Search

Oceanic delphinids that occur in and around Navy operational areas are regularly exposed to intense military sonar broadcast within the frequency range of their hearing. However, empirically measuring the impact of sonar on the behavior of highly social, free-ranging dolphins is challenging. Additionally, baseline variability or the frequency of vocal state-switching among social oceanic dolphins during undisturbed conditions is lacking, making it difficult to attribute changes in vocal behavior to anthropogenic disturbance. Using a network of drifting acoustic buoys in controlled exposure experiments, we investigated the effects of mid-frequency (3–4 kHz) active sonar (MFAS) on whistle production in short-beaked (Delphinus delphis delphis) and long-beaked common dolphins (Delphinus delphis bairdii) in southern California. Given the complexity of acoustic behavior exhibited by these group-living animals, we conducted our response analysis over varying temporal windows (10 min– 5 s) to describe both longer-term and instantaneous changes in sound production. We found that common dolphins exhibited acute and pronounced changes in whistle rate in the 5 s following exposure to simulated Navy MFAS. This response was sustained throughout sequential MFAS exposures within experiments simulating operational conditions, suggesting that dolphins may not habituate to this disturbance. These results indicate that common dolphins exhibit brief yet clearly detectable acoustic responses to MFAS. They also highlight how variable temporal analysis windows–tuned to key aspects of baseline vocal behavior as well as experimental parameters related to MFAS exposure–enable the detection of behavioral responses. We suggest future work with oceanic delphinids explore baseline vocal rates a-priori and use information on the rate of change in vocal behavior to inform the analysis time window over which behavioral responses are measured. [ABSTRACT FROM AUTHOR]

This paper describes an attempt to use data obtained from sodar (sound detection and ranging) for short-term forecasting of PM 10 concentration levels in Krakow. Krakow is one of the most polluted cities in central Europe (CE) in terms of PM 10 concentration. This is due to the high municipal emissions. Thanks to intensive corrective actions taken by the city authorities, these are being effectively eliminated, but the unfavourable topographic location of the city limits natural ventilation. The article describes all these conditions, focusing on presenting the method of short-term correction of air quality for the time needed to take quick corrective actions by the city authorities in the event of anticipated exceedances of the permissible values. Based on several years of measurements of the physical properties of the atmosphere with sodar, the authors of the paper suggest that sodar data could be considered for operational use to generate short-term predictions. The proposed method is based on the use of the spectrum, i.e. the set of amplitudes of signals returning to the sodar receiver from the reflection of a single-frequency sound transmission and its characteristic properties depending on the physical state of the atmosphere. Similar spectra were parameterised with a single numerical value using statistical methods. It was found that, in some cases preceding high concentrations of PM 10 , the spectral parameters had similar values. This made it possible to develop a forecasting method for such concentrations by using data mining to search for conditions in historical data closest to the state of the atmosphere at the time of forecasting. In this part of the study, data from 2017–2018 were used. In the next step, three methods of using the sodar data developed for PM 10 prediction were proposed, comparing them with the method without sodar use. The study results were tested on independent material using data concerning the episodes of high concentrations of pollutants from October 2021 to March 2022 in Krakow. The findings were considered to be encouraging, also taking into account the speed and low cost of preparing the forecast. [ABSTRACT FROM AUTHOR]

During sonar detection, the fuzzy function of hyperbolic frequency modulation (HFM) in the waveform of active signal is in the shape of a "oblique blade," which leads to the coupling characteristics of its fuzzy function in the time dimension and frequency dimension. Doppler causes a frequency shift in the frequency dimension and a time delay in the time dimension, which makes it impossible for a single HFM signal to measure speed accurately. However, the time delay caused by the same moving target to HFM signals with different frequency bands and different pulse widths is also different. A velocity measurement method combining HFM signals (VCH) is proposed, which employs the time delay correlation between the combined HFM signals to achieve the target distance and speed. In the VCH method, the echo signal is no longer matched and filtered as a whole but is divided into two channels and matched and filtered separately. And then, the combined HFM signals are used to obtain the distance and speed of the target. Extensive simulation results show that the proposed method can estimate the distance and speed of moving targets accurately, and it has reference value for engineering application. [ABSTRACT FROM AUTHOR]

Humans robustly associate spiky shapes to words like "Kiki" and round shapes to words like "Bouba." According to a popular explanation, this is because the mouth assumes an angular shape while speaking "Kiki" and a rounded shape for "Bouba." Alternatively, this effect could reflect more general associations between shape and sound that are not specific to mouth shape or articulatory properties of speech. These possibilities can be distinguished using unpronounceable sounds: The mouth-shape hypothesis predicts no Bouba-Kiki effect for these sounds, whereas the generic shape-sound hypothesis predicts a systematic effect. Here, we show that the Bouba–Kiki effect is present for a variety of unpronounceable sounds ranging from reversed words and real object sounds (n = 45 participants) and even pure tones (n = 28). The effect was strongly correlated with the mean frequency of a sound across both spoken and reversed words. The effect was not systematically predicted by subjective ratings of pronounceability or with mouth aspect ratios measured from video. Thus, the Bouba–Kiki effect is explained using simple shape-sound associations rather than using speech properties. [ABSTRACT FROM AUTHOR]

In this paper, we propose the zero-correlation-zone (ZCZ) of radius r on two-dimensional m × n sonar sequences and define the (m , n , r) ZCZ sonar sequences. We also define some new optimality of an (m , n , r) ZCZ sonar sequence which has the largest r for given m and n. Because of the ZCZ for perfect autocorrelation, we are able to relax the distinct difference property of the conventional sonar sequences, and hence, the autocorrelation of ZCZ sonar sequences outside ZCZ may not be upper bounded by 1. We may sometimes require such an ideal autocorrelation outside ZCZ, and we define ZCZ-DD sonar sequences, indicating that it has an additional distinct difference (DD) property. We first derive an upper bound on the ZCZ radius r in terms of m and n ≥ m . We next propose some constructions for (m , n , r) ZCZ sonar sequences, which leads to some very good constructive lower bound on r. Furthermore, this construction suggests that for m and r, the parameter n can be as large as possible indefinitely. We present some exhaustive search results on the existence of (m , n , r) ZCZ sonar sequences for some small values of r. For ZCZ-DD sonar sequences, we prove that some variations of Costas arrays construct some ZCZ-DD sonar sequences with ZCZ radius r = 2 . We also provide some exhaustive search results on the existence of (m , n , r) ZCZ-DD sonar sequences. Lots of open problems are listed at the end. [ABSTRACT FROM AUTHOR]

