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It is a formidable challenge to find the minority carrier recombination lifetime in low-dimensional devices as low-dimensionality increases the surface recombination rate and often reduces the recombination lifetime to a scale of picoseconds. In this work, we demonstrated a simple but powerful method to quantitatively probe the minority carrier recombination lifetime in silicon nanowires or microwires by fitting the experimental photoresponses with our recently established analytical photoresponse principle of photoconductors. The nanowires were passivated with small molecules and Al2O3 to suppress surface recombination, which will increase the minority recombination lifetimes. As expected, the minority carrier recombination lifetime found by this approach increases by orders of magnitude. These wires were also made into PIN diodes, the leakage of which was reduced at least 1 order of magnitude after surface passivation by Al2O3. The minority recombination lifetime found from the leakage current of these devices is largely consistent with what we found from our analytical photoresponse principle. As a further step, we performed scanning photocurrent microscopy to find the minority diffusion length from which we found that the minority recombination lifetime is close to what we found from the analytical photoresponses. In short, this work validated that our analytical response principle is a reliable method to find the minority recombination lifetime in low-dimensional semiconductors. [ABSTRACT FROM AUTHOR]

Building segmentation has extensive research value and application prospects in high-resolution remote sensing image (HRSI) processing. However, complex architectural contexts, varied building morphologies, and non-building occlusions make building segmentation challenging. Compared with traditional methods, deep learning-based methods present certain advantages in terms of accuracy and intelligence. At present, the most popular option is to first apply a single neural network to encode an HRSI, then perform a decoding process through up-sampling or using a transposed convolution operation, and then finally obtain the segmented building image with the help of a loss function. Although effective, this approach not only tends to lead to a loss of detail information, but also fails to fully utilize the contextual features. As an alternative, we propose a novel network called NPSFF-Net. First, using an improved pseudo-Siamese network composed of ResNet-34 and ResNet-50, two sets of deep semantic features of buildings are extracted with the support of transfer learning, and four encoded features at different scales are obtained after fusion. Then, information from the deepest encoded feature is enriched using a feature enhancement module, and the resolutions are recovered via the operations of skip connections and transposed convolutions. Finally, the discriminative features of buildings are obtained using the designed feature fusion algorithm, and the optimal segmentation model is obtained by fitting a cross-entropy loss function. Our method obtained intersection-over-union values of 89.45% for the Aerial Imagery Dataset, 71.88% for the Massachusetts Buildings Dataset, and 68.72% for the Satellite Dataset I. [ABSTRACT FROM AUTHOR]

Detecting changes in multisource heterogeneous images is a great challenge for unsupervised change detection methods. Image-translation-based methods, which transform two images to be homogeneous for comparison, have become a mainstream approach. However, most of them primarily rely on information from unchanged regions, resulting in networks that cannot fully capture the connection between two heterogeneous representations. Moreover, the lack of a priori information and sufficient training data makes the training vulnerable to the interference of changed pixels. In this paper, we propose an edge-oriented generative adversarial network (EO-GAN) for change detection that indirectly translates images using edge information, which serves as a core and stable link between heterogeneous representations. The EO-GAN is composed of an edge extraction network and a reconstructive network. During the training process, we ensure that the edges extracted from heterogeneous images are as similar as possible through supplemented data based on superpixel segmentation. Experimental results on both heterogeneous and homogeneous datasets demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Impact craters are the most significant topographic features on the lunar surface. They are also essential factors affecting the construction of lunar bases and activities on the lunar surface in the future. Nevertheless, it is difficult to extract the impact craters by image processing methods due to the influence of illumination in the lunar polar region. Furthermore, the digital elevation model (DEM) is unable to extract small impact craters. Therefore, we propose an extraction framework for small craters, namely AE-TransUNet+, for the future lunar South Pole region of interest. The algorithm is applied to grayscale images (0.5 m/pixel) captured by the Lunar Reconnaissance Orbital (LRO) narrow-angle camera (NAC). To improve the channel extract and spatial relationship between features, the convolutional block attention modules (CBAMs) and depthwise-separable convolution (DSC) are added to the core TransUNet model, and the redesigned jump connection enhancement module focuses more on small impact crater features. Experiments on the NAC dataset show that AE-TransUNet+ has a higher F1-Score (0.743) and IOU (0.847) compared to other crater detection models. Besides, based on transfer learning, the visualization results of Mars and Mercury show that the proposed method is more efficient in the processing of remote sensing data for different deep space explorations providing better mobility. [ABSTRACT FROM AUTHOR]

Co-substituted U-type hexagonal ferrite bulks, with composition of Ba4Ni2−xCoxFe36O60 (x=0.2, 0.4, 0.6, 0.8), were prepared by a conventional ceramic method. Saturation magnetization (4πMs), coercivity (Hc), and Curie temperature (Tc) were investigated. Anisotropy constant (K1) was calculated by fitting the magnetization curve (M-H) according to the law of approach to saturation, and anisotropy field (Ha) was calculated accordingly. The results reveal that all the samples possess the U-type hexagonal crystallographic structure. With increasing cobalt substitution content (x), the lattice parameters (a and c) almost remain the same owing to the similar radii of Ni2+ (0.72 Å) Co2+ (0.74 Å) ions. 4πMs goes up, while HcHc shows an opposite trend. K1 and Ha monotonously decrease resulting from that cobalt substitution weakens the c-axis orientation. Additionally, Tc increases from 467 °C to 484 °C. [ABSTRACT FROM AUTHOR]

The high frequency magnetic spectra of Sr 2+ or Sr 2+ -Gd 3+ doped Co 2 Z hexaferrites were systematically investigated. Of particular interest is the high impedance modulus measured in such dual-ion modified Z-type polycrystalline hexaferrites that have considerable potential for applications as anti -electromagnetic interference (EMI) over a frequency range of 0.5–5 GHz. The experiments indicate that the combination of Sr 3+ and Gd 3+ cations effectively tailors the anisotropy field while concomitantly increasing magnetization, yielding an increase in both real and imaginary components of the complex permeability. Consequently, this results in an enhanced impedance modulus as high as 384 Ω at 2 GHz. The structure and magnetic properties of the hexaferrite materials were characterized by X-ray diffractometry (XRD), scanning electron microscopy (SEM), vibrating sample magnetometry (VSM), and S-parameter measurements using a vector network analyzer (VNA). Furthermore, the physical origins of the improved high frequency performance are discussed. The present results provide a pathway to the development of anti -EMI hexaferrites operating in the GHz range. [ABSTRACT FROM AUTHOR]