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Forward-looking detection for airborne single-channel radar with beam scanning

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2012年第12期33卷 2240-2245页

作者： Yang, Zhiwei He, Shun Liao, Guisheng National Laboratory of Radar Signal Processing Xidian University Xi'an 710071 China Communication and Information Engineering College Xi'an University of Science and Technology Xi'an 710054 China

Traditional side-looking synthetic aperture radar (SAR) and Doppler beam-sharpening (DBS) technologies cannot generate images of the areas in flight direction with high azimuth resolution. To alleviate this problem, an innovative approach based on real-beam scanning is presented. Moreover, both the feasibility and the applicable conditions of azimuth super-resolution are discussed. According to the model of echoes in which receiver power is modulated by beam gain, a modified Capon algorithm is employed to calculate the spatial spectrum. Numerical simulation indicates that it can achieve forward-looking imaging for an airborne single-channel radar, and its performance is better than that of Capon when strong signals and weak signals are in co-existence.

关键词： Synthetic aperture radar

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Off-Line Handwritten Signature Verification Using Contourlet Transform and Co-occurrence Matrix

Off-Line Handwritten Signature Verification Using Contourlet...

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International Workshop on Frontiers in Handwriting Recognition

作者： Assia Hamadène Youcef Chibani Hassiba Nemmour Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene Algiers Algeria

We address, in this work, a new feature generation method for two different approaches of off-line handwritten signature verification (HSV), writer-dependent and writer-independent HSV. The proposed method uses conjointly the contourlet transform and the co-occurence matrix. The contourlet transform allows capturing contour segment directions of the handwritten signature, while the co-occurrence matrix allows describing the number of directions. Experiments are conducted on the well known CEDAR dataset and the classification through the support vector machines (SVM). The obtained results show the effective use of the Contourlet transform for handwritten signature verification comparatively to the state of the art.

关键词： Handwriting recognition Transforms Computed tomography Vectors Support vector machines Laplace equations Filter banks

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SVM-DSmT combination for off-line signature verification

SVM-DSmT combination for off-line signature verification

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International Conference on Computer, Information and Telecommunication Systems (CITS)

作者： Nassim Abbas Youcef Chibani Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology of Houari Boumedienne Algiers Algeria

We propose in this work a signature verification system based on decision combination of off-line signatures for managing conflict provided by the SVM classifiers. The system is basically divided into three modules: i) Radon Transform-SVM, ii) Ridgelet Transform-SVM and iii) PCR5 combination rule based on the generalized belief functions of Dezert-Smarandache theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Decision making is performed through likelihood ratio. Experiments are conducted on the well known CEDAR database using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework improves the verification accuracy compared to individual SVM classifiers.

关键词： Support vector machines Transforms Biometrics Forgery Training Estimation

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Handwritten Digit Recognition Based on a DSmT-SVM Parallel Combination

Handwritten Digit Recognition Based on a DSmT-SVM Parallel C...

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International Workshop on Frontiers in Handwriting Recognition

作者： Nassim Abbas Youcef Chibani Hassiba Nemmour Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene Algiers Algeria

We propose in this work a new handwritten digit recognition system based on parallel combination of SVM classifiers for managing conflict provided between their outputs. Firstly, we evaluate different methods of generating features to train the SVM classifiers that operate independently of each other. To improve the performance of the system, the outputs of SVM classifiers are combined through the Dezert-Smarandache theory. The proposed framework allows combining the calibrated SVM outputs issued from a sigmoid transformation and uses an estimation technique based on a supervised model to compute the belief assignments. Decision making is performed by maximizing the new Dezert-Smarandache probability. The performance evaluation of the proposed system is conducted on the well known US Postal Service database. Experimental results show that the proposed combination framework improves the recognition rate even when individual SVM classifiers provide conflicting outputs.

关键词： Support vector machines Handwriting recognition Error analysis Databases Accuracy Estimation Character recognition

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One-class versus bi-class SVM classifier for off-line signature verification

One-class versus bi-class SVM classifier for off-line signat...

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： Yasmine Guerbai Youcef Chibani Nassim Abbas Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology of Houari Boumedienne Algiers Algeria

Support vector machines (SVMs) have become an alternative tool for pattern recognitions, and more specifically for Handwritten Signature Verification Systems (HSVS). Usually, the bi-class SVMs (B-SVM) are used for separating between genuine and forged signatures. However, in practice, only genuine signatures are available. In this paper, we investigate the use of one-class SVM (OC-SVM) for handwritten signature verifications. Experimental results conducted on the standard CEDAR database show the effective use of the one-class SVM compared to the bi-class SVM.

