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Apply pipelining empirical mode decomposition to accelerate an emotionalized speech processing

Apply pipelining empirical mode decomposition to accelerate ...

引用

international conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Fu-Hua Chou Jie-Cyun Huang Department of Electronic Engineering Ching Yun University of Technology Taoyuan Taiwan

In this paper, a pipelining empirical mode decomposition is presented to reduce the computing time of the emotionalized spontaneous speaker or speech recognition processing. This is a novel approach for integrating the pipelining technique into the standard empirical mode decomposition of the Hilbert-Huang transform. In addition, there is reduced about 45% of the computing time when the emotionalized spoken signal through our segmentation and pipelining processes. Based on the designed processing of emotionalized spontaneous speaker or speech recognition, the segmented and processed voice signals are recomposed back for constructing the speech and speaker models, or to identify which existed model is the most similar one. In the final part of this paper, a comparison of the speech recognized rate between standard and pipelining empirical mode decompositions are presented, and an equivalent effect in the recognition will be found. In practice, speaker or speech recognitions in an emotionalized spontaneous speech are very difficult. The existing speech recognition methods often fail to capture inherent voiceprint features from an emotionalized speech, such as the voice with a passionate intonation. And some of the existed methods to extract the pure voiceprint from an emotionalized spoken signal are very expensive in computation and time, so that technique is impossible to use in a real-time environment like smart houses. But, this paper presents a solution to improve the emotionalized spontaneous speaker or speech recognition processing to fit the real-time request.

关键词： Pipeline processing Acceleration Speech processing Speech recognition Spectrogram Signal processing Wavelet analysis pattern analysis Speech analysis pattern recognition

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Design of a Form Factor Based Expert System for Detection of Myocardial Infarction Using Supervised Classification Technique

Design of a Form Factor Based Expert System for Detection of...

引用

international conference on Advances in computing, Control, & Telecommunication Technologies (ACT)

作者： Saurabh Pal Madhuchhanda Mitra Department of Instrumentation Engineering Haldia Institute of Technology Haldia India Department of Applied Physics University of Calcutta Kolkata India

A computational approach for detection of myocardial infarction (MI) from ECG wave is presented. ECG signal of an infarcted person are different from that of healthy persons in terms of some specified spatial and temporal parameters. This results in difference in signal complexity for infarcted and healthy ECG. In this work a multiresolution wavelet based method is used for relevant features extraction and wave complexity is measured as form factor. A supervised pattern classification technique is applied for MI pattern recognition. The proposed expert system is tested and validated against 42 subjects taken from physionet database. The obtained sensitivity (Se), specificity (Sp) and accuracy are 95% and 90.8% and 92.8 respectively.

关键词： Expert systems Myocardium Electrocardiography Spatial resolution Signal resolution Feature extraction pattern classification pattern recognition System testing Spatial databases

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Human ear recognition based on block segmentation

Human ear recognition based on block segmentation

引用

international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC

作者： Wang Xiaoyun Yuan Weiqi Computer Vision Group Shenyang University of Technology Shenyang China Department of Mechanical Engineering Shenyang Ligong University Shenyang China

A new human ear recognition approach based on block segmentation is proposed in this paper. In this method, an original ear image is partitioned into several smaller sub-images, then the sub-images are extracted by features, As a result, the lower dimension space features that can replace the original images are obtained. Finally the pattern classification can be implemented by the nearest neighbor classifier. To verify the effectiveness of the block segmentation approach, a various experiments are conducted based on four feature extraction methods. USTB human ear database is applied to test the algorithms. The experimental results indicate that the recognition rates are significantly improved. The recognition rate of the moment invariants based on block segmentation achieves 100% in the experiments. The statistics feature extraction is easy to actualize, and the computational speed of recognition is the fastest.

关键词： Humans Ear Image segmentation Feature extraction pattern classification Nearest neighbor searches Image databases Spatial databases Testing Statistics

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A Novel Edge Based Method to Extract Text in Camera Captured Images

A Novel Edge Based Method to Extract Text in Camera Captured...

引用

international conference on Advances in computing, Control, & Telecommunication Technologies (ACT)

作者： H.K. Chethan G. Hemantha Kumar R. Raghavendra Dept of studies in Computer Science University of Mysore Mysore India Dept of Studies in Comput. Sci. Univ. of Mysore Mysore India

Character recognition has been in importance for several decades. Lot of research interest are now focused on applying pattern recognition and computer vision algorithms on camera captured documents to retrieve information from the documents. This paper presents a novel approach for extracting text in camera captured images using edge based algorithm. Extensive experiments have been carried out on set of camera captured images of natural scenes that vary along light, dimension and orientation.

关键词： Digital cameras Data mining Image converters Kernel Pixel pattern recognition Computer vision Information retrieval Image retrieval Working environment noise

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引用

international Joint conference on Bioinformatics, Systems Biology and Intelligent computing, IJCBS

作者： Huilin Xiong Panfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we present a scheme of similarity measure learning based on kernel optimization. Employing a data-dependent kernel model, the proposed scheme optimizes the spatial distribution of the training data in the feature space, aiming to maximize the class separability of the data in the feature space. The learned similarity measure, derived from the optimized kernel, exhibits a favorable feature to the task of pattern classification, that the spatial resolution of the embedding space is expanded around the boundary areas, and shrunk around the homogeneous areas. Experiments demonstrate that using the learned similarity measure can substantially improve the performances of the K-nearest-neighbor classifier.

