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2015 3rd International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2015)

作者： Ya-dong Wei Min-quan Li Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University

A novel ultra-wideband( UWB) antenna is proposed in this paper,which has a band-notched characteristic for the interference between the UWB applications and the IEEE802.11 a wireless local area network(WLAN) and the Wi MAX /C-band with combination of notched structures. The proposed antenna fed by coplanar waveguide( CPW),with modified U-slots and general U-slot in echelon patch which can be used to reject the frequency 5.15 to 5.825 GHz, 3.3 to 3.6GHz,and 3.7 to 4.2GHz. The simulation results show that the antenna has obvious band-notched characteristics in the notched band which can effectively prevent the interference of these narrow-band communication systems, and also has well radiation characteristics in the whole band.

关键词： UWB antenna dual-notched bands coplanar waveguide(CPW) combination of notched structures

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Application of a multi-factor model in the grain yield prediction

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Metallurgical and Mining Industry 2015年第8期7卷 468-474页

作者： Chen, Lili Guo, Hongjun Laboratory of Intelligent Information Processing Suzhou University Suzhou Anhui China The Key Laboratory of Intelligent Computing and Signal Processing of MOE Anhui University Hefei Anhui China

A multi-factor model for the grain yield prediction based on the previous data and relevant impacting factors is reported. In this model, the weight coefficient of each individual factor that affected the grain yield historically is analyzed by the variation coefficient method and the conversion degree function in the attribute theory. The effects of various factors on the predicted grain yield are then used as standard vectors and subjected to a similarity-based search in the matrix of historical values. The predicted total grain yield is then determined by multiplying the sown area by the unit grain yield which is obtained by the highest similarity between the historical data and those predicted. Compared to the results obtained by the BP nerve network method, this method is simpler, more flexible, less time-consuming, and more accurate.

关键词： Forecasting

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The ICL-TUM-PASSAU approach for the MediaEval 2015 "affective impact of movies" task

The ICL-TUM-PASSAU approach for the MediaEval 2015 "affectiv...

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Multimedia Benchmark Workshop, MediaEval 2015

作者： Trigeorgis, George Coutinho, Eduardo Ringeval, Fabien Marchi, Erik Zafeiriou, Stefanos Schuller, Björn Department of Computing Imperial College London United Kingdom Machine Intelligence and Signal Processing Group Technische Universität München Munich Germany Department of Complex and Intelligent Systems University of Passau Germany

In this paper we describe the Imperial College London, Technische Universitat München and University of Passau (ICL+TUM+PASSAU) team approach to the MediaEval's "Affective Impact of Movies" challenge, which consists in the automatic detection of affective (arousal and valence) and violent content in movie excerpts. In addition to the baseline features, we computed spectral and energy related acoustic features, and the probability of various objects being present in the video. Random Forests, AdaBoost and Support Vector Machines were used as classification methods. Best results show that the dataset is highly challenging for both affect and violence detection tasks, mainly because of issues in inter-rater agreement and data scarcity.

关键词： Adaptive boosting

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Image authentication and self-recovery scheme based on the rehashing model

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Journal of Information Hiding and Multimedia signal processing 2016年第3期7卷 460-474页

作者： Lyu, Wan-Li Chang, Chin-Chen Wang, Feng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd. Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Mathematics and Physics Fujian University of Technology FuzhouFujian350108 China

An effective image tamper localization and self-recovery scheme that uses the rehashing model is proposed in this paper. The aim of the proposed scheme was to reduce the failure rate in detecting image tamper areas and achieve high rates of successful detection. To achieve this aim, the proposed scheme uses a set of hash functions which are called the rehashing model, gained an optimal decreasing collision method to detect tamper areas With an improved and accurate tamper detection rate, the tampered image can be recovered more accurately. The quality of the proposed scheme was tested with gray and color images. The experimental results indicated that the proposed scheme has good performance in locating tamper locations and allows image recovery with an acceptable visual quality up to 50% of the image content tampered. © 2016.

关键词： Hash functions

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Distributed CoMP transmission for cell range expansion with almost blank subframe in downlink heterogeneous networks

Distributed CoMP transmission for cell range expansion with ...

