The paper presents a new approach for the identification of minimum-phase autoregressive (AR) systems in the presence of heavy noise. A damped cosine model for the ramp cepstrum of the one-sided autocorrelation functi...
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The paper presents a new approach for the identification of minimum-phase autoregressive (AR) systems in the presence of heavy noise. A damped cosine model for the ramp cepstrum of the one-sided autocorrelation function of a noise-free AR signal is proposed to estimate the AR parameters. The AR parameters are obtained directly from the estimated damped cosine model parameters. The proposed method overcomes the failure of conventional cepstrum and correlation based techniques in noisy AR system identification at a very low signal-to-noise ratio (SNR). computer simulations are carried out based on. both synthetic AR systems and natural speech signals, showing superior identification results even at an SNR of -5 dB for which most of the existing methods would fail.
We present new algorithms that can be used to extract features from a DNA chromatogram prior to base-calling. The algorithms assume that the inter-base distance has already been equalized using methods such as those p...
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We present new algorithms that can be used to extract features from a DNA chromatogram prior to base-calling. The algorithms assume that the inter-base distance has already been equalized using methods such as those presented in L. Andrade-Cetto and E. Manolakos (2005). We show first how a good estimate of the peak diffusion (spread) can be calculated from the raw trace and without having to known the underlying base sequence. Using the estimated inter-peak distance and peak spread parameters a non-negative least squares problem can be formulated in order to find the weight factors of the multiple shapes immersed in broad peaks, typically found towards the end of the chromatogram. The two algorithms combined provide peak hypotheses that are tested by the subsequent base decisions and scoring stage of the base-caller using probabilistic methods
Accurate estimation of MIMO frequency selective fading channels is important for reliable communication. In this paper, a new channel estimation scheme which relies on uncorrelated aperiodic complementary sets of sequ...
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Accurate estimation of MIMO frequency selective fading channels is important for reliable communication. In this paper, a new channel estimation scheme which relies on uncorrelated aperiodic complementary sets of sequences is proposed. Theoretical analysis and Monte-Carlo simulation show that the estimator achieves the minimum possible Cramer-Rao lower bound (CRLB). Furthermore, low-complexity algorithms for both ASIC/FPGA and DSP implementations are provided, using the special structure of the uncorrelated aperiodic complementary sets of sequences
A first order variable dependence (FOVD) probabilistic graphical model is introduced to capture the complex inter-event dependencies that are present in DNA sequencing data. In this framework, DNA base-calling is addr...
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A first order variable dependence (FOVD) probabilistic graphical model is introduced to capture the complex inter-event dependencies that are present in DNA sequencing data. In this framework, DNA base-calling is addressed as a parameter estimation problem using maximum likelihood methods. The FOVD model accounts for dependencies between neighboring alleles and statistically characterizes the size of signal peaks. Our experimental results suggest that the resulting unsupervised classification base-calling algorithms (i) achieve accuracy that exceeds on average that of the-state-of-the art base-callers, (ii) work well for a variety of data set types without requiring costly recalibration
We propose a new joint multiple matrix diagonalization algorithm for robust blind beamforming. This new algorithm is based on the iterative eigen-decomposition of cumulant matrices. Therefore it can avoid the stabilit...
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We propose a new joint multiple matrix diagonalization algorithm for robust blind beamforming. This new algorithm is based on the iterative eigen-decomposition of cumulant matrices. Therefore it can avoid the stability and misadjustment problems arising among the conventional steepest-descent approaches for constant-modulus or cumulant optimization. Our Monte Carlo simulations show that our proposed algorithm significantly outperforms the JADE algorithm based on the Givens rotation for PSK source signals in terms of signal-to-interference-and-noise ratio for a wide variety of signal-to-noise ratios
Edge detection is a cornerstone in any computer, robotic or machine vision system. Real time edge detection is a pre-process to many critical applications, such as assembly line inspection and surveillance. Wavelets-b...
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Edge detection is a cornerstone in any computer, robotic or machine vision system. Real time edge detection is a pre-process to many critical applications, such as assembly line inspection and surveillance. Wavelets-based algorithms are replacing traditional algorithms, especially the Haar wavelet because of its simplicity. The Haar algorithm uses a multilevel decomposition to produce image edges corresponding to high frequency wavelet coefficients. In this paper, a real time edge detection algorithm based on Haar is analyzed and compared to conventional edge detectors. Other implemented and compared algorithms are the traditional Prewitt algorithm, and, from a newer generation, the Canny algorithm. The real time implementation of all algorithms is accomplished using TI TMS320C6711 card. In case of Haar, the multilevel decomposition improves the results obtained with noisy images. The results show that the Haar-based edge detector has a low execution time with accurate edge results, and thus represents a suitable algorithm for on-line vision system applications. Canny has produced the thinnest edges, but is not suitable for real time processing using the 6711, and falls short in edge results compared to the Haar results. The wavelet-based algorithm has outperformed other edge detectors.
Receiver operating characteristics (ROC) has been widely used as a performance evaluation tool to measure effectiveness of medical modalities. It is derived from a standard detection theory with false alarm and detect...
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