• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer

Center for Artificial Intelligence and Cybersecurity – AIRI

  • Home
  • About Us
    • Center Activities
    • Vision, Mission and Goals
    • Center Faculty
    • Steering Committee
    • Press
  • Research
    • Scientific Projects
    • Research Papers
  • Laboratories
    • Machine Learning
    • Natural Speech & Language Processing
    • Blockchain Technology
    • Information Processing & Pattern Recognition
    • AI in Medicine
    • Data Mining
    • Computer Vision
    • Complex Networks
    • Human-Computer Interaction
    • Maritime Cybersecurity
    • Autonomous Navigation
    • AI in Mechatronics
    • AI in Education
    • Hybrid Computational Methods
    • Drug Design
    • Legal Aspects of AI
    • Ethically Aligned AI
    • Cultural Complexity
  • Collaboration
    • Industry Collaboration
    • Industry Projects
    • International Collaboration
  • News
  • Contact

Improved Parametrized Multiple Window Spectrogram with Application in Ship Navigation Systems

03.03.2022

In analyzing non-stationary noisy signals with time-varying frequency content, it’s convenient to use distribution methods in joint, time and frequency, domains. Besides different adaptive data-driven time-frequency (TF) representations, the approach with multiple orthogonal and optimally concentrated Hermite window functions is an effective solution to achieve a good trade-off between low variance and minimized stable bias estimates. In this paper, we propose a novel spectrogram method with multiple optimally parameterized Hermite window functions, with parameterization which includes a pair of free parameters to regulate the shape of the window functions. The computation is performed in the optimization process to minimize the variable projection (VP) functional problem. The proposed parametrized distribution method improves TF concentration and instantaneous frequency (IF) estimation accuracy, as shown in experimental results for synthetic signals and real-life ship motion response signals. With the optimization of nonlinear least-squares approximation of the ship response signals, the Hermite spectra are centralized, and only up to 15 basis functions are sufficient for concentration improvement in the TF domain.

Authors:
Denis Selimović, Jonatan Lerga, Péter Kovács, Jasna Prpić-Oršić
Journal:
Digital Signal Processing
Publishing date:
25.02.2022
View original article

Primary Sidebar

Latest Projects

Transversal Skills in Applied Artificial Intelligence (TSAAI)

INNO2MARE – Strengthening the capacity for excellence of Slovenian and Croatian innovation ecosystems to support the digital and green transitions of maritime regions

European Digital Innovation Hub Adria Croatia

ABsistemDCiCloud

Machine Learning for Knowledge Transfer in Medical Radiology

Latest Research Papers

Fracture Recognition in Paediatric Wrist Radiographs: An Object Detection Approach

Rapid extraction of skin physiological parameters from hyperspectral images using machine learning

Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals

Latest News

Recognition of the Faculty of Information and Digital Technologies

Assoc. prof. Jonatan Lerga received the Croatian Academy of Sciences and Arts award

Dr. Sc. Nikola Lopac successfully defended his doctoral dissertation

Presentation at the conference “Digital Innovation and Technology for People”

Assoc. prof. dr. sc. Jonatan Lerga presented AIRI Center at the IEEE Rijeka : Computer Society Congress 2021

We provide the expertise for solving real world problems using AI

If your company wants to implement artificial intelligence in your products or services, or increase your level of cybersecurity, our multidisciplinary team of scientists is your ideal partner.

Contact us

Footer

Center for Artificial Intelligence and Cybersecurity
  • jlerga@airi.uniri.hr
  • +385 51 406 500

University of Rijeka

University of Rijeka

About the Center

  • About Us
  • News
  • Privacy Policy
  • Contact

Center Activities

  • Laboratories
  • Scientific Projects
  • Industry Projects
  • Research Papers
  • Industry Collaboration
  • International Collaboration

Footer bottom left

© 2020 Center for Artificial Intelligence and Cybersecurity, all rights reserved.

Designed & developed by Nela Dunato Art & Design