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TOC Alert for Publication# 4159607 2018April 19<![CDATA[Algebraic representation for fractional Fourier transform on one-dimensional discrete signal models]]>122143148779<![CDATA[Fast recovery of weak signal based on three-dimensional curvelet transform and generalised cross validation]]>1221491545287<![CDATA[Localisation of mixed near-field and far-field sources using the largest aperture sparse linear array]]>1221551623164<![CDATA[Performance comparison between matched filter and locally optimal detector for composite hypothesis test with inaccurate noise]]>1221631682096<![CDATA[Mainlobe maintenance using shrinkage estimator method]]>1221691731880<![CDATA[Adaptive complex-EKF-based DOA estimation for GPS spoofing detection]]>1221741812534<![CDATA[Sequential source localisation and range estimation based on shrinkage algorithm]]>1221821871536<![CDATA[Learning using privileged information for HRRP-based radar target recognition]]>1221881974567<![CDATA[Dictionary learning based on M-PCA-N for audio signal sparse representation]]>M ranks of SVD decomposition to update M atoms at a time. Then, in order to further utilise the information from remaining ranks, M-PCA-N is proposed on the basis of M-PCA, by transforming information from the following N non-principal ranks onto the top M principal ranks. The mathematic formula indicates that M-PCA may be seen as a generalisation of K-SVD. Experimental results on the BBC Sound Effects Library show that M-PCA-N not only lowers the MSE between original signal and approximation signal in audio signal sparse representation, but also obtains higher audio signal classification precision than K-SVD.]]>1221982061723<![CDATA[Spatial multiplexing gain in MIMO radars with widely separated antennas]]>1222072132045<![CDATA[Combined speech compression and encryption using chaotic compressive sensing with large key size]]>135 when logistic map is used. A spectral segmental signal-to-noise ratio of -36.813 dB is obtained as a measure of encryption strength. The quality of reconstructed speech is given by means of signal-to-noise ratio (SNR), and perceptual evaluation speech quality (PESQ). For 60% compression ratio the proposed method gives 48.203 dB SNR and 4.437 PESQ for voiced speech segments. However, for continuous speech (voiced and unvoiced), it gives 41.097 dB SNR and 4.321 PESQ.]]>1222142183677<![CDATA[Foetal ECG extraction using non-linear adaptive noise canceller with multiple primary channels]]>1222192272352<![CDATA[Non-linear system modelling based on NARX model expansion on Laguerre orthonormal bases]]>1222282413807<![CDATA[Compressive sensing for microwave breast cancer imaging]]>1222422461974<![CDATA[Design methods for ULA-based directional antenna arrays by shaping the Cramér–Rao bound functions]]>1222472544290