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

共 2037 条记录，以下是1681-1690 订阅

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A Novel Method of Feature Extraction for Minor Crack Identification

A Novel Method of Feature Extraction for Minor Crack Identif...

引用

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

作者： Ping Fan Xinbao Liu School of Information Science and Technology Northwest University Xi'an 710127 China School of Chemical Engineering Northwest University Xi'an 710127 China

ISBN: (纸本)9781509037117

Ultrasonic testing technique has been widely applied for monitoring the metal structure health. It is useful to detect and access the damage condition with the minor crack information being concealed in the ultrasonic signal. Although there has been a large amount of studies, the extraction of robust minor crack features is still a fundamental problem. In this paper, a novel crack identification algorithm is proposed by the wavelet packet transform (WPT) of received signal. With the calculation of sub-band signal energy, the most suitable decomposition level is decided. Then, the features are defined by the correlation coefficient between the damaged signal and undamaged signal. With principal component analysis (PCA), the feature extraction is achieved by reducing the overlapped and redundant ones. Finally, the extracted features are fed into support vector machines (SVM) classier and their outputs are employed to classify the damage type. The performance of the proposed method is confirmed with practical experiment. It indicated that compared with other methods, the proposed algorithm has a higher identification accuracy with more robust features.

关键词： correlation coefficient ultrasonic testing minor crack feature extraction

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Feature extraction of protein secondary structure using 2D convolutional neural network

Feature extraction of protein secondary structure using 2D c...

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

作者： Yihui Liu Yehong Chen Jinyong Cheng Institute of Intelligent Information Processing Qilu University Of Technology China

ISBN: (纸本)9781509037117

In this paper we propose a approach using 2D convolutional neural network(CNN) for the prediction of protein secondary structure. A representation of two dimensional PSSM is directly used to convolve with filters to extract the features. This new representation reflects not only the evolutionary information but also the sequence interaction of residue. The architecture of convolutional neural network has two convolutional layers and one max-pooling layer. The feature maps extracted from second convolutional layer are used to feed to Bayes classifier, in order to build prediction model. The Q3 accuracy 77.7% of 25PDB dataset is achieved based on 3 fold cross validation experiments using CNN features. The performance based on CNN features is better than 73.8% of 25PDB dataset using the original features. Experimental results illustrate that CNN features improve the prediction performance and have the potential ability of the invariance against mutation, insertion, deletion of residue.

关键词： Feature extraction Proteins Neural networks Amino acids Protein engineering Machine learning Filter banks

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Multi-feature based fast depth decision in HEVC inter prediction for VLSI implementation

Multi-feature based fast depth decision in HEVC inter predic...

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

作者： Gaoxing Chen Zhenyu Liu Tetsunori Kobayashi Takeshi Ikenaga Graduate School of IPS Waseda University Kitakyushu-shi Japan TNList Tsinghua University Beijing China Faculty of Science and Engineering Waseda University

ISBN: (纸本)9781509037117

High efficiency video coding (HEVC) is the latest international video compression standard that achieves double compression efficiency than the previous standard H.264/AVC. To increase the compression accuracy, HEVC employs the coding unit (CU) ranging from 8 × 8 to 64 × 64. However, the encoding complexity of HEVC increase a lot since the manifold partition sizes. A lot of works are focused on reducing the complexity but didn't considered the feasibility of hardware implementation. This paper proposes a hardware friendly fast depth range definition algorithm based on multiple features. Block texture feature, quantization feature and block motion feature are utilized. Block texture feature is based on the texture similarity in consecutive frames. Quantization feature is based on the compression regularity of HEVC. Block motion feature is for compensate the difference caused by the moving object. Comparing with the original HEVC, the proposed method can saved about 33.72% of the processing time with 0.76% of BD-bitrate increase on average.

关键词： Complexity theory Quantization (signal) Encoding Very large scale integration Hardware Correlation Partitioning algorithms

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Study on Area Coordination Control System Based on Traffic State Discrimination

Study on Area Coordination Control System Based on Traffic S...

