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Spin manipulations in magnetic nanostructures
Published in Guo-ping Zhang, Georg Lefkidis, Mitsuko Murakami, Wolfgang Hübner, Tomas F. George, Introduction to Ultrafast Phenomena from Femtosecond Magnetism to high-harmonic Generation, 2020
Guo-ping Zhang, Georg Lefkidis, Mitsuko Murakami, Wolfgang Hübner, Tomas F. George
The surge of technological advances in the late 20th century associated with personal computers and the World Wide Web are now recognized as the third industrial revolution.1 Just like the first two industrial revolutions, the third revolution has made the world more productive by way of effective information processing. Computer systems store information in digital format called a binary system, which is in the form of zeros and ones. The potential impact of ultrafast technology on modern technology can be understood through some examples. For a comprehensive review (which includes both theoretical methods and experimental techniques), see Refs. [Zhang et al. (2002b); Kirilyuk et al. (2010)]. In this chapter, we introduce key concepts that are fundamental to the mechanisms of magnetization switching, namely, spin precession, Rabi oscillation, and spin-orbit coupling. Then, the magnetization reversal of an antiferromagnetic NiO cluster is discussed in detail, based on the numerical solution of the time-dependent Schrodinger equation.
Recording and Capturing Audio and Video
Published in Joe Follansbee, Hands-On Guide to Windows Media, 2012
You’ll come across a variety of file formats as you work with computer audio and video. Source file formats, for the purposes of this book and as shown in Table 4-2, are formats used to store audio and video in a computer before they are transformed or encoded into streamable formats. (A format is simply a way to organize data on a storage medium.) To keep things simple, we like to think of these formats as raw formats, just like the raw materials that a foundry uses to make steel.
Image and Its Properties
Published in Ravishankar Chityala, Sridevi Pudipeddi, Image Processing and Acquisition using Python, 2020
Ravishankar Chityala, Sridevi Pudipeddi
Here is an example code snippet where we read an image and write an image. The imwrite function takes the file name and the ndarray of an image as input. The file format is identified using the file extension in the file name. import cv2 img = cv2.imread('image1.png') # cv2.imwrite will take an ndarray. cv2.write('file_name', img)
Simplified Prediction Method for Detecting the Emergency Braking Intention Using EEG and a CNN Trained with a 2D Matrices Tensor Arrangement
Published in International Journal of Human–Computer Interaction, 2023
Hermes J. Mora, Esteban J. Pino
The TensorFlow (TF) algorithm is a Python-open source library for numerical calculus making machine learning faster and easier (Python, 2019). The usage of TF to design and train CNNs is reasonably easy due to the strong support in Artificial Intelligence that has a vast number of functions to manage the input data. We can build a data generator with the (.numpy) extension that corresponds to a Python library implemented for working with N-dimensional arrays. With this in mind, the input data can be used directly as a large array configured by matrices. It is no necessary a dataset as RGB or grayscale images. Implementing a tensor (n-dimensional matrix) implies that the network designer reduces the processing time and computer resources when training the network. Based on Table 2, the dataset for each of the six electrode groups is configured directly as a (.numpy) array, Figure 3(b). By contrast, the image groups used to train our CNN through grayscale images and compare the CNN results are converted into the common image file format (.png). There are six electrode groups (4, 8, 13, 18, 33, 59) that result in six different 2 D-tensors. The height of each 2 D matrix varies according to the number of electrodes. The width of the matrix (400 samples) because there is no variation in the length of segments. The number of images in each image-set is significantly reduced given the number of electrodes used in each group.
Colour filter array demosaicking over compression through modified grey wolf optimization technique
Published in The Imaging Science Journal, 2018
M. S. Safna Asiq, W. R. Sam Emmanuel
The proposed demosaicking algorithm combined with optimized compression techniques is compared using the Kodak dataset of Kodak Eastman Company. The dataset consists of 24 true colour images of 768 × 512 dimensions in tagged image file format. The work was implemented in the MATLAB R2015a platform. The performance is measured using the Peak Signal Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Feature Similarity Index Measure (FSIM). The PSNR, SSIM and FSIM values express the quality measure. Tables 1–3 show the performance of the proposed work. The conventional demosaicking algorithms used for analysis includes Bilinear Interpolation (BI), Gradient-Based Threshold-Free (GBTF) Algorithm [31], (Figures 7 and 8).
A human pericardium biopolymeric scaffold for autologous heart valve tissue engineering: cellular and extracellular matrix structure and biomechanical properties in comparison with a normal aortic heart valve
Published in Journal of Biomaterials Science, Polymer Edition, 2018
Frantisek Straka, David Schornik, Jaroslav Masin, Elena Filova, Tomas Mirejovsky, Zuzana Burdikova, Zdenek Svindrych, Hynek Chlup, Lukas Horny, Matej Daniel, Jiri Machac, Jelena Skibová, Jan Pirk, Lucie Bacakova
A histomorphometric evaluation of the ECM structure was performed using ImageJ analysis software (Image Processing and Analysis in Java; National Institute of Health, USA) [13] from N = 3 NAV tissue donors and N = 3 HP tissue donors. Ten image files were scanned from each immunohistochemically-stained (collagen I, collagen III, elastin) or Alcian blue-stained (GAGs) sample, (100x magnification), giving a total of 30 images of HP and NAV. All images were acquired under identical conditions. The images have been digitized and stored in an uncompressed tagged image file format (image size 2088 × 1550 pixel) with 24-bit RGB. Ten lines of bar profiles were subsequently evaluated for each image sample for an investigation of the structure of the tissue. We evaluated this with the use of so-called ‘integrated density’ – the total amount of collagen I, elastin and mucopolysaccharides in the examined tissue was determined, with the exclusion of non-compacted tissue structures, which are artifacts resulting from the processing of histological samples. The content of investigated ECM proteins (collagen I, collagen III, elastin) and GAGs in the tissue was calculated as the integral of the density of the pixel values (color intensity) along the lines of the bar profiles (one slice intensity per row = ROI, n = 300 for each of the evaluated proteins and GAGs). This is like the tissue thickness of the stained ECM component multiplied by its color intensity [13–16].