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To improve the retrieval result obtained from a pairwise dissimilarity, many variants of diffusion process have been applied in visual re-ranking. In the framework of diffusion process, various contextual similarities can be obtained by solving an optimization problem, and the objective function consists of a smoothness constraint and a fitting constraint. And many improvements on the smoothness constraint have been made to reveal the underlying manifold structure. However, little attention has been paid to the fitting constraint, and how to build an effective fitting constraint still remains unclear. In this article, by deeply analyzing the role of fitting constraint, we firstly propose a novel variant of diffusion process named Hybrid Regularization of Diffusion Process (HyRDP). In HyRDP, we introduce a hybrid regularization framework containing a two-part fitting constraint, and the contextual dissimilarities can be learned from either a closed-form solution or an iterative solution. Furthermore, this article indicates that the basic idea of HyRDP is closely related to the mechanism behind Generalized Mean First-passage Time (GMFPT). GMFPT denotes the mean time-steps for the state transition from one state to any one in the given state set, and is firstly introduced as the contextual dissimilarity in this article. Finally, based on the semi-supervised learning framework, an iterative re-ranking process is developed. With this approach, the relevant objects on the manifold can be iteratively retrieved and labeled within finite iterations. The proposed algorithms are validated on various challenging databases, and the experimental performances demonstrate that retrieval results obtained from different types of measures can be effectively improved by using our methods. [ABSTRACT FROM AUTHOR]

This paper aims at the monitoring of birds and their ecological environment in the island and coastal wetland ecosystems. A new approach of island bird detection is proposed based on the Faster R-CNN (Regions with Convolutional Neural Networks) model under multiple backgrounds. It includes feature extraction, region proposal, bounding box regression, classification into the whole neural network structure. This key technology can automatically achieve automatic bird species identification and quantitative statistics in the faster computation speed. The details of constructing Faster R-CNN are described. In the end, many actual images are utilized to demonstrate the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band > texture feature > polarization decomposition parameter > vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classification algorithm is suitable for the study of mangrove species classification, and the classification accuracy of extremely random trees in this paper is higher than that of random forest (RF), K-nearest neighbor (KNN), and Bayesian (Bayes). The results can provide technical guidance and data support for mangrove species monitoring based on multi-source satellite data. [ABSTRACT FROM AUTHOR]

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-means clustering algorithm is proposed based upon the prior information. This method can solve the fuzzy C-means algorithm''s problem that the clustering quality is greatly affected by the data distributing and the stochastic initializing the centrals of clustering. After the probability statistics of original data, the weights of data attribute are designed to adjust original samples to the uniform distribution, and added in the process of cyclic iteration, which could be suitable for the character of fuzzy C-means algorithm so as to improve the precision. Furthermore, appropriate initial clustering centers adjacent to the actual final clustering centers can be found by the proposed single point adjustment method, which could promote the convergence speed of the overall iterative process and drastically reduce the calculation time. Otherwise, the modified algorithm is updated from multidimensional data analysis to color images clustering. Moreover, with the comparison experiments of the UCI data sets, public Berkeley segmentation dataset and the actual remote sensing data, the real validity of proposed algorithm is proved. [Copyright &y& Elsevier]

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas. [ABSTRACT FROM AUTHOR]

Nanodiamonds (NDs) are emerging as a promising candidate for multimodal bioimaging on account of their optical and spectroscopic properties. NDs are extensively utilized for bioimaging probes due to their defects and admixtures in their crystal lattice. There are many optically active defects presented in NDs called color centers, which are highly photostable, extremely sensitive to bioimaging, and capable of electron leap in the forbidden band; further, they absorb or emit light when leaping, enabling the nanodiamond to fluoresce. Fluorescent imaging plays a significant role in bioscience research, but traditional fluorescent dyes have some drawbacks in physical, optical and toxicity aspects. As a novel fluorescent labeling tool, NDs have become the focus of research in the field of biomarkers in recent years because of their various irreplaceable advantages. This review primarily focuses on the recent application progress of nanodiamonds in the field of bioimaging. In this paper, we will summarize the progress of ND research from the following aspects (including fluorescence imaging, Raman imaging, X-ray imaging, magnetic modulation fluorescence imaging, magnetic resonance imaging, cathodoluminescence imaging, and optical coherence tomography imaging) and expect to supply an outlook contribution for future nanodiamond exploration in bioimaging. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image understanding and interpretation. With the advancement of imaging techniques, it permits to produce higher resolution SAR data and extend data amount. Therefore, intelligent algorithms for high-resolution SAR image classification are demanded. Inspired by deep learning technology, an end-to-end classification model from the original SAR image to final classification map is developed to automatically extract features and conduct classification, which is named deep recurrent encoding neural networks (DRENNs). In our proposed framework, a spatial feature learning network based on long–short-term memory (LSTM) is developed to extract contextual dependencies of SAR images, where 2-D image patches are transformed into 1-D sequences and imported into LSTM to learn the latent spatial correlations. After LSTM, nonnegative and Fisher constrained autoencoders (NFCAEs) are proposed to improve the discrimination of features and conduct final classification, where nonnegative constraint and Fisher constraint are developed in each autoencoder to restrict the training of the network. The whole DRENN not only combines the spatial feature learning power of LSTM but also utilizes the discriminative representation ability of our NFCAE to improve the classification performance. The experimental results tested on three SAR images demonstrate that the proposed DRENN is able to learn effective feature representations from SAR images and produce competitive classification accuracies to other related approaches. [ABSTRACT FROM AUTHOR]

