Pregled bibliografske jedinice broj: 1030208
Signal Feature Recognition in Time-Frequency Domain Using Edge Detection Algorithms
Signal Feature Recognition in Time-Frequency Domain Using Edge Detection Algorithms // Proceedings of the 4th International Conference on Smart and Sustainable Technologies (SpliTech 2019) / Perković, Toni ; Vukojević, Katarina ; Rodrigues, Joel ; Nižetić, Sandro ; Patrono, Luigi ; Šolić, Petar (ur.).
Piscataway (NJ): Institute of Electrical and Electronics Engineers (IEEE), 2019. 1, 4 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1030208 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Signal Feature Recognition in Time-Frequency Domain Using Edge Detection Algorithms
Autori
Milanović, Željka ; Saulig, Nicoletta ; Marasović, Ivan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 4th International Conference on Smart and Sustainable Technologies (SpliTech 2019)
/ Perković, Toni ; Vukojević, Katarina ; Rodrigues, Joel ; Nižetić, Sandro ; Patrono, Luigi ; Šolić, Petar - Piscataway (NJ) : Institute of Electrical and Electronics Engineers (IEEE), 2019
ISBN
978-953-290-091-0
Skup
4th International Conference on Smart and Sustainable Technologies (SpliTech)
Mjesto i datum
Bol, Hrvatska; Split, Hrvatska, 18.06.2019. - 21.06.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Computer vision ; edge detection ; Image segmentation ; Time-frequency domain ; denoising ; nonstationary signals
Sažetak
We propose an approach to discern components in multicomponent, stationary and nonstationary signals by application of edge detection techniques to the time-frequency (TF) plane. The approach is based upon the use of a robust to noise computer vision edge detection algorithms, which can be used to precisely mark the position of the component in the TF plane independent of its time or frequency support. The results show the proposed method correctly detects positions of stationary signals with low error even in signals heavily corrupted by Additive White Gaussian Noise (AWGN) and other color noise environments, tested for Signal-to-Noise Ratio (SNR) of 0dB and 6dB. Positions of stationary components in the TF plane are detected with error of 2\% and nonstationary with error of 6%. Results with synthetic signals and a real-life signal (bat-echolocation) indicate that the method can be used in identifying components in noisy environments using a computationally less costly method that outperforms previously proposed methods by offering faster computational speed and smaller processor workload.
Izvorni jezik
Engleski
