共查询到20条相似文献,搜索用时 15 毫秒
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Jamal Saeedi Karim Faez Mohammad Hassan Moradi 《Circuits, Systems, and Signal Processing》2014,33(8):2583-2604
In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods. 相似文献
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Srinivasarao Chintagunta P. Palanisamy 《Multidimensional Systems and Signal Processing》2018,29(4):1241-1253
This paper deals with the directions of departure (DOD) and directions of arrival (DOA) estimation of coherent and noncoherent targets in bistatic MIMO radar with the electromagnetic vector (EmV) sensors. The high-resolution eigenspace-based methods such as, estimation of signal parameters via rotational invariance technique (ESPRIT), multiple signal classification, etc., fails to estimate DOD and DOA of fully or partially correlated targets. In order to employ these methods, a new pre-processing method is developed based on the spatial smoothing in MIMO radar with the EmV sensors. Then, the directions are estimated using the ESPRIT algorithm. Monte-Carlo simulations are performed to investigate the estimation-accuracy and resolution-capability of the proposed approach, and to compare with no pre-processing and the existing method. The simulation result shows that, the proposed methodology improves the performance significantly. 相似文献
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Jie Han Tao Zhang Zhaoyang Qiu Xiaoyu Zheng 《International Journal of Communication Systems》2019,32(1)
Specific emitter identification can detect emitters automatically by extracting and analyzing features. A novel specific emitter identification method based on 3D‐Hilbert energy spectrum‐based multiscale segmentation (3D‐HESMS) is proposed. First, the time‐frequency energy spectrum is derived via the Hilbert‐Huang transform, that is, a complicated curved surface in a 3D space, namely, the 3D‐Hilbert energy spectrum. The differential box dimension, multifractal dimension, lacunarity change rate, and 3D‐Hilbert energy entropy are extracted to compose the feature vector under multiscale segmentation using fractal theory. Subsequently, communication emitter individual identification is obtained using the 4 features. Finally, the performance and complexity of the 3D‐HESMS method are compared with those of 2 existing methods. Experiments show that the performance of the 3D‐HESMS method is better than those of the 2 other methods. The extracted features with high stability, sufficiency, and identifiability can overcome the negative effects of the changes in signal‐to‐noise ratio and the number of training samples. 相似文献
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通过研究语音信号分形维数的特点,提出一种在语音信号小波变换低频系数的DCT域实现信息隐藏的方法,该方法首先对原始语音进行一级小波分解,计算高频系数的分形维数,在对应帧的低频系数中嵌入秘密信息。秘密信息的嵌入位置和个数利用小波变换高频系数的分形维数决定,嵌入和提取采用查量化表的方法。仿真结果表明,该算法具有较好的鲁棒性和嵌入效率。 相似文献
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提出了一种基于分形理论的麻醉监测诱发脑电信号识别方法.首先给出了麻醉监测中潜伏期听觉诱发脑电信号的数学模型,产生与实际信号相符的模拟脑电信号,然后对脑电信号进行小波降噪,提取降噪后脑电信号的关联维数,最后通过关联维数的大小识别麻醉状态下中潜伏期听觉诱发脑电信号的类型.实验仿真结果表明:提出的识别方法具有较高的识别率. 相似文献
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Ashish Khare Manish Khare Yongyeon Jeong Hongkook Kim Moongu Jeon 《Signal processing》2010,90(2):428-439
The paper presents a novel despeckling method, based on Daubechies complex wavelet transform, for medical ultrasound images. Daubechies complex wavelet transform is used due to its approximate shift invariance property and extra information in imaginary plane of complex wavelet domain when compared to real wavelet domain. A wavelet shrinkage factor has been derived to estimate the noise-free wavelet coefficients. The proposed method firstly detects strong edges using imaginary component of complex scaling coefficients and then applies shrinkage on magnitude of complex wavelet coefficients in the wavelet domain at non-edge points. The proposed shrinkage depends on the statistical parameters of complex wavelet coefficients of noisy image which makes it adaptive in nature. Effectiveness of the proposed method is compared on the basis of signal to mean square error (SMSE) and signal to noise ratio (SNR). The experimental results demonstrate that the proposed method outperforms other conventional despeckling methods as well as wavelet based log transformed and non-log transformed methods on test images. Application of the proposed method on real diagnostic ultrasound images has shown a clear improvement over other methods. 相似文献
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Respiratory sounds are always contaminated by heart sound interference. An essential preprocessing step in some of the heart sound cancellation methods is localizing primary heart sound components. Singular spectrum analysis (SSA), a powerful time series analysis technique, is used in this paper. Despite the frequency overlap of the heart and lung sound components, two different trends in the eigenvalue spectra are recognizable, which leads to find a subspace that contains more information about the underlying heart sound. Artificially mixed and real respiratory signals are used for evaluating the performance of the method. Selecting the appropriate length for the SSA window results in good decomposition quality and low computational cost for the algorithm. The results of the proposed method are compared with those of well-established methods, which use the wavelet transform and entropy of the signal to detect the heart sound components. The proposed method outperforms the wavelet-based method in terms of false detection and also correlation with the underlying heart sounds. Performance of the proposed method is slightly better than that of the entropy-based method. Moreover, the execution time of the former is significantly lower than that of the latter. 相似文献
