There remains some debate about whether beta power effects observed during sentence comprehension reflect ongoing syntactic unification operations (beta-syntax hypothesis), or instead reflect maintenance or updating of the sentence-level representation (beta-maintenance hypothesis). In this study, we used magnetoencephalography to investigate beta power neural dynamics while participants read relative clause sentences that were initially ambiguous between a subject- or an object-relative reading. An additional condition included a grammatical violation at the disambiguation point in the relative clause sentences. The beta-maintenance hypothesis predicts a decrease in beta power at the disambiguation point for unexpected (and less preferred) object-relative clause sentences and grammatical violations, as both signal a need to update the sentence-level representation. While the beta-syntax hypothesis also predicts a beta power decrease for grammatical violations due to a disruption of syntactic unification operations, it instead predicts an increase in beta power for the object-relative clause condition because syntactic unification at the point of disambiguation becomes more demanding. We observed decreased beta power for both the agreement violation and object-relative clause conditions in typical left hemisphere language regions, which provides compelling support for the beta-maintenance hypothesis. Mid-frontal theta power effects were also present for grammatical violations and object-relative clause sentences, suggesting that violations and unexpected sentence interpretations are registered as conflicts by the brain's domain-general error detection system.
MULTIFILE
Abstract gepubliseerd in Elsevier: For patients with intermediate- and high risk prostate cancer, treated with high dose radiotherapy, the CTV generally involves the prostate and (part of) the seminal vesicles (SV) [1,2]. Fiducial markers locate the prostate reliably during radiotherapy [3]. However the SV may move independent from the corpus of the prostate [4–6]. As this should be incorporated in the PTV margin [4,6–8], this may lead to a larger irradiated rectum volume and more gastro-intestinal toxicity [9]. Several studies have shown that rectal and bladder filling are of influence on prostate and SV mobility [10–13]. Using a dietary protocol or applying rectal gas removal could somewhat decrease the prostate and SV mobility [14,15], however, these methods are not very patient friendly. In this study we hypothesize that the vesicles become more rigidly attached to the prostate in case of tumour infiltration. This would imply that in case of extensive infiltration, the prostate and vesicles move as a rigid body and are thus adequately localized by marker-based Image Guided Radiotherapy (IGRT). The aim of this study was to assess the impact of tumour invasion in the SV on their mobility.
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The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
Due to the exponential growth of ecommerce, the need for automated Inventory management is crucial to have, among others, up-to-date information. There have been recent developments in using drones equipped with RGB cameras for scanning and counting inventories in warehouse. Due to their unlimited reach, agility and speed, drones can speed up the inventory process and keep it actual. To benefit from this drone technology, warehouse owners and inventory service providers are actively exploring ways for maximizing the utilization of this technology through extending its capability in long-term autonomy, collaboration and operation in night and weekends. This feasibility study is aimed at investigating the possibility of developing a robust, reliable and resilient group of aerial robots with long-term autonomy as part of effectively automating warehouse inventory system to have competitive advantage in highly dynamic and competitive market. To that end, the main research question is, “Which technologies need to be further developed to enable collaborative drones with long-term autonomy to conduct warehouse inventory at night and in the weekends?” This research focusses on user requirement analysis, complete system architecting including functional decomposition, concept development, technology selection, proof-of-concept demonstrator development and compiling a follow-up projects.
In the past decade, particularly smaller drones have started to claim their share of the sky due to their potential applications in the civil sector as flying-eyes, noses, and very recently as flying hands. Network partners from various application domains: safety, Agro, Energy & logistic are curious about the next leap in this field, namely, collaborative Sky-workers. Their main practical question is essentially: “Can multiple small drones transport a large object over a high altitude together in outdoor applications?” The industrial partners, together with Saxion and RUG, will conduct feasibility study to investigate if it is possible to develop these collaborative Sky-workers and to identify which possibilities this new technology will offer. Design science research methodology, which focuses on solution-oriented applied research involving multiple iterations with rigorous evaluations, will be used to research the feasibility of the main technological building blocks. They are: • Accurate localization based on onboard sensors. • Safe and optimal interaction controller for collaborative aerial transport Within this project, the first proof-of-concepts will be developed. The results of this project will be used to expand the existing network and formulate a bigger project to address additional critical aspects in order to develop a complete framework for collaborative drones.
