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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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Food image-Induced Discrete Emotion Recognition Using a Single-Channel Scalp-EEG Recording

Food Image-Induced Discrete Emotion Recognition Using a Sing...

引用

international congress on image and signal processing, biomedical engineering and informatics

作者： Kunyuan Zhao Dan Xu Yunnan University Kunming China

We construct a food image data set as visual stimulus cues for the field of affective computing. After 40 volunteers rating each of 500 food images in terms of the levels of arousal, valence, liking, and familiarity, 188 food images with high consistency scores are selected as the experimental stimuli that evoked three emotional tags: positive, neutral, and negative. The electroencephalogram (EEG) signals of 20 participants were recorded as each watched food images, comprising a food emotion dataset which can be an effective stimulus material. The results show that the food data sets can induce different emotions and the features of each EEG sub-band of different emotional labels have significant differences. This study proposes an innovative theme of emotional stimuli selection based on the food visual cues and expect to construct the optimal emotional feature space as input of deep learning model to achieve better classification accuracy on the real-time emotional recognition system.

关键词： Electroencephalography Emotion recognition Visualization Feature extraction Real-time systems Brain modeling Psychology

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Numerical Calculation of Partial Differential Equation Deduction in Adaptive Total Variation image Denoising

Numerical Calculation of Partial Differential Equation Deduc...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Minghua Zhao Qin Wang Abdul Nasir Muniru Jiawei Ning Peng Li Bing Li Shaanxi Key Laboratory of Network Computing and Security Technology Xi'an Shaanxi China

Total variation model of image denoising is easy to influence the gradient and lose the details of image. Due to these weaknesses, many adaptive total variation (ATV) models of image denoising have been proposed to eliminate the Gaussian noisy additive in the image. This paper implements the model through a numerical solution, where the gradient descent method is used to derive the partial differential equation (PDE) corresponding to the ATV model. First, based on Euler-Lagrange equation, a detailed derivation process for Partial Differential Equation is used to calculate the ATV model. Then, based on gradient descent method, a numerical calculation of the model is derived from the PDE using the Direct Difference Method. Finally, several different λ parameters are compared to produce different image denoising effects and the appropriate parameter λ value is determined. Experimental results prove that our proposed numerical calculation can effectively realize the ATV denoising model.

关键词： Mathematical model Numerical models Adaptation models image denoising Noise reduction TV Partial differential equations

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Construction of 8PSK MSS-OFDM Set Based on Orthogonal Complementary Sequences

Construction of 8PSK MSS-OFDM Set Based on Orthogonal Comple...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Zhenyu Zhang Guixin Xuan Chenrun Liu Fanxin Zeng Communication Petty Officer Academy PLA Army Engineering University Chongqing China Boston University Boston USA Chongqing Institute of Engineering Chongqing China

Based on orthogonal complementary sequences, a construction method of 8PSK sequence set for M-ary spread spectrum (MSS) orthogonal frequency division multiplexing (OFDM) system is proposed. The purpose of this research is to improve the data rate of MSS-OFDM system while maintaining a low peak-to-average power ratio (PAPR). Compared with traditional BPSK sequence set, the proposed 8PSK sequence set can ensure that MSS-OFDM system obtains three times higher data rate. As an inherent problem of higher order modulation, the bit-error rate performance of MSS-OFDM system will decrease with the shortening of Euclidean distance. However, PAPRs of OFDM symbols may be effectively controlled to a fixed value, which will be helpful to improve the power efficiency of MSS-OFDM system.