Titanium dioxide (TiO2) is a kind of wide-bandgap semiconductor. Nano-TiO2 devices exhibit size-dependent and novel photoelectric performance due to their quantum limiting effect, high absorption coefficient, high surface-volume ratio, adjustable band gap, etc. Due to their excellent electronic performance, abundant presence, and high cost performance, they are widely used in various application fields such as memory, sensors, and photodiodes. This article provides an overview of the most recent developments in the application of nanostructured TiO2-based optoelectronic devices. Various complex devices are considered, such as sensors, photodetectors, light-emitting diodes (LEDs), storage applications, and field-effect transistors (FETs). This review of recent discoveries in TiO2-based optoelectronic devices, along with summary reviews and predictions, has important implications for the development of transitional metal oxides in optoelectronic applications for researchers. [ABSTRACT FROM AUTHOR]

Diamond holds promise for optoelectronic devices working in high-frequency, high-power and high-temperature environments, for example in some aspect of nuclear energetics industry processing and aerospace due to its wide bandgap (5.5 eV), ultimate thermal conductivity, high-pressure resistance, high radio frequency and high chemical stability. In the last several years, p-type B-doped diamond (BDD) has been fabricated to heterojunctions with all kinds of non-metal oxide (AlN, GaN, Si and carbon-based semiconductors) to form heterojunctions, which may be widely utilized in various optoelectronic device technology. This article discusses the application of diamond-based heterostructures and mainly writes about optoelectronic device fabrication, optoelectronic performance research, LEDs, photodetectors, and high-electron mobility transistor (HEMT) device applications based on diamond non-metal oxide (AlN, GaN, Si and carbon-based semiconductor) heterojunction. The discussion in this paper will provide a new scheme for the improvement of high-temperature diamond-based optoelectronics. [ABSTRACT FROM AUTHOR]

The n-type Ce:ZnO (NL) grown using a hydrothermal method was deposited on a p-type boron-doped nanoleaf diamond (BDD) film to fabricate an n-Ce:ZnO NL/p-BDD heterojunction. It shows a significant enhancement in photoluminescence (PL) intensity and a more pronounced blue shift of the UV emission peak (from 385 nm to 365 nm) compared with the undoped heterojunction (n-ZnO/p-BDD). The prepared heterojunction devices demonstrate good thermal stability and excellent rectification characteristics at different temperatures. As the temperature increases, the turn-on voltage and ideal factor (n) of the device gradually decrease. The electronic transport behaviors depending on temperature of the heterojunction at different bias voltages are discussed using an equilibrium band diagram and semiconductor theoretical model. [ABSTRACT FROM AUTHOR]

It is of great need to accurately locate vessels and oil platforms because huge numbers of these marine man-made targets can cause oil spills or illegal invasion problems. Many different types of marine targets in the complex marine environment make it very difficult to deal with the target detection and classification process accurately. This paper proposes a robust Laplacian of Gaussian operator connected domain controller squeeze excitation residual network (LCSE-ResNet) for marine man-made target classification based on optical remote sensing imagery. Vessel and oil platform candidate regions in remote sensing images are extracted by the Laplacian of Gaussian (LoG) operator and the connected domain controller, which are input into the classification neural networks in the following. It is a whole End-to-End structure from original remote sensing images to the precise target information. That clouds, ripples, and the other man-made target disturbances can be excluded effectively means the better robust character in the actual application. Some shape and size features of vessels and oil platforms are considered in the LCSE-ResNet structure, which improves the explanation of the final results. Experimental results demonstrate that the proposed LCSE-ResNet can effectively detect marine man-made targets and effectively distinguish vessels and oil platforms. [ABSTRACT FROM AUTHOR]

