The Amsterdam Sensor Lab is part of the Amsterdam University of Applied Sciences (AUAS) and its goal is to develop application specific sensor systems for applied research. In order to (anonymously) measure, for instance, traffic without influencing people’s behaviour, a pressure sensing sub-tile is under development. It can be placed under a regular (0.3*0.3 m) tile in the pavement and, hence, cannot be seen by the public. Applications may range from evaluating the behaviour of pedestrians in crowds or on large open areas, to measuring the mechanical stress on bridges due to lorry traffic. The resulting data may be valuable to social scientists and municipal decision makers.A preliminary demonstration model has been realized that can detect: weight (pressure), direction, and a speed estimate of pedestrians and cyclists, by measuring the direction and velocity of pressure changes. Data communication is wireless, e.g., via Bluetooth™, to a Raspberry Pi™ or computer for calibration and visualization of the data. The demonstration model has been working satisfactorily for about half a year in the corridors of the AUAS.Pressure changes are measured with strain gauges using low-noise analogue instrumentation amplifiers and digitized with a 16 bit effective resolution. Current consumption is about 50 mA, the minimal detectable pressure is ca. 10 N and the maximal pressure ca. 1500 N. The data is refreshed every 2 ms.New electronics for a second version of the sub-tile (under development) make it possible to detect the tiny signal of a 0.3 gram rubber object falling from a 10 cm height. Investigations and development are going on to increase the measurement range from this low-level (impulse) pressure up to a pressure of about 500 kN, and configuring multiple sub-tiles to a wireless sensor network, thus paving the way to a (smart) sensing pavement. Apart from that, possibilities to give an estimate of the kind of traffic using artificial intelligence will be investigated.
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De openbare ruimte staat vol borden waarmee (vooral) de overheid ons gedrag wil sturen. Die borden vertellen ongewild ook een verhaal over de mensen, de organisatie, het beleid erachter. Dat gaat soms goed, soms minder goed.
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This study evaluates the maximum theoretical exposure to radiofrequency (RF) electromag- netic fields (EMFs) from a Fifth-generation (5G) New Radio (NR) base station (BS) while using four commonly used mobile applications: YouTube for video streaming, WhatsApp for voice calls, Instagram for posting pictures and videos, and running a Video game. Three factors that might affect exposure, i.e., distance of the measurement positions from the BS, measurement time, and induced traffic, were examined. Exposure was assessed through both instantaneous and time-averaged extrapolated field strengths using the Maximum Power Extrapolation (MPE) method. The former was calculated for every measured SS-RSRP (Secondary Synchronization Reference Signal Received Power) power sample obtained with a sampling resolution of 1 second, whereas the latter was obtained using a 1-min moving average applied on the applications’ instantaneous extrapolated field strengths datasets. Regarding distance, two measurement positions (MPs) were selected: MP1 at 56 meters and MP2 at 170 meters. Next, considering the measurement time, all mobile application tests were initially set to run for 30 minutes at both MPs, whereas the video streaming test (YouTube) was run for an additional 150 minutes to investigate the temporal evolution of field strengths. Considering the traffic, throughput data vs. both instantaneous and time-averaged extrapolated field strengths were observed for all four mobile applications. In addition, at MP1, a 30-minute test without a User Equipment (UE) device was conducted to analyze exposure levels in the absence of induced traffic. The findings indicated that the estimated field strengths for mobile applications varied. It was observed that distance and time had a more significant impact than the volume of data traffic generated (throughput). Notably, the exposure levels in all tests were considerably lower than the public exposure thresholds set by the ICNIRP guidelines.INDEX TERMS 5G NR, C-band, human exposure assessment, mobile applications, traffic data, maximum extrapolation method, RF-EMF.
