Current Issue : April - June Volume : 2014 Issue Number : 2 Articles : 5 Articles
This paper models and solves the mathematical problem of interpolating characteristic points of signals by a partial differential\r\nEquation-(PDE-) based approach. The existence and uniqueness results are established in an appropriate space whose regularity is\r\nsimilar to cubic spline one. We show how this space is suitable for the empirical mode decomposition (EMD) sifting process.\r\nNumerical schemes and computing applications are also presented for signal envelopes calculation. The test results show the\r\nusefulness of the new PDE interpolator in some pathological cases like input class functions that are not so regular as in the cubic\r\nsplines case. Some image filtering tests strengthen the demonstration of PDE interpolator performance....
Tongue body segmentation is a prerequisite to tongue image analysis and has recently received considerable\r\nattention. The existing tongue body segmentation methods usually involve two key steps: edge detection and\r\nactive contour model (ACM)-based segmentation. However, conventional edge detectors cannot faithfully detect\r\nthe contour of the tongue body, and the initialization of ACM suffers from the edge discontinuity problem. To\r\naddress these issues, we proposed a novel tongue body segmentation method, GaborFM, which initializes ACM by\r\nperforming fast marching over the two-dimensional (2D) Gabor magnitude domain of the tongue images. For the\r\nenhancement of the contour of the tongue body, we used the 2D Gabor magnitude-based detector. To cope with\r\nthe edge discontinuity problem, the fast marching method was utilized to connect the discontinuous contour\r\nsegments, resulting in a closed and continuous tongue body contour for subsequent ACM-based segmentation.\r\nQualitative and quantitative results showed that GaborFM is superior to the other methods for tongue body\r\nsegmentation....
Close-microphone techniques are extensively employed in many live music recordings, allowing for interference\r\nrejection and reducing the amount of reverberation in the resulting instrument tracks. However, despite the use of\r\ndirectional microphones, the recorded tracks are not completely free from source interference, a problem which is\r\ncommonly known as microphone leakage. While source separation methods are potentially a solution to this\r\nproblem, few approaches take into account the huge amount of prior information available in this scenario. In fact,\r\nbesides the special properties of close-microphone tracks, the knowledge on the number and type of instruments\r\nmaking up the mixture can also be successfully exploited for improved separation performance. In this paper, a\r\nnonnegative matrix factorization (NMF) method making use of all the above information is proposed. To this end, a\r\nset of instrument models are learnt from a training database and incorporated into a multichannel extension of the\r\nNMF algorithm. Several options to initialize the algorithm are suggested, exploring their performance in multiple\r\nmusic tracks and comparing the results to other state-of-the-art approaches....
In this paper authors have presented the performance evaluation of Multiband band orthogonal frequency division (MB-OFDM) system for different modulation schemes. The multi-band system is a solution for OFDM proposed for the ultra wideband physical layer standard. Authors has been evaluated the performance of OFDM communication system with BPSK and QPSK modulation schemes along with ½ and ¾ code rates....
A hyperspectral image (HSI) is always modeled as a three-dimensional tensor, with the first two dimensions indicating\r\nthe spatial domain and the third dimension indicating the spectral domain. The classical matrix-based denoising\r\nmethods require to rearrange the tensor into a matrix, then filter noise in the column space, and finally rebuild the\r\ntensor. To avoid the rearranging and rebuilding steps, the tensor-based denoising methods can be used to process\r\nthe HSI directly by employing multilinear algebra. This paper presents a survey on three newly proposed HSI\r\ndenoising methods and shows their performances in reducing noise. The first method is the Multiway Wiener Filter\r\n(MWF), which is an extension of the Wiener filter to data tensors, based on the TUCKER3 decomposition. The second\r\none is the PARAFAC filter, which removes noise by truncating the lower rank K of the PARAFAC decomposition. And\r\nthe third one is the combination of multidimensional wavelet packet transform (MWPT) and MWF (MWPT-MWF),\r\nwhich models each coefficient set as a tensor and then filters each tensor by applying MWF. MWPT-MWF has been\r\nproposed to preserve rare signals in the denoising process, which cannot be preserved well by using the MWF or\r\nPARAFAC filters. A real-world HYDICE HSI data is used in the experiments to assess these three tensor-based denoising\r\nmethods, and the performances of each method are analyzed in two aspects: signal-to-noise ratio and improvement\r\nof subsequent target detection results....
Loading....