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ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Gi Hyun Lim Byoungjun Chung Il Hong Suh Department of Electronics and Computer Engineering Hanyang University Seoul Korea

Two HMM-based threshold models are suggested for recognition and incremental learning of scenario-oriented human behavior patterns. One is the expected behavior threshold model to discriminate if a monitored behavior pattern is normal or not. the other model is the registered behavior threshold model to detect whether such behavior pattern is already learned. If a behavior patten is detected as a new one, an HMM is generated to represent the pattern, and then the HMM is used to update behavior clusters by hierarchical clustering process.

关键词： Hidden Markov models Adaptation models Humans pattern recognition Cognition Service robots Generators

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An Approach of Soft Computing Applications in Clinical Neurology

An Approach of Soft Computing Applications in Clinical Neuro...

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6th international conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Simic, Dragan Simic, Svetlana Tanackov, Ilija Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Univ Novi Sad Fac Med Novi Sad 21000 Serbia

ISBN: (纸本)9783642212215

this paper briefly introduces various soft computing techniques and presents miscellaneous applications in clinical neurology domain. the aim is to present the large possibilities of applying soft computing to neurology related problems. Recently published data about use of soft computing in neurology are observed from the literature, surveyed and reviewed. this study detects which methodology or methodologies of soft computing are frequently used together to solve the specific problems of medicine. Recent developments in medicine show that diagnostic expert systems can help physicians make a definitive diagnosis. Automated diagnostic systems are important applications of pattern recognition, aiming at assisting physicians in making diagnostics decisions. Soft computing models have been researched and implemented in neurology for a very long time. this paper presents applications of soft computing models of the cutting edge researches in neurology domain.

关键词： Soft Computing neurology neural networks fuzzy logic

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the automatic detection algorithm of tongue cancer stem cells based on fuzzy pattern recognition

The automatic detection algorithm of tongue cancer stem cell...

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international conference on Computer Science & Education (ICCSE)

作者： Kehe Cai Mengyi Liu Yuyu Zhang School of Electronic Information Wuhan University China Computer School Wuhan University China

In this paper, we present a novel recognition algorithm for detecting tongue cancer stem cells with respect to appropriate scaling factors. Our method can be achieved by computer image processing in the condition that the cancer cells are undifferentiated or slightly differentiated, which is of important research significance in the realm of oral medicine. According to the biological natures of tongue cancer stem cells, we select the curvature variance of cell contour, the nuclear-cytoplasmic area ratio, and the average optical density of cytoplasm as the measurement parameters. Using these three biological parameters, the characteristics of cancerous tumor cells can be described and thus classified. therefore, those cells can be categorized under the principle of maximum degree of membership in fuzzy pattern recognition algorithms. In this way, the tongue cancer stem cells can be automatically detected. Desirable recognition results given by our experiments have substantiated the efficiency of our algorithm.

关键词： Cancer Stem cells Tongue pattern recognition Cells (biology) Image edge detection Algorithm design and analysis

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Modulation recognition for hybrid carrier scheme based on weighted-type fractional Fourier transform

Modulation recognition for hybrid carrier scheme based on we...

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international conference on Communications and Networking in China (CHINACOM)

作者： Hong Feng Lin Mei Xuejun Sha Naitong Zhang Communication Research Center Harbin Institute of Technology Harbin Heilongjiang China Shenzhen Graduate School Harbin Institute of Technology Shenzhen Guangdong China Harbin Institute of Technology Harbin Heilongjiang CN

Communication system based on the weighted-type fractional Fourier transform (WFRFT) is a hybrid carrier (HC) scheme which is proposed recently. All current modulation recognition methods are aiming at recognizing single carrier (SC) scheme and multi-carrier (MC) scheme modulated signals, whereas, methods on the WFRFT signals are never seen. In this paper, we propose an idea for recognizing the WFRFT signals. Signals after the single parameter WFRFT, whose higher order cumulants vary with transform orders, present different magnitudes with different carrier schemes. We may recognize SC, HC and MC schemes according to the relationship between higher order cumulants and transform orders.

