This letter proposes a lightweight parallel recurrent–convolutional scheme to improve generalization capability and recognition accuracy while maintaining low computational complexity in resource-constrained underwater acoustic channels. In this scheme, the lightweight convolutional network is used to extract time–frequency features, and the lightweight recurrent network with gated recurrent units is used to capture long-term temporal phase correlations, thereby alleviating the Doppler-induced phase rotation and inter-symbol interference in time-varying multipath underwater acoustic channels. Sea-trial data are collected during shallow-water sea trials with strictly separated training and evaluation datasets. Experimental results on ten underwater acoustic modulation types show that the proposed scheme improves recognition accuracy by 6.2% and reduces computational cost by 22.4%, while exhibiting stronger generalization capability compared with benchmark schemes.

Sonar images are vital in ocean explorations but face transmission challenges due to limited bandwidth and unstable channels. The Just Noticeable Difference (JND) represents the minimum distortion detectable by human observers. By eliminating perceptual redundancy, JND offers a solution for efficient compression and accurate Image Quality Assessment (IQA) to enable reliable transmission. However, existing JND models prove inadequate for sonar images due to their unique redundancy distributions and the absence of pixel-level annotated data. To bridge these gaps, we propose the first sonar-specific, picture-level JND dataset and a weakly supervised JND model that infers pixel-level JND from picture-level annotations. Our approach starts with pretraining a perceptually lossy/lossless predictor, which collaborates with sonar image properties to drive an unsupervised generator producing Critically Distorted Images (CDIs). These CDIs maximize pixel differences while preserving perceptual fidelity, enabling precise JND map derivation. Furthermore, we systematically investigate JND-guided optimization for sonar image compression and IQA algorithms, demonstrating favorable performance enhancements.

In this paper, we propose a novel generalized orthogonal chirp division multiplexing (GOCDM) communication system under doubly selective channels. The GOCDM waveform consists of modulated Zadoff-Chu sequences parameterized on the root index λ, which specifies the chirp rate. Thus, GOCDM subsumes OCDM as a special case (λ = 1) and inherits the full double-spreading feature. To deal with doubly selective channels, the optimal pilot chirp-assisted GOCDM is designed based on a basis expansion model. The optimal structure, placement and number of pilot symbols are proposed. Moreover, the optimal power allocation between pilot and data symbols is also derived. These optimal pilot parameters not only minimize the mean square error of the channel estimation, but also maximize a lower bound of the average channel capacity. Furthermore, it is possible to design the root index λ to achieve a GOCDM system with the optimal training to have higher bandwidth efficiency than the OCDM system. Simulation results corroborate the superior performance of the proposed design for GOCDM.

This article proposes a robust peer-to-peer localization scheme using compact arrays in underwater time-varying channels. The method combines two-way ranging and the time difference of arrival (TDOA) positioning technique, utilizing three elements of the compact array to achieve accurate localization. To better adapt to time-varying channels caused by the inherent changes in the water medium, and improve localization accuracy, this article adopts a TDOA correction method based on channel feature matching using the correlation between the received signals of different elements of the compact array. This method combines line-of-sight with non-line-of-sight information of multipath signals to correct the TDOA. The specific localization steps are as follows. First, multipath features are extracted from the single-frame received signal of an array element through the Savitzky-Golay filter and dynamic time warping (DTW) algorithm. Then, based on the obtained multipath features of multiframe signals, hierarchical clustering is used to extract the channel features from the perspective of the current array element. Finally, the target position is estimated by combining two-way ranging with the TDOA values corrected by DTW from different array elements. The accuracy and stability of the proposed method are validated through simulations and pool experiments by comparing it with the traditional methods.

G-quadruplexes (G4s) are nucleic acid secondary structures with important regulatory functions. Single-nucleotide variants (SNVs), one of the most common forms of genetic variation, can potentially impact the formation of G4 structures if they occur within G4 regions. However, there is currently a lack of software tools specifically designed to assess such effects. Here, we present an R/Bioconductor package named G4SNVHunter, which enables rapid detection of variants that may disrupt G4 structures. This tool, based on the core principles of the G4Hunter algorithm, can provide precise quantitative assessment of the propensity for G4 formation within genomic sequences. Specialized experimental methods can then be designed based on the results provided by G4SNVHunter to further verify the specific functions of the affected G4 structures, facilitating deeper insights into the biological impacts of genetic variants from the perspective of G4 structures. To showcase the functionality of the G4SNVHunter package, we analyzed the Neandertal and Denisovan archaic introgressed variants detected by the Sprime software, and identified approximately 5,800 variants located within G4 regions, among which around 230 may impair G4 structure formation propensity. The source code for the G4SNVHunter package has been publicly released under the MIT license at https://github.com/rongxinzh/G4SNVHunter and https://bioconductor.org/packages/devel/bioc/html/G4SNVHunter.html.

