dr inż. Tomasz Kocejko | Gdańsk University of Technology

Page content

dr inż. Tomasz Kocejko

Contact:

email:
tomkocej@pg.edu.pl
website:
https://mostwiedzy.pl/tomasz-kocejko,539630-1

Positions:

Assistant professor

dr inż. Tomasz Kocejko

Publications:

  1. Publication
    • M. Romanowska-Kocejko
    • A. Braczko
    • A. Jędrzejewska
    • M. Żarczyńska-Buchowiecka
    • T. Kocejko
    • B. Kutryb-zając
    • M. Hellmann

    - MICROVASCULAR RESEARCH - Year 2025

    Long COVID is a complex pathophysiological condition. However, accumulating data suggests that COVID-19 is a systemic microvascular endothelial dysfunction with different clinical manifestations. In this study, a microvascular function was assessed in long COVID patients (n = 33) and healthy controls (n = 30) using flow-mediated skin fluorescence technique (FMSF), based on measurements of nicotinamide adenine dinucleotide fluorescence...

    Full text to download in external service

  2. Publication

    This study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. The primary objective is to explore an enhanced method for estimating FH probability, surpassing the currently recommended Dutch Lipid Clinic Network (DLCN) Score. The models were trained using the largest Polish cohort...

    Full text to download in external service

  3. Publication

    - Year 2024

    Pedestrians, as vulnerable road users, pose safety challenges for autonomous vehicles (AVs). Their behavior, often unpredictable and subject to change, complicates AV-pedestrian interactions. To address this uncertainty, AV s can enhance safety by communicating their planned trajectories to pedestrians. In this research, we explore the interaction between pedestrians and autonomous vehicles within an industrial environment, focusing...

    Full text to download in external service

  4. Publication

    - Year 2024

    This paper presents the development and preliminary testing of a fall detection algorithm that leverages OpenPose for real-time human pose estimation from video feeds. The system is designed to function optimally within a range of up to 7 meters from ground-level cameras, focusing exclusively on detected human silhouettes to enhance processing efficiency. The performance of the proposed approach was evaluated using accuracy values...

    Full text to download in external service

  5. Publication

    - Year 2024

    This paper introduces a Smart City solution designed to run on edge devices, leveraging NVIDIA's DeepStream SDK for efficient urban surveillance. We evaluate five object-tracking approaches, using YOLO as the baseline detector and integrating three Nvidia DeepStream trackers: IOU, NvSORT, and NvDCF. Additionally, we propose a custom tracker based on Optical Flow and Kalman filtering. The presented approach combines advanced machine...

    Full text to download in external service

data from Bridge of Knowledge open in new tab Bridge of Knowledge

Projects: