Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

As a typical p-type semiconductor gas-sensing material, LaFeO₃ has good response stability to H₂S, but its responsiveness is low, and the detection limit is not low enough for large-scale use in the field of gas sensors. To obtain better performance, we synthesized Pd modified LaFeO₃ using the sol–gel method. A total of 3 wt% of Pd–LaFeO₃ with a high specific surface area had the highest response to H₂S (36.29–1 ppm) at 120 °C, with relatively fast response–recovery times (19.62/15.22 s), and it had higher selectivity to H₂S with other gases. Finally, we detected the H₂S concentrations in the air around the shrimps, and the H₂S concentrations that we obtained by the 3 wt% Pd–LaFeO₃ in this study were within 10% of those obtained by GC–MS. According to the experimental results, noble-metal surface modification improves the performance of gas-sensing materials, and Pd–LaFeO₃ has considerable potential in H₂S detection.

Direction-of-arrival (DOA) mismatch can degrade the performance of adaptive beamforming algorithms. Thus, a projection method is proposed to correct this mismatch. In a beamforming algorithm, the DOA error is usually regarded as a steering vector error which is corrected using a steering vector optimization algorithm. This approach can provide an optimal steering vector but ignores the actual DOA estimate. The proposed algorithm provides correction after DOA estimation but before beamforming to improve both the DOA estimation accuracy and beamforming gain. First, the signal-to-noise ratio (SNR) of the signal is estimated and used to regularize the covariance matrix. Then, an estimated steering vector with DOA close to the true value is determined based on a minimum number of projections. Numerical results are presented to verify the effectiveness of the proposed method for DOA estimation correction. In most cases, this method improves the performance of the beamforming algorithms without changing them.

The passive detection of low-altitude signal sources is studied using an improved cross-correlation method in the time–frequency domain. A matching template is designed for signal cross-correlation, and a cross-correlation threshold is used to determine whether a signal source is present or not. An improved cross-correlation method is also proposed to estimate the direction of arrival and communication frequency of a signal source. Furthermore, the distance and signal-to-noise ratio are estimated using an energy detector. Outdoor data from a bridge in the Jimo District, Qingdao, and indoor data from a research laboratory are used for performance evaluation. The results obtained show that the proposed method can provide better passive detection of low-altitude signal sources compared to several well-known algorithms in the literature. In addition, this method is more suitable for long-distance detection.