87 research outputs found
An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles
No full textThis paper develops an event-related potential (ERP)-based adaptive model for the control of humanoid robot movements in an environment cluttered with obstacles based on live video feedback. This model adaptively determines the repetition number according to an individual's mental state to speed up the robot control cycle. N200 and P300 potential features increase in the frontal and occipital areas when using robot images as visual stimuli, so it is able to effectively recognize target visual stimuli by processing Fisher's linear discriminant analysis (FLDA) and to identify a subject's intention by using support vector machine (SVM), in parallel. The offline evaluations show that, compared with a nonadaptive model, the adaptive model increases the accuracy rate from 88.8% to 92.9%, a change of 4.1%, and the information transfer rate (ITR) from 41.3 to 46.3 bits/min, a change of 5.0 bits/min. Eight subjects participated in telepresence controlling a NAO humanoid robot to move in an office environment cluttered with obstacles. The successful maneuvers demonstrate that the brain-controlled humanoid robot can be applied for surveillance and exploration in unknown environments based on live video feedback, which are evaluated by using new metrics for the performance of the brain-robot interaction (BRI) system
DisQ: Disentangling Quantitative MRI Mapping of the Heart
No full textQuantitative MRI (qMRI) of the heart has become an important clinical tool for examining myocardial tissue properties. Because heart is a moving object, it is usually imaged with electrocardiogram and respiratory gating during acquisition, to “freeze” its motion. In reality, gating is more-often-than-not imperfect given the heart rate variability and nonideal breath-hold. qMRI of the heart, consequently, is characteristic of varying image contrast as well as residual motion, the latter compromising the quality of quantitative mapping. Motion correction is an important step prior to parametric mapping, however, a long-standing difficulty for registering the dynamic sequence is that the contrast across frames varies wildly: depending on the acquisition scheme some frames can have extremely poor contrast, which fails both traditional optimization-based and modern learning-based registration methods. In this work, we propose a novel framework named DisQ, which Disentangles Quantitative mapping sequences into the latent space of contrast and anatomy, fully unsupervised. The disentangled latent spaces serve for the purpose of generating a series of images with identical contrast, which enables easy and accurate registration of all frames. We applied our DisQ method to the modified Look-Locker inversion recovery (MOLLI) sequence, and demonstrated improved performance of T1 mapping. In addition, we showed the possibility of generating a dynamic series of baseline images with exactly the same shape, strictly registered and perfectly “frozen". Our proposed DisQ methodology readily extends to other types of cardiac qMRI such as T2 mapping and perfusion.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Medical Imagin
Overall energy reduction for hard real-time systems based on threshold work-demand analysis
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Rate-monotonic scheduling for reducing system-wide energy consumption for hard real-time systems
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