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Robust speech features based on wavelet transform with application to speaker identification 总被引:2,自引:0,他引:2
Hsieh C.-T. Lai E. Wang Y.-C. 《Vision, Image and Signal Processing, IEE Proceedings -》2002,149(2):108-114
An effective and robust speech feature extraction method is presented. Based on the time-frequency multiresolution property of the wavelet transform, the input speech signal is decomposed into various frequency channels. For capturing the characteristics of an individual speaker, the linear predictive cepstral coefficients of the approximation channel and entropy value of the detail channel for each decomposition process are calculated. In addition, an adaptive thresholding technique for each lower resolution is also applied to remove the influence of noise interference. Experimental results show that using this mechanism not only effectively reduces the influence of noise interference but also improves the recognition performance. Finally, the proposed method is evaluated on the MAT telephone speech database for text-independent speaker identification using the group vector quantisation identifier. Some popular existing methods are also evaluated for comparison, and the results show that the proposed feature extraction algorithm is more effective and robust than the other existing methods. In addition, the performance of the proposed method is very satisfactory even in a low SNR environment corrupted by Gaussian white noise. 相似文献
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针对瞬态干扰严重影响天波超视距雷达(OTHR)目标检测性能的问题,提出了一种基于小波影响锥的瞬态干扰抑制方法。该方法利用一维离散平稳小波变换确定信号的奇异点(瞬态干扰),然后将每一奇异点对应的影响锥内的小波细节系数置零,最后通过一维逆离散平稳小波变换重构数据。该方法避免了杂波抑制和插值重构,运算量小,实用性强。对天波雷达实测数据的处理实验表明:提出的方法是有效的。 相似文献
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基于小波变换的雪崩光电二极管信号检测方法 总被引:4,自引:1,他引:3
空间激光通信中的常用调制方式是脉冲位置调制(PPM).雪崩光电二极管(APD)具有高增益、高灵敏度和响应速度快的特点,因而成为空间激光通信中的首选信号探测器件。针对空间激光通信的脉冲位置调制信道.分析了空间光通信系统中雪崩光电二极管探测噪声的特点和类别.根据小波变换具有提取信号局部特征的能力,提出了一种基于小波变换的雪崩光电二极管信号检洲方法。采用四种不同的阈值选择算法选取小波系数以恢复信号.并进行了初步的仿真实验和分析。结果表明,自适应阈值选择算法取得的信号恢复结果最佳.可有效减弱强背景噪声的影响.提高雪崩光电二极管信号检测系统的性能。 相似文献
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根据烟雾的半透明性特征和分形性质,提出一种基于频域增强和差分盒维数的烟雾图像分割算法。首先,利用小波变换空-频域的特性,通过对图像多层分解后小波系数的加权处理得到烟雾纹理增强图像;然后,运用差分盒维数方法遍历图像,计算出各像素分形维数值,由阈值法得到烟雾分割图像。最后,通过形态学膨胀运算使分割图像更加完整。实验结果表明,该算法能有效利用小波频域增强的特点,减小烟雾薄弱区内背景的影响,使该区域烟雾的分形维数更多地集中于阈值内,提高了烟雾分割的准确性。 相似文献
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Abdolhossein Fathi Fatemeh Faraji-kheirabadi 《Signal, Image and Video Processing》2016,10(8):1433-1440
In this study, a new compression algorithm for ECG signal is proposed based on selecting important subbands of wavelet packet transform (WPT) and applying subband-dependent quantization algorithm. To this end, first WPT was applied on ECG signal and then more important subbands are selected according to their Shannon entropy. In the next step, content-based quantization and denoising method are applied to the coefficients of the selected subbands. Finally, arithmetic coding is employed to produce compressed data. The performance of the proposed compression method is evaluated using compression rate (CR), percentage root-mean-square difference (PRD) as signal distortion, and wavelet energy-based diagnostic distortion (WEDD) as diagnostic distortion measures on MIT-BIH Arrhythmia database. The average CR of the proposed method is 29.1, its average PRD is <2.9 % and WEDD is <3.2 %. These results demonstrated that the proposed method has a good performance compared to the state-of-the-art compression algorithms. 相似文献
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Multiscale Wiener filter for the restoration of fractal signals:wavelet filter bank approach 总被引:4,自引:0,他引:4
Bor-Sen Chen Chin-Wei Lin 《Signal Processing, IEEE Transactions on》1994,42(11):2972-2982
A filter bank design based on orthonormal wavelets and equipped with a multiscale Wiener filter is proposed in this paper for signal restoration of 1/f family of fractal signals which are distorted by the transmission channel and corrupted by external noise. First, the fractal signal transmission process is transformed via the analysis filter bank into multiscale convolution subsystems in time-scale domain based on orthonormal wavelets. Some nonstationary properties, e.g., self-similarity, long-term dependency of fractal signals are attenuated in each subband by wavelet multiresolution decomposition so that the Wiener filter bank can be applied to estimate the multiscale input signals. Then the estimated multiscale input signals are synthesized to obtain the estimated input signal. Some simulation examples are given for testing the performance of the proposed algorithm. With this multiscale analysis/synthesis design via the technique of the wavelet filter bank, the multiscale Wiener filter can be applied to treat the signal restoration problem for nonstationary 1/f fractal signals 相似文献
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Wavelet transform has been found to be an effective tool for the time-frequency analysis of non-stationary and quasi-stationary signals. Recent years have seen wavelet transform being used for feature extraction in speech recognition applications. In the paper a sub-band feature extraction technique based on an admissible wavelet transform is proposed and the features are modified to make them robust to additive white Gaussian noise. The performance of this system is compared with the conventional mel frequency cepstral coefficients (MFCC) under various signal to noise ratios. The recognition performance based on the eight sub-band features is found to be superior under the noisy conditions compared with MFCC features. 相似文献