the evaluation methodology, the methods and models used by participants, and the results achieved
Learning the syntax of plant assemblages
No full textInternational audienceTo address the urgent biodiversity crisis, it is crucial to understand the nature of plant assemblages. The distribution of plant species is shaped not only by their broad environmental requirements but also by micro-environmental conditions, dispersal limitations, and direct and indirect species interactions. While predicting species composition and habitat type is essential for conservation and restoration purposes, it remains challenging. In this study, we propose an approach inspired by advances in large language models to learn the ‘syntax’ of abundance-ordered plant species sequences in communities. Our method, which captures latent associations between species across diverse ecosystems, can be fine-tuned for diverse tasks. In particular, we show that our methodology is able to outperform other approaches to (1) predict species that might occur in an assemblage given the other listed species, despite being originally missing in the species list (16.53% higher accuracy in retrieving a plant species removed from an assemblage than co-occurrence matrices and 6.56% higher than neural networks), and (2) classify habitat types from species assemblages (5.54% higher accuracy in assigning a habitat type to an assemblage than expert system classifiers and 1.14% higher than tabular deep learning). The proposed application has a vocabulary that covers over 10,000 plant species from Europe and adjacent countries and provides a powerful methodology for improving biodiversity mapping, restoration and conservation biology. As ecologists begin to explore the use of artificial intelligence, such approaches open opportunities for rethinking how we model, monitor and understand nature