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Based on the benefits of AlN solar-blind ultraviolet (SUBV) detectors in the field of photovoltaics, rare Earth element (RE) yttrium (Y) was doped and investigated to regulate the sensitization range of AlN films by dual-target co-sputtering with different Y target sputtering power. The results showed that the structure of the film changed from AlN (002) to a mixture of AlYN (002) and (100) and then to amorphous with increased Y content, accompanied by the transformation from pebble-like shape morphology to amorphous morphology. The adjusted band gap (5.65–4.51 eV) of AlYN films was widened by Y doped, notably, the film S4 (with Y content of 8.1 at.%) completely covered the solar-blind response band. In particular, there were AlYN and Y crystals formed in the film S2 (with Y content of 1.28 at.%) with low resistivity and high carrier mobility. Based on which, metal-semiconductor-metal (MSM) type photodetectors (PDs) with Ag/Al/AlYN MSM interdigitated electrodes (IDE) structures were fabricated, in which the film S2 exhibited the best photo-detection characteristics at −2 V bias, with photo-to-dark current ratio (PDCR), responsivity (R), external quantum efficiency (EQE), and specific detectivity ( ${D}^{\ast } $ ) of 154.9, $35.7~\mu $ A/W, $2.27\times 10^{-{2}}$ %, and $1.42\times 10^{{10}}$ Jones, respectively, while possessing significant self-powered characteristics (PDCR was 121.5).

This letter considers the distributed fusion for Labeled Multi-Bernoulli (LMB) filters under the multi-sensor multi-target tracking scenario. In practice, a novel fusion method that combines the label-free strategy with the fusion method based on the Arithmetic Average (AA) is proposed. Firstly, the label-free version of the LMB posterior is obtained. Then, the corresponding Probability Hypothesis Density (PHD) is acquired and fused by the merging-based PHD-AA fusion method. Finally, the labels of the fused distributions are reassigned. The simulation shows that the distributed LMB filter using the proposed fusion method can achieve more accurate multi-target state estimates with relatively low computational burden under clutter and missed detection environment compared to other existing methods.

AbstractAt present, most machine learning bathymetry retrieval models use the band reflectance as the inversion feature only, without considering features related to the water substrate and pixel spatial correlation. In this study, in addition to band reflectance, two features, Depth-Invariant Index (DII) and pixel location, are taken into account. Two machine learning algorithms, Random Forest (RF) and Back Propagation (BP) neural network are used to retrieve bathymetry. The effects of the two features on the accuracy and performance of bathymetry retrieval are explored. The results show that: (i) Machine learning algorithms are generally superior to the widely used Stumpf model. Stumpf model performs better only in the depth range of 8–16 m, with a Root Mean Square Error (RMSE) of 0.85 m, but has poor performance in other depth ranges. (ii) Compared with models that use Band Reflectance (BR) only, DIIb,g(blue-green DII) + BR model, Location and Location + BR models are all superior to the BR model for RF and BP algorithms. It means that DII and location features are very effective in improving the bathymetry retrieval accuracy.

In recent years, there has been significant progress in fully supervised learning for smoke image segmentation. However, these methods rely heavily on manually annotated labels, which is difficult for the field of smoke segmentation that lacks public datasets. In this work, we address this issue by employing a semi-supervised semantic segmentation approach that iteratively assigns pseudo-labels to unlabeled data, reducing the need for smoke-labeled data. However, traditional self-training methods tend to prioritize reliable pseudo-labels and overlook the potential of unreliable pseudo-labels. At the same time, there may be false reliable pseudo-labels that introduce false supervisory signals, leading to the confirmation bias problem. To alleviate these problems, we propose a novel method called the Dual-Branch Pseudo-label Selection (DBPS). DBPS focuses on two aspects: selecting more reliable pseudo-labels and exploiting the potential of unreliable pseudo-labels. Intuitively, one branch identifies potentially incorrect regions within reliable pseudo-labels, while the other identifies potentially correct regions within unreliable pseudo-labels. This dual-branch approach ensures a more comprehensive and balanced pseudo-label selection process. In addition, we propose a multi-scale decoder representation head that leverages pixel-level contrastive learning to learn discriminative feature representations. This allows us to enable the gathering of similar samples and the scattering of dissimilar samples. The experimental results on the synthetic smoke dataset demonstrate the effectiveness of our proposed method in mitigating the noise of pseudo-labels and achieving outstanding performance, particularly in challenging scenarios with a limited number of labeled data. [ABSTRACT FROM AUTHOR]

