Segment Anything Model (SAM) has revolutionized the way of segmentation due to its remarkable capacity for generalized segmentation. However, SAM’s performance may decline when applied to tasks involving domains that differ from natural images. Nonetheless, by employing fine-tuning techniques, SAM exhibits promising capabilities in specific domains, such as medicine and planetary science. Notably, there is a lack of research on the application of SAM to sonar imaging. In this paper, we aim to address this gap by conducting a comprehensive investigation of SAM’s performance on sonar images. Specifically, we evaluate SAM with various settings on sonar images. Moreover, we fine-tune SAM for sonar images using effective methods both with prompts and for semantic segmentation. The experimental results reveal a substantial enhancement in the performance of the fine-tuned SAM, increasing from 0.24 to 0.75 in mIoU. This underscores the promising potential of SAM for sonar image segmentation applications. Additionally, even when only 2 out of the 11 categories are utilized for training, the model with box prompt sustains an mIoU of 0.69, showcasing its outstanding capability for general segmentation in sonar images. The code is available at https://github.com/wangsssky/SonarSAM.

Ocean perception has always been an important research content in ocean development. Forward-looking sonar (FLS) is widely used in marine perception due to its advanced performance, real-time properties, and good observation effect. Through the stitching of multiple FLS images, large-scale ocean geomorphology can be realized. However, most of the existing stitching methods of FLS images come from the stitching methods of optical images, which do not consider the unique characteristics of FLS images. Therefore, the result of FLS image stitching is prone to blur and misalignment. Through the analysis of the correspondence between the sonar motion and the pixel motion on the FLS polar image, it is found that the pixel motion in the midline area of the polar image has good stability. According to this characteristic, an FLS image registration method based on a midline template matching algorithm is proposed in this article. At the same time, the motion model of the sonar is introduced as a new constraint to the stitching process creatively, which makes the sonar image sequence stitch more accurately. In the end, the comparison experiments verify that the proposed method has better motion estimation accuracy and achieves a better stitching effect.

Affected by acoustic transmission loss, angular responses of sediments, time varying gain (TVG) residuals and etc., side-scan sonar (SSS) images have the problem of gray attenuation perpendicular to the track direction. This problem seriously affects the subsequent stitching, detection, or other tasks. In this case, it is necessary to perform gray scale correction. The existing methods have a good correction effect on flat terrain but do not consider SSS images with rugged terrain. This paper presents a gray scale correction method for SSS images with rugged terrain based on compensating acoustic attenuation. Firstly, the influence of transmission loss and the incident angle is analyzed, and a multiplicative attenuation model is established. Then, the image is segmented along the attenuation direction, and the segmentation intensity is calculated by using the multiplicative model for the echo points. Finally, the maximum segmentation intensity is taken as the target intensity, and the non-shaded points of the image are compensated and corrected. The experimental results show that our method ensures the gray consistency of flat terrain on SSS images with rugged seafloor, highlights the seabed topography, and significantly improves the effect of image mosaic.

High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).

BACKGROUND: To construct gene co-expression networks, it is necessary to evaluate the correlation between different gene expression profiles. However, commonly used correlation metrics, including both linear (such as Pearson's correlation) and monotonic (such as Spearman's correlation) dependence metrics, are not enough to observe the nature of real biological systems. Hence, introducing a more informative correlation metric when constructing gene co-expression networks is still an interesting topic. RESULTS: In this paper, we test distance correlation, a correlation metric integrating both linear and non-linear dependence, with other three typical metrics (Pearson's correlation, Spearman's correlation, and maximal information coefficient) on four different arrays (macrophage and liver) and RNA-seq (cervical cancer and pancreatic cancer) datasets. Among all the metrics, distance correlation is distribution free and can provide better performance on complex relationships and anti-outlier. Furthermore, distance correlation is applied to Weighted Gene Co-expression Network Analysis (WGCNA) for constructing a gene co-expression network analysis method which we named Distance Correlation-based Weighted Gene Co-expression Network Analysis (DC-WGCNA). Compared with traditional WGCNA, DC-WGCNA can enhance the result of enrichment analysis and improve the module stability. CONCLUSIONS: Distance correlation is better at revealing complex biological relationships between gene profiles compared with other correlation metrics, which contribute to more meaningful modules when analyzing gene co-expression networks. However, due to the high time complexity of distance correlation, the implementation requires more computer memory.

