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This paper is devoted to dealing with the problem of global attitude synchronization for quaternion-based multiple rigid bodies, regardless of the general directed topologies of networks and arbitrary initial orientations of rigid bodies. A novel canonical quaternion is constructed to represent all physical attitudes of rigid bodies such that the pseudo-synchronization of their quaternion representations (namely, the quaternions' vector parts of all rigid bodies reach agreement on some identical value, whereas their scalar parts do not) can be precluded. Moreover, to reduce unnecessary communication requirements of rigid bodies, a hybrid triggering mechanism involving both the time regulation and neighbors' non-real-time information is proposed, with which a distributed protocol is developed by leveraging the constructed canonical quaternion. It is shown that the presented protocol for rigid bodies over directed networks can simultaneously realize the global attitude synchronization and naturally exclude the Zeno behavior. In addition, these observations are also validated via the application of our hybrid triggering protocol to networked spacecraft.
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http://dx.doi.org/10.1016/j.isatra.2023.07.016 | DOI Listing |
Ann Biomed Eng
September 2025
LaBS - Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
Understanding spine biomechanics is essential for maintaining posture under static and dynamic conditions, relying on a balance of muscular and gravitational forces. Computational musculoskeletal (MSK) models are increasingly being used in biomechanical research as non-invasive alternatives to in vivo and in vitro methods. Two main MSK modeling strategies are multibody (MB) models, which simplify the spine using rigid vertebrae and intervertebral joints to study muscle recruitment, and finite element (FE) models, which provide detailed tissue representation but often rely on oversimplified loading conditions.
View Article and Find Full Text PDFRegen Ther
December 2025
Univ Toulouse, Inserm, ToNIC, Toulouse, France.
Background: Brain regeneration after injury is a challenge being tackled by numerous therapeutic strategies in pre-clinical development. There is growing interest in scaffolds implanted in brain lesions. Developments in 3D printing offer the possibility of designing complex structures of varying compositions adapted to tissue anatomy.
View Article and Find Full Text PDFIEEE Trans Biomed Eng
September 2025
Objective: This work proposes a method to estimate the kinematic parameters of multi-joint systems where direct measurement is infeasible, such as joints of the hand.
Methods: Our novel data-driven estimation method uses "workspace manifold mapping" that relies on a unique geometry that arises in exponential representations of 3D motions of two rigid bodies connected via one- or two-degree-of-freedom (DOF) joints. We describe and verify our "Generative Topographic Mapping algorithm with kinematic constraints" (GTM-KC) using simulated data and motion capture data for a 2-DOF bio-inspired mechanical linkage.
Int J Mol Sci
August 2025
School of Medicine, Royal College of Surgeons in Ireland, Medical University of Bahrain (RCSI-MUB), Adliya P.O. Box 15503, Bahrain.
Parkinson's disease (PD) is a common neurodegenerative disorder caused by progressive loss of dopaminergic neurons in the substantia nigra and the presence of Lewy bodies. While PD is most recognized by its motor symptoms (resting tremor, rigidity, bradykinesia, and postural instability), cognitive decline (CD) may become apparent as PD progresses, leading to Parkinson's disease dementia (PDD). Type 2 diabetes mellitus (T2DM) and insulin resistance (IR) are risk factors for dementia, especially Alzheimer's disease; however, their influence on dementia in PD is underexplored.
View Article and Find Full Text PDFIperception
August 2025
Communication Science Laboratories, NTT Inc., Japan.
In dynamic visual scenes, many materials-including cloth, jelly-like bodies, and flowing liquids-undergo non-rigid deformations that convey information about their physical state. Among such cues, we focus on deformation-based motion-defined as the spatial shifts of image deformation. Studying deformation-based motion is essential because it lies at the intersection of motion perception and material perception.
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