关键词： Fourier transforms Phase shift keying pattern recognition Optics OFDM

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Classification of unaligned sequences based on prototype motifs representation

Classification of unaligned sequences based on prototype mot...

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Colombian conference on Computing (CCC)

作者： G. A. Arango-Argoty J. A. Jaramillo-Garzón S. Röthlisberger C. G. Castellanos-Domínguez Signal Processing and Recognition Group Universidad Nacional de Colombia sede Manizales Manizales Colombia research center of ITM Instituto Tecnológico Metropolitano Medelln Colombia

Estimating the function of unknown proteins is one of the main goals in bioinformatics. In the last few years, many pattern recognition algorithms have been developed, usually focusing on global information of the protein. Conversely, predictions can be done through the identification of functional sub-sequence patterns or motifs, but most methods for motifs detection require a predefined fixed window size and aligned sequences. To confront these problems we proposed a method based on variable length motifs detection using the continuous wavelet transform and a dissimilarity space representation for evaluating the performance of our approach. A Support Vector Machine classifier is used as predictor. the highest Sn = 96.67% and Sp = 96.79%, across 10-fold cross validation, is obtained for proteins kinases. Average results Sn = 81.17% and Sp = 87.43% are comparable to current state of the art.

关键词： Proteins Amino acids Wavelet transforms Prototypes bioinformatics Support vector machines

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A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints

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FOOD MICROBIOLOGY 2011年第4期28卷 782-790页

作者： Panagou, Efstathios Z. Mohareb, Fady R. Argyri, Anthoula A. Bessant, Conrad M. Nychas, George-John E. Agr Univ Athens Dept Food Sci Technol & Human Nutr Lab Microbiol & Biotechnol Foods Athens 11855 Greece Cranfield Univ Bioinformat Grp Cranfield MK43 0AL Beds England Cranfield Univ Appl Mycol Grp Cranfield MK43 0AL Beds England

A series of partial least squares (PLS) models were employed to correlate spectral data from FTIR analysis with beef fillet spoilage during aerobic storage at different temperatures (0, 5, 10,15, and 20 C) using the dataset presented by Argyri et al. (2010). the performance of the PLS models was compared with a three-layer feed-forward artificial neural network (ANN) developed using the same dataset. FTIR spectra were collected from the surface of meat samples in parallel with microbiological analyses to enumerate total viable counts. Sensory evaluation was based on a three-point hedonic scale classifying meat samples as fresh, semi-fresh, and spoiled. the purpose of the modelling approach employed in this work was to classify beef samples in the respective quality class as well as to predict their total viable counts directly from FTIR spectra. the results obtained demonstrated that both approaches showed good performance in discriminating meat samples in one of the three predefined sensory classes. the PLS classification models showed performances ranging from 72.0 to 98.2% using the training dataset, and from 63.1 to 94.7% using independent testing dataset. the ANN classification model performed equally well in discriminating meat samples, with correct classification rates from 98.2 to 100% and 63.1 to 73.7% in the train and test sessions, respectively. PLS and ANN approaches were also applied to create models for the prediction of microbial counts. the performance of these was based on graphical plots and statistical indices (bias factor, accuracy factor, root mean square error). Furthermore, results demonstrated reasonably good correlation of total viable counts on meat surface with FTIR spectral data with PLS models presenting better performance indices compared to ANN. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Artificial neural networks Aerobic storage Beef fillets FTIR Machine learning Meat spoilage Partial least squares regression pattern recognition

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A New Scheme for 3D Face recognition Based on 2D Gabor Wavelet Transform Plus LBP

A New Scheme for 3D Face Recognition Based on 2D Gabor Wavel...

