• 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
    • Vision, Mission and Goals
    • Center Activities
    • 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

A Fast Signal Denoising Algorithm Based on the LPA-ICI Method for Real-Time Applications

27.03.2017

We propose a new algorithm for denoising of additive white Gaussian noise-corrupted signals, based on the intersection of confidence intervals (ICI) algorithm, called the fast intersection of confidence intervals (FICI) algorithm. The proposed approach combines the FICI algorithm, used for the adaptive filter support size selection, with the local polynomial approximation (LPA) method, used as a filter design tool. The LPA-FICI method, when compared to the existing ICI-based denoising method, reduces the computational complexity by up to N times, where N is the number of signal samples, resulting in significantly faster algorithm execution time, while maintaining the estimation accuracy close to the one achieved using the original ICI-based method. Furthermore, the proposed modifications allow the use of the LPA-FICI method in real-time signal processing. In conducted simulations, we have confirmed advantages of the proposed method on two commonly used benchmark signals corrupted with various noise strengths.

The research was conducted at the Faculty of Engineering, University of Rijeka (www.riteh.uniri.hr).

Authors:
Ivan Volaric, Jonatan Lerga, Victor Sucic
Journal:
Circuits, Systems, and Signal Processing
Publishing date:
27.03.2017
View original article

Primary Sidebar

Latest Projects

ABsistemDCiCloud

Machine Learning for Knowledge Transfer in Medical Radiology

Estimating River Discharges in Highly Stratified Estuaries

Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis (InfoCoV)

European Network for assuring food integrity using non-destructive spectral sensors

Latest Research Papers

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Entropy-Based Concentration and Instantaneous Frequency of TFDs from Cohen’s, Affine, and Reassigned Classes

Coupled encoding methods for antimicrobial peptide prediction: How sensitive is a highly accurate model?

The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data

Improved Parametrized Multiple Window Spectrogram with Application in Ship Navigation Systems

Latest News

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

Prof. dr. sc. Ana Mestrovic participated at the Panel on perspectives and real-life applications of AI organized by IEEE Technology and Engineering Management Society

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