Cargando…
Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets †
Accurate detection and tracking of moving targets in underwater environments pose significant challenges, because noise in acoustic measurements (e.g., SONAR) makes the signal highly stochastic. In continuous marine monitoring a further challenge is related to the computational complexity of the sig...
Autores principales: | Testolin, Alberto, Diamant, Roee |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287689/ https://www.ncbi.nlm.nih.gov/pubmed/32456024 http://dx.doi.org/10.3390/s20102945 |
Ejemplares similares
-
Automated detection of dolphin whistles with convolutional networks and transfer learning
por: Nur Korkmaz, Burla, et al.
Publicado: (2023) -
Underwater Acoustic Target Tracking: A Review
por: Luo, Junhai, et al.
Publicado: (2018) -
Sparse Convolutional Denoising Autoencoders for Genotype Imputation
por: Chen, Junjie, et al.
Publicado: (2019) -
A New Underwater Acoustic Signal Denoising Technique Based on CEEMDAN, Mutual Information, Permutation Entropy, and Wavelet Threshold Denoising
por: Li, Yuxing, et al.
Publicado: (2018) -
A Graph Localization Approach for Underwater Sensor Networks to Assist a Diver in Distress †
por: Diamant, Roee, et al.
Publicado: (2021)