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the 6th international conference on Computer Science & Education(第六届国际计算机新科技与教育学术会议 ICCSE 2011)

作者： Yi Jin Yizhi Wang Qiuqi Ruan Xueqiao Wang School of Computer & Information Technology Beijing Jiaotong University Beijing P.R.China100044

In this paper, a novel method based on Gabor Wavelet Transform plus Local Binary pattern is proposed for feature representation in 3D face recognition. Firstly, to enable the application of the new feature representation of the framework, Iterative Closet Point (ICP) is utilized as preprocessing to detect the nose tip by aligning a pair of symmetrical 3D face models of the same person, crop the interest of region, and then generate the corresponding depth images. Secondly, multi-scales and multi-orientations Gabor Wavelet Transform is proposed for feature representation. then, LBP operator is used to describe the local features future. For each face region, the statistical histogram is utilized to represent the 3D face accordingly. Finally, LSDA based strategy is presented to address the recognition task. the new algorithm benefits mostly from two aspects: One aspect is that Gabor wavelets are promoted for their useful properties, such as invariance to illumination, rotation, scale and translations, in feature extraction. the other is that the LBP operator not only describe the local features which have been demonstrated to be quite considerable in recognition task, but also reduces more coefficients for image representation. Experiments based on the FRGC 3D face database demonstrate the effectiveness and efficiency of the new method. Results show that our new algorithm outperforms the other popular approaches reported in the literature and achieves a much higher verification rate at 0.1 FAR.

关键词： 3D face recognition 2D Gabor wavelet Transform Local Binary pattern Iterative Closet Point (ICP)

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A feature selection-based framework for human activity recognition using wearable multimodal sensors 11

A feature selection-based framework for human activity recog...

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Proceedings of the 6th international conference on Body Area Networks

作者： Mi Zhang Alexander A. Sawchuk University of Southern California Los Angeles CA

ISBN: (纸本)9781936968299

Human activity recognition is important for many applications. this paper describes a human activity recognition framework based on feature selection techniques. the objective is to identify the most important features to recognize human activities. We first design a set of new features (called physical features) based on the physical parameters of human motion to augment the commonly used statistical features. To systematically analyze the impact of the physical features on the performance of the recognition system, a single-layer feature selection framework is developed. Experimental results indicate that physical features are always among the top features selected by different feature selection methods and the recognition accuracy is generally improved to 90%, or 8% better than when only statistical features are used. Moreover, we show that the performance is further improved by 3.8% by extending the single-layer framework to a multi-layer framework which takes advantage of the inherent structure of human activities and performs feature selection and classification in a hierarchical manner.

关键词： human activity recognition wearable multimodal sensors feature selection feature design pattern recognition

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Practical applications of intelligent systems :Porceedings of the sixth international conference on intelligent systems and knowledge engineering, Shanghai, China, Dec 2011 (ISKE2011) /

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丛书名： Advances in Intelligent and Soft Computing

2011年

作者： international conference on intelligent systems and knowledge engineering(6th :2011 :Shanghai China)editors Yinglin Wang and Tianrui Li. Wang Yinglin. Li Tianrui.

ISBN: (纸本)9783642256578

Proceedings oftheSixthinternational conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. the book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. the proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University, China.

关键词： Intellegent Systems.Knowledge engineering

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Forest Fire Smog Feature Extraction Based on Pulse-Coupled Neural Network

Forest Fire Smog Feature Extraction Based on Pulse-Coupled N...

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2011 6th IEEE Joint international Information Technology and Artificial Intelligence conference(ITAIC 2011)

作者： Wu Jiang School of Science Beijing Forestry University Beijing,China Huang Rule Information Centre Beijing Forestry University Beijing,China Xu Ziyue School of Technology Beijing Forestry University Beijing,China Han Ning School of Technology Beijing Forestry University Beijing,China

A novel algorithm for image-based forest fire smog feature extraction based on Pulse-Coupled neural network (PCNN) is *** PCNN is derived from the phenomena of synchronous pulse burst in mammals' visual *** outputs of PCNN represent unique features of imported images,and has been proved to be invariant to translation,rotation and *** this paper the image from video surveillance monitor is split into three dimensions in RGB color space,then input each dimension to PCNN to extract texture feature,the one-class support vector machine is applied to predict the feature in order to test feature's *** experimental results show that the algorithm we propose accurately distinguishes smog and non-smog images which outperform both the traditional Euclidean distance algorithms and the algorithms based on grey level co-occurrence matrix (GLCM) we applied in our earlier *** recognition accuracy is 98% with robustness on our smog image database.

关键词： Forest fire smog recognition,Pulse-Coupled neural network (PCNN),Support vector machine (SVM),Feature extraction,pattern recognition

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