关键词： Peak to average power ratio Binary phase shift keying Indexes OFDM modulation

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Generating Syndrome Name Based on Partial-Ordered Attributes Theory

Generating Syndrome Name Based on Partial-Ordered Attributes...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Jingmin Luan Xulong Liu Wenxue Hong Jialin Song School of Computer and Communication Engineering Northeast University at Qinhuangdao Qinhuangdao Hebei P.R. China School of Electrical Engineering YanShan University Qinhuangdao Hebei P.R. China

this paper focuses on the syndrome name generation method of syndrome differential based on the traditional Chinese medicine (TCM) theory. Based on the theory of TCM diagnosis, a multi-hierarchies complex model of Disease-Syndrome-Symptom has been established. Using the quantitative method, the model of three-dimensional knowledge base which expresses the relationships between syndromes and syndrome elements has been built. By targeting the clinical request for TCM diagnosis, a new method of generating the syndrome name of TCM diagnosis has been proposed based on the expression theory of structural partial-ordered attribute diagram. By using a series of heart diseases as the formal context, the TCM diagnosis knowledge has been discovered by the structural partial-ordered attribute diagram. Experiment demonstrates that the structural diagram can clearly express the relationships between syndrome name and syndrome element without crossover. The generated syndrome name is consonant with the theory of TCM diagnosis, the efficiency and validity of this method has been certificated.

关键词： Diseases Solid modeling Clinical diagnosis Knowledge based systems Formal concept analysis Pathology

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An Improved Method for the Magnetometer Calibration Based on Ellipsoid Fitting

An Improved Method for the Magnetometer Calibration Based on...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Fangzhou Hu Yinfeng Wu Yang Yu Jing Nie Wei Li Qi Gao School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Capital Aerospace Machinery Company Beijing China

The three-axis MEMS magnetometer is widely applied to assist the inertial sensor to estimate the attitude (especially heading) in strapdown inertial navigation system. It is necessary that the system errors of the magnetometer are analyzed and compensated, just like the inertial sensor. The system errors that contain biases, scale factors, nonorthogonal angles and iron effects can be quantified by the calibration. In general, error items can be derived based on the geomagnetic field vector observation. In most magnetometer calibration algorithm, the data from the sensor is processed directly by ellipsoid fitting without any pretreatment. In order to guarantee the reliability of the data, this paper proposes a new method for testing the rationality and gross errors of the calibration data based on geometric parameters of the ellipsoid. The experimental results show that the unreasonable data is detected, so that the calibration accuracy is improved to some extent.

关键词： Calibration Magnetometers Mathematical model Magnetic flux Ellipsoids Magnetic domains Fitting

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Adapting Random Forest Classifier Based on Single and Multiple Features for Surface Electromyography signal Recognition

Adapting Random Forest Classifier Based on Single and Multip...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Tao Zhou Olatunji Mumini Omisore Wenjing Du Lei Wang Yuan Zhang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Information Science and Engineering University of Jinan Jinan China

Surface Electromyography (sEMG) signals have been recently adopted in developing control models for wearable interventional robots and related surgical systems' mechanical control problems. However, adapting sEMG signal models for intravascular robotic interventions is yet to receive any attention due to inability of obtaining continuously reliable signals from the human experts and real-time decoding and integration of the control outputs into the the robotic mechanism. Pattern recognition modeling and classification are vital steps towards developing effective sEMG-based signal models for surgeon-robot intravascular procedures, and these are focused in this paper. For this purpose, nine single features and nine groups of multiple features were investigated for hand motion recognition with the random forest classifier. The proposed random forest classifier was trained and evaluated using a dataset with recordings of twelve finger motions made by ten subjects from a publicly accessible database in the Ninapro project. An average motion classification accuracy of 84.11±3.99% and a best result of subject of 92.94% was obtained from the adapted classifier. Besides this, we also employed three performance metrics to explore more performance detail of the experimental results, and to showed the learning process of the training process.

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Research on Clothing image Classification by Convolutional Neural Networks 11

Research on Clothing Image Classification by Convolutional N...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Chen, Lili Han, Runping Xing, Shaopeng Ru, Shuiqiang Beijing Inst Fash Technol Sch Informat Engn Beijing 100029 Peoples R China Beijing Inst Fash Technol Chinese Fash & Technol Res Inst Beijing 100029 Peoples R China Beijing Dahao Technol Corp Ltd Beijing 100029 Peoples R China

ISBN: (纸本)9781538676042

In recent years, clothing image classification methods based on convolutional neural networks(CNNs) have attracted plenty of attention with the increasing demand for high accuracy clothing image classification in many fields. In this paper, five different CNNs are designed to implement clothing image classification, which are the conventional CNN, the CNN containing inception module, the CNN containing inception module and residual block, two transfer learned CNNs. The experimental results show that all the networks are capable of achieving good classification, among which the transfer learned CNN have higher classification accuracy.