The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis, YFP) possesses the ability to detect distance through echolocation signals, and its sonar signal signature is adjusted to detect different targets. In order to understand the vocal characteristics of YFPs in different behavioral states and their differential performance, we recorded the vocal activities of YFPs in captivity during free-swimming, feeding, and nighttime resting and quantified their signal characteristic parameters for statistical analysis and comparison. The results showed that the number of vocalizations of the YFPs in the daytime free-swimming state was lower than that in the feeding and nighttime resting states, and the echolocation signals emitted in these three states showed significant differences in the −10 dB duration, −3 dB bandwidth, −10 dB bandwidth, and root-mean-square (RMS) bandwidth. Analysis of the resolution of the echolocation signals of the YFPs using the ambiguity function indicated that their distance resolution could reach the millimeter level. These results indicate that the echolocation signal characteristics of YFPs present diurnal differences and that they can be adjusted with changes in their detection targets. The results of this study can provide certain scientific references and foundations for the studies of tooth whale behavioral acoustics, and provide relevant scientific guidance for the conservation and management of YFPs. [ABSTRACT FROM AUTHOR]

Although research has been conducted on the simultaneous operation of multiple autonomous underwater vehicles (AUVs) to improve the efficiency of oceanographic surveys, it has become clear that sonar interference occurs between AUVs that are in close proximity. In this study, a formation control system was developed for actual operation at sea, based on the assumption of an operational system with an autonomous surface vehicle (ASV) and multiple AUVs. A control algorithm was implemented to maintain the AUVs within the desired range of the ASV, ensure communication, and avoid sonar interference by sending commands to increase or decrease the speed of the AUVs. Simulations were performed to verify the effectiveness of the developed method for the operation of one ASV and 10 AUVs. Evaluation scores for the distance between vehicles were introduced to quantify the effectiveness of the algorithm. The proposed method obtained the highest scores for formation control in the assumed operational scenarios. This confirms the effectiveness of the proposed method in avoiding the side-by-side relationships that cause sonar interference. [ABSTRACT FROM AUTHOR]

In recent years, there has been a trend toward expanding the operating frequency range and increasing the output power of Sound Navigation and Ranging (SONAR) systems to enhance their acoustic detection capabilities. However, due to this increase in operational power, the electrical capacity of amplifiers for SONAR system operation also increases, necessitating High-Power Amplifiers. When configured with a single amplifier, as in conventional methods, the volume of amplifiers increases due to volumetric increases in heat dissipation, components, and windings. These issues are detrimental to SONAR amplifier installation, mobility, maintenance, and equipment lifespan due to stress on individual components. Additionally, amplifiers for SONAR systems are comprised of power conversion devices, transformers for LC filters and matching, necessitating consideration of LC filters and matching transformers for enhancing voltage quality and efficiency to improve amplifier performance transmitted to SONAR transducers. However, previous research has focused on single-amplifier design methods, neglecting such considerations. Therefore, this paper proposes a design technique that overcomes the drawbacks of using the conventional design method by configuring multiple H-bridge inverters in a cascade format and utilizes one of the optimization algorithms, Particle Swarm Optimization (PSO), to derive amplifier design techniques that optimize component parameters for enhancing high-capacity amplifier performance. Subsequently, theoretical analysis, simulations, and experimental results comparing the proposed high-power amplifier design method with conventional single-amplifier design methods demonstrate similar error rates in operational frequency bands. [ABSTRACT FROM AUTHOR]

The Paddlefish (Polyodon spathula) has been extirpated from portions of its native range due to anthropogenic habitat degradation and fragmentation, most notably the impoundment of rivers. To mitigate some of these losses in Oklahoma, Paddlefish have been stocked into reservoirs throughout the state, with variable success in establishing self‐sustaining populations. Two factors thought to contribute to success of Paddlefish stocking are spawning substrate and prey availability, which were quantified in six reservoirs and nine reservoir tributaries. Side‐scan sonar and supervised classification of aerial imagery were used to classify 4517‐ha of river substrate upstream of the river‐reservoir interface in reservoir tributaries. Zooplankton community structure, water clarity, and nutrient availability were also assessed in the same reservoirs and tributaries. One tributary had suitable spawning substrate (>40%), and the rest had minimal (<1.5%), which suggested that availability of suitable spawning substrate was not directly correlated with Paddlefish stocking success. Reservoirs with self‐sustaining Paddlefish populations had higher abundance of large zooplankton (copepods and cladocerans) than reservoirs without a reproducing population. Notably, tributaries associated with Lake Texoma, the one known example of failed restoration, were much more turbid than other rivers. We conclude that abiotic factors such as water clarity may contribute more to variable recruitment than spawning substrate or zooplankton abundance by mediating foraging success of Paddlefish post‐larvae. [ABSTRACT FROM AUTHOR]

That Loch Ness Monsters, "Nessies", might exist has been argued by many people on the basis of films, sonar contacts, photos, and eyewitness accounts. At the same time, some observers have insisted that these are not real creatures, nothing but misperceptions of natural phenomena or known animals. Their arguments typically address the weakest evidence (eyewitness accounts) rather than the strongest objective evidence of sonar and film. Such evasion or avoidance constitutes a strategy of misdirection, as does the insistence that, since Nessies are not recognized by "science," therefore taking their possible existence seriously amounts to doing pseudo-science. Debunkers are arguably irrational and go beyond the evidence in declaring that Nessies are definitely non-existent. Although Nessie fans do believe that they exist, most will admit that final proof requires a specimen or detailed close-up of unequivocally valid photography and insist only that enough evidence exists to warrant further searches: that is not pseudo-science. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Ladar (SAL) is a sensor that combines laser detection technology with synthetic aperture technology to achieve ultra-high-resolution imaging. Due to its extremely short wavelength, SAL is more sensitive to motion errors. The micrometer-level motion will affect the target's azimuth focus. This article proposes an SAL motion compensation method based on Symmetric Triangular Linear Frequency Modulation Continuous Wave (STLFMCW) segmented interference, utilizing the characteristics of a triangular wave, to solve the problem of target azimuth defocusing. This article first establishes an STLFMCW echo signal model based on the SAL system under the influence of motion errors. Secondly, the radial velocity gradient along the azimuth direction is extracted using the triangular-wave-positive and -negative frequency modulation signals segmented interference method. Then, for the initial phase wrapping problem, the frequency spectral cross-correlation method is used to accurately estimate the initial radial velocity error. The radial velocity gradient is integrated along the azimuth to obtain the platform motion trajectory. Finally, the compensation functions are constructed to complete the echo Range Cell Migration (RCM) correction and residual phase compensation, resulting in a focused SAL image. This article verifies the practical effect of this method in eliminating motion errors using only one-period STLFMCW signal through simulation and real experiments. The quantitative results show that compared with the traditional method, the proposed method reduces the azimuth Peak Sidelobe Ratio (PSLR) by 8dB and the Integrated Sidelobe Ratio (ISLR) by 9 dB. This method has significant improvements and is of great significance for high-resolution FMCW SAL imaging. [ABSTRACT FROM AUTHOR]

