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Highly Accurate and Autonomous Programmable Platform for Providing Air Pollution Data Services to Drivers and Public

Highly Accurate and Autonomous Programmable Platform for Providing Air Pollution Data Services to Drivers and the Public (HAPADS) is ambitious project that will custom design and build a novel smart and autonomous air monitoring platform which, will enable end-users (drivers, transport companies, municipalities and the at-large public) to make information-driven decisions to mitigate air pollution exposure for the people. One of the objectives of the project is the development of novel detectors such as microwave-based NO2 sensor and time-delay-integration (TDI) image-based particulate matter (PM) sensor that can be used both in-vehicle and on-vehicle. Moreover, a programmable multiprocessor hardware for data acquisition and signal processing with parallel edge computing and deep learning algorithms support will be developed together with embedded software with air pollution calibration modeling and multi-objective optimization. Data quality of low-cost sensors is often questionable, therefore existing air quality sensors must be manually calibrated for a given deployment site, making them unsuitable for mobile deployment. HAPADS will devise and implement specialized embedded software for mobile MPs that are automatically self-calibrated for a new deployment location. The mobile sensors can be used to provide the data to public entities to make a pollution map in near real time. The main parts of the project, such as PM sensors or Mobile Platform (in particular implementation of particle detectors in the form of specialized ASIC integrated circuits and the implementation of a modern mobile computing platform with optimized algorithms), will be implemented at GUT. Cooperation with other scientific and research institutions, including reputable universities and institutions from Poland and Norway and institutions specializing in air quality monitoring, will allow the staff to gain further scientific and practical experience and to present the results of their work in the form of high-quality publications.

Details

Project's acronym:
HAPADS
Financial Program Name:
EEA and Norway grants - Applied Research Programme
Organization:
Narodowe Centrum Badań i Rozwoju (NCBR) (The National Centre for Research and Development)
Agreement:
NOR/POLNOR/HAPADS/0049/2019-00 z dnia 2021-05-18
Realisation period:
2020-10-01 - 2023-10-01
Project manager:
dr hab. inż. Marek Wójcikowski
Team members:
Realised in:
Department of Microelectronic Systems
External institutions
participating in project:
  • Norwegian Institute for Air Research (Norway)
  • Wroclaw University of Science and Technology (Poland)
  • Logistics Enhancement Systems and Services Sp. z o.o. (Poland)
  • AGH University of Science and Technology (Poland)
  • University of Tromsø – The Arctic University of Norway (Norway)
Project's value:
6 999 075.00 PLN
Request type:
International Research Programmes
Domestic:
International project
Verified by:
Gdańsk University of Technology

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Catalog Projects

Year 2023

  • Accurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensors
    Publication

    - Year 2023

    Monitoring air pollution is a critical step towards improving public health, particularly when it comes to identifying the primary air pollutants that can have an impact on human health. Among these pollutants, particulate matter (PM) with a diameter of up to 2.5 μ m (or PM2.5) is of particular concern, making it important to continuously and accurately monitor pollution related to PM. The emergence of mobile low-cost PM sensors...

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  • Adaptive Wavelet-Based Correction of Non-Anechoic Antenna Measurements
    Publication

    - Year 2023

    Non-anechoic measurements represent an affordable alternative to evaluation of antenna performance in expensive, dedicated facilities. Due to interferences and noise from external sources of EM radiation, far-field results obtained in non-ideal conditions require additional post-processing. Conventional correction algorithms rely on manual tuning of parameters, which make them unsuitable for reliable testing of prototypes. In this...

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