The hydrothermal approach has been used to fabricate a heterojunction of n-aluminum-doped ZnO nanorods/p-B-doped diamond (n-Al:ZnO NRs/p-BDD). It exhibits a significant increase in photoluminescence (PL) intensity and a blue shift of the UV emission peak when compared to the n-ZnO NRs/p-BDD heterojunction. The current voltage (I-V) characteristics exhibit excellent rectifying behavior with a high rectification ratio of 838 at 5 V. The n-Al:ZnO NRs/p-BDD heterojunction shows a minimum turn-on voltage (0.27 V) and reverse leakage current (0.077 μA). The forward current of the n-Al:ZnO NRs/p-BDD heterojunction is more than 1300 times than that of the n-ZnO NRs/p-BDD heterojunction at 5 V. The ideality factor and the barrier height of the Al-doped device were found to decrease. The electrical transport behavior and carrier injection process of the n-Al:ZnO NRs/p-BDD heterojunction were analyzed through the equilibrium energy band diagrams and semiconductor theoretical models. [ABSTRACT FROM AUTHOR]

N-TiO 2 nanorod clusters (Ncs)/p-degenerated boron-doped diamond (n-TiO 2 Ncs/p-DBDD) film heterojunction was prepared by hydrothermal method, and the photoelectric properties depending on temperature dependence were tested. Three distinct emission peaks of 402 nm, 429 nm and 454 nm were detected by the photoluminescence (PL) test under 355 nm ultraviolet light and excited in the blue-green region light. The heterojunction exhibited good rectification characteristics and thermal stability from room temperature to 180 °C. The forward current first decreases and then increases with increasing temperature. The 160 °C is considered to be the ideal high temperature for the best performance of the heterojunction, with the rectification ratio reaching 20.667. The carrier transport mechanisms and tunneling process of n-TiO 2 Ncs/p-DBDD heterojunction with temperature are analyzed in detail through the established band structure diagram. This study provides an effective way method for the potential development of high-temperature stable optoelectronic devices. [Display omitted] • Tested the high-temperature (RT-180 °C) electrical performance of n-TiO 2 Ncs/p-DBDD heterojunction devices for the first time. • The heterojunction exhibited good rectification characteristics and thermal stability from room temperature to 180 ° C. • The 160 °C is considered to be the ideal high-temperature for the best performance of the heterojunction, with the rectification ratio reaching 20.667. • he mechanism for the enhanced optical and electrical properties of this heterojunction is revealed. • This work provides a new path for the design and application of diamond-based devices. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) plays an irreplaceable role in the monitoring of marine oil spills. However, due to the limitation of its imaging characteristics, it is difficult to use traditional image processing methods to effectively extract oil spill information from SAR images with coherent speckle noise. In this paper, the convolutional neural network AlexNet model is used to extract the oil spill information from SAR images by taking advantage of its features of local connection, weight sharing, and learning for image representation. The existing remote sensing images of the oil spills in recent years in China are used to build a dataset. These images are enhanced by translation and flip of the dataset, and so on and then sent to the established deep convolutional neural network for training. The prediction model is obtained through optimization methods such as Adam. During the prediction, the predicted image is cut into several blocks, and the error information is removed by corrosion expansion and Gaussian filtering after the image is spliced again. Experiments based on actual oil spill SAR datasets demonstrate the effectiveness of the modified AlexNet model compared with other approaches. [ABSTRACT FROM AUTHOR]