To combat the variety uncertainties in topologies, dynamical models and disturbances, this paper presents a distributed sliding mode control strategy for formation control of multiple AVs. In this scheme, all collected information of each AV is used for its control and different perturbations are dealt with separately to reduce the contractions among them. Furthermore, a distributed adaptive algorithm is designed to replace the witching part for smoothness of control. The convergence of sliding surfaces of both two controllers is analysed theoretically. The sliding dynamics is affected by both the feedback and interaction topology. The existing decoupling method to handle the variety topologies can be used to synthesize the sliding dynamics. Finally, this approach has been applied to vehicular platooning and validated by several comparative simulations. The results show that the proposed method can control multiple AVs better than the state feedback strategy.

Stretchable and conductance-stable composites are critical for progress in flexible electronics. Tremendous efforts have focused on engineering electrical conductors from liquid metal-embedded elastomers. While these elastomers enable elastic compliance and deformability, the embedded liquid metal is vulnerable to puncture that causes undesired electrical failure, and most of the conductive elastomers are strain-sensitive with degenerative conductance upon deformations. Here, we combined analytical and numerical analyses with experimental studies to investigate the effects of the innovatively designed continuous annular-shaped branch channels, inspired by the Tesla valve, on the performances of the LM-embedded conductive composite. Theoretically, in the process of mechanical deformations, the as-proposed continuous annular-shaped architecture can dissipate the pressure energy of the fluidic LM to enhance the fracture toughness of the composite, while the LM flow could be accelerated as the deformation was removed to maintain the percolated conductive path, leading to excellent conductive stability. Experimentally, the as-fabricated composite conductor with continuous annular-shaped branch channels can be stretched up to ∼80% (more than two times higher than conventional LM-embedded composite), and upon fully activating the conductive pathway, a very high conductivity of ∼4 × 104 S m−1and conductance change of less than 2% under varied deformations, including stretching, flexing, twisting and compressing, are realized. It is believed that this novel design strategy will extend the understanding and application opportunities of LM as functional materials in the development of flexible electronics.

In this study, a nitrogen–boron codoped carbon layer, Na3V2(PO4)2F3sample, obtained by using an ionic liquid–acrylic acid copolymer as the nitrogen–boron source was used as the cathode material for sodium-ion batteries. The optimized and modified nitrogen and boron codoped carbon layer, Na3V2(PO4)2F3(denoted as NVPF-PCNB-20), illustrated better rate capability and cycling performance. The discharge capacities of NVPF-PCNB-20 at 0.5C and 10C were 109 and 90 mAh g–1, respectively, and the capacity retention rate was 93.2% after 100 cycles at 0.5C and 92.8% after 750 cycles at 10C. Through in situ X-ray diffraction analysis of NVPF-PCNB-20, the results show that the modified Na3V2(PO4)2F3has excellent cycle reversibility. The scanning electron microscopy and transmission electron microscopy images reveal that NVPF-PCNB-20 particles were finer and covered by a uniform coating. The results show that the ionic liquid–acrylic acid copolymer not only make the material dispersion more uniform but also enhance the electronic conductivity and sodium storage performance of Na3V2(PO4)3F3effectively. This study may provide an effective way to synthesize nitrogen and boron codoped carbon-coated Na3V2(PO4)2F3with excellent electrochemical performance.