E2E-WAVE achieves +5 dB PSNR and real-time 16 FPS 128x128 video over 2.3 kbps underwater channels by learning waveforms that favor semantic similarity on decoding errors.
Underwater acoustic sensor networks: research challenges,
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Derives closed-form solutions for KL-divergence belief merging and a visit-weighted variant, reducing complexity to O(N|S|) and outperforming standard methods in simulations with noisy sensors and long communication gaps.
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E2E-WAVE: End-to-End Learned Waveform Generation for Underwater Video Multicasting
E2E-WAVE achieves +5 dB PSNR and real-time 16 FPS 128x128 video over 2.3 kbps underwater channels by learning waveforms that favor semantic similarity on decoding errors.
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Robust Multi-Agent Target Tracking in Intermittent Communication Environments via Analytical Belief Merging
Derives closed-form solutions for KL-divergence belief merging and a visit-weighted variant, reducing complexity to O(N|S|) and outperforming standard methods in simulations with noisy sensors and long communication gaps.