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2010 3rd International Congress on Image and Signal Processing, CISP 2010

作者： Chen, Xing Hong, Kun Wang, Hai-Tao Yunnan Provincial Key Laboratory of Computer Application Kunming University of Science and Technology Kunming China Institute of Science and Technology Information of Kunming Kunming China

ISBN: (纸本)9781424465149

Textures is one of the keys in image pattern recognition. To realize the computer vision for textures, it is necessary that understand textures rationally. The key of understanding textures is explaining them by the human's signs and approaches. We believe the mathematical structure of categories is similar to human's rational intelligence. In this paper, we describe the common textures by notes and term in categories theory. It means categories are useful for textures and image recognition. The combination of categories and neural network is worth to research which will gradually reveal the unity way of human's percept and rationality. ©2010 IEEE.

关键词： Image texture

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Asymptotic Analysis of Transmission Capacities for Overlaid Spread-Spectrum Wireless Networks

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China Communications 2010年第3期7卷 81-90页

作者： Huarui Wu Di Li Changchuan Yin Department of Computer Application National Engineering Research Center for Information Technology in Agriculture Beijing 100097 China Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing 100876 China

We study the transmission capacities of two coexisting spread-spectrum wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. We defi ne transmission capacity as the product among the density of transmissions, the transmission rate, and the successful transmission probability. The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, while the SR network limits its interference to the PR network by carefully controlling the density ofits transmitters. Considering two types of spread-spectrum transmission schemes (FH-CDMA and DS-CDMA) and the channel inversion power control mechanism, we quantify the transmission capacities for these two networks based on asymptotic analysis. Our results show that if the PR network permits a small increase ofits outage probability, the sum transmission capacities of the two networks (i.e., the overall spectrumefficiency per unit area) will be boosted significantly over that of a single network.

关键词： transmission capacity ad hoc network spread spectrum stochastic geometry

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An improved sift method for pitch estimation of speech

An improved sift method for pitch estimation of speech

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2010 International Conference on Apperceiving Computing and Intelligence Analysis, ICACIA 2010

作者： Hong, Wang Jin'Gui, Pan Institute of Computer Application and Research Changji University Changji China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781424480265

This paper presents an improved SIFT (Simplified Inverse Filtering Technique) method for accurate pith estimation. In order to save computing time as well as ensuring the precision of autocorrelation, different re-sampling ratios are utilized during the process of LPC (Linear Predictive Coding) coefficients analysis and inverse filtering respectively;Furthermore, for the sake to satisfy the range and accuracy of pitch frequency simultaneousl y, Hamming-weighting is adopted for searching the reliable peak value on the autocorrelation curves, and a four-point non-linear pitch-smoothing algorithm is designed to avoid incoherent errors for an example in transient speech frames. Finally, the smoothed pitch contour is extracted and time-normalized pitch frequencies are calculated, which can then be used as the feature of a speech utterance in speech recognition or speaker recognition systems. Further Experiments show that the present method for pitch estimation of speech has good performance. © 2010 IEEE.

关键词： Autocorrelation

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Modified MFCCs for robust speaker recognition

Modified MFCCs for robust speaker recognition

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International Conference on Intelligent Computing and Intelligent Systems

作者： Wang, Hong Pan, Jin'gui State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute of Computer Application and Research Changji University Changji China

ISBN: (纸本)9781424465835

Mel-scale frequency cepstrum coefficienls (MFCCs) are commonly used katues in speaker recognition systems, but MFCC values are not very robust in the presence of noise. thus, the modified MFCCs (named as SMN-CMN-MFCC) based on the general noisy speech model is proposed in this paper, which uses spectrum mean normalization (SMN) to suppress the additive noise, and uses cepstral mean normalization (CMN) to remove the effect of convolutional noise. Theoretical analyses show that the combination of SMN and CMN can inhibit additive and convolutional noise at the same time. To verify the performance of the SMN-CMN-MFCC, \\e have conducted some speaker recognition tesls. With the same convolutional noise component, the additive white noise experimenls and the additive factory noise experimenls show that SMN-CMN-MFCC provides 10.5% and 9.6% relative improvement than the conventional MFCC and ΔMFCC features, respectively. ©2010 IEEE.

关键词： Additive noise

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RETRACTED ARTICLE: A novel forecasting model of fuzzy time series based on K-means clustering

RETRACTED ARTICLE: A novel forecasting model of fuzzy time s...

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2nd International Workshop on Education technology and computer Science, ETCS 2010

作者： Chi, Kai Fu, Fang-Ping Che, Wen-Gang School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Key Laboratory of Computer Technology and Application of Yunnan Province Kunming University of Science and Technology Kunming China

ISBN: (纸本)9780769539874

Fuzzy time series (FTS) is an effective method in forecasting problems due to its salient capabilities of tracking uncertainty and vagueness in observation data. However, in FTS forecasting, it is required about 5-7 intervals in the universe of discourse, as a result, the method of partition intervals become a major consideration. Recently, some studies have demonstrated that the method using length-variant intervals is able to improve the forecasting accuracy at a large scale. In this paper, a K-means clustering technique is used while selecting the length of each interval, and then a novel model of FTS is introduced accordingly. The empirical results show that this model provides a more general platform for partitioning universe of discourse, and gets higher forecasting accuracy rates than those of the existing methods. © 2010 IEEE.