The underwater domain is a challenging environment for robotics because widely used electromagnetic devices must be substituted by acoustic equivalents, much slower and noisier. In this paper a two‐dimensional pose simultaneous localization and mapping (SLAM) system for an Autonomous Underwater Vehicle based on inertial sensors and a mechanical profiling sonar is presented. Two main systems are specially designed. On the one hand, a dead reckoning system based on Lie Theory is presented to track integrated pose uncertainty. On the other hand, a rigid scan matching technique specialized for acoustic data is proposed, which allows one to estimate the uncertainty of the matching result. Moreover, Bayesian–Gaussian mixtures models are introduced to the scan matching problem and the registration problem is solved by an optimization in Lie groups. The SLAM system is tested on real data and executed in real time with the robotic application. Using this system, section maps at constant depth can be obtained from a three‐dimensional underwater domain. The presented SLAM system constitutes the first achievement towards an underwater Active SLAM application. [ABSTRACT FROM AUTHOR]

Underwater image recognition is crucial for underwater detection applications. Fish classification has been one of the emerging research areas in recent years. Existing image classification models usually classify data collected from terrestrial environments. However, existing image classification models trained with terrestrial data are unsuitable for underwater images, as identifying underwater data is challenging due to their incomplete and noisy features. To address this, we propose a cross-modal augmentation via fusion (CMAF) framework for acoustic-based fish image classification. Our approach involves separating the process into two branches: visual modality and sonar signal modality, where the latter provides a complementary character feature. We augment the visual modality, design an attention-based fusion module, and adopt a masking-based training strategy with a mask-based focal loss to improve the learning of local features and address the class imbalance problem. Our proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/WilkinsYang/CMAF. [ABSTRACT FROM AUTHOR]

Wideband direction-of-arrival (DOA) estimation is an important task for passive sonar signal processing. Nowadays, sparse Bayesian learning (SBL) attracts much attention due to its good performance. However, performance degrades in the existence of strong interference. This problem can be solved by combining the beamformer and the SBL. The beamformer is a useful tool to suppress interference. Then, the SBL can easily estimate the DOA of the targets from the beamformer power outputs (BPO). Unfortunately, the latter step needs to compute the matrix inversion frequently, which brings some computational burden to the sonar system. In this paper, the BPO-based SBL is modified. A sequential solution is provided for the parameters in the BPO probabilistic model. In this manner, only one signal precision parameter involved in the probabilistic model is updated in each iteration and the matrix inversion is avoided during the iteration, thus reducing the computational burden. Simulation and experimental results show that the proposed method maintains high estimation precision in the interference environment. At the same time, its computational efficiency is almost three times higher in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To make an Autonomous Underwater Vehicle (AUV) chase a moving target ship that generates wake, wake-responsive guidance can be used. This scenario is applicable to making an underwater torpedo pursue a moving target ship until hitting the target. The objective of our research is to make an AUV pursue a target ship assisted by passive sonar sensors as well as wake sensors. To track a maneuvering target without losing the target, the AUV applies both passive sonar sensors and two wake sensors. Two wake sensors are utilized to decide the turn direction of the AUV in zig-zag maneuvers. In practice, sharp maneuvers of the target can cause the AUV to exit the target's wake abruptly. As the target ship's wake is not detected by wake sensors and the AUV needs to search for the target ship, the AUV's passive sonar measures the direction of sound generated from the target ship. Thereafter, the AUV chases the target ship until the target's wake is detected again. As far as we know, our paper is novel in addressing wake-responsive AUV guidance assisted by passive sonar sensors. The effectiveness of the proposed guidance is verified using computer simulations. [ABSTRACT FROM AUTHOR]

For health and impact studies of water systems, monitoring underwater environments is essential, for which multi-frequency single- and multibeam echosounders are commonly used state-of-the-art technologies. However, the current scarcity of sediment reference datasets of both bottom backscatter angular response and water column scattering hampers empirical data interpretation. Comprehensive reference data derived from measurements in a controlled environment should optimize the use of empirical backscatter data. To prepare for such innovative experiments, we conducted a feasibility experiment in the Delta Flume (Deltares, The Netherlands). Several configurations of sonar data were recorded of the flume floor and suspended sediment plumes. The results revealed that flume reverberation was sufficiently low and that the differential settling of fine-sand plumes in the water column was clearly detected. Following this successful feasibility test, future comprehensive experiments will feature multi-frequency multi-angle measurements on a variety of sediment types, additional scatterers and sediment plumes, resulting in reference datasets for an improved interpretation of underwater backscatter measurements for scientific observation and sustainable management. [ABSTRACT FROM AUTHOR]

In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot's position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments. [ABSTRACT FROM AUTHOR]

Rice cultivation on southern plantation landscapes was a mainstay and major cash crop along the southeast coastal region of the United States in the eighteenth and nineteenth centuries. The workforce involved in growing rice was primarily enslaved Africans and freed African Americans of the Gullah Geechee culture, whose ancestors were forcibly brought over from West Africa, often due to their superior knowledge of rice cultivation. Today, in the tidal regions of major rivers from Florida to North Carolina, the vestiges of complex canal and waterway systems engineered for rice cultivation are still evident today. Within these systems, there are various wood-built, water-control structures in the form of structural dike supports, floodgates and sluices, levee repairs, bulkheads and boat landings. In this study, a combination of visual observations using non-motorized vessels, combined with a customized side scan sonar system designed specifically for shallow water surveys, was used to identify and map dozens of artifacts within the shallow canal and waterway systems of Eagles Island, North Carolina, where there is historical evidence of significant rice growing in the region. The study focused on the northern third of the island, where rice fields are essentially intact, and was designated Eagles Island Rice District. The results of the 2-year study identified 45 rice-related archaeological structures being discovered, in addition to a nineteenth century watercraft. The novel and effective survey techniques employed in this study can be adopted elsewhere within the southeastern United States to locate, map and preserve the knowledge and engineering expertise of the Gullah Geechee, upon whom the region's economic success depended. [ABSTRACT FROM AUTHOR]