Due to the limited bandwidth and severe noise interference in underwater acoustic channels, underwater image transmission typically requires a coding scheme with a high compression ratio and strong robustness. However, existing semantic communication methods are predominantly designed for terrestrial wireless scenarios and do not fully consider the unique characteristics of underwater images and channel features. To address these problems, we propose an efficient semantic communication scheme of underwater images guided by physical priors. Specifically, we design a semantic encoder-decoder with a hybrid architecture of CNN and Swin Transformer for efficient semantic feature extraction and reconstruction. Meanwhile, we design an underwater physical prior fusion module, which leverages physical prior knowledge from underwater imaging to optimize the encoding process, preserving and enhancing essential semantic features. Additionally, a noise-aware training strategy is proposed to guide the network in learning the noise characteristics of measured underwater acoustic channels, improving its robustness in intricate marine conditions. Extensive experiments verify that our method outperforms existing schemes regarding transmission efficiency and image quality.

Orthogonal chirp division multiplexing (OCDM) has emerged as an attractive modulation scheme due to its double-spreading property in both the time and frequency domains. Meanwhile, it is well known that multiple-input multiple-output (MIMO) techniques enhance spatial diversity to combat channel fading. Thus, MIMO-OCDM is promising for reliable high rate wireless communications. When deploying MIMO-OCDM systems, most studies assume perfect channel knowledge at the receivers. However, channel estimation is crucial and should be considered in the system design. In this paper, we apply Alamouti code for MIMO-OCDM over frequency-selective fading channels. In order to facilitate the channel estimation, three pilot-aided transmission (PAT) schemes are proposed, where the pilots are inserted in the frequency, time, and Fresnel domains, respectively. The first two schemes enjoy higher bandwidth efficiency, while the third one is more resistant to burst interference. The channel estimation methods and the optimal PAT designs are developed accordingly for these PAT schemes. Simulation results corroborate the superior performance of the proposed MIMO-OCDM system and channel estimation methods.

Abstract Since the penetration depth of visible light is limited in muddy and dark deep marine environment, sonar imaging, which is independent of natural light, has been attracting much attention in underwater detections. However, the visual quality of sonar image is inevitably damaged during the acquisition and the transmission in the sophisticated and dynamic underwater environment. The quality decrease can further bring negative impacts on oceanic information analysis. To measure the quality decrease of sonar images, this paper proposes a contour‐aware model for sonar image quality assessment (CaMSIQA). The CaMSIQA metric is established upon the conclusion that the quality of sonar images can be defined as a measure of their utilities in real application scenarios. We exploit a contour information statistic (CIS) model that is critical to object recognition of sonar images here. Quantifying the changes of the CIS model between the unimpaired and test sonar images makes it possible to perceive the quality decrease of sonar images. Extensive experimental results have demonstrated the effectiveness of the proposed method compared with current mainstream image quality assessment (IQA) methods without full reference information.

Underwater acoustic channel (UAC) is featured as fast time-varying characteristic, and challenges the transmission designs. To countermine the time variation effect, we propose an adaptive design for orthogonal frequency division multiplexing (OFDM) transmission systems by utilizing the long-term stability of the second-order statistics of the channel state information (CSI). We derive the analytical expression of signal-to-interference-plus-noise-ratio (SINR) at each subcarrier to reach the target error performance based on the statistical information of the CSI. Thereafter, a new adaptive coding and bit-power loading algorithm with low computational complexity is proposed to pursuit the highest achievable bit rate with fixed error rate. The validity of SINR calculations and the effectiveness of the proposed adaptive algorithm are demonstrated under various conditions, wherein both simulated and measured channels have been tested.

Underwater hostile channel conditions challenge video transmission designs. The current designs often treat video coding and transmission schemes as individual modules. In this study, we develop an adaptive transceiver with channel state information (CSI) by taking into account the importance of video components and channel conditions. The design is more effective than the traditional ones. However, in practical systems, perfect CSI may not be available. Therefore, we compare the imperfect CSI case with existing schemes, and validate the effectiveness of our design through simulations and measured channels in terms of a better peak signal-to-noise ratio and a higher video structural similarity index.

Zheng and Tse have demonstrated that there exists an optimal trade-off between diversity gain and multiplexing gain. To realise the predicted optimal trade-off, we propose a new multiple-input multiple-output transmission scheme for a concatenated coding and space–time (ST) modulation system aimed at applications that require flexible trade-off between performance and data rate. The proposed scheme is multi-layered with linear ST modulation to allow various multiplexing gain. Through a judicious design of the inner ST modulation, the optimisation of spatial multiplexing is made simple. Moreover, a joint iterative receiver based on MMSE criterion with a priori information is developed to reduce computational complexity. Simulation results are provided to demonstrate the merits of the new design.