Fish species' biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (<i>Miichthys miiuy</i>), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring.

Fish species' biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (<i>Miichthys miiuy</i>), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring.

Underwater acoustic communication is heavily influenced by intersymbol interference caused by the delay spread of multipaths. In this article, communication sequences transmitted from a drifting source were received by a fixed acoustic vector receiver system consisting of an accelerometer-based vector sensor and a pressure sensor, which can measure the three-directional components of vector quantity and pressure at a point. The underwater acoustic communication experiment was conducted in water approximately 30 m deep off the south coast of Geoje Island, South Korea, in May 2017 during the Korea Reverberation Experiment. Acceleration signals received by the vector sensor were converted to pressure-equivalent particle velocities, which were then used as input for a four-channel communication system together with acoustic pressure. These four channels have multipaths with different amplitudes but the same delay times, providing directional diversity that differs from the spatial diversity provided by hydrophone arrays. To improve the communication performance obtained from directional diversity, the Multichannel Combined Bidirectional Block-based Time Reversal Technique was used, which combines bidirectional equalization with time-reversal diversity and block-based time reversal that was robust against time-varying channels. Communication performance was compared with the outcomes produced by several other time reversal techniques. The results show that the Multichannel Combined Bidirectional Block-based Time Reversal Technique using a vector sensor achieved superior performance under the environmental conditions considered in this article.

Under Frequency Load Shedding (UFLS) is a critical last-resort measure used to stabilize system frequency in power systems following a major loss of generation. Traditional UFLS methods are primarily based on stability criteria, which may overlook the potential unintended negative impacts on certain loads. To address this issue, we propose a novel Reinforcement Learning (RL)-based UFLS scheme that combines local frequency measurements with high-resolution load criticality functions. By leveraging data-driven models, the proposed approach learns optimal load-shedding strategies tailored to specific emergency conditions. Numerical results obtained in the IEEE 39-Bus system show that this data-augmented approach can successfully shift load shedding to resilient regions while maintaining system stability and ensuring a robust response to under-frequency events.

Target tracking using high-resolution sensors often suffers from multiple detections generated by a single target, leading to a typical multiple detection (MD) problem that requires distinguishing between true target detections and clutter. To address this issue, a Multiple Detection-Integrated Probabilistic Data Association (MD-IPDA) algorithm is combined with a Gaussian Process (GP) for robust single-target tracking in cluttered environments. The MD-IPDA framework utilizes the target existence probability (TEP) as a track scoring mechanism to ensure reliable track maintenance. Meanwhile, GP is employed to construct measurement model for multiple scattering points, based on predefined contour points determined by the target's shape. The performance of the proposed extended target tracking (ETT) method is validated through simulation experiments and further assessed using the information reduction factor (IRF), allowing direct comparison with existing ETT algorithms.

Underwater acoustic wave propagates through multipath channels formed by interactions with the ocean boundaries such as the sea surface and bottom, and each element of the multipath has a different propagation angle each other. Acoustic vector sensors can receive not only acoustic pressure but also vector components (two or three components) such as acoustic acceleration or particle velocity, and each particle velocity signal has different multipath characteristics depending on directionality. In communication systems, directional diversity gain can be obtained if these particle velocity signals are used for multichannel combining. However, since the delay times of each component within the multipath of particle velocity are the same due to the point location of the single vector sensor, the gain from the directional diversity might be smaller than the gain from the spatial diversity of the hydrophone array. In this talk, a study on performance improvement of the communication system of a single vector sensor was conducted in terms of various types of diversity through the communication data obtained from KOREX-17. It is shown that the bidirectional block-based time reversal technique with channel parameter-based weighting gave the best performance.

The underwater soundscape was recorded in Seaview Bay off Inexpressible Island, Ross Sea region Marine protected area, for three days in December 2021. Leopard seal Hydrurga leptonyx vocalizations were a prominent sound source that led to variations in ambient sound pressure levels in a frequency range of approximately 150 to 4,500 Hz. Among the 14 call types previously identified, except ultrasound vocalizations, six types of broadcast calls were classified, and their acoustic characteristics were analyzed. We focused on the acoustic characteristics of four low-frequency calls, clustered in a relatively narrow bandwidth, which have been relatively less studied. We identified a new call type of a triple ascending trill consisting of three trill parts, expanding upon the findings of previous studies. The audio data extracted from leopard seal vocalization videos, recorded by a monitoring camera on sea ice, enhanced the reliability of identifications of the underwater triple ascending trill. We present the unique results of underwater passive acoustic monitoring conducted at Seaview Bay, designated as Antarctic Specially Protected Area No 178. Our results could contribute to the development of detection and localization algorithms for leopard seal vocalizations and can be used as fundamental data for studies related to the vocalization and behavior of this species.

In realistic radar tracking, target measurement uncertainty (TMU) in terms of both detection probability and measurement error covariance is significantly affected by the target-to-radar (T2R) geometry. However, existing posterior Cramer-Rao Lower Bounds (PCRLBs) rarely investigate the fundamental impact of T2R geometry on target measurement uncertainty and eventually on mean square error (MSE) of state estimate, inevitably resulting in over-conservative lower bound. To address this issue, this paper firstly derives the generalized model of target measurement error covariance for bistatic radar with moving receiver and transmitter illuminating any type of signal, along with its approximated solution to specify the impact of T2R geometry on error covariance. Based upon formulated TMU model, an improved PCRLB (IPCRLB) fully accounting for both measurement origin uncertainty and geometry-dependent TMU is then re-derived, both detection probability and measurement error covariance are treated as state-dependent parameters when differentiating log-likelihood with respect to target state. Compared to existing PCRLBs that partially or completely ignore the dependence of target measurement uncertainty on T2R geometry, proposed IPCRLB provides a much accurate (less-conservative) lower bound for radar tracking in clutter with geometry-dependent TMU. The new bound is then applied to radar trajectory control to effectively optimize T2R geometry and exhibits least uncertainty of acquired target measurement and more accurate state estimate for bistatic radar tracking in clutter, compared to state-of-the-art trajectory control methods.