As a powerful tool for solving nonlinear complex system control problems, the model-free reinforcement learning hardly guarantees system stability in the early stage of learning, especially with high complicity learning components applied. In this paper, a reinforcement learning framework imitating many cognitive mechanisms of brain such as attention, competition, and integration is proposed to realize sample-efficient self-stabilized online learning control. Inspired by the generation of consciousness in human brain, multiple actors that work either competitively for best interaction results or cooperatively for more accurate modeling and predictions were applied. A deep reinforcement learning implementation for challenging control tasks and a real-time control implementation of the proposed framework are respectively given to demonstrate the high sample efficiency and the capability of maintaining system stability in the online learning process without requiring an initial admissible control.

As a common phenomenon, most modern detectors for forward looking sonar (FLS) images rely heavily on pyramid structure and stream fusion to enhance information and improve performance. But for FLS images with fuzzy boundaries, complex gradients and low information resolution, excavating more potential information representation is necessary. Considering the fuzzy boundaries and complex gradients of FLS images, a transformer feature fusion network (TFFN) based on the transformer stack structure is proposed to promote information fusion. Additionally, we also propose a novel method called ellipse quality evaluation (EQE) to improve the reliability of localization quality estimation, reduce the false detection rate caused by low resolution, and thus improve the detection performance. Finally, we present an anchor-free method based on TFFN and EQE, called TransYOLO. Experiments show that our detector has reached an advanced level in the FLS dataset. Compared with the state-of-the-art detectors, the proposed detector has the best detection performance when it is qualitatively and quantitatively analyzed on the four evaluation metrics in the FLS dataset. Code and models are available athttps://github.com/Elaine-54/TransYOLO.

BACKGROUND: Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms. RESULTS: In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge. CONCLUSION: In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

This paper presents the constraint dynamic modelling of a six-link elbow-bracing manipulator. This system is kinematically redundant when it is asked to perform spatial trajectory tracking tasks. Hence the extra degrees of freedom (DOFs) can be used to assign additional motion such as constraint forces control without violating end-effect's functions, which can improve the manipulator's performance such as minimizing energy requirements. Since the control of the constraint forces will not affect the end-effect's position, the hybrid force and position control method is proposed. The control scheme consists of two terms: constraint forces control with the incorporation of proportional (p) controller and trajectory tracking control. In addition, the motion equations of motors are incorporated into the constraint dynamics of the system. So that the energy consumption can be calculated by integrating the product of the voltage and current. This study is based on our previous works, which can achieve the control of three constraint forces. Finally, simulation experiments along with comparative studies of previous works such as: with no constraint force and one constraint force are conducted. The results show that the proposed method achieves prior energy-efficient performance and tracking accuracy.

In the medical field and in industry application, a novel type of micro biped robot with multi DOF that can swim smoothly in water or aqueous medium has urgently been demanded. The fish-like microrobot is one of the micro and miniature devices, which is installed with sensing and actuating elements. This paper describes the new structure and motion mechanism of a hybrid type of underwater microrobot using ICPF actuator, and discusses the swimming and floating possibility of the microrobot in water. Characteristic of the underwater microrobot is measured by changing the frequency and the amplitude of input voltage. The experimental results indicate that the swimming speed of proposed underwater microrobot can be controlled by changing the frequency of input voltage; the moving direction (upward or downward) can be controlled by changing the amplitude and the frequency of input voltage

Since data packets may get lost somewhere in the Internet connections, for real-time applications such as Internet-based teleoperation, delay boundary prediction plays an important role in determining properly whether a packet is lost or not. The predictors currently employed are lowpass filters based on the autoregressive and moving average (ARMA) models. However, recent studies and the results of the experiments in this paper show that the traditional ARMA model is not suitable because sometimes delays develop with quick and evident variation. In this paper, we present a novel adaptive algorithm for delay boundary prediction based on the maximum entropy principle (MEP). The results of our 3 successive working day experiments on 9 links which consists of academic, commercial and governmental ones among Northern America, Asia and Europe show that the MEP algorithm proposed has a better performance than the traditional ARMA method.

For Internet based teleoperation, the most difficult and distinct part is the unavoidable time-varying delays between human operators and remote robotic devices. Currently, the RTTs (roundtrip time or we can call it delay) are mostly treated as given conditions in application level. In this paper, after a statistical analysis of the huge RTT time series collected densely in a few continuous days using some linear and nonlinear methods, it is found that the RTT time series has a rather high degree of linear correlation between observations. It could be inferred that there is no high nonlinear dependence among observations. Thus, RTT is linearly predictable. We use the MEP (maximum entropy principle) method developed to predict next RTT value (one step ahead) and the results confirm our findings.