Explore the vast range of titles available.

\"[This] is a knife skills class in book form and an introduction to the best knives you can buy from all over the world. From a premier knife purveyor and go-to knives expert, this comprehensive guide details the elements of buying and caring for good knives, including sharpening and knife skills\"--Amazon.com.

Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

Background Improvement of image quality in radiology, including the maxillofacial region, is important for diagnosis by enhancing the visual perception of the original image. One of the most used modification methods is sharpening, in which simultaneously with the improvement, due to edge enhancement, several artifacts appear. These might lead to misdiagnosis and, as a consequence, to improper treatment. The purpose of this study was to prove the feasibility and effectiveness of automatic sharpening detection based on neural networks. Methods The in-house created dataset contained 4290 X-ray slices from different datasets of cone beam computed tomography images were taken on 2 different devices: Ortophos 3D SL (Sirona Dental Systems GmbH, Bensheim, Germany) and Planmeca ProMax 3D (Planmeca, Helsinki, Finland). The selected slices were modified using the sharpening filter available in the software RadiAnt Dicom Viewer software (Medixant, Poland), version 5.5. The neural network known as \"ResNet-50\" was used, which has been previously trained on the ImageNet dataset. The input images and their corresponding sharpening maps were used to train the network. For the implementation, Keras with Tensorflow backend was used. The model was trained using NVIDIA GeForce GTX 1080 Ti GPU. Receiver Operating Characteristic (ROC) analysis was performed to calculate the detection accuracy using MedCalc Statistical Software version 14.8.1 (MedCalc Software Ltd, Ostend, Belgium). The study was approved by the Ethical Committee. Results For the test, 1200 different images with the filter and without modification were used. An analysis of the detection of three different levels of sharpening (1, 2, 3) showed sensitivity of 53%, 93.33%, 93% and specificity of 72.33%, 84%, 85.33%, respectively with an accuracy of 62.17%, 88.67% and 89% ( p  < 0.0001). The ROC analysis in all tests showed an Area Under Curve (AUC) different from 0.5 (null hypothesis). Conclusions This study showed a high performance in automatic sharpening detection of radiological images based on neural network technology. Further investigation of these capabilities, including their application to different types of radiological images, will significantly improve the level of diagnosis and appropriate treatment.

In the context of earth observation and remote sensing, super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing its details. In recent years, numerous methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Most of those methods depend on various tuning parameters that affect how effective they are. This paper’s aim is twofold. Firstly, we propose to use Bayesian optimization at a reduced scale to select tuning parameters. Secondly, we choose tuning parameters for eight S2 super-resolution methods and compare them using real and synthetic data. While all the methods give good quantitative results, Area-To-Point Regression Kriging (ATPRK), Sentinel-2 Sharpening (S2Sharp), and Sentinel-2 Symmetric Skip Connection convolutional neural network (S2 SSC) perform markedly better on several datasets than the other methods tested in this paper.

We prove that if ( X , d , m ) is a metric measure space with m ( X ) = 1 having (in a synthetic sense) Ricci curvature bounded from below by K > 0 and dimension bounded above by N ∈ [ 1 , ∞ ) , then the classic Lévy-Gromov isoperimetric inequality (together with the recent sharpening counterparts proved in the smooth setting by Milman for any K ∈ R , N ≥ 1 and upper diameter bounds) holds, i.e. the isoperimetric profile function of ( X , d , m ) is bounded from below by the isoperimetric profile of the model space. Moreover, if equality is attained for some volume v ∈ ( 0 , 1 ) and K is strictly positive, then the space must be a spherical suspension and in this case we completely classify the isoperimetric regions. Finally we also establish the almost rigidity: if the equality is almost attained for some volume v ∈ ( 0 , 1 ) and K is strictly positive, then the space must be mGH close to a spherical suspension. To our knowledge this is the first result about isoperimetric comparison for non smooth metric measure spaces satisfying Ricci curvature lower bounds. Examples of spaces fitting our assumptions include measured Gromov–Hausdorff limits of Riemannian manifolds satisfying Ricci curvature lower bounds, Alexandrov spaces with curvature bounded from below, Finsler manifolds endowed with a strongly convex norm and satisfying Ricci curvature lower bounds; the result seems new even in these celebrated classes of spaces.

