Interior tomography is to reconstruct the interior region of interest (ROI) from the projection data just across the ROI. One kind of interior reconstruction methods is based on the inversion of truncated Hilbert tran...
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Interior tomography is to reconstruct the interior region of interest (ROI) from the projection data just across the ROI. One kind of interior reconstruction methods is based on the inversion of truncated Hilbert transform (thT) when there is a known sub-region inside ROI. However, the result via this method is usually to be degraded by noise in real data case. In this paper, we propose to incorporate the total variation (TV) minimization constraint into the thT-based interior tomography to improve the reconstruction quality. therein, we first carry out projection-on-convex-sets (POCS) iteration on each chord, and then we perform a soft-threshold based TV minimization on the intermediate image. In order to validate the proposed method, we conduct both simulated and real data experiments. the results show that with TV constraint the proposed method can lead to better ROI with less noise.
In this paper we discuss a new strategy to create ensemble of classifiers based on the multi objective evolutionary optimization. Instead of using feature selection technique which has been widely used in multi object...
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In this paper we discuss a new strategy to create ensemble of classifiers based on the multi objective evolutionary optimization. Instead of using feature selection technique which has been widely used in multi objective evolutionary approaches for ensemble generating, we have used a bagging-and-boosting-like strategy which also covers problems with lower dimensional feature spaces in which using feature selection technique may lead to ambiguous subspaces. After creating classifiers based on the amount of error created for each class, a multi-objective genetic algorithm has used to combine them to provide a set of powerful ensembles. Comprehensive experiments demonstrate the effectiveness of the proposed strategy.
In this work, we present an algorithm for estimating the fundamental frequency in speech signals. Our approach is based on the spectral compression by the autocorrelation of the speech multi-scale product analysis. It...
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In this work, we present an algorithm for estimating the fundamental frequency in speech signals. Our approach is based on the spectral compression by the autocorrelation of the speech multi-scale product analysis. It consists of operating the product of compressed copies of the original spectrum on the multi-scale product autocorrelation. the multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. the wavelet used is the quadratic spline function with a support of 0.8 ms. We estimate the pitch for each time frame based on its multi-scale product autocorrelation of the harmonic product spectrum structure. We evaluate our approach on the Keele database. Experimental results show the effectiveness of our method presenting a good performance surpassing the other algorithms.
In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple sc...
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In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, GA optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. this optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with automatic threshold. By using the method an optimistic result of image restoration was obtained. the experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. the method provided a foundation for target recognition in the dust environments.
Following is a continuation of the list of titles and authors: Approach to pattern Classification When Little Is Known. By Terrence L. Fine. Fast method for probabilistic and Fuzzy Cluster Analysis Using Association M...
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Following is a continuation of the list of titles and authors: Approach to pattern Classification When Little Is Known. By Terrence L. Fine. Fast method for probabilistic and Fuzzy Cluster Analysis Using Association Measures. By Enrique H. Ruspini. Estimation of the Information in a Two Class Sample. By G. A. Butler and H. B. Ritea. Non-Parametric Method with Applications to patternrecognition and Mode Estimation. By Demetrios A. Michalopoulos. On the Estimation of the Inverse of a Covariance Matrix. By Alejandro A. Lopez-Toledo. Conditions for Perfect Discrimination for Independent Measurements. By Anil K. Jain and B. Chandrasekaran. Some Second-Order Techniques for Selecting Subsets of patternrecognition Properties. By Earl E. Gose and Francis B. H. Wu.
To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. this paper evaluates two types of time-domai...
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ISBN:
(纸本)9781509032884
To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. this paper evaluates two types of time-domain EMG features, one independent feature and one combined feature including four features. the selected features from eight subjects with 13 finger movements were tested with four decomposed multi-class support vector machines (SVM), four decomposed linear discriminant analyses (LDA) and a multi-class LDA. the classification accuracy, training, and classification time are compared. the results have shown that the combined features decrease error rate, and binary tree based decomposition multiclass classifiers yield the highest classification success rate (88.2%) with relatively low training and classification time.
Modern service-based applications (SBAs) operate in highly dynamic environments where both underlying resources and the application demand can be constantly changing which external SBA components might fail. thus, the...
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the recognition of human activities captured by a wearable photo-camera is especially suited for understanding the behavior of a person. However, it has received comparatively little attention with respect to activity...
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At present, in the field of radar emitter classification, theoretical simulation is mostly used to carry out algorithm research. However, there are few schemes to study signal classification in real electromagnetic en...
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ISBN:
(纸本)9781665447300
At present, in the field of radar emitter classification, theoretical simulation is mostly used to carry out algorithm research. However, there are few schemes to study signal classification in real electromagnetic environment using actual hardware. therefore, this paper proposes a radar emitter classification scheme based on HackRF Software Defined Radio (SDR) and deep learning to solve the problem of weak engineering practice. Firstly, the GNU Radio development environment is used to realize the integration design of real space signal transceiver and time-frequency analysis algorithm application on HackRF hardware platform. then, a classification model with 11 layers network is constructed to automatically extract the deep features of intra-pulse signal time-frequency image. Finally, the classification performance of eight kinds of signals in real electromagnetic environment is tested. the total recognition accuracy of this scheme is more than 83% under 6dB low Signal-to-Noise Ratio (SNR), which proves the effectiveness of the scheme, and provides an important basis for practical engineering application in the future.
We propose a visual query method for image retrieval, in which the user expresses the composition of the target image by selecting one of the composition types presented by the system, and also propose an image classi...
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We propose a visual query method for image retrieval, in which the user expresses the composition of the target image by selecting one of the composition types presented by the system, and also propose an image classification method to derive composition types from an image database. From the viewpoint of communication between the system and its user, we point out the problem of visual query methods using concrete images as queries. though suited to convey visual image properties, they grow the ambiguity in the system's interpretation of queries. To manage this tradeoff, we implemented our methods in a prototype system and derived composition types from an image database to show how our query method works in image retrieval.
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