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International Conference on intelligent computing and Internet of Things (ICIT)

作者： Yi Wang Yanjun Hu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui Province China

ISBN: (纸本)9781479975358

The almost blank subframe (ABS) has been studied in 3GPP as a way to mitigate downlink interference experienced by cell range expansion (CRE) user equipments (UEs). However, the throughput of CRE UEs highly depends on the ratios of ABS which are statically configured in many situations. The coordinated multi-point (CoMP) is one of the key solutions standardized in LTE-A which can be implemented in CRE with ABS to exploit the abundant spatial resources. And distributed cell selection could help guarantee the UEs' performance under different range expansion biases. In this paper, a distributed CoMP method for CRE with ABS is proposed in heterogeneous networks. Simulation results show that our proposed algorithm provides considerable performance gains in the spectrum efficiency with different bias and users settings.

关键词： Algorithm design and analysis Macrocell networks Antennas

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Hierarchical Covering Algorithm

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Tsinghua Science and Technology 2014年第1期19卷 76-81页

作者： Jie Chen Shu Zhao Yanping Zhang Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University

The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.

关键词： deep architecture hierarchy fuzzy equivalence relation covering tree MNIST dataset

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Speech Endpoint Detection Based on EMD and Higher Order Statistics in Noisy Environments

Speech Endpoint Detection Based on EMD and Higher Order Stat...

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2015 5th International Conference on Information Engineering for Mechanics and Materials(ICIMM 2015)

作者： Dexiang Zhang Jiaxing Li Zihong Chen Key Lab.of Intelligent Computing and Signal Processing Anhui University The School of Electrical Engineering and Automation of Anhui University

Accurate endpoint detection is crucial for speech recognition accuracy. This paper presents a new technique for speech endpoint detection in a noisy environment based on the empirical mode decomposition(EMD) algorithm and higher order statistics. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions(IMFs), which is a zero-mean AM-FM component. Then higher order statistics of the IMF components can be used to extract the desired feature for endpoint detection. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experimental results show that the performance of the proposed algorithm is noticeable in the real speech signal tests with different SNR.

关键词： Endpoint Detection EMD Higher Order Statistics Noisy Environments

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Exploring the importance of individual differences to the automatic estimation of emotions induced by music 5

Exploring the importance of individual differences to the au...

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5th International Workshop on Audio/Visual Emotion Challenge, AVEC 2015 - co-Located with MM 2015

作者： Sagha, Hesam Coutinho, Eduardo Schuller, Björn Department of Complex and Intelligent Systems University of Passau Germany Department of Computing Imperial College London London United Kingdom Machine Intelligence and Signal Processing Group Technische Universität München Germany Department of Music University of Liverpool United Kingdom

ISBN: (纸本)9781450337434

The goal of this study was to evaluate the impact of the inclusion of listener-related factors (individual differences) on the prediction of music induced affect. A group of 24 subjects listened to a set of music excerpts previously demonstrated to express specific emotional characteristics (in terms of Arousal and Valence), and we collected information related to listeners' stable (personality, emotional intelligence, attentiveness, music preferences) and transient (mood, and physiological activity) states. Through a series of regression analysis we identified those factors which have a significant explanatory power over the affective states induced in the listeners. Our results show that incorporating information related to individual differences permits to identify more accurately the affective states induced in the listeners, which differ from those expressed by the music. © 2015 ACM.

关键词： Regression analysis

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Fusion of Polarization Image Using Bidimensional Empirical Mode Decomposition

Fusion of Polarization Image Using Bidimensional Empirical M...

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International Conference on Computer Science and Mechanical Automation

作者： Dexiang Zhang Zihong Chen Jiaxing Li Jingjing Zhang Key Lab. of Intelligent Computing and Signal Processing Anhui University The School of Electrical Engineering and Automation Anhui University

ISBN: (纸本)9781467391672

Empirical mode decomposition (EMD) provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Bidimensional empirical mode decomposition (BEMD) techniques decompose an image into several bidimensional intrinsic mode functions (BIMFs) and a bidimensional residue (BR). Firstly, several polarization images can be decomposed into several BIMFs with multi-scales using BEMD. For the BIMF coefficients, the teager energy-based method is used. For the each BIMF coefficients, the area-based teager energy larger value of information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused image. Experimental results show that the proposed algorithm gives more satisfactory results than the traditional image fusion algorithms in preserving the edges and texture information.

关键词： Bidimensional empirical mode decomposition Image decomposition Polarization image Image fusion

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