Purpose: The low-frequency noise radiated by ships has a significant impact on marine animals; however, traditional ship sound-absorbing materials are not optimal for low-frequency noise control. Therefore, an acoustic metasurface suitable for low-frequency noise control is devised using a spatial folding structure, which is composed of folded channels and perforated structural units. Methods: Numerical methods are used to investigate the acoustic characteristics and absorption mechanism of the metasurface, and the impact of common structural factors on the acoustic performance of the metasurface is studied. An experimental model is produced with 3D printing technology, and the structure's absorption coefficient is examined. Results: A composite acoustic metasurface is designed by coupling multiple cells, and the designed metasurface structure achieves continuous broadband sound absorption in the range of 125–200 Hz. The structure exhibits subwavelength absorption characteristics and has an average absorption coefficient of 0.886 in the target frequency range, with a thickness of 1/27 of the wavelength. Conclusion: Folded channels extend the propagation path of acoustic waves, lead to lower absorption frequencies, and the coupled multicell design broadens the absorption bandwidth. The proposed coupled multicell folded low-frequency acoustic metasurface structure has excellent sound absorption performance and provides new ideas for the design of marine low-frequency broadband acoustic absorbers. [ABSTRACT FROM AUTHOR]

By using sound to visualize the real-world environment, audio games can offer a completely new game experience. In this paper, we study how audio cues can be visualized in audio games and their effects on the players' navigation skills, gaming experience, and immersion. Also, factors that impact the system performance in terms of navigation using audio components. To this end, we propose an audio game in which blindfolded players move through a physical maze with the help of audio cues. To realize this game, a sonar headset is designed and built based on ultrasonic sensors, which maps the external environment features into musical sounds. Actually, the design of the sonar headset is the result of design and research efforts. The gaming environment of this game is a physical maze, where the blindfolded players enter the maze while wearing the sonar headset. A user study was conducted to evaluate the effect of using different sound mapping techniques on navigation performance and playing experience in the game proposed in this paper. The results show that producing musical sound can lead to better navigation performance, game experience, and immersion in players. [ABSTRACT FROM AUTHOR]

The scarcity and difficulty in acquiring Side-scan sonar target images limit the application of deep learning algorithms in Side-scan sonar target detection. At present, there are few amplification methods for Side-scan sonar images, and the amplification image quality is not ideal, which is not suitable for the characteristics of Side-scan sonar images. Addressing the current shortage of sample augmentation methods for Side-scan sonar, this paper proposes a method for augmenting single underwater target images using the CBL-sinGAN network. Firstly, considering the low resolution and monochromatic nature of Side-scan sonar images while balancing training efficiency and image diversity, a sinGAN network is introduced and designed as an eight-layer pyramid structure. Secondly, the Convolutional Block Attention Module (CBAM) is integrated into the network generator to enhance target learning in images while reducing information diffusion. Finally, an L1 loss function is introduced in the network discriminator to ensure training stability and improve the realism of generated images. Experimental results show that the accuracy of shipwreck target detection increased by 4.9% after training with the Side-scan sonar sample dataset augmented by the proposed network. This method effectively retains the style of the images while achieving diversity augmentation of small-sample underwater target images, providing a new approach to improving the construction of underwater target detection models. [ABSTRACT FROM AUTHOR]

An Autonomous Underwater Helicopter (AUH) is a disk-shaped, multi-propelled Autonomous Underwater Vehicle (AUV), which is intended to work autonomously in underwater environments. The near-bottom area sweep in unknown environments is a typical application scenario, in which the complete coverage path planning (CCPP) is essential for AUH. A complete coverage path planning approach for AUH with a single beam echo sounder, including the initial path planning and online local collision avoidance strategy, is proposed. First, the initial path is planned using boustrophedon motion. Based on its mobility, a multi-dimensional obstacle sensing method is designed with a single beam range sonar mounted on the AUH. The VFH+ algorithm is configured for the heading decision-making procedure before encountering obstacles, based on their range information at a fixed position. The online local obstacle avoidance procedure is simulated and analyzed with variations of the desired heading direction and corresponding polar histograms. Finally, several simulation cases are set up, simulated and compared by analyzing the heading decision in front of different obstacle situations. The simulation results demonstrate the feasibility of the complete coverage path planning approach proposed, which proves that AUH completing a full coverage area sweep in unknown environments with a single beam sonar is viable. [ABSTRACT FROM AUTHOR]

This paper investigates the terrain-following problem for an autonomous underwater vehicle (AUV) from control perspectives with full consideration of terrain characteristics. By equipping the AUV with three simple single-beam echo sounders, a set of precise along-track bottom slopes are obtained in a real-time manner, and the occurrence of the lost bottom lock phenomenon caused by a single sonar altimeter is eliminated. A slope-based data processing method is developed, which enables an AUV to characterize seafloor features with complementary sensing modalities to generate proper adaptive height-modified values. In order to keep a fixed height when maneuvering over rugged terrains, a back-stepping depth control is implemented by adjusting horizontal rudder angle, and Lyapunov theory is adopted to analyze the asymptotic stability of the resulting terrain-following control system. At last, simulation results indicate the feasibility and effectiveness of the proposed methodology with a discussion of various sensor configurations. [ABSTRACT FROM AUTHOR]

Coprime and nested sparse arrays are two widely researched sparse arrays. These arrays, requiring fewer sensors compared to full uniform linear arrays (ULAs), still have a high number of degrees of freedom. The reduced sensor count in sparse arrays contributes to cost and system complexity reduction, and it also lessens the computational burden. This study delves into the computational efficiency aspect of sparse arrays, conducting a comparative analysis of coprime and nested arrays in classifying underwater stationary and moving sources using real sonar data. Leveraging two machine learning-based classification algorithms—support vector machine (SVM) and convolutional neural network (CNN)—the study aims to assess the extent of computational time savings achievable with sparse arrays and the associated trade-offs in accuracy. Simulation results reveal that both coprime and nested arrays can achieve near 100% classification accuracy for SVM and CNN models when an appropriate number of snapshots is selected for data processing. Additionally, within a suitable range of number of snapshots, computational time can be significantly reduced compared to full ULA, with only a minimal compromise in accuracy (< 2%). The study underscores the effectiveness of machine learning-based classifiers in source identification using sonar data, highlighting their high accuracy. However, it also notes that while a more powerful algorithm enhances operational capacity, it may concurrently escalate the complexity of the algorithm.

