AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on Non-Maximum Suppression (NMS) with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD

The role of echocardiography in prognosis for dysfunction and abandonment of radiocephalic arteriovenous fistula in elderly Chinese patients on hemodialysis

The objective of this study was to examine the impact of cardiac structure and function at baseline on the outcomes associated with arteriovenous fistula (AVF) in patients on hemodialysis (HD). Patients who initiated HD aged ≥70 years and received a mature AVF creation were included retrospectively. Echocardiographic parameters measured within 1 week before AVF creation were acquired. The observational period for each patient was from the point of AVF creation to the last time of follow‐up unless AVF abandonment or death occurred. Kaplan‐Meier and Cox proportional hazard regression analyses were conducted. A total of 82 elderly Chinese HD patients with mature radiocephalic AVF (RCAVF) and EF ≥50% were analyzed. During the median study period of 26.8 (12‐40) months, 42 (51.2%) experienced RCAVF dysfunction and 34 (41.5%) progressed to abandonment. Primary and cumulative patencies at 6, 12, 24, and 36 months were 81%, 73%, 48%, 38%, and 84%, 81%, 68%, 55%, respectively. Left ventricle end‐diastolic volume (LVEDV) ≤103.5 mL (HR = 2.5, P = .019) and the right side of RCAVF (HR = 3.59, P = .003) significantly predicted RCAVF dysfunction. The main pulmonary artery internal diameter (MPAID) ≤21.5 mm (HR = 4.3, P = .001) as well as the right side (HR = 2.95, P = .047) were the independent predictors for RCAVF abandonment. In conclusion, LVEDV, MPAID assessed by echocardiography and the right side of RCAVF, showed significant predictive implications for the outcomes of RCAVF. Disparities among nationalities in the areas of utilization and patency of AVFs necessitate additional studies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156158/2/sdi12871.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156158/1/sdi12871_am.pd

A Novel Phase Compensation Method for Urban 3D Reconstruction Using SAR Tomography

Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain

A Novel Phase Compensation Method for Urban 3D Reconstruction Using SAR Tomography

Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain

Demonstration and Analysis of an Extended Adaptive General Four-Component Decomposition

The overestimation of volume scattering is an essentialshortcoming of the model-based polarimetric syntheticaperture radar (PolSAR) target decomposition method. It islikely to affect the measurement accuracy and result in mixedambiguity of scattering mechanism. In this paper, an extendedadaptive four-component decomposition method (ExAG4UThs)is proposed. First, the orientation angle compensation (OAC)is applied to the coherency matrix and artificial areas areextracted as the basis for selecting the decomposition method.Second, for the decomposition of artificial areas, one of the twocomplex unitary transformation matrices of the coherency matrixis selected according to the wave anisotropy (Aw). In addition, thebranch condition that is used as a criterion for the hierarchicalimplementation decomposition is the ratio of the correlationcoefficient (Rcc). Finally, the selected unitary transformationmatrix and discriminative threshold are used to determine thestructure of the selected volume scattering models, which aremore effectively to adapt to various scattering mechanisms. Inthis paper, the performance of the proposed method is evaluatedon GaoFen-3 full PolSAR data sets for various time periods andregions. The experimental results demonstrate that the proposedmethod can effectively represent the scattering characteristics ofthe ambiguous regions and the oriented building areas can bewell discriminated as dihedral or odd-bounce structures

Flattened dispersion Ge 11.5 As 24 Se 64.5 glass waveguide for correlated photon generation: Design and analysis

A realizable waveguide structure providing ultra-low anomalous dispersion is achievable by changing the effective index of the top cladding using a SiO2 layer. Using atomic layer deposition to produce the silica layer, the dispersion can be tuned with a

A Bragg-Like Point Extraction Method for Co-polarization Channel Imbalance Calibration

Calibration by distributed targets is an important part of polarimetric calibration (PolCAL) without corner reflectors (CRs). Currently, both the correlation of HH and VV ($R_{\text{hhvv}}$) and the equivalent number of looks (ENL) constitute the common method for the extraction of Bragg-like points (BLPs) in order to calibrate the co-polarization channel imbalance $k$. However, strict assumptions about distributed targets and complex mathematical expressions regarding ENL limit the impact analysis of $k$ on $R_{\text{hhvv}}$ and ENL; thus, the fixed thresholds for $R_{\mathrm{hhvv}}$ and ENL can be obtained only by comparing the calibration errors with the CRs or simulated values in order to calibrate $k$ accurately. In some Earth observation and lunar exploration without CRs, the abovementioned conditions are not satisfied. In this article, a BLP extraction method is proposed for calibrating co-polarization channel imbalance $k$ in PolCAL. ${H}/{\bar{\alpha }}$ decomposition is utilized to deduce the specific scattering impacts of BLPs by different $k$ values first. Then, a dynamic selection method is proposed to reduce the influence of fixed thresholds and improve the extraction accuracy. Multiscene polarimetric synthetic aperture radar images from AIRSAR, ALOS, and GF-3 are utilized to verify the effectiveness of the proposed algorithm in the extraction of BLPs and the calibration results of $k$ obtained by applying the extracted BLPs

