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Micro/nano structures with morphological gradients possess unique physical properties and significant applications in various research domains. This study proposes a straightforward and precise method for fabricating micro/nano structures with morphological gradients utilizing single-voxel synchronous control and a nano-piezoelectric translation stage in a two-photon laser direct writing technique. To address the defocusing issue in large-scale fabrication, a methodology for laser focus dynamic proactive compensation was developed based on fluorescence image analysis, which can achieve high-precision compensation of laser focus within the entire range of the nano-piezoelectric translation stage. Subsequently, the fabrication of micro/nano dual needle structures with morphological gradients were implemented by employing different writing speeds and voxel positions. The minimum height of the tip in the dual needle structure is 80 nm, with a linewidth of 171 nm, and a dual needle total length reaching 200 μm. Based on SEM (scanning electron microscope) and AFM (atomic force microscope) characterization, the dual needle structures fabricated by the method proposed in this study exhibit high symmetry and nanoscale gradient accuracy. Additionally, the fabrication of hexagonal lattice periodic structures assembled from morphological gradient needle structures and the size gradient Archimedean spiral structures validate the capability of the single voxel-based fabrication and proactive focus compensation method for complex gradient structure fabrication. [ABSTRACT FROM AUTHOR]

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The polymer derived SiC fibers with high mechanical properties are mainly used as reinforcing materials for ceramic matrix composites (CMCs), which are applied in various fields. However, there is a problem that amorphous SiC fibers are decomposed by oxygen impurities and excess carbon at high temperature. In this study, SiC fibers prepared under various atmospheric-pressure near the decomposition temperature exhibited morphological gradient with various mechanical and electrical properties. In particular, the mechanical properties and electrical properties of polymer-derived SiC fibers showed an inverse correlation depending on the surface layer such as carbon-rich or amorphous SiCxOy.

For high-performance thermoplastic materials, material behavior results from the degree of crystallization and the distribution of crystalline phases. Due to the less stiff amorphous and the stiffer and anisotropic crystalline phases, the microstructural properties are inhomogeneous. Thus, imaging of the microstructure is an important tool to characterize the process-induced morphology and the resulting properties. Using focusing ultrasonic transducers with high frequency (25 MHz nominal center frequency) enables the imaging of specimens with high lateral resolution, while wave propagation is related to the elastic modulus, density and damping of the medium. The present work shows experimental results of high-frequency ultrasonic spectroscopy (HF-US) applied to injection-molded polyether-ether-ketone (PEEK) tensile specimens with different process-related morphologies. This work presents different analysis procedures, e.g., backwall echo, time of flight and Fourier-transformed time signals, facilitating the mapping of gradual mechanical properties and assigning them to different crystalline content and morphological zones. [ABSTRACT FROM AUTHOR]

The Arabic language is characterized by a great diversity in its writing styles in terms of the shape, size, and percentage of inclination and methods of drawing. Despite this great diversity, the Arabic styles are also characterized by a huge similarity that makes it difficult for traditional methods of machine learning to overcome with Arabic manuscripts. In this paper we present a Morphological Gradient Convolutional Neural Network to Classify the Arabic styles (MG-CNN). The model is a combination of two methods: a morphological gradient to detect images' contours and a convolutional neural network to extract images features and classify them. Due to the absence of Arabic styles dataset, we created an image database from the book "Teach Yourself Arabic styles: Naskh, Rokaa, Farissi, Tolot, Diwani" (Mehdi, Teach yourself arabic styles: Naskh, Rokaa, Farissi, Tolot, Diwani, 2005) and then we use augmentation methods to increase number of images while preserving the characteristics of each style. Our architecture gives a high accuracy of 100% for the training dataset and 99.50% for the validation dataset. [ABSTRACT FROM AUTHOR]

Aiming at the problems of color distortion, noise interference and long running time in the dark channel prior algorithm with large depth of field, an image defogging method based on multi-scale wavelet transform and improved fusion dark channel was proposed. Firstly, the foggy image was decomposed by two-level wavelet transform, the high-frequency component was denoised by soft threshold, and the low-frequency component was defogged by improved adaptive fusion dark channel. Finally, a local linear model was used to correlate the high-frequency and low-frequency component coefficients for wavelet reconstruction. The experiment show that the proposed algorithm has high defogging effect and can improve the quality of defogging image. [ABSTRACT FROM AUTHOR]

