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• An all-weather real-time detection method for ore based on near-infrared light and zero-crossing characteristics was proposed to detect target ores. • Acquisition of images based on infrared–visible radiation detection model(IVRDM). • Fast and accurate extraction of the laser centerline based on the zero-crossing points characteristics. • A neighborhood splicing method was proposed to detect ore size parameters. • The experiments show that the proposed method has strong adaptability and real-time performance. To address the low efficiency of ore detection methods caused by the complex lighting environment of mining sites, an all-weather real-time ore detection method based on near-infrared structured light and zero-crossing points characteristics was proposed. Firstly, an image acquisition method based on the infrared–visible radiation detection model (IVRDM) is proposed to avoid structured light imaging being disturbed by a strong sunlight environment; Then, the centerline of the light bar is extracted quickly and accurately based on the zero-crossing point characteristics; Finally, the light bar neighborhood splicing detection method was proposed to realize the real-time detection and 3D measurement of the target ore. The experimental results show that the proposed method can effectively resist strong sunlight interference and faster detection speed, the average extraction time of light bar centerline per frame is below 1.8 ms, and the error rate of ore size detection is <5 %. [ABSTRACT FROM AUTHOR]

• A proposal for an automatic flight measurement methodology based on shape decomposition. It solves the measurement of the local shape to be measured of a large work-piece that is not in a field of view and the measurement of complex shapes. • A proposal for an accurate extraction method of image contour line based on zero-crossing point feature. It can accurately extract the theoretical boundary points of the figure outline. • A proposal of a seed point search algorithm based on difference interval. For the images in this paper, the algorithm is efficient and can better meet the real-time performance of the system. • The establishment of a large work-piece measurement system based on machine vision. Its functionality is the combination of large work-piece plane dimensions with local 3D parameter measurement. The existing size measurements of large workpieces have drawbacks of slow speed, low-precision, and separation of planar size and 3D parameters measurements. An automatic flight measurement (AFM)-based methodology was proposed to measure large workpieces' planar and local 3D parameters. Firstly, the automatic flight path planning of the measured workpiece is carried out through shape decomposition before acquire the images with a bi-telecentric lens. Then, we measure the planar size of the workpiece through zero-crossing point (ZCP) feature contour extraction, and parameter fitting. Finally, we achieve local 3D parameter measurement through the seed point search algorithm to extract laser lines, system calibration, and the point cloud data (PCD) processing. We also measured the curvature and roughness of the curved workpiece. The experimental results obtained from multiple large-size workpieces show that the plane error of the method in this paper is within 0.05 mm within the measurement range of 216 mm × 236 mm, and the relative error of the local height measurement is within 1 %. Therefore, the proposed method has strong adaptability and robustness and can meet the measurement requirements of the large-sized plane and curved workpieces. [ABSTRACT FROM AUTHOR]

• Summarizing the major technical barriers that limit the precision of FMCW LiDAR range. • Modeling of four key factors effects on ranging precision, including Chirp nonlinearity, phase noise, optical path difference drift, material dispersion. • Proposing a novel measurement method for high-precision ranging, which can be used to obtain micrometer-accurate measurements in both active and passive environments. Frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) is considered as one of the most promising 3D imaging technologies by virtue of its single-photon detection sensitivity, high interference immunity, and high precision. In this paper, major technical barriers that limit the precision of FMCW LiDAR range are summarized and analyzed, and solutions to improve the measurement precision are presented. In addition, a novel method for high precision ranging of FMCW LiDAR is proposed, which can be applied to measure target distance in both active and passive environments. The proposed method has a precision of 11 μm in passive environments. [ABSTRACT FROM AUTHOR]

