Matching Pursuit Coefficients Research Articles

Matching pursuit based indices for examining physiological differences of meditators and non-meditators: An HRV study

Since meditation has recently been used as a complementary therapy, the study of physiological effects of different kinds of meditation has been augmented over the last decade. In this study, a robust method based on the matching pursuit (MP) algorithm was proposed to examine the heart rate variability (HRV) differences of meditators and non-meditators. The non-meditators were comprised of metronomic breathing (MB) and spontaneous nocturnal breathing (SNB) groups. The meditators were comprised of pre Chinese Chi meditation (pCCM), during CCM (dCCM), pre Kundalini yoga meditation (pKYM), and during KYM (dKYM). Following MP based decomposition of HRV into its sparse parts, eleven indices were extracted for the MP coefficients. The efficiency of the indices was also examined through statistical significance between the groups. It was shown that for almost all indices, significant differences between the classes were observed. Furthermore, a probabilistic neural network (PNN) with a variable sigma (σ) value, was trained to classify the physiological responses of the groups. A maximum accuracy of 99.61% was detected for dKYM using 5-fold cross validation scheme. It was also shown that lower σ values give higher classification rates and upper σ values provide more robust accuracies. In conclusion, the proposed method is a promising technique for showing significant differences between HRV responses of different non-meditator and meditator groups.

Human identification using a new matching Pursuit-based feature set of ECG

Background and ObjectiveIn recent years, many attempts have been made to design reliable systems for identifying individuals using biometrics. Electrocardiogram (ECG) biometric is one of the newest methods that not only offers unique characteristics of individuals for human identification, but also the possibility of counterfeiting it is negligible. In this paper, our objective was to develop an identification system using a non-fiducial one-lead ECG feature set based on a sparse algorithm. MethodsThe ECG signals of 90 participants were decomposed using a matching pursuit (MP) and several statistical and nonlinear measures were extracted from the MP coefficients. Then, the performance of ECG characteristics delivered by MP analysis in human identification was evaluated by the probabilistic neural network (PNN) and k-nearest neighbor (kNN) with one vs. all strategy. The role of the feature set in classification rates was also tested in different modes, including linear attributes, nonlinear indices, all features, features selected by principal component analysis (PCA), and features selected by linear discriminant analysis (LDA). ResultsExperimental results showed that (1) the highest recognition rate was 99.68%; (2) the performance of the PNN was superior to the kNN; and (3) selecting features with LDA resulted in higher identification rates. ConclusionsThe results are prominent from the performance perspective because it gives higher recognition rates over the group of 90 participants. The great performance of the proposed identification system advocates that it can be employed confidently in different smart systems.

An accurate emotion recognition system using ECG and GSR signals and matching pursuit method

BackgroundThe purpose of the current study was to examine the effectiveness of Matching Pursuit (MP) algorithm in emotion recognition. MethodsElectrocardiogram (ECG) and galvanic skin responses (GSR) of 11 healthy students were collected while subjects were listening to emotional music clips. Applying three dictionaries, including two wavelet packet dictionaries (Coiflet, and Daubechies) and discrete cosine transform, MP coefficients were extracted from ECG and GSR signals. Next, some statistical indices were calculated from the MP coefficients. Then, three dimensionality reduction methods, including Principal Component Analysis (PCA), Linear Discriminant Analysis, and Kernel PCA were applied. The dimensionality reduced features were fed into the Probabilistic Neural Network in subject-dependent and subject-independent modes. Emotion classes were described by a two-dimensional emotion space, including four quadrants of valence and arousal plane, valence based, and arousal based emotional states. ResultsUsing PCA, the highest recognition rate of 100% was achieved for sigma = 0.01 in all classification schemes. In addition, the classification performance of ECG features was evidently better than that of GSR features. Similar results were obtained for subject-dependent emotion classification mode. ConclusionsAn accurate emotion recognition system was proposed using MP algorithm and wavelet dictionaries.