The target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of maize, a phenotyping monitoring technology based on an internal gradient algorithm is proposed for acquiring the target region and diameter of maize stems. Observations were conducted during the small bell stage of maize. First, color images of maize plants were captured by an Intel RealSense D435i camera. The color information in the color image was extracted by the hue saturation value (HSV) color space model. The maximum between-class variance (Otsu) algorithm was applied for image threshold segmentation to obtain the main stem of maize. Median filtering, image binarization, and morphological opening operations were then utilized to remove noise from the images. Subsequently, the morphological gradient algorithm was applied to acquire the target region of maize stems. The similarity between the three types of gradient images and the manually segmented image was evaluated by pixel ratio extraction and image quality assessment indicators. Evaluation results indicated that the internal gradient algorithm could more accurately obtain the target region of maize stems. Finally, a checkerboard was employed as a reference for measurement assistance, and the stem diameter of maize was calculated by the pinhole imaging principle. The mean absolute error of stem diameter was 1.92 mm, the mean absolute percentage error (MAPE) was 5.16%, and the root mean square error (RMSE) was 2.25 mm. The R² value was 0.79. With an R² greater than 0.7 and a MAPE within 6%, the phenotyping monitoring technology based on the internal gradient algorithm was proven to accurately measure the diameter of maize stems. The application of phenotyping monitoring technology based on the internal gradient algorithm in field conditions provides technological support for smart agriculture. [ABSTRACT FROM AUTHOR]

Micro/nano structures with morphological gradients possess unique physical properties and significant applications in various research domains. This study proposes a straightforward and precise method for fabricating micro/nano structures with morphological gradients utilizing single-voxel synchronous control and a nano-piezoelectric translation stage in a two-photon laser direct writing technique. To address the defocusing issue in large-scale fabrication, a methodology for laser focus dynamic proactive compensation was developed based on fluorescence image analysis, which can achieve high-precision compensation of laser focus within the entire range of the nano-piezoelectric translation stage. Subsequently, the fabrication of micro/nano dual needle structures with morphological gradients were implemented by employing different writing speeds and voxel positions. The minimum height of the tip in the dual needle structure is 80 nm, with a linewidth of 171 nm, and a dual needle total length reaching 200 μm. Based on SEM (scanning electron microscope) and AFM (atomic force microscope) characterization, the dual needle structures fabricated by the method proposed in this study exhibit high symmetry and nanoscale gradient accuracy. Additionally, the fabrication of hexagonal lattice periodic structures assembled from morphological gradient needle structures and the size gradient Archimedean spiral structures validate the capability of the single voxel-based fabrication and proactive focus compensation method for complex gradient structure fabrication.

For high-performance thermoplastic materials, material behavior results from the degree of crystallization and the distribution of crystalline phases. Due to the less stiff amorphous and the stiffer and anisotropic crystalline phases, the microstructural properties are inhomogeneous. Thus, imaging of the microstructure is an important tool to characterize the process-induced morphology and the resulting properties. Using focusing ultrasonic transducers with high frequency (25 MHz nominal center frequency) enables the imaging of specimens with high lateral resolution, while wave propagation is related to the elastic modulus, density and damping of the medium. The present work shows experimental results of high-frequency ultrasonic spectroscopy (HF-US) applied to injection-molded polyether-ether-ketone (PEEK) tensile specimens with different process-related morphologies. This work presents different analysis procedures, e.g., backwall echo, time of flight and Fourier-transformed time signals, facilitating the mapping of gradual mechanical properties and assigning them to different crystalline content and morphological zones.

The target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of maize, a phenotyping monitoring technology based on an internal gradient algorithm is proposed for acquiring the target region and diameter of maize stems. Observations were conducted during the small bell stage of maize. First, color images of maize plants were captured by an Intel RealSense D435i camera. The color information in the color image was extracted by the hue saturation value (HSV) color space model. The maximum between-class variance (Otsu) algorithm was applied for image threshold segmentation to obtain the main stem of maize. Median filtering, image binarization, and morphological opening operations were then utilized to remove noise from the images. Subsequently, the morphological gradient algorithm was applied to acquire the target region of maize stems. The similarity between the three types of gradient images and the manually segmented image was evaluated by pixel ratio extraction and image quality assessment indicators. Evaluation results indicated that the internal gradient algorithm could more accurately obtain the target region of maize stems. Finally, a checkerboard was employed as a reference for measurement assistance, and the stem diameter of maize was calculated by the pinhole imaging principle. The mean absolute error of stem diameter was 1.92 mm, the mean absolute percentage error (MAPE) was 5.16%, and the root mean square error (RMSE) was 2.25 mm. The R² value was 0.79. With an R² greater than 0.7 and a MAPE within 6%, the phenotyping monitoring technology based on the internal gradient algorithm was proven to accurately measure the diameter of maize stems. The application of phenotyping monitoring technology based on the internal gradient algorithm in field conditions provides technological support for smart agriculture.

