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Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using discrete wavelet transform on EEG and Inertial Signals

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Big Data Mining and Analytics 2024年第1期7卷 188-204页

作者： Yan Zhao Ming Guo Xiangyong Chen Jianqiang Sun Jianlong Qiu School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China School of Automation and Electrical Engineering Linyi UniversityLinyi 276000China

Walking as a unique biometric tool conveys important information for emotion recognition.Individuals in different emotional states exhibit distinct walking patterns.For this purpose,this paper proposes a novel approach to recognizing emotion during walking using electroencephalogram(EEG)and inertial signals.Accurate recognition of emotion is achieved by training in an end-to-end deep learning fashion and taking into account multi-modal fusion.Subjects wear virtual reality head-mounted display(VR-HMD)equipment to immerse in strong emotions during walking.VR environment shows excellent imitation and experience ability,which plays an important role in awakening and changing emotions.In addition,the multi-modal signals acquired from EEG and inertial sensors are separately represented as virtual emotion images by discrete wavelet transform(DWT).These serve as input to the attention-based convolutional neural network(CNN)fusion model.The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features.To effectively improve the performance of the recognition system,the proposed decision fusion algorithm combines Critic method and majority voting strategy to determine the weight values that affect the final decision results.An investigation is made on the effect of diverse mother wavelet types and wavelet decomposition levels on model performance which indicates that the 2.2-order reverse biorthogonal(rbio2.2)wavelet with two-level decomposition has the best recognition performance.Comparative experiment results show that the proposed method outperforms other existing state-of-the-art works with an accuracy of 98.73%.

关键词： walking multi-modal fusion virtual reality emotion recognition discrete wavelet transform attention mechanism

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Detection and recognition of veterinary drug residues in beef using hyperspectral discrete wavelet transform and deep learning

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 224-232页

作者： Rongchang Jiang Jingxin Shen Xinran Li Rui Gao Qinghe Zhao Zhongbin Su College of Electrical and Information Northeast Agricultural UniversityHarbin 150030China Shandong Academy of Agricultural Machinery Sciences Jinan 250100China Harbin Big Data Center Harbin 150030China

A fast,non-destructive recognition method for veterinary drug residues in beef was proposed to mitigate the laborious sample preparation and long detection times associated with conventional chemical detection techniques.Control beef samples free of veterinary drug residues and four groups of beef sprayed with relevant concentrations of metronidazole,ofloxacin,salbutamol,and dexamethasone under ambient conditions were analyzed by 400-1000 nm hyperspectral imaging followed by multiplicative scatter correction preprocessing.Data dimension reduction was performed using Competitive Adaptive Reweighted Sampling(CARS),Principal Component Analysis(PCA),and discrete wavelet transform(DWT)based on Haar,db3,bior1.5,sym5,and rbio1.3 wavelet basis functions.Treated data were subjected to Convolutional Neural Network(CNN),Multilayer Perceptron(MLP),Random Forest(RF),and Support Vector Machine(SVM)modelling.CNN,MLP,SVM,and RF algorithms achieved overall accuracies of 91.6%,88.6%,87.6%,and 86.2%,respectively,when combined with DWT(wavelet basis functions and numbers of transform layers being Haar-4,db3-2,bior1.5-4,and sym5-3,respectively).The algorithm Kappa coefficients(0.89,0.86,0.85,and 0.83,respectively)and time consumption for prediction(140.60 ms,57.85 ms,70.67 ms,and 87.16 ms,respectively)were also superior to models based on CARS and PCA.DWT combined with deep learning can shorten prediction times,considerably improve the accuracy of classification and recognition,and alleviate the Hughes phenomenon,thus providing a new method for the fast,non-destructive detection and recognition of veterinary drug residues in beef.

