Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera +2 more
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Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients. [PDF]
Khodami F +3 more
europepmc +1 more source
Challenges with the kinematic analysis of neurotypical and impaired speech: Measures and models. [PDF]
Mücke D +4 more
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Kinematic analysis of an unrestrained passenger in an autonomous vehicle during emergency braking. [PDF]
Santos-Cuadros S +3 more
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Kinematic Analysis and Application to Control Logic Development for RHex Robot Locomotion. [PDF]
Burzyński P +3 more
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Simulation and kinematic analysis of a 3-DOF marine antenna pedestal focusing on singularity avoidance and its effects on angular velocity and angular acceleration. [PDF]
GholamiOmali A, Alizadeh M, Sadedel M.
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Validation of inertial measurement units based on waveform similarity assessment against a photogrammetry system for gait kinematic analysis. [PDF]
Blanco-Coloma L +7 more
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Kinematical analysis of Wunderlich mechanism
Mechanism and Machine Theory, 2013Abstract We analyze the configuration space of Wunderlich mechanism using tools of computational algebraic geometry. We provide the complete description of the configuration space of the Wunderlich mechanism by computing the prime decomposition of the relevant variety and analyzing all of its prime components. We analyze also the effect of the choice
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