Search

Empirical studies have demonstrated that humans possess the remarkable capacity to distinguish whether a glass of water is hot or cold solely by the sound of pouring it. However, the underlying physical mechanisms governing the disparities in the acoustic signatures of hot versus cold water remain to be deciphered. In this paper, we conducted a series of experiments to extract the intrinsic features of pouring sounds at contrasting temperatures. The results of our spectral analysis revealed that the sound of pouring hot water exhibited more pronounced low-frequency components and diminished high-frequency components relative to cold water. High-speed photographic evidence elucidated that pouring hot water could generate larger air bubbles in greater abundance. We conjecture that the Minnaert resonance arising from these larger entrained bubbles in hot water produces a lower-frequency acoustic signature, thereby constituting the foundational mechanistic explanation for the auditory distinction between pouring hot and cold water.

Reconfigurable intelligent surfaces (RISs) have become a promising technology to meet the requirements of energy efficiency and scalability in future six-generation (6G) communications. However, a significant challenge in RISs-aided communications is the joint optimization of active and passive beamforming at base stations (BSs) and RISs respectively. Specifically, the main difficulty is attributed to the highly non-convex optimization space of beamforming matrices at both BSs and RISs, as well as the diversity and mobility of communication scenarios. To address this, we present a greenly gradient based meta learning beamforming (GMLB) approach. Unlike traditional deep learning based methods which take channel information directly as input, GMLB feeds the gradient of sum rate into neural networks. Coherently, we design a differential regulator to address the phase shift optimization of RISs. Moreover, we use the meta learning to iteratively optimize the beamforming matrices of BSs and RISs. These techniques make the proposed method to work well without requiring energy-consuming pre-training. Simulations show that GMLB could achieve higher sum rate than that of typical alternating optimization algorithms with the energy consumption by two orders of magnitude less., Comment: 5 pages, 8 figures. Accepted in IEEE ICC 2024 (GCSN symposium)