Earticle

RFID technology has been widely used in many fields. Lots of information could be obtained from the enormous RFID devices. Therefore, classification of the trajectory of these moving objects is significant to predict the trends of the moving objects. This paper introduces an innovative algorithm, using pattern recognition approach. This algorithm could be divided into several steps. First of all, the coarse-fine layer classification approach is used for clustering the trajectories. That aims to cut down the trajectories according to the difference phases. Secondly, in the sub-set of the trajectory, searching set is established through detecting the neighbor domains which have been classified at different phases. Finally, the hierarchical classification approach is used for classifying the sub-trajectory. By using the approach, the experimental results imply the feasibility and practicality of the proposed approach on figuring out the familiar trajectory of the RFID-enabled moving objects. It is observed that With the increasing of neighbor value , the linear trends is obvious from the figure, thus, the neighbor query decreases and the magnification ratio outperforms to TRACLUS method. Additionally, the proposed algorithm uses coarse-fine strategy at phases-to-phases, saving the time spend on large number of distance calculation at various phase.

A practical speech enhancement system consists of two major components, the estimation of noise power spectrum, and the estimation of speech.In single channel speech enhancement systems, most algorithms require an estimation of average noise spectrum since a secondary channel is not available. This requires a reliable speech/silence detector. Thus the speech/silence detection can be a determining factor for the performance of the whole speech enhancement system. The speech/silence detection finds out the frames of the noisy speech that contain only noise. If the speech/silence detection is not accurate then speech echoes and residual noise tend to be present in the enhanced speech. The performance of noise estimation algorithm is usually a tradeoff between speech distortion and noise reduction. In existing methods, noise is estimated only during speech pauses and these pauses are identified using Voice Activity Detector (VAD). This paper describes novel noise estimation method to estimate noise in non-stationary environments. This approach uses an algorithm that classifies noisy speech signal into pure speech, quasi speech and non-speech frames based on adaptive thresholds without using of VAD.Speech presence is determined by computing the ratio of the noisy speech power spectrum to its local minimum, which is computed by averaging past values of the noisy speech power spectra with a look-ahead factor. To evaluate proposed method performance, segmental SNR as evaluation criteria and compared with weighted average noise estimation method. The simulation results of the proposed algorithm shows better performance than conventional methods.

In this paper, we applied the visibility graph algorithm to change the well log data into a complex network. The degree distribution of this complex network existed exponential distribution, which indicated the scale free property of our network. Compared the original curve with the corresponding degree and cluster coefficient, we found that the shape information of original well log has been inherited by the network. The hierarchical coefficient of the complex network is about 2, which indicates the hierarchical structure in the network. All the data show that it is feasible and significant to translate the well log data into a complex network.

We propose a new technique for enhancing high frequency geometric features and geometric texture for scatter point clouds. We generalize the classical High-Boost filtering of signal and image processing to the geometric feature manipulation for 3D point clouds. We get a smoothed version of the input point clouds by Adaptive Moving Least Squares (MLS) based point clouds smoothing operator. The distance fields between the point clouds and their corresponding smoothed version are regarded as the high frequency geometric features and geometric textures intuitively. High Boost operations are performed by iteratively updating the position of the input points along the normal direction which is proportion to the distance between the point clouds and their corresponding smoothed versions with given geometric enhancement scale factor. The effectiveness of the proposed method is demonstrated by several examples with both synthetic and real scanned point clouds.

Glaucoma which is a leading cause of blindness in the world is not a single disease but a group of disorders with diverse clinical manifestations. If not controlled at an early stage, it causes irreversible damage to vision. Careful evaluation of Optic nerve head structure and its documentation is extremely important for diagnosis of the disease and to monitor its progression. The optic nerve head comprises of the optic disc and the optic cup. ‘Optic cup’ is one of the imperative and crucial factors in disease diagnosis and monitoring. The aim of the research is to compare various existing optic cup segmentation methods and report their comparative performance. A modification in calculating threshold has been suggested which improves the accuracy.

In order to effectively extract the focused regions from the source images and inhibit the blocking artifacts of the fused image, a novel adaptive block-based image fusion scheme based on sparse features is proposed. The source images are decomposed into principal and sparse matrices by a newly developed robust principal component analysis (RPCA) decomposition. The problem of multi-focus image fusion is transformed into a problem of choosing the sparse features of the sparse matrices to form a feature space. An optimal subdivision of blocks of the sparse matrices is obtained by using a quad tree structure to inhibit the blocking artifacts. The focused regions of the source images are detected by the local sparse feature of the blocks and integrated to construct the resulting fused image. Experimental results show that the proposed scheme can significantly inhibit the blocking artifacts and improve the fusion quality compared to the other existing fusion methods in terms of some objective evaluation indexes, such as structural similarity, mutual information and the edge information transferred from the source images to the fused image.

In this paper Time-varying Auto regressive model (TVAR) based approach for instantaneous frequency(IF) estimation of the frequency modulated signal in noisy environment is presented. Covariance method is applied for least square estimation of time-varying autoregressive parameters. Time-varying parameters are expressed as a linear combination of constants multiplied by basis functions. Then, the time-varying frequencies are extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. The experimental results are presented for IF estimation, prediction and power spectrum estimation of non stationary signals. we have also discussed the spectral resolution ability of TVAR Model. Simulation and experimental results demonstrate that TVAR is an effective solution to non-stationary signal analysis and has strong capability in signal time-frequency feature extraction.

An efficient Stone’s BSS (ESBSS) algorithm is proposed based on the joint between original Stone’s BSS (SBSS) and genetic algorithm (GA). Original Stone’s BSS has several advantages compared with independent component analysis (ICA) techniques, where the BSS problem in Stone’s BSS is simplified to generalized eigenvalue decomposition (GEVD), but it’s susceptible to the local minima problem. Therefore, GA is used to overcome this problem and to enhance the separation process. Performance of the proposed algorithm is first tested through a different pdf source, followed by artifact extraction test for EEG mixtures then compared with the original Stone’s BSS (SBSS) and other BSS algorithms. The results demonstrate proposed algorithm efficiency in real time blind extraction of both super-Gaussian and sub-Gaussian signals from their mixtures.