关键词： Forecasting

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RETRACTED ARTICLE: A modified model of fuzzy time series for forecasting exchange rates

RETRACTED ARTICLE: A modified model of fuzzy time series for...

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2nd International Workshop on Education technology and computer Science, ETCS 2010

ISBN: (纸本)9780769539874

Financial forecasting has become an important and challenging task for both researchers and investors. In order to improve the forecasting accuracy rate, in this paper, a modified heuristic model of fuzzy time series using FCM is proposed. Using the daily prices of USD/JPY and USD/CHY exchange rates as testing data, the empirical results show that the proposed method is able to get better forecasting results and higher accuracy than those of traditional econometric methods and some existing models of fuzzy time series. © 2010 IEEE.

关键词： Engineering education

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RETRACTED ARTICLE: Second-order difference heuristic model of fuzzy time series based on particle swarm optimization

RETRACTED ARTICLE: Second-order difference heuristic model o...

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作者： Chi, Kai Che, Wen-Gang Fu, Fang-Ping Zhuo, Ming-Kun School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Key Laboratory of Computer Technology and Application of Yunnan Province Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781424463503

Fuzzy time series (FTS) has become an effective method for forecasting some typical time series-enrollments, stock price and daily price of foreign exchange-due to its salient capacities for dealing with uncertainty, vagueness in historical observations. However, how to partition the universe of discourse and how to construct fuzzy logic relationships are two unsolved problems which may affect the forecasting accuracy. In order to supply an effective platform, relatively speaking, this paper proposed a novel heuristic model of fuzzy time series based on particle swarm optimization (PSO). The empirical results show that the presented model not only provides more effective fuzzy logic relationship matrix, but obtains higher forecasting accuracy rates than the existing models. © 2010 IEEE.

关键词： Particle swarm optimization (PSO)

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Parameter learning for multi-factors of entity answer extracting

Parameter learning for multi-factors of entity answer extrac...

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2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Zong, Huanyun Yu, Zhengtao Mao, Cunli Zou, Junjie Guo, Jianyi School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Institute of Intelligent Information Processing Computer Technology Application Key Laboratory of Yunnan Province Kunming China

ISBN: (纸本)9781424459346

Entity extraction involves multi-factors, and the different factor has an impact on the answer in varying degrees, this paper presents a machine learning approach to parameter learning for entity answer. Firstly, in view of characteristics of the Question Answering System (QA), we define three elements of the text score, passage score and entity score which influenced the answer extraction, also give the relevant computational method about them. Then collect 400 entity answers of product, person, and organization according to TREC2009 entity task requirements. With the help of search engines, retrieve related pages and calculate the score of the various factors related to the answer respectively. Thereafter compute the score of entity answers according to a linear combination of the various factors. Define an initial score to extract the entity answer and get a sorted list of answers. Finally, mark these entities answer to obtain the correct marked answers corpus, then build parameter learning model by the EM algorithm iterate gradually to find the optimal answer weight of different factors that influenced the answer extraction. We carried on the experiment in the TREC2009 entity task;it shows very good results for this method. The accuracy of entity answer has achieved 88.93%. ©2010 IEEE.

关键词： Parameter estimation

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Performance test of network simulation based on split-object model

Performance test of network simulation based on split-object...

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International Forum on Information technology and applications

作者： Wu, Ping Wu, Bin Dept. Network Management Center Yunnan Academy of Scientific and Technical Information Kunming 650051 China Computer Technology Application Key Laboratory Kunming University of Science and Technology Kunming 650051 China

ISBN: (纸本)9780769541150

The network simulation (NS) is an important issue in the study of network technology. However, The error between the network performance simulation on split-object model and the true network environment is considerable apparent. In order to obtain the NS result approaching the reality, in this paper, we analyze the structure of split-object mode and find reasons to cause the error. According to the definition of NS performance's index, we remodel the split-object model by choosing the proper function and equation in the control theory and deducing the logical coefficent in them. We further evaluate the efficiency and the availability of the new model under NS-2 platform. The results show that the new mode is more suitable for network simulation with higher accuracy. © 2010 IEEE.

关键词： mathematical analysis network simulation performance test split-object model network analog Performance Tests mathematical analysis Control Theory object information model Networking Techniques

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Evaluation techniques for oil gas reservoir based on artificial neural networks techniques

Evaluation techniques for oil gas reservoir based on artific...

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International Conference on Business Intelligence and Financial Engineering

作者： Pan, Hong Yan He, Hong Department of Computer Sciences Tianjin Broadcast and TV University Tianjin China Tianjin Key Laboratory for Control Theory and Application in Complicated Systems Tianjin University of Technology Tianjin China

ISBN: (纸本)9780769541167

By using BP artificial nerve network's error reversion transmission. Summing up various data of comprehensive logging can solve the problem of low accurate rate for identifying oil, gas, water zones. The software provides nerve network reservoir interpretation model by studying and training the initial data of tested oil. Practice proves the overall coincidence rate of interpretation reaches 97%. It can more efficiently reflects logging technique's advantage of wellsite quick evaluation oil, gas, water zones. The application in this technique improves the level of logging data interpretation and evaluation. © 2010 IEEE.

关键词： Neural networks

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