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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Kun Qian Christoph Janott Jun Deng Clemens Heiser Winfried Hohenhorst Michael Herzog Nicholas Cummins Bjorn Schuller Chair of Complex & Intelligent Systems Universität Passau Germany Machine Intelligence & Signal Processing group Technische Universität München Germany Institute for Medical Engineering Technische Universität München Germany EERING GmbH Gilching Germany Department of Otorhinolaryngology/Head and Neck Surgery Technische Universität München Germany Clinic for ENT Medicine Alfried Krupp Krankenhaus Essen Germany Clinic for ENT Medicine Head and Neck Surgery Cottbus Germany Department of Computing Imperial College London U.K

In this paper, we present a comprehensive comparison of wavelet features for the classification of snore sounds. Wavelet features have proven to be efficient in our previous work;however, the benefits of wavelet transform energy (WTE) and wavelet packet transform energy (WPTE) features were not clearly established. In this study, we firstly present our updated snore sounds database, expanded from 24 patients (collected by one medical centre) to 40 patients (collected by three medical centres). We then study the effects of varying frame sizes and overlaps for extraction of the wavelet low-level descriptors, the effect of which have yet to be fully established. We also compare the performance of the WTE and WPTE features when fed into multiple classifiers, namely, Support Vector Machines (SVM), K-Nearest Neighbours, Linear Discriminant Analysis, Random Forests, Extreme Learning Machines, Kernel Extreme Learning Machines, Multilayer Perceptron, and Deep Neural Networks. Key results presented indicate that, when fed into a SVM, WTE outperforms WPTE (one-tailed z-test, p < 0.002). Further, WPTE can achieve a significant improvement when trained by a k-nearest neighbours classifier (one-tailed z-test, p < 0.001).

关键词： Support vector machines Feature extraction Wavelet packets Surgery Kernel

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Comparative analysis of identification methods for wheat leaf diseases

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International Agricultural Engineering Journal 2016年第3期25卷 197-203页

作者： Zhang, D.Y. Xie, X.H. Liang, D. Huang, L.S. Zhang, X.Q. Teng, L. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University School of Electronic and Information Engineering Hefei Anhui230601 China

How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image processing technique was applied to segment the infected regions of disease leaves. Twenty disease features were extracted, and eighteen larger weight features were selected by Relief-F algorithm to generate the models of Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Back Propagation Neural Network (BPNN). Subsequently, these models were used to identify two kinds of wheat diseases, namely, wheat stripe rust and powdery mildew. Total 136 samples, including 68 training samples and 68 test samples with different infection severities were used to study the recognition capabilities of the three models. Results showed that high predictive accuracies in identification of two wheat diseases with varying severity for all three models. Overall accuracy of RVM was 89.71%, which was superior to 83.82% of SVM and inferior to 92.64% of BPNN. Meanwhile, the recognition accuracies of SVM, RVM and BPNN models for mild-to-moderate disease were 83.33%, 88.33% and 91.67%, respectively. The prediction time of RVM was less than those of SVM and BPNN, with differences as large as 7.96 and 31.68 times, respectively. Therefore, RVM appeared to be the most suitable for real-time identifying wheat leaf diseases among the three models, which can provide important technical support for wheat diseases management.

关键词： Support vector machines

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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Multi-view Metric Learning for Multi-view Video Summarization

Multi-view Metric Learning for Multi-view Video Summarizatio...

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International Conference on Cyberworlds

作者： Linbo Wang Xianyong Fang Yanwen Guo Yanwei Fu MOE Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Data Science Fudan University China

Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.

关键词： Extraterrestrial measurements Optimization Manifolds Visualization Minimization Eigenvalues and eigenfunctions

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Cross lingual speech emotion recognition using canonical correlation analysis on principal component subspace

Cross lingual speech emotion recognition using canonical cor...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hesam Sagha Jun Deng Maryna Gavryukova Jing Han Björn Schuller Chair of Complex & Intelligent Systems University of Passau Passau Germany Machine Intelligence & Signal Processing group Technische Universität München Munich Germany Department of Computing Imperial College London London UK

This paper proposes an analytical approach based on Kernel Canonical Correlation Analysis (KCCA) for domain adaptation. To generate paired instances for KCCA, we mapped source and target data onto both source and target principal components. We performed pair-wise domain adaptation between four emotional speech corpora with different languages (English, German, Italian, and Polish) to validate the approach. We compared our approach with the Shared-Hidden-Layer Auto-Encoder (SHLA) and kernel based principal components. On average, the proposed approach yields higher classification performance.

关键词： Kernel Correlation Databases Speech Principal component analysis Training Emotion recognition

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Robust low-rank tensor factorization by cyclic weighted median

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Science China(Information Sciences) 2015年第5期58卷 145-155页

作者： MENG DeYu ZHANG Biao XU ZongBen ZHANG Lei GAO ChenQiang Institute for Information and System Sciences and Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Department of Computing Hong Kong Polytechnic University Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications

Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.

