At the 5Groningen field lab, the next generation of wireless technology is being put to the test in an experiment with a prototype involving real-time decision-making using 5G edge computing. One of the applications envisioned is a smart police vest that can fully automatically detect threats like firearms and stabbing weapons.
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The development of information and communication technologies (ICT) has led to many innovative technologies. The integration of technologies such as the internet of things (IoT), cloud computing, and machine learning concepts have given rise to Industry 4.0. Fog and edge computing have stepped in to fill the areas where cloud computing is inadequate to ensure these systems work quickly and efficiently. The number of connected devices has brought about cybersecurity issues. This study reviewed the current literature regarding edge/fog-based cybersecurity in IoT to display the current state.
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This article deals with automatic object recognition. The goal is that in a certain grey-level image, possibly containing many objects, a certain object can be recognized and localized, based upon its shape. The assumption is that this shape has no special characteristics on which a dedicated recognition algorithm can be based (e.g. if we know that the object is circular, we could use a Hough transform or if we know that it is the only object with grey level 90, we can simply use thresholding). Our starting point is an object with a random shape. The image in which the object is searched is called the Search Image. A well known technique for this is Template Matching, which is described first.
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Brochure from the Inauguration of Klaas Dijkstra, professor Computer Vision and Data Science
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![I’M A.I. IMage Processing and Artificial Intelligence [Inaugurele rede]](https://publinova-harvester-content-prod.s3.amazonaws.com/thumbnails/files/previews/pdf/20250506132441657988.LS20Brochure20Professorship20CVDS20FINAL20WEB2028129-thumbnail-400x300.png)
More people voted in 2024 than any other year in human history, while often relying on the internet for political information. This combination resulted in critical challenges for democracy. To address these concerns, we designed an exhibition that applied interactive experiences to help visitors understand the impact of digitization on democracy. This late-breaking work addresses the research questions: 1) What do participants, exposed to playful interventions, think about these topics? and 2) How do people estimate their skills and knowledge about countering misinformation? We collected data in 5 countries through showcases held within weeks of relevant 2024 elections. During visits, participants completed a survey detailing their experiences and emotional responses. Participants expressed high levels of self-confidence regarding the detection of misinformation and spotting AI-generated content. This paper contributes to addressing digital literacy needs by fostering engaging interactions with AI and politically relevant issues surrounding campaigning and misinformation.
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Lectorale rede waarin wordt ingegaan op de manier waarop de mens nu binnen zijn natuurlijke omgeving functioneert. Dit wordt getypeerd als een ‘mismatch’. Tegelijkertijd is de lector er ook van overtuigd dat de technologie uiteindelijk zorgt voor een beter leven.
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Van oorsprong worden televisieprogramma’s op een lineaire manier aangeboden aan kijkers: een omroep bepaalt in welke volgorde programma’s worden getoond. Dit verandert echter langzaam. Ondemand mogelijkheden via internet en settopboxen zorgen ervoor dat kijkers zelf kunnen bepalen wanneer ze welk programma willen kijken. De Nederlandse Publieke Omroep (NPO) speelt op die mogelijkheden in met onder meer de dienst Uitzending Gemist, een NPOapplicatie voor mobiele apparaten en verschillende themakanalen. Eén van die themakanalen is NPO Spirit. Ze biedt via internet ondemand video’s aan op het gebied van levensbeschouwing, spiritualiteit en diversiteit. Het aanbod is zo pluriform mogelijk. Dat wil zeggen dat verschillende religies en levensbeschouwingen naast elkaar worden aangeboden. NPO Spirit formuleert haar propositie dan ook als volgt: “NPO Spirit laat de kijker genieten en brengt op toegankelijke wijze (nieuwe) inzichten!" De uiteindelijke doelstelling van dit project is om méér mensen en meer verschillende groepen te bereiken. Het eindresultaat bestaat uit (1) een specifiek overzicht voor NPO Spirit van relevante trefwoorden, groepen ( hubs ) en sleutelfiguren ( influencers ) op internet, en (2) een algemene werkwijze om vanuit een organisatie of merk te bepalen welke groepen, trefwoorden en sleutelfiguren op het internet relevant zijn. Dit moet ertoe leiden dat NPO Spirit beter in staat raakt om video’s naar consumenten ‘toe te brengen’.
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Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
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Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for a given coordination graph is therefore a central subroutine in cooperative multi-agent reinforcement learning (MARL). Much research in MARL focuses on how to gradually update the parameters of the coordination graph, whilst leaving the solving of the coordination graph up to a known typically exact and generic subroutine. However, exact methods { e.g., Variable Elimination { do not scale well, and generic methods do not exploit the MARL setting of gradually updating a coordination graph and recomputing the joint action to select. In this paper, we examine what happens if we use a heuristic method, i.e., local search, to select joint actions in MARL, and whether we can use outcome of this local search from a previous time-step to speed up and improve local search. We show empirically that by using local search, we can scale up to many agents and complex coordination graphs, and that by reusing joint actions from the previous time-step to initialise local search, we can both improve the quality of the joint actions found and the speed with which these joint actions are found.
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