Matching pursuit for direction of arrival estimation in the presence of Gaussian noise and impulsive noise

Two high-resolution direction of arrival (DOA) estimation approaches of non-stationary narrowband signals based on matching pursuit (MP) are developed. The first sensor output is considered as the reference and decomposed by MP. As the MP is a linear decomposition, the obtained MP coefficients contain the steering vector information. So, the MP coefficients corresponding to the leading decomposition atoms are used to develop the MP-MUSIC algorithm for the DOA estimation. In addition, the chosen MP atoms are used to implement the modified spatial time–frequency distribution (STFD) based on Wigner Ville (WV) distribution as well, and this method named MP-WV. It has been demonstrated that these two methods can be applied for underdetermined problems and are robust against Gaussian and impulsive noises. The authors show that using either coefficients or chosen atoms to estimate the DOA in array processing by considering the source discriminative capability outperforms the conventional MUSIC and STFD. Some simulation results showing the performance of the two proposed approaches based on MP, conventional MUSIC and STFD are presented.

High-resolution direction finding of non-stationary signals using matching pursuit

One of the main goals of time–frequency (TF) signal representations in non-stationary array processing is to equip multi-antenna receivers with the ability to separate sources in the TF domain prior to direction finding. This permits high-resolution direction-of-arrival (DOA) estimation of individual sources and of more sources than sensors. In this paper, we use linear decomposition of sensor data based on matching pursuit (MP). The leading atoms of the MP, which capture most of the source TF signatures, can be different for different sources and, as such, provide the desired source discrimination. The MP coefficients with high signal-to-noise ratio (SNR) and corresponding to the leading decomposition atoms are used to develop the MP-MUSIC DOA estimation for non-stationary source signals. We demonstrate the source discriminatory capability of the proposed technique using linear FM, nonlinear FM, and other non-stationary signals. Further, we compare MP-MUSIC performance with conventional MUSIC and the time–frequency MUSIC, which incorporates bilinear transforms.

On the statistics of matching pursuit angles

Matching pursuit decompositions have been employed for signal coding. For this purpose, matching pursuit coefficients need to be quantized. However, their behavior has been shown to be chaotic in some cases; posing difficulties to their modeling and quantizer design. In this work, a different approach is presented. Instead of trying to model the statistics of matching pursuit coefficients, the statistics of the angle between the residue signal and the element selected in each iteration of the matching pursuit are studied, what allows to model matching pursuits coefficients indirectly. This approach results in a simple statistical model. This is so because one observes that the statistics of such angles do not vary substantially after the first matching pursuit iteration, and can be approximately modeled as independent and identically distributed. Moreover, it is also observed that the probability density functions of matching pursuit angles are reasonably modeled by a single probability density function. This function depends only on the dictionary employed and not on the signal source. The derived statistical model is validated by employing it to design Lloyd–Max quantizers for matching pursuit coefficients. The Lloyd–Max quantizers obtained show good rate×distortion performance when compared to the state-of-the-art methods.

Adaptive Rate-Distortion Optimal In-Loop Quantization for Matching Pursuit

In this paper, an adaptive in-loop quantization technique is proposed for quantizing inner product coefficients in matching pursuit. For each matching pursuit (MP) stage a different quantizer is used based on the probability distribution of MP coefficients. The quantizers are optimized for a given rate budget constraint. Additionally, our proposed adaptive quantization scheme finds the optimal quantizers for each stage based on the already encoded inner product coefficients. Experimental results show that our proposed adaptive quantization scheme outperforms existing quantization methods used in matching pursuit image coding.

Modulus quantization for matching-pursuit video coding

Overcomplete signal decomposition using matching pursuits has been shown to be an efficient technique for coding motion-residual images in a hybrid video coder. Unlike orthogonal decomposition, matching pursuit uses an in-the-loop modulus quantizer which must be specified before coding begins. This complicates the quantizer design, since the optimal quantizer depends on the statistics of the matching-pursuit coefficients which in turn depend on the in loop quantizer actually used. In this paper, we address the modulus quantizer design issue, specifically developing frame-adaptive quantization schemes for the matching-pursuit video coder. Adaptive dead-zone subtraction is shown to reduce the information content of the modulus source, and a uniform threshhold quantizer is shown to be optimal for the resulting source. Practical two-pass and one-pass algorithms are developed to jointly determine the quantizer parameters and the number of coded basis functions in order to minimize coding distortion for a given rate. The compromise one-pass scheme performs nearly as well as the full two-pass algorithm, but with the same complexity as a fixed-quantizer design. The adaptive schemes are shown to outperform the fixed quantizer used in earlier works, especially at high bit rates, where the gain is as high as 1.7 dB.