关键词： hyperspectral beef veterinary drug residues discrete wavelet transform convolutional neural network deep learning

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Digital Image Watermarking Based on discrete wavelet transform

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Journal of Computer Science & Technology 2002年第2期17卷 129-139页

作者：丁玮齐东旭等 CADLaboratory InstituteofComputingTechnologyTheCineseAcademyofSciencesBeijing100080P.R.China CADResearchCenter NorhChinaUniversityofTechnologyBeijing100041P.R.China

This paper aims at digital watermark which is a new popular research topic recently,presents some methods to embed digital watermark based on modifying frequency coefficients in discrete wavelet transform(DWT) domian,Fist,the,the present progress of digital watermark is briefly introduced;after that,starting from Pitas's method and discarding his pseudo random number method,the authors use a digital image scrambling technology as preprocessing for digital watermarking ,Then the authors discuss how to embed a 1-bit digital image as watermark in frequency domain.Finally another digital watermarking method is given in which3-D DWT is used to transform a given digtial image .Based on the experimental results ,it is shown that the proposed methods are robust to a large extent.

关键词： discrete wavelet transform digital image watermarking digital image scrambling

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Interpolation of Images Using discrete wavelet transform to Simulate Image Resizing as in Human Vision

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International Journal of Automation and computing 2010年第1期7卷 9-16页

作者： Rohini S. Asamwar Kishor M. Bhurchandi Abhay S. Gandhi Department of Electronics and Communication Engineering Shri Ramdeobaba Kamla Nehru Engineering College Department of Electronics and Computer Science Visvesvaraya National Institute of Technology

This paper presents discrete wavelet transform （DWT） and its inverse （IDWT） with Haar wavelets as tools to compute the variable size interpolated versions of an image at optimum computational load. As a human observer moves closer to or farther from a scene, the retinal image of the scene zooms in or out, respectively. This zooming in or out can be modeled using variable scale interpolation. The paper proposes a novel way of applying DWT and IDWT in a piecewise manner by non-uniform down- or up-sampling of the images to achieve partially sampled versions of the images. The partially sampled versions are then aggregated to achieve the final variable scale interpolated images. The non-uniform down- or up-sampling here is a function of the required scale of interpolation. Appropriate zero padding is used to make the images suitable for the required non-uniform sampling and the subsequent interpolation to the required scale. The concept of zeroeth level DWT is introduced here, which works as the basis for interpolating the images to achieve bigger size than the original one. The main emphasis here is on the computation of variable size images at less computational load, without compromise of quality of images. The interpolated images to different sizes and the reconstructed images are benchmarked using the statistical parameters and visual comparison. It has been found that the proposed approach performs better as compared to bilinear and bicubic interpolation techniques.

关键词： discrete wavelet transform nomuniform sampling zeroeth level discrete wavelet transform （DWT） interpolation human vision.

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Low complexity reconfigurable architecture for the 5/3 and 9/7 discrete wavelet transform

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Journal of Systems Engineering and Electronics 2006年第2期17卷 303-308页

作者： Xiong Cheng yi Tian Jinwen Liu Jian Coll. of Electronic Information Engineering South Center Univ. for Nationalities Wuhan 430074 P.R. China Inst. of Pattern Recognition & Artificial Intelligence Key Lab. of Education Ministry for Image Processing and Intelligent Control Huazhong Univ. of Science & Technology Wuhan 430074 P.R. China

Efficient reconfigurable VLSI architecture for 1-D 5/3 and 9/7 wavelet transforms adopted in JPEG2000 proposal, based on lifting scheme is proposed. The embedded decimation technique based on fold and time multiplexing, as well as embedded boundary data extension technique, is adopted to optimize the design of the architecture. These reduce significantly the required numbers of the multipliers, adders and registers, as well as the amount of accessing external memory, and lead to decrease efficiently the hardware cost and power consumption of the design. The architecture is designed to generate an output per clock cycle, and the detailed component and the approximation of the input signal are available alternately. Experimental simulation and comparison results are presented, which demonstrate that the proposed architecture has lower hardware complexity, thus it is adapted for embedded applications. The presented architecture is simple, regular and scalable, and well suited for VLSI implementation.

