Cloud-based lighting system for smart cities

The aim of the project is a definition of novel multimodal system for intelligent lamps, providing functions of Internet of Things, integrated with a cloud computing service. The system will be proposed, built and evaluated. Optimal methods of environmental data acquisition from sensors and monitoring cameras will be developed and evaluated. Cloud and fog computing technologies will be applied for efficient data postprocessing, fusion and decision making. As a result the lighting conditions will be continuously monitored, and light emitting elements (LEDs, mirrors, lenses) optimally adjusted to provide required light temperature, intensity, directional characteristics. Project partners will build and examine hardware and communication layers, and Politechnika Gdanska will focus on data analysis layer: acquisition, pre-processing, synchronization, computations in cloud and fog architectures, decision making. PG will as well conduct a research on influence of light parameters on human. That will include ability of a driver to recognise objects on the road, reflex and reactions, well-being, and circadian rhythm. Algorithms of data processing and inference will form a set of basic services, combined into complex services by defining decision rules. All functions will be provided to an end user as a service: predefined applications and building blocks to facilitate creation of own solutions for smart city. Integration with external data sources is planned, including infrastructure of smart city and connected cars by a V2X (vehicle-to-everything) protocol. As a side result, the PG team will increase its competence in key technologies such as Internet of Things, programming of distributed applications, data analysis, fusion, and decision making.

Details

Project's acronym:

INFOLIGHT

Financial Program Name:

Program Operacyjny Inteligentny Rozwój

Organization:

Narodowe Centrum Badań i Rozwoju (NCBR) (The National Centre for Research and Development)

Agreement:

POIR.04.01.04-00-0075/19 z dnia 2019-09-24

Realisation period:

2020-04-01 - 2023-06-29

Project manager:

dr hab. inż. Piotr Szczuko

Team members:

manager dr hab. inż. Piotr Szczuko
Expert prof. dr hab. inż. Andrzej Czyżewski
Specialist dr inż. Piotr Odya
IT specialist mgr inż. Szymon Zaporowski
IT specialist mgr inż. Dawid Weber
Specialist dr inż. Arkadiusz Harasimiuk
Specialist dr hab. inż. Józef Kotus
Specialist mgr inż. Adam Kurowski
Specialist dr inż. Karolina Marciniuk
Specialist mgr inż. Paweł Spaleniak
Specialist dr inż. Maciej Szczodrak
Specialist dr hab. inż. Grzegorz Szwoch

Realised in:

Department of Multimedia Systems

External institutions participating in project:

Reva-Siled Sp. z o.o. (Poland)
Siled Sp. z o.o,. (Poland)

Project's value:

7 134 301.47 PLN

Request type:

Fundusz Strukturalny

Domestic:

Domestic project

Verified by:

No verification

Papers associated with that project

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total: 3

Year 2021

Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift
Publication
- Year 2021
While recent computer vision algorithms achieve impressive performance on many benchmarks, they lack robustness - presented with an image from a different distribution, (e.g. weather or lighting conditions not considered during training), they may produce an erroneous prediction. Therefore, it is desired that such a model will be able to reliably predict its confidence measure. In this work, uncertainty estimation for the task...

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Estimation of Average Speed of Road Vehicles by Sound Intensity Analysis
Publication
- J. Kotus
- G. Szwoch
- SENSORS - Year 2021
Constant monitoring of road traffic is important part of modern smart city systems. The proposed method estimates average speed of road vehicles in the observation period, using a passive acoustic vector sensor. Speed estimation based on sound intensity analysis is a novel approach to the described problem. Sound intensity in two orthogonal axes is measured with a sensor placed alongside the road. Position of the apparent sound...

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Year 2020

Toward Robust Pedestrian Detection With Data Augmentation
Publication
- S. Cygert
- A. Czyżewski
- IEEE Access - Year 2020
In this article, the problem of creating a safe pedestrian detection model that can operate in the real world is tackled. While recent advances have led to significantly improved detection accuracy on various benchmarks, existing deep learning models are vulnerable to invisible to the human eye changes in the input image which raises concerns about its safety. A popular and simple technique for improving robustness is using data...

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