An Innovative Supervised Classification Algorithm for PolSAR Image Based on Mixture Model and MRF

The Wishart mixture model is an effective tool for characterizing the statistical distribution of polarimetric synthetic aperture radar (PolSAR) data. However, due to the difficulty in determining the equivalent number of looks, the Wishart mixture model has some problems in terms of practicality. In addition, the flexibility of the Wishart mixture model needs to be improved for complicated scenes. To improve the practicality and flexibility, a new mixture model named the relaxed Wishart mixture model (RWMM) is proposed. In RWMM, the equivalent number of looks is no longer considered a constant for the whole PolSAR image but a variable that varies between different clusters. Next, an innovative algorithm named RWMM-Markov random field (RWMM-MRFt) for supervised classification is proposed. A new selection criterion for adaptive neighborhood systems is proposed in the algorithm to improve the classification performance. The new criterion makes effective use of PolSAR scattering information to select the most suitable neighborhood for each center pixel in PolSAR images. Three datasets, including one simulated image and two real PolSAR images, are utilized in the experiment. The maximum likelihood classification results demonstrate the flexibility of the proposed RWMM for modeling PolSAR data. The proposed selection criterion shows superior performance than the span-based selection criterion. Among the mixture model-based MRF classification algorithms, the proposed RWMM-MRFt algorithm has the highest classification accuracy, and the corresponding classification maps have better anti-noise performance

An Innovative Supervised Classification Algorithm for PolSAR Image Based on Mixture Model and MRF

The Wishart mixture model is an effective tool for characterizing the statistical distribution of polarimetric synthetic aperture radar (PolSAR) data. However, due to the difficulty in determining the equivalent number of looks, the Wishart mixture model has some problems in terms of practicality. In addition, the flexibility of the Wishart mixture model needs to be improved for complicated scenes. To improve the practicality and flexibility, a new mixture model named the relaxed Wishart mixture model (RWMM) is proposed. In RWMM, the equivalent number of looks is no longer considered a constant for the whole PolSAR image but a variable that varies between different clusters. Next, an innovative algorithm named RWMM-Markov random field (RWMM-MRFt) for supervised classification is proposed. A new selection criterion for adaptive neighborhood systems is proposed in the algorithm to improve the classification performance. The new criterion makes effective use of PolSAR scattering information to select the most suitable neighborhood for each center pixel in PolSAR images. Three datasets, including one simulated image and two real PolSAR images, are utilized in the experiment. The maximum likelihood classification results demonstrate the flexibility of the proposed RWMM for modeling PolSAR data. The proposed selection criterion shows superior performance than the span-based selection criterion. Among the mixture model-based MRF classification algorithms, the proposed RWMM-MRFt algorithm has the highest classification accuracy, and the corresponding classification maps have better anti-noise performance

Atmospheric Particles Are Major Sources of Aged Anthropogenic Organic Carbon in Marginal Seas

Deposition of atmospheric particulates is a major pathway for transporting materials from land to the ocean, with important implications for climate and nutrient cycling in the ocean. Here, we report the results of year-round measurements of particulate organic carbon (POC) and black carbon (BC) in atmospheric aerosols collected on Tuoji Island in the coastal Bohai-Yellow Sea of China (2019–2020) and during a cruise in the western North Pacific. Aerosol POC contents ranged from 1.9 to 11.9%; isotope values ranged from −18.8 to −29.0‰ for δ13C and −150 to −892‰ for Δ14C, corresponding to 14C ages of 1,235 to 17,780 years before present (BP). Mass balance calculations indicated that fossil carbon contributed 19–66% of the POC, with highest values in winter. BC produced from fossil fuel combustion accounted for 18–54% of the POC. “Old” BC (mean 6,238 ± 740 yr BP) was the major contributor to POC, and the old ages of aerosol POC were consistent with the 14C ages of total OC preserved in surface sediments of the Bohai-Yellow Sea and East China Sea. We conclude that atmospheric deposition is an important source of aged OC sequestered in marginal sea sediments and thus represents an important sink for carbon dioxide from the atmosphere