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- W4387087517 abstract "Wireless underwater acoustic networking is a key technology with many applications in military and commercial settings. Unlike their radio-frequency (RF) counterpart, underwater acoustic communications are constrained by the limited bandwidth, and impaired by stronger Doppler spread, interference, and frequency selective fading. Furthermore, the underwater channel is completely unregulated. To enable successful underwater spectrum access, spectrum sensing techniques are an important tool to separate ambient interference and undesirable signals from real, desired, transmitted signals. For these reasons, this article introduces SonAIr (Sonar + AI), a real-time system that utilizes Deep Learning (DL) techniques to classify and localize signals in the underwater channel. We collected and curated a real-world underwater acoustic signal dataset spanning 7 days in 2 different locations and multiple transceiver configurations, which will be made publicly available. Additionally, we designed and trained a novel two-stage Convolutional Neural Network (CNN) to identify signal modulations and bandwidths with an Intersection-Over-Union (IOU) as high as 0.89 on days unseen during training. Furthermore, we implemented and validated our classifier in real-time with an underwater testbed, achieving a CNN processing latency of 17 ms." @default.
- W4387087517 created "2023-09-28" @default.
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- W4387087517 date "2023-06-01" @default.
- W4387087517 modified "2023-09-28" @default.
- W4387087517 title "SonAIr: Real-Time Deep Learning For Underwater Acoustic Spectrum Sensing and Classification" @default.
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- W4387087517 doi "https://doi.org/10.1109/dcoss-iot58021.2023.00011" @default.
- W4387087517 hasPublicationYear "2023" @default.
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