Airborne 3D imaging using ultrasound is a promising sensing modality for robotic applications in harsh environments. Over the last decade, several high-performance systems have been proposed in the literature. Most of these sensors use a reduced aperture microphone array, leading to artifacts in the resulting acoustic images. This paper presents a novel in-air ultrasound sensor that incorporates 1024 microphones, in a 32-by-32 uniform rectangular array, in combination with a distributed embedded hardware design to perform the data acquisition. Using a broadband Minimum Variance Distortionless Response (MVDR) beamformer with Forward-Backward Spatial Smoothing (FB-SS), the sensor is able to create both 2D and 3D ultrasound images of the full-frontal hemisphere with high angular accuracy with up to 70 $\mathrm { \text {dB}}$ main lobe to side lobe ratioin the array response in a single-source scenario. This paper describes both the hardware infrastructure needed to obtain such highly detailed acoustical images, as well as the signal processing chain needed to convert the raw acoustic data into said images. Utilizing this novel high-resolution ultrasound imaging sensor, we wish to investigate the limits of both passive and active airborne ultrasound sensing by utilizing this virtually artifact-free imaging modality.

Fish stock assessment is crucial for sustainable marine fisheries management in rangeland ecosystems. To address the challenges posed by the overfishing of offshore fish species and facilitate comprehensive deep-sea resource evaluation, this paper introduces an improved fish sonar image detection algorithm based on the you only look once algorithm, version 5 (YOLOv5). Sonar image noise often results in blurred targets and indistinct features, thereby reducing the precision of object detection. Thus, a C3N module is incorporated into the neck component, where depth-separable convolution and an inverse bottleneck layer structure are integrated to lessen feature information loss during downsampling and forward propagation. Furthermore, lowercase shallow feature layer is introduced in the network prediction layer to enhance feature extraction for pixels larger than $ 4 \times 4 $. Additionally, normalized weighted distance based on a Gaussian distribution is combined with Intersection over Union (IoU) during gradient descent to improve small target detection and mitigate the IoU's scale sensitivity. Finally, traditional non-maximum suppression (NMS) is replaced with soft-NMS, reducing missed detections due to occlusion and overlapping fish targets that are common in sonar datasets. Experiments show that the improved model surpasses the original model and YOLOv3 with gains in precision, recall and mean average precision of 2.3%, 4.7% and 2.7%, respectively, and 2.5%, 6.3% and 6.7%, respectively. These findings confirm the method's effectiveness in raising sonar image detection accuracy, which is consistent with model comparisons. Given Unmanned Underwater Vehicle advancements, this method holds the potential to support fish culture decision-making and facilitate fish stock resource assessment.

The ability to identify asymptotic scattering is critical for the continuous operation of modern combat systems. In the active sonar equation, acoustic target strength is crucial. The plane-wave propagation target strength equation predicts far-field reradiated intensity. Submarines defend themselves by being invisible. Sonar can be used to detect submarines. Saltwater readily absorbs radio waves. Commercial fishing and scientific ocean exploration both make use of sonar technology. Acoustic wave reflection reduces the area requirements of ocean depth sections, therefore submarine designers take this into consideration. The Target Strength (TS) of a sonar target is used to assess its size. The Pressure Acoustic-Boundary Element Model (PA-BEM) of the High-Frequency Boundary Model (HFB) illuminates and simplifies the TS analysis of the Benchmark Target Echo Strength Simulation (BeTTSi) benchmark submarine. We explore how subsea shape, material qualities, and operation frequency affect acoustic target strength using comprehensive models. The findings underline the need of correctly characterising the structural sections of the submarine and their impact on dispersion. They also help with research into the submarine’s acoustic signature, detectability, and potential detection mitigation measures.

In this study, the mechanisms of jet atomization were analyzed based on a frequency analysis of atomization sounds in the audible range (∼20 kHz). Jet atomization is a two-dimensional, high-speed atomization using a diaphragm, and interesting acoustic signals and atomization phenomena were detected on hydrophobic and hydrophilic diaphragms. The hydrophilic diaphragm strongly interacted and resonated with the surface wave, resulting in symmetrical jet atomization relative to the diaphragm. The resonance between the diaphragm and the surface wave was supported by a calculation of the eigenfrequency of the diaphragm and the coincidence of the droplet diameters as calculated from Lang's equation. Notably, the diaphragm excited by the ultrasonic transducer acted as a new transducer vibrating perpendicular to the transducer. As a result, when the diaphragm and the surface wave were in resonance at 2.4 MHz, a symmetrical two-dimensional high-speed jet atomization was generated in the direction perpendicular to the transducer's vibration direction. This study also revealed that the atomization state can be determined based on the acoustic analysis. This acoustic analysis of atomization sounds can be applied in more advanced atomization control, such as for providing uniform dispersions of droplets containing DNA, drugs, or microplastics. [ABSTRACT FROM AUTHOR]

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.

