Cloud-based lighting system for smart cities - Project - Bridge of Knowledge

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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.
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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:
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:
European Founds
Domestic:
Domestic project
Verified by:
No verification

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Year 2023

  • Autoencoder application for anomaly detection in power consumption of lighting systems
    Publication

    - IEEE Access - Year 2023

    Detecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning...

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  • Detection of Water on Road Surface with Acoustic Vector Sensor
    Publication

    - SENSORS - Year 2023

    This paper presents a new approach to detecting the presence of water on a road surface, employing an acoustic vector sensor. The proposed method is based on sound intensity analysis in the frequency domain. Acoustic events, representing road vehicles, are detected in the sound intensity signals. The direction of the incoming sound is calculated for the individual spectral components of the intensity signal, and the components...

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Year 2022

  • Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters
    Publication

    - ENERGIES - Year 2022

    Smart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challenges with respect to the monitoring function. This article presents research results indicating the practical feasibility of real‐time...

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  • Machine learning applied to acoustic-based road traffic monitoring
    Publication

    - Year 2022

    The motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...

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  • Robust Object Detection with Multi-input Multi-output Faster R-CNN
    Publication

    - Year 2022

    Recent years have seen impressive progress in visual recognition on many benchmarks, however, generalization to the out-of-distribution setting remains a significant challenge. A state-of-the-art method for robust visual recognition is model ensembling. However, recently it was shown that similarly competitive results could be achieved with a much smaller cost, by using multi-input multi-output architecture (MIMO). In this work,...

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Year 2021

  • Acoustic Detector of Road Vehicles Based on Sound Intensity
    Publication

    - SENSORS - Year 2021

    A method of detecting and counting road vehicles using an acoustic sensor placed by the road is presented. The sensor measures sound intensity in two directions: parallel and perpendicular to the road. The sound intensity analysis performs acoustic event detection. A normalized position of the sound source is tracked and used to determine if the detected event is related to a moving vehicle and to establish the direction of movement....

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  • Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift

    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

    - 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

    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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