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In 2021, Citython editions were held for the European cities of Eindhoven (Netherlands), Bilbao and Barcelona (Spain), Hamburg (Germany), and Lublin (Poland). Within this project, BUAS contributed to the organization of CITYTHON Eindhoven in cooperation with CARNET (an initiative by CIT UPC) and City of Eindhoven – an event which gives young talent the opportunity to work with mentors and experts for the development of innovative urban solutions. Participants of CITYTHON Eindhoven worked on three challenges:- Traffic safety in school zones - Travel to the campus- Make the city healthy The event took place between 18 May and 2 June 2021 with various experts, for example from ASML, City of Eindhoven and University of Amsterdam, giving inspirational talks and mentoring students throughout the ideation and solutions development process. The teams presented their solutions during the Dutch Technology Week and the winners were announced by Monique List-de Roos (Alderman Mobility and Transport, City of Eindhoven) on 2 June 2021. The role of BUAS within this project was to assist City of Eindhoven with the development of the challenges to be tackled by the participating teams, and find relevant speakers and mentors who would be supporting the students for the development of their solutions and jury members who would determine the winning teams. The project ended with a round table “Green and Safe Mobility for all: 5 Smart City(thon) Case studies” on November 17 organized as part of Smart City Expo World Congress 2021 in Barcelona. This project is funded by EIT Urban Mobility, an initiative of the European Institute of Innovation and Technology (EIT), a body of the European Union. EIT Urban Mobility acts to accelerate positive change on mobility to make urban spaces more livable. Learn more: eiturbanmobility.eu.Collaborating partnersCARNET (Lead organisation); Barcelona Institute of Technology for Habitat; Barcelona City Council; Bilbao City Hall; City of Hamburg; City of Eindhoven,; City of Lublin; Digital Hub Logistics Hamburg; Technical University of Catalonia, Tecnalia; UPC Technology Center.
The SPRONG group, originating from the CoE KennisDC Logistiek, focuses on 'Low Impact in Lastmile Logistics' (LILS). The LILS group conducts practical research with local living labs and learning communities. There is potential for more collaboration and synergy for nationwide scaling of innovations, which is currently underutilized. LILS aims to make urban logistics more sustainable and facilitate necessary societal transitions. This involves expanding the monodisciplinary and regional scope of CoE KennisDC Logistiek to a multidisciplinary and supra-regional approach, incorporating expertise in spatial planning, mobility, data, circularity, AI, behavior, and energy. The research themes are:- Solutions in scarce space aiming for zero impact;- Influencing behavior of purchasers, recipients, and consumers;- Opportunities through digitalization.LILS seeks to increase its impact through research and education beyond its regions. Collaboration between BUas, HAN, HR, and HvA creates more critical mass. LILS activities are structured around four pillars:- Developing a joint research and innovation program in a roadmap;- Further integrating various knowledge domains on the research themes;- Deepening methodological approaches, enhancing collaboration between universities and partners in projects, and innovating education (LILS knowledge hub);- Establishing an organizational excellence program to improve research professionalism and quality.These pillars form the basis for initiating and executing challenging, externally funded multidisciplinary research projects. LILS is well-positioned in regions where innovations are implemented and has a strong national and international network and proven research experience.Societal issue:Last-mile logistics is crucial due to its visibility, small deliveries, high costs, and significant impact on emissions, traffic safety, and labor hours. Lastmile activities are predicted to grow a 20% growth in the next decade. Key drivers for change include climate agreements and energy transitions, urban planning focusing on livability, and evolving retail landscapes and consumer behavior. Solutions involve integrating logistics with spatial planning, influencing purchasing behavior, and leveraging digitalization for better data integration and communication. Digital twins and the Physical Internet concept can enhance efficiency through open systems, data sharing, asset sharing, standardization, collaboration protocols, and modular load units.Key partners: Buas, HR, HAN, HvAPartners: TNO, TU Delft, Gemeente Rotterdam, Hoger Onderwijs Drechtsteden, Significance, Metropolitan Hub System, evofenedex, Provincie Gelderland, Duurzaam Bereikbaar Heijendaal, Gemeente Alphen aan den Rijn, Radboud Universiteit, I&W - DMI, DHL, TLN, Noorderpoort, Fabrications, VUB, Smartwayz, RUG, Groene Metropoolregio.
Bicycle manufacturing currently falls behind the fast technological developments in automotive industries. We propose to design, develop and test a smart cycling eco-system where bicycles communicate in realtime with each other, and with the urban transport infrastructure (e.g. traffic lights) to optimize the use and improve traffic safety, economical value, and efficiency. This require technologies and mechanisms to allow monitoring the bike, understanding the cyclist and the context, as well as data sharing between cyclists, industry, service providers, government, and urban planners. The new eco-system can drive decision-making, behaviour incentivisation, and ultimately investment, across government, and beyond. A key ingredient is an AI-enabled IoT ecosystem in which data is securely collected, shared, processed in combination with other data sources, and made available to establish new services. This allows to reliably identify relevant events (like dangerous situations), detect trends (like decreasing performance of components, allowing maintenance to be performed in time), and give new insights to the user (such as health and performance).