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

作者： Limin Zhang Guifen Chen Tianyu Zhang School of Electronic & Information Engineering Changchun University of Science and Technology

ISBN: (纸本)9781509037117

In recent decades, due to the gradual emergence of urban traffic problems, Development of Urban Intelligent Traffic Control System is getting to arouse the study of researcher. Therefore, this paper mainly studied on intelligent traffic control of the city from a regional perspective. This paper firstly introduces the division of traffic control sub-region. Secondly, this paper discriminated the traffic state of traffic control sub-region by fuzzy control method. Finally, this paper designed the various components of the coordinate control system. Using traffic control method based on the intersection and coordinate intersection to complete signal phase distribution and switching. Using the timing control, single fuzzy control and coordinated fuzzy control of the paper proposed to simulate. Results demonstrated the average vehicle delay time of coordination fuzzy control is minimal, the performance is the best.

关键词： coordinated control traffic area sub-region divided traffic state fuzzy control

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Research and Implementation of 3D SLAM Algorithm Based on Kinect Depth Sensor

Research and Implementation of 3D SLAM Algorithm Based on Ki...

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

作者： Ce Li Haozuo Wei Tian Lan College of Electrical and Information Engineering Lanzhou University of Technology

ISBN: (纸本)9781509037117

For the 3D perception problem of mobile robots in unknown indoor environments, a practical approach to building 3D maps using a low-cost Kinect sensor is proposed. Successive frames of RGB-D information are captured during the robots movements. First, SIFT(Scale-invariant Feature Transform)detector is applied to color images for extracting and matching stable feature points. Then, combined with the GTM(Graph Transformation Matching) algorithm to eliminate the possible existence of false matching points and complete the initial registration, so as to estimate the rough relative transfer between the image frames. Finally, using the ICP(Iterative Closest Point) algorithm, the robot's motion parameters are continuously updated to update the robot posture, and based on this, the 3D map of the indoor environment is created based on the combination of depth information. Experimental results validate the feasibility and effectiveness of the approach.

关键词： game player indoor environment Robot Mobile robots images supersonic low altitude missile Sensors

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Ultra Low Power All-Digital CMOS Sensor Read Out Circuit for Optically Powered biomedical Systems

Ultra Low Power All-Digital CMOS Sensor Read Out Circuit for...

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

作者： Asli Yelkenci Okan Zafer Batur Baykal Sarioglu Department of Electrical and Electronics Engineering Istanbul Bilgi University Eyup Turkey 34060

ISBN: (纸本)9781509037117

In this paper, an ultra low power all digital sensor read out circuit architecture which requires very low power is proposed. The proposed circuit is targeted for optically powered biomedical applications. The read-out circuit is utilized for the measurement of capacitive and resistive type of transducers. The measurement method is based on the fact that both capacitive and resistive sensors can be utilized to introduce delay in the signal transmission path. The proposed circuit architecture is composed of entirely digital components and it measures the introduced delay using only single input clock signal. The proposed architecture is implemented on UMC 180 nm CMOS technology and simulation results are presented. The proposed circuit operates with 1.2 V supply that is generated by the optical power harvesting and charge pump unit, while consuming 70.4 μW. The results confirm that the proposed circuit can be utilized in optically powered biomedical applications for carrying out capacitive and resistive sensor measurements.

关键词： biomedical optical imaging Optical sensors Clocks Delays Radiation detectors Optical transmitters Low-power electronics low-power electronics Optical transmitters biomedical optical imaging Radiation detectors Timing devices optical sensors capacitance delay Particle detectors readout biomedical digital sensor

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Automatic Feature Point Detection Using Deep Convolutional Networks for Quantitative Evaluation of Facial Paralysis

Automatic Feature Point Detection Using Deep Convolutional N...

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

作者： Hiroki Yoshihara Masataka Seo Truc Hung Ngo Naoki Matsushiro Yen-Wei Chen College of Information Science and Engineering Ritsumeikan University Siga Japan Department of Otorhinolayngology Osaka Police Hospital Japan

ISBN: (纸本)9781509037117

Feature point detection is an important pre-processing step for quantitative evaluation of facial paralysis. Since the conventional methods such as active shape model (ASM) or active appearance model (AAM) are trained by using normal face and they are not possible to detect the feature points accurately for the face with paralysis. In this paper, we propose an automatic and accurate feature point detection method for quantitative evaluation of facial paralysis using deep convolutional neural networks (DCNN). The proposed method consists of two steps. We first use AAM for initial feature point detection. In the second step, a patch with the detected point at the center is used as an input of DCNN for refinement. Experiments demonstrated that the proposed method can significantly improve the detection accuracy of the conventional AAM.