• We introduce a new continuous cross-layer attention transmission (CCLAT) mechanism in CNN, which makes full use of all hierarchical attentions through locally dense connections of attention maps. • Based on CCLAT mechanism, we just use a simple attention module to construct a novel residual dense attention fusion block (RDAFB), and employ it as a building block to design an effective architecture named RDAFNet for dynamic scene deblurring. • Our experiments on benchmark datasets show that the proposed model outperforms the state-of-the-art methods and demonstrate the effectiveness of CCLAT mechanism. The deep convolutional neural networks (CNNs) using attention mechanism have achieved great success for dynamic scene deblurring. In most of these networks, only the features refined by the attention maps can be passed to the next layer and the attention maps of different layers are separated from each other, which does not make full use of the attention information from different layers in the CNN. To address this problem, we introduce a new continuous cross-layer attention transmission (CCLAT) mechanism that can exploit hierarchical attention information from all the convolutional layers. Based on the CCLAT mechanism, we use a very simple attention module to construct a novel residual dense attention fusion block (RDAFB). In RDAFB, the attention maps inferred from the outputs of the preceding RDAFB and each layer are directly connected to the subsequent ones, leading to a CCLAT mechanism. Taking RDAFB as the building block, we design an effective architecture for dynamic scene deblurring named RDAFNet. The experiments on benchmark datasets show that the proposed model outperforms the state-of-the-art deblurring approaches, and demonstrate the effectiveness of CCLAT mechanism. The source code is available on: https://github.com/xjmz6/RDAFNet. [ABSTRACT FROM AUTHOR]

IMB5036 is a pyridazinone compound with antiproliferative and antitumour activity against hepatoma and pancreatic cancer. In this study, we attempted to identify the target protein of IMB5036 and test its potential for overcoming multidrug resistance and inducing pyroptosis. We examined the effects of IMB5036 on cancer cells by in vitro assays, a molecular docking model and in vivo tumour models. We performed pull-down experiments using biotinylated IMB5036 and identified the binding proteins. Gene knockdown were used to investigate the oncogenic role of KH-type splicing regulatory protein (KSRP). Western blot was used to detect for mechanism-associated molecules. IMB5036 could overcome resistance to multiple chemotherapeutic drugs at the cellular level and in vivo. Furthermore, IMB5036 was not a P-glycoprotein (P-gp) substrate and downregulated the expression of P-gp. We identified KSRP as a binding protein of IMB5036. The knockdown of KSRP inhibited the proliferation of MCF7 and MCF7/adriamycin (MCF7/ADR) cells. In addition, IMB5036 induced pyroptosis in both MCF7 and MCF7/ADR cells via KSRP. We found IMB5036 binds to KSRP and overcomes multidrug resistance via gasdermin E (GSDME)-dependent pyroptosis. [Display omitted] [ABSTRACT FROM AUTHOR]

L 0 regularization is highly suitable for estimating the latent image in the blur kernel estimation step of the two-step process of blind image deblurring. To tackle L 0 regularization problem, the method randomly selects gradient truncation threshold or only depends on the experience of repeated experiments, which often leads to the damage of edges in the latent image and inaccurate blur kernel. The proposed blind deblurring model based on edge-preserving of L 0 -regularized gradient (EP-L 0 RG) prior has the great adaptability to the parameters selection for retaining the edges. The experimental results state that the proposed method not only reduces the blindness and difficulty of the parameter settings, but also estimates a more accurate kernel and improves the deblurring performance. Comparative experiments demonstrated that the proposed method provides the optimal and quantitative results. [ABSTRACT FROM AUTHOR]

X-ray angiograms, which suffer from low-contrast and noise, need to be improved by both the image enhancement and denoising techniques. However, the goals of these two tasks usually conflict, which makes it difficult to efficiently combine the enhancement and denoising in one scheme. To deal with this problem, we propose a novel spatial-frequency filtering (SFF) scheme for high-contrast X-ray cardiovascular angiogram image restoration. The proposed scheme includes the following four parts: (1) low-quality X-ray angiogram images are decomposed into the base layer with strong structures and redundant parts by employing a relative total variation (RTV) method. (2) the base layer is enhanced by a proposed improved histogram equalization (IHE) method. (3) The detail layer with weak structures is extracted by using a short-time Fourier transform (STFT) and is further enhanced by a proposed adaptive correction parameter. (4) The improved image is the combination of results obtained by the base layer and the detail layer. Experimental results in both qualification and quantitation validate that the proposed method performs better than the state-of-the-arts on contrast enhancement, structure preservation, and noise reduction. [ABSTRACT FROM AUTHOR]