Since the visible and infrared images have different imaging mechanisms, the difficulty of image registration has greatly increased. The grayscale difference between visible and infrared images is very disadvantageous for extracting feature points in homogenous region, but they both retain the obvious contour edge in the scene. After using the morphological gradient method, the grayscale edge of visible and infrared images can be obtained and their similarity is greatly improved, and their difference may be seen as the difference in brightness or grayscale. Therefore, we proposed a novel algorithm to realise real-time adaptive registration of visible and infrared images using morphological gradient and C_SIFT. Firstly, the morphological gradient method is used to extract the rough edges of visible and infrared images for aligning their visual features as a single similar type. Secondly, the C_SIFT feature detection operator is used to detect and extract feature points from the extracted edges. The C_SIFT uses the centroid method to describe the main direction of feature points, makes rotation invariance feasible. Finally, to verify the effectiveness of the proposed algorithm, we carried out a series of experiments in eight various scenarios. The experimental results show that the proposed algorithm has achieved good experimental results. The registration of visible and infrared images can be completed quickly by the proposed algorithm, and the registration accuracy is satisfactory. [ABSTRACT FROM AUTHOR]

Pansharpening refers to the fusion of remotely sensed multispectral and panchromatic images which are characterized by different levels of spectral–spatial resolutions and acquired for the same location by optical remote sensing satellite sensors. In this paper, we propose a pansharpening algorithm based on morphological extended-half-gradient. Popular quality metrics employing two assessment methods, namely reduced resolution assessment and full resolution assessment, are used for performance measurement. For validating the efficiency of the proposed algorithm, we compare its performance with that of morphological half-gradient-based fusion procedure and a few other popular image fusion algorithms. We also propose the best possible bias factor in the formulation of the proposed algorithm by experimentation on varied values. Three real datasets acquired by WorldView-4, SPOT-6 and QuickBird-2 are used in the experimentation. The results affirm that the proposed algorithm offers improved image fusion than using the morphological half-gradient. This successful demonstration of the proposed algorithm proves the potential of morphological image processing operations to be useful in the achievement of efficient pansharpening. This work also underlines the need for more computational efficiency in image fusion. [ABSTRACT FROM AUTHOR]

Highlights • Single end cable and overhead line protection in MTDC grids using morphological gradient of voltage (MGV); • Discrimination between internal and external faults; • Protection stability against breaker opening and opposite pole faults; • Threshold setting based on line series inductor and maximum fault resistance; • Fast, reliable and low sampling frequency protection up to a significant high fault resistance. Abstract This paper presents a new method for the protection of transmission lines in voltage source converter based multi-terminal HVDC grids. The fault generated travelling waves at the faulted line ends are filtered by series inductors. The filtered voltage waveform processed by morphological gradient accompanied by fault voltage drop is employed for fast single-end protection of HVDC lines. The proposed method is stable against external DC and AC faults, as well as faults at opposite pole and the breaker opening transients from the adjacent lines. Moreover, it does not require communications between the line ends, it is capable to detect and discriminate relatively high resistance faults, and uses relatively low sampling rate. The effectiveness of the proposed protection method is validated by simulation study on a four-terminal HVDC grid including modular multilevel voltage source converters. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the requirements for high-precision products could not be satisfied due to the low cutting precision of traditional automatic cutting machines, a method of using machine vision technology to improve the cutting precision of the cutting machine was proposed by studying the structure of the cutting machine. Firstly, the improved morphological gradient filter operator was used to find the rough edge of the sheet, and then the sub-pixel fine positioning was performed by the gray scale method. Finally, the sub-pixel edge points of meeting the requirements were fitted into a straight line by least square method. Then the offset of the sheet was calculated according to the geometric relationship, and was used to guide the correction platform to compensate the deviation. The method was simulated and tested by image edge positioning experiment designed by Matlab. The results indicate that the cutting precision of the traditional automatic cutting machine can reach 0. 03 mm theoretically, which meets the precision requirements of most high-precision products. [ABSTRACT FROM AUTHOR]

Abstract Mapping channel edges is a significant issue in 3D seismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing the modified top-hat algorithm, we calculated the maximum curvelet coefficients in all sub-bands. Employing top-hat in curvelet domain is more effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edge map resulting from the weighted average of all sub-edge images was obtained from the structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithm was applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors. Highlights • Mapping channel edges is a significant issue in 3D seismic interpretation. • Curvelet transform is a proper scale-space representation for curve singularities. • Curvelet transform is used to enhance channel boundaries in seismic horizon slice. • Morphological gradient algorithm detects channel edges. • The experimental results are comparable with the Canny, Sobel and LoG filters. [ABSTRACT FROM AUTHOR]