关键词： Training Feature point detection Deep convolutional neural network Facial paralysis Patch

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On the Typical Pulse Recognition Based on Support Vector Machine

On the Typical Pulse Recognition Based on Support Vector Mac...

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

作者： Wanbing Li Shangxiong Sheng Yongxin Chou College of Mechanical Engineering Xijing University School of Electrical and Automatic Engineering Changshu Institute of Technology

ISBN: (纸本)9781509037117

The physiological and pathological information obtained by the single-point or complex multi-point pressure sensor is still less. In this paper, we adopt the pulse image sensor which can reflect the change of the pulse-taking skin surface particularly and comprehensively, we use the MM-3 pulse model (Group A) as the subjects produced by Shanghai University of Traditional Chinese Medicine to study the characteristic parameters in different pulse conditions. Through the area changes from the pulse dynamic image to get the pulse waveform, and extract the frequency characteristic and pulse frequency value, at the same time extract the pulse length and pulse width from the three-dimensional reconstruction of the frame at the maximum amplitude. Finally, Support Vector Machine (SVM) are used to recognize the different pulse conditions, and to find out the features which can characterize the normal pulse, the slippery pulse, the slow pulse and the soft pulse. Experimental results show that the SVM is used in four kinds of pulse condition recognition the rate can reach 90% or more.

关键词： Pulse recognition Pulse image Pulse characteristics SVM

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A New Elastography Method for Liver Stiffness Based on Ultrasound Spectral Analysis

A New Elastography Method for Liver Stiffness Based on Ultra...

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

作者： Hao Pan Chun Yi Lin Jian Hua Zhou School of Electrical and Information Engineering South China University of Technology Sun Yat-Sen University Cancer Center

ISBN: (纸本)9781509037117

A new elastography method based on ultrasound spectral analysis is proposed. The experimental data source is Ultrasound radio frequency (RF) signal. It is decomposed by three levels Discrete Wavelet Packet Transform, and each sub-band energy spectral sequence is achieved. Fourth-order polynomial is used to fit these sub-band energy spectral sequences based on the least square method. The local elastic parameters (LEPs) are acquired as features from the fitted polynomial coefficients. These parameters correlate to tissue stiff. At last the elastic parameters are coded by pseudo color and map stiff of tissue. To validate its effectiveness, it is applied on in vivo liver tissue. The experiment result shows that these elastic parameters between normal and cirrhosis are different. This new elastography method is effective to characterize liver stiffness.

关键词： Elastography Local Elastic Parameters Wavelet Packet Decompsition

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Anomaly Recognition of Ultra Low Frequency Electric Data Based on Artificial Neural Network

Anomaly Recognition of Ultra Low Frequency Electric Data Bas...

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

作者： Jianqin An Yiyao Son Zhong Li Department of Disaster Information Engineering Institute of Disaster Prevention Sanhe Hebei China

ISBN: (纸本)9781509037117

In order to search the abnormal data of seismic satellite quickly, we extract the ultra low frequency electric field waveform data of 10 days before the Wenchuan earthquake in this paper. The back propagation neural network classification model is designed and the self-organizing feature map network clustering model is used to verify back propagation network using the mean, variance, skewness and kurtosis as the feature information. Results show that anomaly areas are located in the larger area of the south of the epicenter of the Wenchuan earthquake, which is perhaps due to the powerful seismic wave energy of south of Wenchuan causing the strong spatial ionospheric disturbance;the correct recognition rate of the normal class of back propagation net classifier reaches 98.13%, the recognition rate of the exception class of that is 96.75%;The clustering results of self-organizing feature map network and the back propagation network classifier are practically in good agreement.

关键词： ultra low frequency electric field signal computer application artificial neural network DEMETER satellite

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