Phase Properties of Different HfO2 Polymorphs: A DFT-Based Study

Background: Hafnium Dioxide (HfO2) represents a hopeful material for gate dielectric thin films in the field of semiconductor integrated circuits. For HfO2, several crystal structures are possible, with different properties which can be difficult to describe in detail from an experimental point of view. In this study, a detailed computational approach has been shown to present a complete analysis of four HfO2 polymorphs, outlining the intrinsic properties of each phase on the basis of atomistic displacements. Methods: Density functional theory (DFT) based methods have been used to accurately describe the chemical physical properties of the polymorphs. Corrective Hubbard (U) semi-empirical terms have been added to exchange correlation energy in order to better reproduce the excited-state properties of HfO2 polymorphs. Results: the monoclinic phase resulted in the lowest cohesive energy, while the orthorhombic showed peculiar properties due to its intrinsic ferroelectric behavior. DFT + U methods showed the different responses of the four polymorphs to an applied field, and the orthorhombic phase was the least likely to undergo point defects as oxygen vacancies. Conclusions: The obtained results give a deeper insight into the differences in excited states phenomena in relation to each specific HfO2 polymorph.

Forensic Camera Identification in Social Networks via Camera Fingerprint

Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.

Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

The development of abnormal skin pigment cells can cause a skin cancer called melanoma. Melanoma can be cured if diagnosed and treated in its early stages. Various studies using various technologies have been developed to conduct early detection of melanoma. This research was conducted to diagnose melanoma skin cancer with digital image processing techniques on the dermoscopic image of skin cancer. The diagnosis is made by classifying dermoscopic images based on the types of Common Nevus, Atypical Nevus or Melanoma. Pre-processing is done by changing the RGB image to grayscale (grayscaling), smoothing image using median filtering, and image segmentation based on binary images of skin lesions. The value of Contrast, Correlation, Energy and Homogeneity obtained from the texture feature extraction of the GLCM method is used in the next step, which is the classification process with the Multi-SVM algorithm. The proposed research method shows high accuracy results in diagnosing skin cancer

Efficient Online Object Tracking Scheme for Challenging Scenarios

Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.

A Novel Image Encryption Technique Based on Mobius Transformation

The nonlinear transformation concedes as S-box which is responsible for the certainty of contemporary block ciphers. Many kinds of S-boxes are planned by various authors in the literature. Construction of S-box with a powerful cryptographic analysis is the vital step in scheming block cipher. Through this paper, we give more powerful and worthy S-boxes and compare their characteristics with some previous S-boxes employed in cryptography. The algorithm program planned in this paper applies the action of projective general linear group P G L 2 , G F 2 8 on Galois field G F 2 8 . The proposed S-boxes are constructed by using Mobius transformation and elements of Galois field. By using this approach, we will encrypt an image which is the preeminent application of S-boxes. These S-boxes offer a strong algebraic quality and powerful confusion capability. We have tested the strength of the proposed S-boxes by using different tests, BIC, SAC, DP, LP, and nonlinearity. Furthermore, we have applied these S-boxes in image encryption scheme. To check the strength of image encryption scheme, we have calculated contrast, entropy, correlation, energy, and homogeneity. The results assured that the proposed scheme is better. The advantage of this scheme is that we can secure our confidential image data during transmission.

A simple fragment-based method for van der Waals corrections over density functional theory

Modelling intermolecular noncovalent interactions between large molecules remains a challenge for electron structure theory community. This is due to the high cost of calculating electron correlation energy. Fragment-based methods usually fare well in reducing the cost of computations in such systems while quantum Drude oscillators turn out to be a good model for van der Waals interactions. In this paper, we have developed a simple yet effective method based on oscillator methods for calculating van der Waals interactions between molecular fragments as a correction to low-cost DFT functional PBE. We have tested our method on S66X8 with significant success.

Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

The Density Functional Theory and Beyond: Example and Applications

Density Functional Theory is one of the most widely used methods in quantum calculations of the electronic structure of matter in both condensed matter physics and quantum chemistry. Despite the importance of the density functional theory to find the correlation-exchange energy, but this quantity remains inaccurate. So we have to go beyond DFT to correct this quantity. In this framework, the random phase approximation has gained importance far beyond its initial field of application, condensed matter physics, materials science, and quantum chemistry. RPA is an approach to accurately calculate the electron correlation energy.