In the past decades, even though the performance of electronic devices based on MoS 2 has reached a very high level, the service life, failure rate and stability of the devices often deteriorate with the increase of temperature when they work in high temperature or harsh environment. In this work, n-MoS 2 NSs/p-type boron-doped diamond (BDD) film heterojunction was fabricated by sol–gel method to investigate the temperature dependence of the optoelectronic transport behavior of the device, and the electrical properties of the heterojunction device at high temperature (RT-180 °C) were tested. The heterojunction presented all rectification characteristics from room temperature to 180 °C and exhibited good thermal stability. With the increase in temperature, the rectification ratio reached the maximum at 120 °C and turned into backward diodes at 160 °C and 180 °C. The mechanism of the current and rectification ratio of n-MoS 2 NSs/p-BDD heterojunction changing with temperature was mainly attributed to the decrease of tunneling current caused by the shift of Fermi level of the two semiconductors at high temperature. The heterojunction undergoes a transition from tunneling current to thermal excitation current, and the shift of Fermi level to the valence band of n-MoS 2 provides support for the transition to reverse diode. In accordance with the carrier transport behavior following ohmic laws, the current conduction of composite tunneling and the space charge limitation under different bias voltages and temperatures were also discussed, which provides theoretical and experimental basis for optimizing the design of new photoelectric devices in the field of high temperature optoelectronics. [Display omitted] • Tested the high-temperature (RT-180 °C) electrical performance of n-MoS 2 NSs/p-BDD heterojunction devices. • The n-MoS 2 NSs/p-BDD heterojunction presented all rectification characteristics from RT to 180 °C and exhibited good thermal stability. • The rectification ratio of n-MoS 2 NSs/p-BDD heterojunction reached the highest value at 120 °C and transformed into backward diode at 160 °C and 180 °C. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) have been widely utilized in time series analysis and modeling, wherein generators and discriminators interact to generate realistic data. However, when addressing the challenge of imputing continuous missing values in time series, the generator struggles to learn meaningful features due to the loss of local information, and the discriminator's loss function exhibits significant deviations in determining the probability, making it difficult to effectively update model parameters during training. In response to these issues, this study presents a novel imputation model called cue Wasserstein generative adversarial network with gradient penalty (CWGAIN-GP). CWGAIN-GP incorporates the contextual cue information matrixs into the generator, enabling obtain and capture the potential data evolution features that hidden beyond the missing positions. This approach constrains the generator's generation results to be closer to the true values probability distribution. Meanwhile, it optimizes the generator and discriminator neural network structure and loss function computation strategy of the original GAN model, which not only improves the accuracy of continuous missing data interpolation, but also improves the training stability of the model. Finally, We used three imputation accuracies on two real-world datasets, including experiments on imputation accuracy for eight comparison models, ablation study, and experiments on the effect of consecutive missing lengths. The experimental results show that CWGAIN-GP achieves impressive performance in imputing continuous missing time series. [ABSTRACT FROM AUTHOR]

The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches. [ABSTRACT FROM PUBLISHER]

• The uniaxial compressive properties of A-UHPC after high temperature were studied. • The uniaxial compression stress–strain constitutive model of A-UHPC after high temperature was established and verified. • The deterioration mechanism of the A-UHPC after high temperature was revealed by using the SEM. In this study, a total of 72 prism specimens were manufactured to investigate the steel fiber volume fraction and exposure temperature on the uniaxial compression performance of the aluminate cement-based ultra-high performance concrete (A-UHPC). The main uniaxial compression performances of the A-UHPC after high temperature were examined, including the apparent characteristic, mass loss, failure mode, compressive strength, peak strain, initial elastic modulus and compressive stress–strain curve. As the exposure temperature increased, the failure mode of the non-fiber specimen showed a trend of transition from brittleness to ductility, the uniaxial compressive strength first increased and then decreased and the peak strain gradually increased. However, the initial elastic modulus continued to decrease. Subsequently, the calculation formulas of the uniaxial compressive strength, peak strain and elastic modulus considering the influence of temperature were proposed through regression analysis, and the uniaxial compression stress–strain constitutive model of the A-UHPC with 3% volume steel fiber was established and verified. Finally, the deterioration mechanism of the A-UHPC after high temperature was revealed by SEM for microscopic phase tests. [ABSTRACT FROM AUTHOR]

This study employs polarimetric synthetic aperture radar (Pol-SAR) to examine the scattering properties of continuous slow-release oil slicks on the sea surface. The objective is to extract and analyze the general polarization scattering properties of continuous slow-release slicks, i.e., those slicks that consist of substances released at a fairly slow and relatively constant rate, and to determine the influence of the slick formation process on these properties. Using multi-polarization feature parameters derived from the averaged coherency matrix, we find that the scattering mechanisms related to the continuous slow-release slicks differ from those of anthropogenic slicks, possibly as a result of the multiple scattering mechanisms that occur between the interfaces formed by the thick slick layer. Combinations of entropy (H) and modified anisotropy (A12) are relatively robust parameters for identifying continuous slow-release slicks under different sea conditions, and may serve as a reference parameter for slick detection. [ABSTRACT FROM AUTHOR]