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

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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Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary auto-correlation function

Python-Based Simulation of Non-Gaussian Stationary Random Pr...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Bykhovsky, Dima Tochilovsky, Netanel Rudyak, Alexander Shamoon College of Engineering Electrical and Electronics Engineering Department Beer-Sheva Israel

ISBN: (纸本)9798350327816

Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must have a pre-defined distribution and autocorrelation function (ACF). It is challenging to model non-Gaussian distributions as using a linear filter can alter the target distribution. To address this issue, we wrote a Python package that implements one of the prominent methods in the field of random process simulation. To the best of our knowledge, it is the first publicly available implementation of such a simulator in Python. © 2023 IEEE.

关键词： auto-correlation function Nak-agami distribution probability distributions Python simulation stationary process time series

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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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Dynamic Texture Analysis Using auto-correlation function of Histogram Similarity Measure From Galois-Field Texture Representation of Water Flow Video

Dynamic Texture Analysis Using Auto-correlation Function of ...

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International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

作者： Sirenden, Bernadus H. Mursanto, Petrus Wijonarko, Sensus Univ Indonesia Fac Comp Sci Depok Indonesia Indonesian Inst Sci Res Ctr Phys Tangerang Selatan Indonesia

ISBN: (纸本)9781728189222

This paper propose a method for determining the periodicity of dynamic textures from water video using spatial feature similarity measure of Galois-Field (GF) texture representation. auto correlation function are use to analyze the extracted spatial feature from the representation to determine the periodicity of dynamic textures. There are two type of spatial feature to be compared, the first is histogram and second is normalize cumulative histogram (NCH). Two type of experiment are conducted, the first is virtual rotation where video is rotated virtually from 0 degrees until 360 degrees, the second is actual rotation where camera are rotated physically. Experiments show that although GF improves the performance of the Histogram similarity measure, overall NCH shows better performance. In virtual rotation experiment, GF representation prove to minimize variability due to rotation of camera, the maximum variability produce by NCH is 27%, while when GF are not use the maximum variability is 106%. Contrary, in actual rotation experiment, GF is not proven to minimize variability where NCH produce maximum variability is 57%, while where GF are not use the maximum variability is 9%. The difference in variability pattern between virtual and actual rotation, shows that Galois Field is good at handling dynamic texture rotation, but not against other factors that affect variability.

关键词： Dynamic Texture Analysis Galois Field Histogram Normalize Cumulative Histogram auto-correlation function Video Processing Water Surface Velocity Open Channel Flow Measurement

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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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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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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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A Novel Sorting Method Based on auto-correlation function of Coherent Radar Signal

A Novel Sorting Method Based on Auto-correlation Function of...

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2017第二届电气工程，机械工程与自动化国际会议

作者： Wei Qing-yun Chen Bei Wang Jing Han Jun Tang Yu-wen Air Force Early Warning Academy

In order to solve the increasing-batch problem in signal sorting, a novel sorting method based on auto-correlation function of coherent radar signal is proposed. By using the instantaneous auto-correlation function of coherent signal, the discrimination model of ratio of peak values was built and applied in sorting simulation. Simulation results show that the proposed approach can solve the Increasing-batch problem for CW, LFM, NLFM one sorting when the signal-to-noise rate(SNR) above 5d B. The validity of the approach is demonstrated by experimental results.

关键词： Sorting Coherency auto-correlation function Ratio of Peak Values

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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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Feature Extraction for Complicated Radar PRI Modulation Modes Based on auto-correlation function

Feature Extraction for Complicated Radar PRI Modulation Mode...

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2016 IEEE Advanced Information Management,Communicates,Electronic and automation Control Conference(IMCEC 2016)

作者： Zhongya Shi Hua Wu Wendi Shen Siyi Cheng You Chen Air Force Engineering University

ISBN: (纸本)9781509001668

In order to recognize complicated PRI modulation modes,a classify algorithm based on auto-correlation and normalization is *** mathematical model of four PRI modulation modes is given *** the basis of making autocorrelation and normalization to four kinds of PRI modulation modes,it defines three characteristic quantities: density of peak value,intension of monotone and energy of *** to these characteristic quantities,3 dimensional feature space is set *** comparing characteristic quantity with threshold value,PRI modulation mode can be recognized *** simulation result shows that the characteristic quantities perform well in distinguishing four kinds of PRI modulation modes and the correct recognition rate is high when pulse sequence length is short and pulse miss is serious.

关键词： PRI modulation mode auto-correlation function Recognition of radar emitter Feature extraction

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