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auto-correlation function analysis of crystallization in amorphous SiGe thin films

APPLIED SURFACE SCIENCE 2003年 212卷 339-343页

作者： Chiang, TF Wu, WW Cheng, SL Lin, HH Lee, SW Chen, LJ Natl Tsing Hua Univ Dept Mat Sci & Engn Hsinchu 300 Taiwan

The existence of medium-range ordering structures or nanocrystallites in as-deposited amorphous SiGe thin films has been demonstrated by high-resolution transmission electron microscopy in conjunction with auto-correlation function analysis. The density of nanocrystallites decreases in amorphous SiGe samples annealed at 300-350 degreesC then increases in samples annealed at 400-450 degreesC with annealing temperature. The observations can be interpreted in terms of free energy change with annealing temperature. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： auto-correlation function amorphous Si SiGe crystallization

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auto-correlation function analysis of crystallization in amorphous SiGe thin films

Auto-correlation function analysis of crystallization in amo...

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11th International Conference on Solid Films and Surfaces (ICSFE-11)

作者： Chiang, TF Wu, WW Cheng, SL Lin, HH Lee, SW Chen, LJ Natl Tsing Hua Univ Dept Mat Sci & Engn Hsinchu 300 Taiwan

关键词： auto-correlation function amorphous Si SiGe crystallization

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G-L fractional differential operator modified using auto-correlation function: Texture enhancement in images

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AIN SHAMS ENGINEERING JOURNAL 2018年第4期9卷 1689-1704页

作者： Hemalatha, S. Anouncia, S. Margret VIT Univ Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

Texture plays an important role in the low-level image analysis and understanding in the field of computer vision. Texture based image enhancement is very important in many applications. In order to attain texture enhancement in images, a modified version of the Grunwald-Letnikov (G-L) definition based fractional differential operator is proposed in this paper. Considering the G-L based fractional differential operator's basic definition and implementation, a filter is devised and its applicability for texture enhancement is analyzed. Subsequently, the filter is modified by considering the auto-correlation effect between pixels in a neighborhood. Experiments are carried out on a number of standard texture-rich images and it is proved that the modified filter enhances the image contrast by nonlinearly enhancing the image textural features. In addition, the texture enhancement is quantitatively proven by a few Gray Level Co-occurrence Matrix (GLCM) measures, such as contrast, correlation, energy and homogeneity. Their % of Improvement is discussed in detail and the substantial improvement attained by the modified G-L FD operator over the basic G-L FD operator is well proved. (C) 2016 Ain Shams University.

关键词： Image texture Fractional differentiation auto-correlation function Texture enhancement G-L definition Gray level co-occurrence matrix

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Radar Emitter Identification Based on auto-correlation function and Bispectrum via Convolutional Neural Network

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IEICE TRANSACTIONS ON COMMUNICATIONS 2021年第12期E104B卷 1506-1513页

作者： Xiao, Zhiling Yan, Zhenya Nanjing Res Inst Elect Technol Nanjing Peoples R China

This article proposes to apply the auto-correlation function (ACF), bispectrum analysis, and convolutional neural networks (CNN) to implement radar emitter identification (REI) based on intrapulse features. In this work, we combine ACF with bispectrum for signal feature extraction. We first calculate the ACF of each emitter signal, and then the bispectrum of the ACF and obtain the spectrograms. The spectrum images are taken as the feature maps of the radar emitters and fed into the CNN classifier to realize automatic identification. We simulate signal samples of different modulation types in experiments. We also consider the feature extraction method directly using bispectrum analysis for comparison. The simulation results demonstrate that by combining ACF with bispectrum analysis, the proposed scheme can attain stronger robustness to noise, the spectrograms of our approach have more pronounced features, and our approach can achieve better identification performance at low signal-to-noise ratios.

关键词： radar emitter identification auto-correlation function bispectrum analysis convolutional neural networks

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The auto-correlation function Aided Sparse Support Matrix Machine for EEG-Based Fatigue Detection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第2期70卷 836-840页

作者： Li, Yuxiang Wang, Dongqing Liu, Feng Qingdao Univ Sch Elect Engn Qingdao 266071 Peoples R China Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

Due to support vector machine (SVM) methods generally vectorizing the input matrix and destroying the location dependence characteristic of EEG signals, the support matrix machine (SMM) methods have been presented to incorporate input signal with its location information into the input data matrix and to perform the classification algorithm. In this brief, we investigate an auto-correlation function based sparse support matrix machine (ACF-SSMM) algorithm to optimize and classify the EEG fatigue signals. The main contents include: 1) to input a matrix EEG signal with location information and compress the redundant features by using the sparse principle;2) to extend the input matrix by adding data through an ACF transform, which takes into account information containing the previous/current instants, to accurately express the memory dynamics of the EEG signals;3) to perform multiple similar classification algorithms on the EEG fatigue dataset (SEED-VIG dataset) to verify effectiveness of the presented ACF-SSMM algorithm in EEG-based fatigue detection. The experiments get better results, and show the effectiveness of the proposed algorithm.

关键词： Support matrix machine electroencephalogram (EEG) fatigue detection auto-correlation function

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Generalized spot auto-focusing method with a high-definition auto-correlation function in transmission electron microscopy

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MICROSCOPY 2019年第5期68卷 395-412页

作者： Isakozawa, Shigeto Baba, Misuzu Amano, Junpei Sakamoto, Shohei Baba, Norio Kogakuin Univ Res Inst Sci & Technol 2665-1 Nakano Hachioji Tokyo 1920015 Japan Kogakuin Univ Major Informat Grad Sch 2665-1 Nakano Hachioji Tokyo 1920015 Japan

The spot auto-focusing (AF) method with a unique high-definition auto-correlation function (HD-ACF) proposed in the previous paper is improved and is now applicable to general specimens at a wide range of magnifications. According to the definition where the AF is defocused to obtain the highest resolution, the proposed method achieves the sharpest HD-ACF profile in the AF spot image. The relationship where the sharpest HD-ACF profile gives the highest resolution is theoretically explained, and practical AF examples for different specimens and magnifications are experimentally demonstrated. Specimens include a yeast cell thin section at 10-k magnification, a standard grating replica used as a ruler at 50-k, a crystal lattice of graphitized carbon at 400-k and a 60 degrees-tilted thin section (yeast cell) at 10-k. Different procedures are prepared to actively identify the defocus position that gives the sharpest HD-ACF profile. Every AF result demonstrates the highest-resolution image.

关键词： spot auto-focusing auto-correlation function high-definition auto-correlation function auto-correlation radial distribution function Fourier ring correlation electron tomography

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An auto-correlation function Based Technique for Discrimination of Internal Fault and Magnetizing Inrush Current in Power Transformers

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第4期43卷 399-411页

作者： Samet, Haidar Ghanbari, Teymoor Ahmadi, Mohammad Shiraz Univ Sch Elect & Comp Engn Dept Power & Control Shiraz Iran

Abstract-This article presents an efficient method for discriminating magnetizing inrush and internal fault currents in three-phase transformers based on the auto-correlation function. For each phase, the auto-correlation function of the differential current is calculated for a specified range of lags. Their standard deviation is determined in the next step. Criterion function is simply defined as the difference between the maximum and minimum of the auto-correlation function lags standard deviations for the three phases. Performance of the proposed method is evaluated and compared with eight well-known methods by a large number of simulation and experimental data, covering different cases. The results confirm that the proposed technique is more efficient than the other methods from speed response, reliability, and accuracy points of view. To eliminate the current transformer saturation impact on the performance of the method, the currents are compensated by a current transformer saturation correction algorithm. Also, the proposed algorithm is evaluated in the case of a transformer that is fed through a feeder with series capacitors.

关键词： magnetizing internal fault auto-correlation function inrush current transformer protection

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Spot auto-focusing and spot auto-stigmation methods with high-definition auto-correlation function in high-resolution TEM

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MICROSCOPY 2018年第2期67卷 75-88页

作者： Isakozawa, Shigeto Fuse, Taishi Amano, Junpei Baba, Norio Kogakuin Univ Res Inst Sci & Technol 2665-1 Nakano Hachioji Tokyo 1920015 Japan Kogakuin Univ Major Informat Grad Sch 2665-1 Nakano Hachioji Tokyo 1920015 Japan

As alternatives to the diffractogram-based method in high-resolution transmission electron microscopy, a spot auto-focusing (AF) method and a spot auto-stigmation (AS) method are presented with a unique high-definition auto-correlation function (HD-ACF). The HD-ACF clearly resolves the ACF central peak region in small amorphous-thin-film images, reflecting the phase contrast transfer function. At a 300-k magnification for a 120-kV transmission electron microscope, the smallest areas used are 64 x 64 pixels (similar to 3 nm(2)) for the AF and 256 x 256 pixels for the AS. A useful advantage of these methods is that the AF function has an allowable accuracy even for a low s/n (similar to 1.0) image. A reference database on the defocus dependency of the HD-ACF by the pre-acquisition of through-focus amorphous-thin-film images must be prepared to use these methods. This can be very beneficial because the specimens are not limited to approximations of weak phase objects but can be extended to objects outside such approximations.

关键词： spot auto-focusing spot auto-stigmation auto-correlation function high-definition auto-correlation function low s/n image high-resolution TEM

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Characterization of line edge roughness in resist patterns by using Fourier analysis and auto-correlation function

Characterization of line edge roughness in resist patterns b...

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15th International Microprocesses and Nanotechnology Conference

作者： Yamaguchi, A Komuro, O Hitachi Ltd Cent Res Lab Kokubunji Tokyo 1858601 Japan Hitachi High Technol Corp Hitachi Ibaraki 3128504 Japan

We have evaluated periodicity of line edge roughness (LER) in electron beam (EB), KrF, and ArF resists. A set of 512 edge-point coordinates was obtained for a scanning electron microscope (SEM) image of one edge by using an in-house image-processing program. The edge-point fluctuations were given and analyzed by Fourier transform and auto-correlation function. Fourier analysis has shown that (1) the spatial frequency (f) distribution of LER can be described with the 1/f-rule and (2) an additional component appears at around 100 nm in the results from several kinds of ArF resists. The auto-correlation-function method gives histograms of the basic period of LER. The results from this method are consistent with those from Fourier analysis and show that several ArF resist samples have a specific period distribution at around 240 nm. Results from both of these methods show that not only the resist materials but also the lithographic tools and processes affect the period distribution.

关键词： line edge roughness periodicity Fourier analysis auto-correlation function

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Radar Signal Classification Based on auto-correlation function and Directed Graphical Model

Radar Signal Classification Based on Auto-Correlation Functi...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Wang, Chao Gao, Hao Zhang, Xudong Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781509027088

This paper proposes a new radar signal classification algorithm based on auto-correlation function (ACF) and directed graphical model (DGM). The ACFs of analytic radar signals are calculated to magnify the discrimination of signals of different categories. A simple de-noising approach is introduced to purify the ACFs. Four features are extracted from the purified ACF. A DGM is used to represent the joint probability distribution of the four features along with the category and to classify unknown radar signals. Simulation results show the effectiveness of this classification algorithm.

关键词： radar signal classification auto-correlation function directed graphical model

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