In response to the demand for efficient and high-precision detection in marine capture fishery, here the omnidirectional fishery sonar was developed to detect the targets in long distance using digital broadband and multiple beams. Broadband technology was used to effectively suppress the interference of interface and volume reverberation. Multiple beams were to improve the resolution of target detection. A distributed architecture of signal processing was also constructed for the omnidirectional fishery sonar using a field programmable gate array (FPGA). The omnidirectional fishery sonar was divided into multiple sectors. The underwater acoustics was simulated to determine the mutual constraints between the three main influencing parameters, namely the size of the transducer array, the maximum distance of detection, and the optimal point of operating frequency. The key parameters were then selected for the fishery sonar, according to the typical fishery vessels in China. The preferred frequency band of the signal was 20-30 kHz. A systematic investigation was also conducted on the effects of noise, interface reverberation, and volume reverberation on the detection performance of omnidirectional fishery sonar. A parallel and serial broadband beamforming FPGA was utilized to reduce the on-chip storage by 90% during operation; the Delta Sigma module was adopted to improve the signal-to-noise ratio of the transmission signal, where the noise within 20-30 kHz shifted to the frequency band above 50 kHz. A one-step stability of the predictive beam was improved using two-point recursive interpolation. The real-time stability errors of the beam were achieved at less than 1.6° (-0.8°-+0.8°) under significant sway of fishing vessels. The core components were also developed, such as a cylindrical transducer array, a highly integrated receiver and transmitter, and a 256 multi-channel signal processor, and system integration. 256 channels were independent receiving and transmitting for the signals in the 20-30 kHz frequency band. The scanning detection capabilities were for horizontal 360° and vertical 70°. The omnidirectional multi-beam fishery sonar was installed on the deep-sea fishery vessel. Environmental and simulated target detection tests were carried out on the fish school. The sea test results showed that the omnidirectional fishery sonar better performed stable, clear and continuous detection of environmental targets, where the detection ability of the 0 dB fish target reached more than 1 500 m. The key indicators of the self-developed omnidirectional sonar can fully meet the needs of marine fishing scenarios. The finding can greatly improve the detection performance of fishing for large-scale autonomous production. [ABSTRACT FROM AUTHOR]

Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe values, which hinder accurate target localization in underwater imagery. To address this, a Compressed Sensing (CS) method is proposed by reconstructing echo signals to suppress correlation noise between orthogonal waveforms. A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are used to multiply received echo signals to yield compressed measurements. A sparse recovery algorithm is applied to optimize signal reconstruction before joint transmit–receive beamforming forms a 2D sonar image in the angle-range domain. Numerical simulations and lake experimental results confirm the effectiveness of the proposed method, by obtaining a lower sidelobe sonar image under sub-Nyquist sampling rates as compared with other approaches. [ABSTRACT FROM AUTHOR]

Sonar systems are frequently used to classify objects at a distance by using the structure of the echoes of acoustic waves as a proxy for the object's shape and composition. Traditional synthetic aperture processing is highly effective in solving classification problems when the conditions are favourable but relies on accurate knowledge of the sensor's trajectory relative to the object being measured. This article provides several new theoretical tools that decouple object classification performance from trajectory estimation in synthetic aperture sonar processing. The key insight is that decoupling the trajectory from classification-relevant information involves factoring a function into the composition of two functions. The article presents several new general topological invariants for smooth functions based on their factorisations over function composition. These invariants specialise to the case when a sonar platform trajectory is deformed by a non-small perturbation. The mathematical results exhibited in this article apply well beyond sonar classification problems. This article is written in a way that supports full mathematical generality. [ABSTRACT FROM AUTHOR]

The frequency–azimuth (FRAZ) spectrum is a critical characteristic in passive target detection and tracking, as it encapsulates information regarding the signal's frequency and azimuth. However, due to the inherent limitations in the sonar array's physical aperture and the analysis time of the system, the signal often suffers from undersampling in both spatial and temporal dimensions. This undersampling leads to energy leakage across the azimuth and frequency domains, adversely affecting the resolution of the FRAZ spectrum. Such a reduction in resolution hampers multitarget resolution and feature extraction. To address these challenges, this study introduces a deconvolution-based FRAZ spectrum estimation method tailored for uniform linear arrays. The proposed method initiates by decoupling the azimuth and frequency in the FRAZ spectrum, forming a two-dimensional point scattering function that possesses shift-invariance. Subsequent to this, the power spectrum and the two-dimensional point scattering function undergo deconvolution using the Richardson–Lucy (R–L) iterative algorithm. The final stage involves calculating the signal azimuths and frequencies based on the deconvolution results from the preceding step. Comparative analyses involving simulations and sea test results reveal that the proposed method achieves a narrower main lobe width and diminished background noise in contrast to traditional FRAZ spectrum estimation techniques. This improvement is instrumental in minimizing the target's energy leakage in both the azimuth and frequency domains. [ABSTRACT FROM AUTHOR]

The Mechanoelectrical transduction (MET) channels of the mammalian hair cells are essential for converting sound stimuli into electrical signals that enable hearing. However, the impact of acoustic overstimulation, a leading cause of hearing loss, on the MET channel function remains poorly understood. In this study, I investigated the effect of loud sound-induced temporary threshold shift (TTS) on the transduction response phase across a wide range of sound frequencies and amplitudes. The results demonstrated an increase in the transduction response phase following TTS, indicating altered transduction apparatus function. Further investigations involving the reduction of extracellular calcium, a known consequence of TTS, replicated the observed phase changes. Additionally, reduction of potassium entry confirmed the specific role of calcium in regulating the transduction response phase. These findings provide novel insights into the impact of loud sound exposure on hearing impairment at the transduction apparatus level and highlight the critical role of calcium in modulating sound transduction. Considering that over 1 billion teenagers and young adults globally are at risk of hearing loss due to unsafe music listening habits, these results could significantly enhance awareness about the damaging effects of loud sound exposure. [ABSTRACT FROM AUTHOR]

Low frequency acoustic methods are a common tool for seabed stratigraphy mapping. Due to the efficiency in seabed mapping compared to geotechnical methods, estimating geotechnical sediment properties from acoustic surveying is attractive for many applications. In this study, co-located geotechnical and geoacoustic measurements of different seabed sediment types in shallow water environments (<5 m of water depth) are analyzed. Acoustic impedance estimated from sediment properties based on laboratory testing of physical samples is compared to acoustic impedance deduced from CHIRP sonar measurements using an inversion approach. Portable free fall penetrometer measurements provided in situ sediment strength. The results show that acoustic impedance values deduced from acoustic data through inversion fall within a range of ±25% of acoustic impedance estimated from porosity and bulk density. The acoustic measurements reflect variations in shallow sediment properties such as porosity and bulk density (~10 cm below seabed surface), even for very soft sediments (su < 3 kPa) and loose sands (~20% relative density). This is a step towards validating the ability of acoustic methods to capture geotechnical properties in the topmost seabed layers. [ABSTRACT FROM AUTHOR]

