Classifying marine mammals signal using cubic splines interpolation combining with triple loss variational auto-encoder.

In practical applications of passive sonar principles for extracting characteristic frequencies of acoustic signals, scientists typically employ traditional time-frequency domain transformation methods such as Mel-frequency, Short time Fourier transform (STFT), and Wavelet transform (WT). However, these solutions still face limitations in resolution and information loss when transforming data collected over extended periods. In this paper, we present a study using a two-stage approach that combines pre-processing by Cubic-splines interpolation (CSI) with a probability distribution in the hidden space with Siamese triple loss network model for classifying marine mammal (MM) communication signals.

Bacterial Cellulose Aerogels Derived from Pineapple Peel Waste for the Adsorption of Dyes.

Valorization of pineapple peel waste is an attractive research topic because of the huge quantities of this byproduct generated from pineapple processing industries. In this study, the extract from pineapple waste was collected to produce a hydrogel-like form containing bacterial cellulose fibers with a three-dimensional structure and nanoscale diameter by the fermentation process. The bacterial cellulose suspension was subsequently activated by freeze-drying, affording lightweight aerogels as potential adsorbents in wastewater treatment, in particular the adsorptive removal of organic dyes.

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network.

This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure.