Augmenting Aquaculture Efficiency through Involutional Neural Networks and Self-Attention for Oplegnathus Punctatus Feeding Intensity Classification from Log Mel Spectrograms.

Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and deep learning architecture. Raw audio waveforms are first transformed into Log Mel Spectrograms, and a fusion of features such as the Discrete Wavelet Transform, the Gabor filter, the Local Binary Pattern, and the Laplacian High Pass Filter, followed by a well-adapted deep model, is proposed to capture crucial spectral and spectral information that can help distinguish between the various forms of fish feeding behavior.

Exploring Vibration Transmission Rule of an Artificial Spider Web for Potential Application in Invulnerability of Wireless Sensor Network.

Significant similarities exist between a spider web and wireless sensor network in terms of topology. Combining the unique advantages of the spider web in nature, such as invulnerability and robustness, with communication technology of a wireless sensor network presents high research value and broad development prospects. In this paper, a sort of a spiral artificial spider web based on 3D printing and its associated vibration testing device is proposed, which is used to study the transmission rule of vibration information of the spider web under given excitation conditions.