Sonar imaging technology is widely used in the field of marine and underwater monitoring because sound waves can be transmitted in elastic media, such as the atmosphere and seawater, without much interference. In underwater object detection, due to the unique characteristics of the monitored sonar image, and since the target in an image is often accompanied by its own shadow, we can use the relative relationship between the shadow and the target for detection. To make use of shadow-information-aided detection and realize accurate real-time detection in sonar images, we put forward a network based on a lightweight module. By using the attention mechanism with a global receptive field, the network can make the target pay attention to the shadow information in the global environment, and because of its exquisite design, the computational time of the network is greatly reduced. Specifically, we design a ShuffleBlock model adapted to Hourglass to make the backbone network lighter. The concept of CNN dimension reduction is applied to MHSA to make it more efficient while paying attention to global features. Finally, CenterNet's unreasonable distribution method of positive and negative samples is improved. Simulation experiments were carried out using the proposed sonar object detection dataset. The experimental results further verify that our improved model has obvious advantages over many existing conventional deep learning models. Moreover, the real-time monitoring performance of our proposed model is more conducive to the implementation in the field of ocean monitoring. [ABSTRACT FROM AUTHOR]

The channel in the marine environment is a time-varying and space-varying channel. Pulse-truncated continuous wave (PCW) speed measurement is often used in sonar, but the instability effect of PCW signal in the channel limits the effectiveness of speed measurement. Hyperbolic frequency modulation (HFM) signal is insensitive to Doppler; therefore, HFM signals are widely used in ranging and velocity measurement of sonar and radar. However, due to the filtering effect of the marine environment, the HFM signal of a single frequency band may cause excessive transmission loss, and the echo energy may be too weak to detect the target. Based on the analysis of the influence of speed on the distance measurement of HFM signal, a pulse sequence method based on HFM for speed measurement (PHS) is proposed, which uses HFM signals of different frequency bands and pulse widths in the pulse sequence to perform speed measurement. Extensive simulation results show that PHS method not only guarantees the speed measurement but also makes full use of the energy of the HFM sequence to improve the accuracy of the distance measurement. And PHS method is valuable to the practical application of engineering. [ABSTRACT FROM AUTHOR]

The paper presents the concept of the method of determining the direction of ultrasonic signal arrival, i.e., the azimuth and elevation angles. This method is an extension of the previous approach which was proposed to determine only the azimuth angle. The approach is based on the indirect phase determination. This makes it possible to tolerate spacing of receivers greater than half the wavelength of the received signal. At the same time, it provides increased measurement accuracy and reduced hardware requirements. To check the robustness of the method, simulations were carried out for the geometric arrangement of the receivers of the sonar module, for which the method was then implemented. This sonar module was used in the conducted experiments. The results of these simulations and experiments are included in the paper and discussed. [ABSTRACT FROM AUTHOR]

In sea ice–covered polar oceans, profiling Argo floats are often unable to surface for 9 months or longer, rendering acoustic RAFOS tracking the only method to obtain unambiguous under-ice positions. Tracking RAFOS-enabled floats has historically relied on the ARTOA3 software, which had originally been tailored toward nonprofiling floats in regions featuring the sound fixing and ranging (SOFAR) channel with acoustic ranges of approximately 1000 km. However, in sea ice–covered regions, RAFOS tracking is challenged due to (i) reduced acoustic ranges of RAFOS signals, and (ii) enhanced uncertainties in float and sound source clock offsets. A new software, built on methodologies of previous ARTOA versions, called artoa4argo, has been created to overcome these issues by exploiting additional float satellite fixes, resolving ambiguous float positions when tracking with only two sources and systematically resolving float and sound source clock offsets. To gauge the performance of artoa4argo, 21 RAFOS-enabled profiling floats deployed in the Weddell Sea during 2008–12 were tracked. These have previously been tracked in independent studies with a Kalman smoother and a multiconstraint method. The artoa4argo improves tracking by automating and streamlining methods. Although artoa4argo does not necessarily produce positions for every time step, which the Kalman smoother and multiconstraint methods do, whenever a track location is available, it outperforms both methods. Significance Statement: Argo is an international program that collects oceanic data using floats that drift with ocean currents and sample the water column from 2000-m depth to the surface every 7–10 days. Upon surfacing, the float acquires a satellite position and transmits its data via satellite. In polar regions, with extensive seasonal sea ice coverage, floats are unable to surface for many months. Thus, any under-ice samples collected are missing positions, hampering their use in scientific endeavors. Since monitoring of polar regions is imperative to better understand and predict the effects of climate change, hydroacoustic tracking is employed there. Here a new acoustic tracking software, artoa4argo, is introduced, which improves tracking of these floats. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DoA) estimation is an important part in sonar signal processing, providing a reliable foundation for tasks, such as underwater object detection and tracking. Although the deep learning model has powerful data fitting capabilities, accurately estimating the orientation of multiple targets with a single model remains a challenging task. To address this challenge, we enhance the permutation invariant training (PIT) technique and propose two different types of methods: multi-group classification with PIT (MC-PIT) and multi-group regression with PIT (MR-PIT). These two frame-level PIT schemes utilize a single model for both training and testing in multi-target scenarios. Furthermore, we evaluate the performance of MR-PIT and MC-PIT with different network backbones and demonstrate that the frame-level PIT has excellent portability. Compared with the model trained with the general multi-label strategy, simulation experiments show that our proposed methods have better multi-target DoA estimation performance. Finally, when the array configuration of simulated and recorded data are consistent, the model with frame-level PIT can achieve good performance on recorded data even only trained on simulation data. [ABSTRACT FROM AUTHOR]

This paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques. [ABSTRACT FROM AUTHOR]

Plant root imaging is crucial for progress in various domains such as plant breeding and crop optimization. Traditionally, root tomography involves invasive methods that disrupt plant systems and yield non-reproducible results. As a result, non-invasive techniques, particularly electrical tomography, have gained significant attention. Despite the advantages, these techniques have limitations in terms of radiation efficiency and directivity due to suboptimal antenna design. This paper presents a comprehensive simulation on antenna design optimization focusing on dimensions, spacing, and integration of advanced algorithms. A micropatch transducer antenna was engineered for an existing in-silico plant root setup operating within a 3–5 MHz frequency range. The optimized dimensions of the antenna are 109.32 mm × 140.67 mm × 2.55 mm, and it resonates effectively within a frequency range of 3.1–5.68 MHz. Using scalar minimization techniques, patch transducers were interconnected into an antenna array with an optimized 3 mm spacing. Utilizing multi-objective optimization algorithm based on sperm fertilization procedure and shuffled frog leaping algorithm, optimal frequencies were obtained at 3,989,796.88 Hz and 3,989,951.83 Hz, respectively. Validated using CADFEKO software, the proposed antenna design demonstrated distinctive voltage distribution, superior directivity of 9.24 dBi, gain of 9.15 dBi, and 98.6% radiation efficiency when compared to the existing silicon-based root tomography antenna setups. [ABSTRACT FROM AUTHOR]

