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• Realization of reception tests of multiple types of target signals using the DASF. • Given the specific experimental procedure for detecting underwater sound using DASF. • Analyzed the characteristics of DASF receiving underwater signals. The distributed acoustic sensing (DAS) technology based on phase-sensitive optical time domain reflectometry (Φ-OTDR) has been widely and well applied on land. Meanwhile, the distributed acoustic sensing optical fiber (DASF) based on Φ-OTDR, as a new type of sensor, is still in the initial stage of research, gets much attention in recent years because of its long-distance monitoring, high sensitivity, anti-electromagnetic interference, good concealment and so on. Performances of DASF in sensing different kinds of acoustic source signals were given by experiments in shallow water, which included single frequency pulse signal in the fixed position, the moving single frequency continuous pulse signal, the moving broadband signal, and the signal generated by the sailing surface target. Depth of the active sound source was changed to examine the performance of DASF in sensing signals in different depths. The specific experimental process was given, and the experimental results were processed and analyzed. Additionally, wave components of data generated from of the sailing surface vessel were explored with the time delay difference between the equivalent array elements of DASF. Results show that DASF can sense the above varieties of underwater acoustic signals well, and the physical field generated by the sailing surface vessel in shallow water include acoustic field and flow field, in which the propagation velocity of the former is faster than that of the latter. [ABSTRACT FROM AUTHOR]

Structural superlubricity, which results from the cancelation of lateral force between contacted smooth solid surfaces with weak interaction, achieves ultralow friction and wear, inspiring disruptive innovations in applications on microscale. It is generally believed that this phenomenon requires the contact interface being ultra-clean, ensured by either a clean environment or mechanical cleaning of the contact. However, in this paper, the experimental observation of structural superlubricity with a contaminant-rich interface is unambiguously observed. After intentionally introducing airborne contaminants into microscale structural superlubric incommensurate graphite contacts, we find that the important features of structural superlubricity, the nearly-zero friction coefficient, ultralow friction, and symmetry in friction, are all well preserved. Moreover, contaminants are found to reduce the friction of a commensurate contact but increase the friction of an incommensurate contact, i.e., playing opposite roles. A theoretical model is proposed to fully grasp the contaminant effect. Our results bring challenges to the current understanding about the mechanism of structural superlubricity and provide solid support for its application under practical conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Emotions are recognized using the occurrences of auto-detected phonological units. • Phonemes are clustered together based on the similarity of formant characteristics. • Experiment results indicate reduced computational cost and increased robustness. Speech Emotion Recognition (SER) has numerous applications including human-robot interaction, online gaming, and health care assistance. While deep learning-based approaches achieve considerable precision, they often come with high computational and time costs. Indeed, feature learning strategies must search for important features in a large amount of speech data. In order to reduce these time and computational costs, we propose pre-processing step in which speech segments with similar formant characteristics are clustered together and labeled as the same phoneme. The phoneme occurrence rates in emotional utterances are then used as the input features for classifiers. Using six databases (EmoDB, RAVDESS, IEMOCAP, ShEMO, DEMoS and MSP-Improv) for evaluation, the level of accuracy is comparable to that of current state-of-the-art methods and the required training time was significantly reduced from hours to minutes. [ABSTRACT FROM AUTHOR]