encitraan medis atau medical imaging adalah suatu cara untuk mendapatkan informasi citra medis tanpa harus menggunakan tindakan operasi atau bedah. Proses diagnosis dalam pencitraan medis akan memberikan informasi terkait bentuk, lokasi, objek yang di teliti, atau disebut dengan ROI (Region of Interest). Pada Penelitian ini dibuat sebuah rancangan metoda segmentasi secara komputasi menggunakan teknik watershed dengan filter sobel dan morphological gradient untuk menganalisis daerah tumor dan mengurangi efek segmentasi berlebihan yang muncul pada teknik watershed, pada citra otak dengan tinjauan tiga slice hasil citra MRI yang berbeda yaitu axial, koronal dan sagital. Hasil percobaan dari dua metoda kombinasi teknik watershed makers dan morphological gradient menghasilkan segmentasi baik mengurangi segmentasi yang berlebihan serta hasil yang lebih tajam, dengan hasil pengujian kualitas citra dengan metoda SNR (Signal Noise to Ratio) untuk setiap slice adalah axial 5.73 dB, koronal 6.38 dB dan sagital 5.96 dB dengan waktu rata-rata komputasi adalah 1.20 s dan kombinasi segmentasi menggunakan filter sobel untuk masing-masing slice adalah axial 5.68 dB, koronal 6.28 dB, dan sagital 5.27 dB dengan waktu rata-rata komputasi adalah1.80 s. Medical imaging is a way to get the medical image without using surgery. The process of diagnosis in medical imaging will provide information regarding the shape, location, objects in conscientious, or ROI (Region of Interest). In this research created a design method of segmentation computation using the technique watershed with filter Sobel and morphological gradient to analyze the region of the tumor and reduce the effects of segmentation excessive appearing on technique watershed, the image of the brain from three slice results MRI axial, coronal and sagittal planes. The experimental results of the two methods combination of techniques and morphological gradient watershed makers produce better segmentation reduces excessive segmentation and image are sharper than segmentation using Sobel filter, with image quality results SNR (Signal Noise to Ratio) for each slice is 5.73 dB axial, coronal and sagittal 6.38 dB 5.96 dB average time computing is 1.20 s and the combination of segmentation using Sobel filter for each slice is 5.68 dB axial, coronal 6.28 dB, and sagittal 5.27 dB with an average time of computing adalah1.80 s.

In current individual tree crown (ITC) delineation methods for high-resolution multispectral imagery, either a spectral band or a brightness component of the multispectral image is employed in delineation with reference to edges or shapes of crowns, whereas spectra of tree crowns are seldom taken into account. Such methods normally perform well in coniferous forests with obvious between-crown shadows, but fail in dense deciduous or mixed forests, in which tree crowns are close to each other, between-crown shadows and boundaries are unobvious, whereas adjacent tree crowns may be of distinguishable spectra. In order to effectively delineate crowns in dense deciduous or mixed forests, a new ITC delineation method using both brightness and spectra of the image is proposed in this study. In this method, a morphological gradient map of the image is first generated, treetops of multi-scale crowns are extracted from the gradient map and refined regarding the spectral differences between neighboring crowns, the gradient map is segmented using a watershed approach with treetops as markers, and the resulting segmentation map is refined to yield a crown map. Evaluated on images of a rainforest and a deciduous forest, the proposed method more accurately delineated adjacent broad-leaved tree crowns with similar brightness but different spectra than the other two typical ITC delineation algorithms, achieving a delineation accuracy of up to 76% in the rainforest and 63% in the deciduous forest.

This research proposes a novel fusion scheme for non-subsampled shearlet transform (NSST) which is based on simplified model of pulse coupled neural network (PCNN). The images to be fused are acquired from Postgraduate Institute of Medical Education and Research, Chandigarh, India, and internet repository. The image database contains computed tomography and T2-weighted magnetic resonance images. The images to be fused are decomposed into approximation and detail sub-bands using NSST. The regional energy-based activity measure with consistency verification is applied to fuse the approximation sub-band of NSST. The novel morphological gradient of detail sub-bands is fed as external stimulus to PCNN to fuse detail sub-bands. The proposed method is compared with five state-of-the-art fusion schemes visually and using five fusion performance parameters. It is observed that the resultant images of the proposed fusion scheme show appropriate fusion characteristics and retain the bone, CSF and edema details in the clinical format required for disease evaluation by the radiologists. The proposed scheme requires lesser computational time than other state-of-the-art PCNN-based fusion schemes. [ABSTRACT FROM AUTHOR]

This study proposes a novel scheme using mathematical morphology to effectively discriminate between the internal fault and inrush currents of power transformers. The proposed scheme consists of two parts. The first part is based on morphological gradient (MG), for discrimination of currents occurring without considering the current transformer (CT) saturation. The second one uses a symmetrical criterion involving MG to identify internal fault current under CT saturation. To demonstrate its compatibility and efficiency, the scheme is verified employing the data simulated using PSCAD/EMTDC and data collected from laboratory experiment, respectively. The simulation and experimental results indicate that the proposed scheme can improve the accuracy of inrush current identification, even under certain extreme conditions. [ABSTRACT FROM AUTHOR]