Background: The aim of this work is to provide the currently missing evidence that may allow an update of the Paediatric Dosage Card provided by the European Association of Nuclear Medicine (EANM) for conventional PET/CT systems. Methods: In a total of 2082 consecutive [18F]FDG-PET scans performed within the EuroNet-PHL-C2 trial, the administered [18F]FDG activity was compared to the activity recommended by the EANM Paediatric Dosage Card. None of these scans had been rejected beforehand by the reference nuclear medicine panel of the trial because of poor image quality. For detailed quality assessment, a subset of 91 [18F]FDG-PET scans, all performed in different patients at staging, was selected according to pre-defined criteria, which (a) included only patients who had received substantially lower activities than those recommended by the EANM Paediatric Dosage Card, and (b) included as wide a range of different PET systems and imaging parameters as possible to ensure that the conclusions drawn in this work are as generally valid as possible. The image quality of the subset was evaluated visually by two independent readers using a quality scoring system as well as analytically based on a volume-of-interest analysis in 244 lesions and the healthy liver. Finally, recommendations for an update of the EANM Paediatric Dosage Card were derived based on the available data. Results: The activity recommended by the EANM Paediatric Dosage Card was undercut by a median of 99.4 MBq in 1960 [18F]FDG-PET scans and exceeded by a median of 15.1 MBq in 119 scans. In the subset analysis (n = 91), all image data were visually classified as clinically useful. In addition, only a very weak correlation (r = 0.06) between activity reduction and tumour-to-background ratio was found. Due to the intended heterogeneity of the dataset, the noise could not be analysed statistically sound as the high range of different imaging variables resulted in very small subsets. Finally, a suggestion for an update of the EANM Paediatric Dosage Card was developed, based on the analysis presented, resulting in a mean activity reduction by 39%. Conclusion: The results of this work allow for a conservative update of the EANM Paediatric Dosage Card for [18F]FDG-PET/CT scans performed with conventional PET/CT systems. [ABSTRACT FROM AUTHOR]

This review article delves into the latest advancements and transformative impacts of remotely sensed methods in measuring water depth and underwater topography. It particularly focuses on topobathymetric data acquisition which is crucial for numerous applications including navigation, flood control, coastal erosion, sea-level rise predictions, and mapping, modelling, and monitoring of offshore resources. Traditionally, measuring depths, distribution, and volumes of submerged resources has been challenging due to time, cost, labor, and reliance on sophisticated, mostly airborne technologies. These conventional methods often struggled in remote, hazardous, or shallow water areas. Recent advances in Light Detection and Ranging (LiDAR) technology, especially its miniaturization and integration with unoccupied aerial systems (UAS), have significantly improved the collection of three-dimensional bathymetric data. This progress presents effective solutions for previously faced challenges. Despite these advancements, there's a need for a thorough assessment of the state of LiDAR-based topobathymetric mapping and the impact of the increasing accessibility of UAS in this field. Our systematic review scrutinizes topobathymetric LiDAR technology and research, centering on acquisition platforms and methods in coastal and riverine environments for mapping, planning, monitoring, and inspections. We document the spread of topobathymetric LiDAR research over time, geographically, across disciplines, and in various publication outlets. Utilizing the VOSviewer analytical tool, we conducted a bibliometric keywordbased network analysis, evaluating the distribution of topobathymetric citations, and their bibliographic coupling, co-citation, and co-authorship relationships. Our findings reveal the innovative yet emerging state of UAS applications in topobathymetry and the ongoing reliance on LiDAR technology for 3-D underwater data acquisition. This study provides a fundamental assessment of current trends and developments in coastal and riverine bathymetric applications, highlighting the evolving landscape of remotely sensed methods in underwater topography. [ABSTRACT FROM AUTHOR]

Research articles in all branches of modern science are crowded with the abbreviated technical terms known as acronyms, a phenomenon that was essentially unknown before World War II. Apart from an introduction to the notion of acronyms and its short history, the paper discusses from a historical perspective the connections between acronyms and eponyms in science. Moreover, it charts how acronyms and abbreviations became so common in physics and astronomy that the excessive use of them came to be considered a symptom of a contagious disease. While many science acronyms are exotic and little used, and some downright bizarre, others have become household words that in some cases are not even recognized to be acronymic constructions. The paper briefly examines the naming histories of some of these successful acronyms in physics and astronomy, among them radar, sonar, maser, laser, and pulsar. [ABSTRACT FROM AUTHOR]

In the equilibrium range of the wave spectrum's high-frequency tail, energy levels are proportional to the wind friction velocity. As a consequence of this intrinsic coupling, spectral tail energy levels can be used as proxy observations of surface stress and wind speed when direct observations are unavailable. Proxy observations from drifting wave-buoy networks can therefore augment existing remote sensing capabilities by providing long dwell observations of surface winds. Here we consider the skill of proxy wind estimates obtained from observations recorded by the globally distributed Sofar Spotter network (observations from 2021 to 2022) when compared with collocated observations derived from satellites (yielding over 20 000 collocations) and reanalysis data. We consider physics-motivated parameterizations (based on frequency−4 universal tail assumption), inverse modeling (estimate wind speed from spectral energy balance), and a data-driven approach (artificial neural network) as potential methods. Evaluation of trained/calibrated models on unseen test data reveals comparable performance across methods with generally of order 1 m s−1 root-mean-square difference with satellite observations. [ABSTRACT FROM AUTHOR]

Source enumeration with large snapshots is feasible for a static uniform circular sonar array (UCSA). However, the dimension of the signal subspace in the array coordinate system exceeds the number of signal sources due to the rotation of the UCSA, which makes standard source enumeration ineffective. To address this issue, a spatial focusing approach for source enumeration with rotating UCSAs is presented. The approach is based on the concept of phase‐mode excitation and makes use of the UCSA heading information to achieve spatial focusing. The proposed algorithm provides reliable source enumeration with a high signal‐to‐noise ratio (SNR) when compared to the previous signal‐subspace transformation (SST) method. Simulations demonstrate that the proposed algorithm outperforms the other three methods in the conditions of a low SNR and a small angular separation. [ABSTRACT FROM AUTHOR]