关键词： Support vector machines Databases Biological system modeling Physiology Classification algorithms Equations Pattern recognition

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Localized detection of speech recognition errors

Localized detection of speech recognition errors

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IEEE Spoken Language technology Workshop

作者： Svetlana Stoyanchev Philipp Salletmayr Jingbo Yang Julia Hirschberg Department of Computer Science Columbia University USA Signal Processing and Speech Communication Laboratory Graz University of Technology Austria

We address the problem of localized error detection in Automatic Speech Recognition (ASR) output. Localized error detection seeks to identify which particular words in a user's utterance have been misrecognized. Identifying misrecognized words permits one to create targeted clarification strategies for spoken dialogue systems, allowing the system to ask clarification questions targeting the particular type of misrecognition, in contrast to the “please repeat/rephrase” strategies used in most current dialogue systems. We present results of machine learning experiments using ASR confidence scores together with prosodic and syntactic features to predict whether 1) an utterance contains an error, and 2) whether a word in a misrecognized utterance is misrecognized. We show that by adding syntactic features to the ASR features when predicting misrecognized utterances the F-measure improves by 13.3% compared to using ASR features alone. By adding syntactic and prosodic features when predicting misrecognized words F-measure improves by 40%.

关键词： Accuracy Speech recognition Syntactics Humans Speech Training Feature extraction

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MFCC and vector quantization for Arabic fricatives speech/speaker recognition

MFCC and vector quantization for Arabic fricatives speech/sp...

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： Fatma Zohra. Chelali Amar. Djeradi Speech communication and signal processing laboratory Faculty of Electronics engineering and computer science University of Science and Technology of Houari Boumedienne Algiers Algeria

This article develops a speaker-dependent Arabic phonemes recognition system using MFCC analysis and the VQ-LBG algorithm. The system is examined with and without vector quantization in order to analyze the effect of compression in an acoustic parameterization phase. Our experimental results show that vector quantization using a codebook of size 16 achieves good results compared to the system without quantization for a majority of the phonemes studied.

关键词： Mel frequency cepstral coefficient Speech Feature extraction Vectors Speaker recognition Speech recognition

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Whisper denoising in Joint Time-Frequency Domain based on Real-Valued Discrete Gabor Transform

Whisper denoising in Joint Time-Frequency Domain based on Re...

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2012 International Conference on Mechanical Engineering and Materials(ICMEM 2012)

作者： Jian Zhou Cheng Huang Man Zhang Liang Tao Li Zhao School of Computer Science and Technology Anhui University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University

Whispered speech can be effectively used for quiet and private communications over mobile phones and is also the communication means for ENT patients under a regime of voice rest. However,little progress has been made on the denoising of whispered speech in noisy environment because of its special acoustic *** this paper, we propose a whisper denoising algorithm in joint time-frequency domain based on real-valued discrete Gabor transform(RDGT). Noisy whisper is first transformed into the joint time-frequency domain by fast real-valued discrete Gabor transform. The MMSE based log-amplitude estimator is derived under speech presence uncertainty hypothesis. Clean whisper spectral is then estimated by inverse transform of RDGT. Experimental results show that the proposed algorithm is very effective in avoiding the musical residual noise and retaining weak speech components.

关键词： Real-Valued Discrete Gabor Transform(RDGT) joint time-frequency domain whisper denoising

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Contracting community for computing maximum flow

Contracting community for computing maximum flow

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IEEE International Conference on Granular Computing (GRC)

作者： Yanping Zhang Xiansheng Xu Bo Hua Shu Zhao School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

In this paper, we propose a novel method named Contracting Community Approach (CCA) to get the maximum flow of flow network. Firstly, we contract communities in the original network. Then, we apply classic algorithms on the contracted network to approximately solve the maximum flow problem. Experimental results show that the efficiency of the proposed algorithm. For sparse networks, the size of network is reduced to 58.38% averagely and the correctness of maximum flow is over 95%. For middle dense networks, the size of network is reduced to 65.77% averagely. For dense networks, the size of network is reduced to 64.84% averagely. And the correctness of maximum flow even reach 100% both in many middle dense and dense cases in our experiments.

关键词： Educational institutions Artificial neural networks Video recording

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Segmentation and Recognition Strategy of Handwritten Connected Digits Based on the Oriented Sliding Window

Segmentation and Recognition Strategy of Handwritten Connect...

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International Workshop on Frontiers in Handwriting Recognition

作者： Abdeljalil Gattal Youcef Chibani Université de Tébessa Algeria Speech communication and signal processing laboratory Faculty of Electronics and Data processing University of Science and Technology Houari Boumediene Algiers Algeria

In this paper, we propose a system to recognize handwritten digit strings, which constitutes a difficult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-recognition of handwritten connected digits based on the oriented sliding window. The proposed approach allows separating adjacent digits according the connection configuration by finding at the same time the interconnection points between adjacent digits and the cutting path. The segmentation-recognition using the global decision module allows the rejection or acceptance of the processed image. Experimental results conducted on the handwritten digit database NIST SD19 show the effective use of the sliding window for segmentation-recognition.

关键词： IP networks Image segmentation Handwriting recognition Databases NIST Skeleton

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