关键词： VLSI discrete wavelet transform lifting scheme embedded decimation reeonfigurable.

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A lightweight symmetric image encryption cryptosystem in wavelet domain based on an improved sine map

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Chinese Physics B 2024年第3期33卷 266-276页

作者：陈柏池黄林青蔡述庭熊晓明张慧 School of Advanced Manufacturing Guangdong University of TechnologyJieyang 522000China School of Integrated Circuits Guangdong University of TechnologyGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangzhou 510006China

In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive image.In this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively tested.Drawing upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and speed.Initially,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and diffusion.Then leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all sub-bands.Simulations and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC.

关键词： image encryption discrete wavelet transform 1D-chaotic system selective encryption Gaussianization operation

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A New Segmentation Method of CR Images Based on discrete wavelet transform and Mathematics Morphology

A New Segmentation Method of CR Images Based on Discrete Wav...

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20th Congress of the International Commission for Optics

作者： LI Zhea,b and . HUANG Lian-qingaa Changchun Institute of Optics ,Fine Mechanics and Physics of Chinese Academy of Sciences,Changchun, China b Postgraduate School of Chinese Academy of Sciences, Beijing, China

In this paper, we propose a segmentation method of CR(computed radiography) images with being rid of under-segmentation and over-segmentation. An under-segmentation occurs when pixels belonging to dif

关键词： Watershed Algorithm Under-segmentation discrete wavelet transform CR image Over-segmentation

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Research on Digital Watermarking Technology Based on discrete wavelet transform

Research on Digital Watermarking Technology Based on Discret...

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作者： Dingying Tan Huiling Liu Huili Wang Min Zou Pingping Chen School of Medical Information Engineering Guangzhou University of Chinese Medicine

By using the advantages of discrete wavelet transform in digital watermark embedding,extraction and detection,a corresponding basic model was established.Using MATLAB as a tool for simulation experiments,the experimental results show that the discrete wavelet transform can extract the watermark signal under the common JPEG compression,noise,shear and rotation,filtering and other attacks,and has high robustness.

关键词： Digital watermarking discrete wavelet transform Robustness

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Stator Fault Diagnosis of Induction Motor Based on discrete wavelet Analysis and Neural Network Technique

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Chinese Journal of Electrical Engineering 2023年第1期9卷 142-157页

作者： Abdelelah Almounajjed Ashwin Kumar Sahoo Mani Kant Kumar Sanjeet Kumar Subudhi Electrical Engineering Department C.V Raman Global UniversityBhubaneswar 752054India Electronics and Communication Engineering Department C.V Raman Global UniversityBhubaneswar 752054India

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit(ITSC)faults in induction motors(IMs)is proposed.Determining the incipient ITSC fault and its severity is challenging for several reasons.The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault.Moreover,the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity.The objective of this study is the early detection of incipient ITSC faults.Furthermore,it aims to determine the percentage of shorted turns in the faulty phase,which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform.A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet.The statistical parameters L1 and L2 norms are computed for the detailed coefficients.These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault.Combining L1 and L2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim.An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process.The classifier output defines the percentage of defective turns with a high level of accuracy.The competency of the adopted methodology is validated via simulations and experiments.The results confirm the merits of the proposed method,with a classification test correctness of 95.29%.

关键词： discrete wavelet transform induction motor inter-turn short circuit fault neural networks statistical parameters

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Use of discrete wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool

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Structural Durability & Health Monitoring 2018年第2期12卷 111-127页

作者： C.K.Madhusudana N.Gangadhar Hemantha Kumar S.Narendranath National Institute of Technology Karnataka SurathkalMangalorePin-575025India

This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)technique.The decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected features.From the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.

关键词： Fault diagnosis face milling decision tree discrete wavelet transform support vector machine

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