In this paper, a Taylor cone model in the nanoscale is configured using the many-body dissipative particle dynamics method. The sharpening process of the Taylor cone and the breakup process at different electric field intensities and different charge concentrations are systematically investigated. Under a strong electric field, the hemispherical droplet is sharpened over time and evolves into a conic one. Then the conic cusp emits a thin jet. Finally, the cone shrinks into a semi-sphere after jet breaking. These deformation processes occur several times until no charges are emitted from the conic cusp. It is found that the electric field force is responsible for jet emitting, while the Coulombic force causes a jet breakup. With the rising of the intensity of the electric field, the breakup times also increase. However, the breakup times decrease with the rising of the charge concentration. It indicates that a conductive liquid with low electrical conductivities and subjected to a strong electric field is more prone to undergo pulsating breakups.

Presented are the results of metallographic studies of the structure and properties of a cultivator share, hardened by spot electromechanical processing in its various high-wear areas. In the process of research, the manufacturer was offered recommendations on the technology of forming parts in the process of their manufacture. It has been established that spot electromechanical processing allows to obtain hemispherical hardened areas with a hardness of HV 7000 ... 7600 MPa, which will provide the effect of self-sharpening of their cutting parts during the operation of the cultivator shares. Spot electromechanical processing with two tools allows to provide through hardening of the cultivator share along the ends of the share wings. The proposed technology for hardening cultivator shares during their manufacture and repair will increase the durability of these parts during their operation.

An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen flow conditions and geometries given the pixelated shape of the object. In particular, we consider Reynolds Averaged Navier–Stokes (RANS) flow solutions over airfoil shapes as training data. The CNN can automatically detect essential features with minimal human supervision and is shown to effectively estimate the velocity and pressure field orders of magnitude faster than the RANS solver, making it possible to study the impact of the airfoil shape and operating conditions on the aerodynamic forces and the flow field in near-real time. The use of specific convolution operations, parameter sharing, and gradient sharpening are shown to enhance the predictive capabilities of the CNN. We explore the network architecture and its effectiveness in predicting the flow field for different airfoil shapes, angles of attack, and Reynolds numbers.

In the literature of pan-sharpening based on neural networks, high resolution multispectral images as ground-truth labels generally are unavailable. To tackle the issue, a common method is to degrade original images into a lower resolution space for supervised training under the Wald’s protocol. In this paper, we propose an unsupervised pan-sharpening framework, referred to as “perceptual pan-sharpening”. This novel method is based on auto-encoder and perceptual loss, and it does not need the degradation step for training. For performance boosting, we also suggest a novel training paradigm, called “first supervised pre-training and then unsupervised fine-tuning”, to train the unsupervised framework. Experiments on the QuickBird dataset show that the framework with different generator architectures could get comparable results with the traditional supervised counterpart, and the novel training paradigm performs better than random initialization. When generalizing to the IKONOS dataset, the unsupervised framework could still get competitive results over the supervised ones.

Photovoltaic devices based on metal halide perovskites are rapidly improving in efficiency. Once the Shockley–Queisser limit is reached, charge-carrier extraction will be limited only by radiative bimolecular recombination of electrons with holes. Yet, this fundamental process, and its link with material stoichiometry, is still poorly understood. Here we show that bimolecular charge-carrier recombination in methylammonium lead triiodide perovskite can be fully explained as the inverse process of absorption. By correctly accounting for contributions to the absorption from excitons and electron-hole continuum states, we are able to utilise the van Roosbroeck–Shockley relation to determine bimolecular recombination rate constants from absorption spectra. We show that the sharpening of photon, electron and hole distribution functions significantly enhances bimolecular charge recombination as the temperature is lowered, mirroring trends in transient spectroscopy. Our findings provide vital understanding of band-to-band recombination processes in this hybrid perovskite, which comprise direct, fully radiative transitions between thermalized electrons and holes. Radiative bimolecular processes will dominate charge-carrier recombination in hybrid perovskite solar cells operating near the Shockley-Queisser limit. Here, the authors show that such processes are the inverse of absorption and increase as distribution functions sharpen towards lower temperatures.