关键词： tensor factorization low-rank weighted median filter tensorface

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Modeling nonlinear responses in metallic metamaterials by the FDTD solution to Maxwell-hydrodynamic equations

Modeling nonlinear responses in metallic metamaterials by th...

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IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： Ming Fang Kaikun Niu Zhixiang Huang Wei E.I. Sha Xianliang Wu Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China School of Electronic and Information Engineering Hefei Normal University Hefei China

A nonperturbative and universal FDTD model is proposed to investigate the nonlinear responses from metallic metamaterials. By coupling the hydrodynamic equation to the Maxwell equation, the multiphysics model enables to investigate both linear and nonlinear dynamics of free electron gas within the metallic metamaterials without any approximations and prior assumptions. Moreover, the proposed FDFD model has a potential to capture nonlocal response and other quantum effects from metallic metamaterials.

关键词： Mathematical model Finite difference methods Time-domain analysis Hydrodynamics Metamaterials Numerical models Computational modeling

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Strongly enhanced and directionally tunable second-harmonic radiation from a plasmonic particle-in-cavity nanoantenna

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Physical Review A 2016年第5期94卷 053825-053825页

作者： Xiaoyan Y. Z. Xiong Li Jun Jiang Wei E. I. Sha Yat Hei Lo Ming Fang Weng Cho Chew Wallace C. H. Choy Department of EEE The University of Hong Kong Hong Kong Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230000 China Ames Laboratory and Department of Physics and Astronomy Iowa State University Iowa 50011 USA Department of ECE The University of Illinois at Urbana-Champaign Illinois 61801 USA

Second-harmonic (SH) generation is tremendously important for nonlinear sensing, microscopy, and communication systems. One of the great challenges of current designs is to enhance the SH signal and simultaneously tune its radiation direction with a high directivity. In contrast to the linear plasmonic scattering dominated by a bulk dipolar mode, a complex surface-induced multipolar source at the doubled frequency sets a fundamental limit to control the SH radiation from metallic nanostructures. In this work, we harness a plasmonic hybridization mechanism together with a special selection rule governing the SH radiation to achieve the high-intensity and tunable-direction emission by a metallic particle-in-cavity nanoantenna (PIC-NA). The nanoantenna is modelled with a first-principle, self-consistent boundary element method, which considers the depletion of pump waves. The giant SH enhancement arises from a hybridized gap plasmon resonance between the small particle and the large cavity that functions as a concentrator and reflector. Centrosymmetry breaking of the PIC-NA not only modifies the gap plasmon mode boosting the SH signal, but also redirects the SH wave with a unidirectional emission. The PIC-NA has a significantly larger SH conversion efficiency compared to existing literature. The main beam of the radiation pattern can be steered over a wide angle by tuning the particle's position.

关键词： Geometrical & wave optics High-order harmonic generation Plasmonics

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Two weight Zpk -codes, p odd prime

arXiv

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arXiv 2017年

作者： Shi, Minjia Sepasdar, Zahra Solé, Patrick Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Mathematical Sciences of Anhui University Hefei230601 China Department of Pure Mathematics Ferdowsi University of Mashhad P. O. Box 1159 Mashhad91775 Iran CNRS/LTCI Telecom ParisTech University of Paris-Saclay Paris75 013 France Department of Mathematics Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

We show that regular homogeneous two-weight Zpk -codes where p is odd and k ≥ 2 with dual Hamming distance at least four do not exist. The proof relies on existence conditions for the strongly regular graph built on ... 详细信息

关键词： Hamming distance

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REVERSIBLE DATA HIDING IN ENCRYPTED IMAGE BASED ON BLOCK HISTOGRAM SHIFTING

REVERSIBLE DATA HIDING IN ENCRYPTED IMAGE BASED ON BLOCK HIS...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Zhaoxia Yin Andrew Abel Xinpeng Zhang Bin Luo School of Communication and Information Engineering Shanghai University Shanghai 200072 P.R. China. Computing Science and Mathematics University of Stirling. Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei 230601 P.R. China.

ISBN: (纸本)9781479999897

Since there is good potential for practical applications such as encrypted image authentication, content owner identification and privacy protection, reversible data hiding in encrypted image (RDHEI) has attracted increasing attention in recent years. In this paper, we propose and evaluate a new separable RDHEI framework. Additional data can be embedded into a cipher image previously encrypted using Josephus traversal and a stream cipher. A block histogram shifting (BHS) approach using self-hidden peak pixels is adopted to perform reversible data embedding. Depending on the keys held, legal receivers can extract only the embedded data with the data hiding key, or, they can decrypt an image very similar to the original with the decryption key. They can extract both the embedded data and recover the original image error-free if both keys are available. The results demonstrate that higher embedding payload, better quality of decrypted-marked image and error-free image recovery are achieved.

关键词： signal processing in encrypted domain (SPED) Reversible data hiding in encrypted images (RDHEI) Privacy protection Hidden data images Keys Keys Cays Imagery (Psychotherapy) embedding Image Error-free operations Locks

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