关键词： component Convolutional neural network Inception module Residual block Transfer learning Pre-trained Inception-v3

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Dynamic Alterations of EEG Oscillations and Networks from Resting to Deep Meditation

Dynamic Alterations of EEG Oscillations and Networks from Re...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Meiyun Wang Menglin Guo Xu Wang Ting Xue Zhuo Wang Han Li Tianjiao Xu Bin He Donghong Cui Shanbao Tong Xiaoli Guo School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Psychotic Disorders Shanghai China Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai China Center for Neuroengineering Carnegie Mellon University Pittsburgh USA School of Biomedical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai China

Meditation is a progressive process in which meditators may experience different stages to finally reach a deep meditation state. However, the temporal-dynamic process of meditation remains unclear. In this study, we recruited 38 experienced Tibetan Buddhism meditators to explore the dynamic process from resting to deep meditation from the aspects of EEG oscillations and networks. We found that the delta power was reduced and the transitivity of the delta network was increased immediately after the start of meditation. Then, the beta and gamma power increased, and the clustering coefficient and global efficiency of the gamma network increased at the deep meditation substage. Our results suggested that the participants entered into a wakeful and non-judgmental state immediately after the start of meditation, and then achieved a more efficient information processing and transferring only at a deep meditation state. Our study showed that meditation was a time-varying process, deep meditation was a different substage dominated by high frequency EEG oscillations and more small-world like brain networks.

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Constitution Identification of Tongue image Based on CNN 11

Constitution Identification of Tongue Image Based on CNN

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Zhou, Hao Hu, Guangqin Zhang, Xinfeng Beijing Univ Technol Beijing Peoples R China

ISBN: (纸本)9781538676042

The physique of traditional Chinese medicine (TCM) is the quality of our body and the tongue image is a manifestation based on the metabolism of the body. The constitution can be easily and objectively identified by the image of the tongue. In this paper, the classical convolution neural network (CNN) and gray level co-occurrence matrix, minimum enclosing rectangle and edge curve are used to extract the features of human tongue. Then different classifier are used to classify different constitution, and finally by comparing the accuracy and complexity of the two methods, we proposed a method constitution identification of TCM which is based on tongue images. The data set used in the experiment is provided and acquired by the Department of TCM in the hospital of Beijing University of Technology. In this paper, the accuracy of the three types of physique classification of Qi deficiency, damp heat and phlegm dampness is 63%, and the accuracy of traditional machine learning algorithm is respectively 30%, 56% and 66%. It is of great significance to clinical, teaching and scientific research of TCM by making the most of the deep learning network and the auxiliary identification of physique.

关键词： CNN tongue image constitution of TCM

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The Influence of Clipping on the Performance of a Low Bit Rate Parametric Speech Coder

The Influence of Clipping on the Performance of a Low Bit Ra...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Penglong Wu Xia Zou Meng Sun Jun He Xingyu Zhang The Army Engineering University of PLA Nanjing China Institution of Information and Communication of National University of Defense Technology Wuhan China

This paper presents a study of the influence of clipping on low bit rate parametric speech coder. By selecting three parameters of MELP vocoder: the fundamental frequency, voicing and LPC, the influence of different clipping degree on the MELP vocoder is studied. In the experimental stage, the performance of the MELP vocoder at different clipping degree are evaluated by using pitch estimation accuracy rate, voicing decision error rate and average Log-spectral distance. And then, the reasons which lead to a decrease in the performance of parameter extraction are analyzed. The experimental results show that clipping has a certain influence on the accuracy of pitch estimation and voicing decision. However, the accuracy of pitch estimation and voicing decision can keep high accuracy even when the clipping degree is large, which indicates that the pitch and voicing are strongly resistant to clipping. For LPC, the increase of the clipping degree increases the average Log-spectral distance, resulting in an inaccurate estimation of LPC.

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