关键词： Kernel Euclidean distance pattern recognition Area measurement Performance evaluation Extraterrestrial measurements Support vector machines Hilbert space Training data Spatial resolution

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Study on Chinese handwriting identification based on texture analysis

Study on Chinese handwriting identification based on texture...

引用

international conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Jun Feng Xu Gao School of Computing and information engineering Shijiazhuang Railway Institute Shijiazhuang China

As a kind of behavior-based personal identification techniques, automated Chinese handwriting identification becomes a hot topic in pattern recognition and machine learning research area. There are lots of key issues worthy researching. In this paper, the Chinese handwriting identification technology based on texture analysis is discussed. Firstly, a practical Chinese handwriting image samples library CHSL2007 is established for the comparison of exist algorithms and further research. Then the methods of feature extraction based on texture analysis are explored and the pairwise SVM classifier is utilized. The experiment results of texture analysis based on Gabor filter is compared with DB6 wavelet filter and demonstrate that the former is more suitable for handwriting identification on CHSL2007. Finally, the sheet recognition rate is defined and can be arrived at above 99.50% for CHSL2007.

关键词： Image texture analysis Gabor filters pattern recognition Machine learning Libraries Machine learning algorithms Feature extraction Support vector machines Support vector machine classification Wavelet analysis

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Building Context Aware Network of Wireless Sensors Using a Novel pattern recognition Scheme Called Hierarchical Graph Neuron

Building Context Aware Network of Wireless Sensors Using a N...

引用

international conference on Semantic computing

作者： Amir H. Basirat Asad I. Khan Clayton School of IT Monash University Clayton VIC Australia

ISBN: (纸本)9781424449620

The capability to support plethora of new diverse applications has placed wireless sensor network (WSN) technology at threshold of an era of significant potential growth. In this regard, pattern recognition especially in real-time applications plays a paramount role in securing the network against malicious activity. In this paper, an attempt is made to introduce a novel method using a highly scalable and distributed associative memory technique, called hierarchical graph neuron (HGN), while its effectiveness is analyzed from different points of view. The proposed approach not only enjoys from conserving the limited power resources of resource-constrained sensor nodes, but also can be scaled effectively to address scalability issues, which are of primary concern in wireless sensor networks. In addition, the algorithm overcomes the issue of crosstalk available in the original GN algorithm, and thus not only promises to deliver accurate results, but also can be deployed for diverse types of applications in a multidimensional domain.

关键词： Wireless sensor networks Context awareness pattern recognition Neurons Resource description framework Production Proposals Semantic Web Ontologies Marine vehicles

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Handwritten Character recognition Based on BP Neural Network

Handwritten Character Recognition Based on BP Neural Network

引用

international conference on Genetic and Evolutionary computing (WGEC)

作者： Xin Wang Ting-lei Huang Xiao-yu Liu School of Computer and Control Guilin University of Electronic and Technology Guilin China

This paper researches on the issue of computer recognition to the handwritten character images, including lowercase letters and Arabic numerals. In this paper, we preprocess on characters in order to unified the basic features. And then, we apply the basic method of making the grids to extract the features of character, and classify the respectives. At last, we apply the latest heuristic modifications of Backpropagation algorithm to recognize the handwritten characters successfully. The basic datas of this research are testing and debugging on visual studio 2005, a large number of test experiments' datas show that the discrimination of heuristic modifications of Backpropagation algorithm is up to 95 percentage, further improving validity and correctness of this latest algorithm.

关键词： Character recognition Neural networks Feature extraction Handwriting recognition Backpropagation algorithms Computer networks Image recognition Humans System testing pattern recognition

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Positioning for herd management

Positioning for herd management

引用

IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Torben Godsk Department of Computer Science Aarhus University Denmark Danish Agricultural Advisory Service Denmark

Modern herd management requires aiding technologies for farmers to overview their constantly growing herds. Such an aiding system could use GNSS-based positioning to track the animals, and perform real time pattern recognition on the tracks, in order to detect and inform the farmer if one should behave abnormally, which might indicate a change of state regarding the animal's health or welfare. In order to develop a prototype of such a system there are several issues, that needs to be investigated. This report opens numerous discussions regarding these issues but none of them are followed by a closure; instead the discussions remain open issues for my further research.

关键词： Cows Global Positioning System Position measurement Satellites Animals Prototypes Time measurement Technology management Real time systems pattern recognition

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ICDAR 2009 Page Segmentation Competition

ICDAR 2009 Page Segmentation Competition

引用

international conference on Document Analysis and recognition

作者： Apostolos Antonacopoulos Stefan Pletschacher David Bridson Christos Papadopoulos Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Sanford Manchester UK

This paper presents an objective comparative evaluation of layout analysis methods in realistic circumstances. It describes the Page Segmentation competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2009 and presents the results of the evaluation of four submitted methods. Two state-of-the art methods are also compared as well as the three methods from the ICDA2007 Page Segmentation competition. The results indicate that although methods continue to mature, there is still a considerable need to develop robust methods that deal with everyday documents.

关键词： Image segmentation Image analysis Text analysis pattern analysis pattern recognition Image recognition Art Robustness Image enhancement Pixel

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