Cross-Term-Free Time-Frequency Distribution Reconstruction via Lifted Projections

Cataloged from PDF version of article.A crucial aspect of time-frequency (TF) analysis is the identification of separate components in a multicomponent signal. The Wigner-Ville distribution is the classical tool for representing such signals, but it suffers from cross-terms. Other methods, which are members of Cohen's class of distributions, also aim to remove the cross-terms by masking the ambiguity function (AF), but they result in reduced resolution. Most practical time-varying signals are in the form of weighted trajectories on the TF plane, and many others are sparse in nature. Therefore, in recent studies the problem is cast as TF distribution reconstruction using a subset of AF domain coefficients and sparsity assumption. Sparsity can be achieved by constraining or minimizing the l(1) norm. In this article, an l(1) minimization approach based on projections onto convex sets is proposed to obtain a high-resolution, cross-term-free TF distribution for a given signal. The new method does not require any parameter adjustment to obtain a solution. Experimental results are presented

AM/FM signal estimation with micro-segmentation and polynomial fit

Cataloged from PDF version of article.Amplitude and phase estimation of AM/FM signals with parametric polynomial representation require the polynomial orders for phase and amplitude to be known. But in reality, they are not known and have to be estimated. A well-known method for estimation is the higher-order ambiguity function (HAF) or its variants. But the HAF method has several reported drawbacks such as error propagation and slowly varying or even constant amplitude assumption. Especially for the long duration time-varying signals like AM/FM signals, which require high orders for the phase and amplitude, computational load is very heavy due to nonlinear optimization involving many variables. This paper utilizes a micro-segmentation approach where the length of segment is selected such that the amplitude and instantaneous frequency (IF) is constant over the segment. With this selection first, the amplitude and phase estimates for each micro-segment are obtained optimally in the LS sense, and then, these estimates are concatenated to obtain the overall amplitude and phase estimates. The initial estimates are not optimal but sufficiently close to the optimal solution for subsequent processing. Therefore, by using the initial estimates, the overall polynomial orders for the amplitude and phase are estimated. Using estimated orders, the initial amplitude and phase functions are fitted to the polynomials to obtain the final signal. The method does not use any multivariable nonlinear optimization and is efficient in the sense that the MSE performance is close enough to the Cramer–Rao bound. Simulation examples are presented

Fast insect damage detection in wheat kernels using transmittance images

We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.12 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.90 ± 0.02. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93 ± 0.03

Identification of insect damaged wheat kernels using transmittance images

We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.2 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.86. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.1 at the TP Rate of 0.8 and an AUC of 0.92. © 2004 IEEE

Phase retrieval of sparse signals from Fourier Transform magnitude using non-negative matrix factorization

Signal and image reconstruction from Fourier Transform magnitude is a difficult inverse problem. Fourier transform magnitude can be measured in many practical applications, but the phase may not be measured. Since the autocorrelation of an image or a signal can be expressed as convolution of x(n) with x(-n), it is possible to formulate the inverse problem as a non-negative matrix factorization problem. In this paper, we propose a new algorithm based on the sparse non-negative matrix factorization (NNMF) to estimate the phase of a signal or an image in an iterative manner. Experimental reconstruction results are presented. © 2013 IEEE

CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation

The detection of curvilinear structures in medical images, e.g., blood vessels or nerve fibers, is important in aiding management of many diseases. In this work, we propose a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography (OCT-A), color fundus image, and corneal confocal microscopy (CCM). Instead of the U-Net based convolutional neural network, we propose a novel network (CS-Net) which includes a self-attention mechanism in the encoder and decoder. Two types of attention modules are utilized - spatial attention and channel attention, to further integrate local features with their global dependencies adaptively. The proposed network has been validated on five datasets: two color fundus datasets, two corneal nerve datasets and one OCT-A dataset. Experimental results show that our method outperforms state-of-the-art methods, for example, sensitivities of corneal nerve fiber segmentation were at least 2% higher than the competitors. As a complementary output, we made manual annotations of two corneal nerve datasets which have been released for public access

Effectiveness of TMC AI Applications in Case Studies

DTFH61-16-D00030Traffic incident detection is a crucial task in traffic management centers (TMCs) that typically manage large highway networks with limited staff. An effective automatic incident-detection approach could benefit TMCs by helping to report abnormal events in a timely and accurate manner and optimize operating resources. During the past decades, researchers have made significant progress in developing such automatic approaches. Nevertheless, the majority of the developed approaches have shown limited success in the field, largely because of concerns about their often-costly false alarms (e.g., misdispatching response teams to a nonexistent incident). Fortunately, recent advances in artificial intelligence (AI) are expected to provide opportunities for improving conventional TMC operations. This project aimed to propose an AI-based incident-detection framework that can leverage large-scale sensor data along with advanced learning algorithms to improve the performance of incident detection. Researchers investigated the generic algorithmic problems in designing a detection approach and emphasized the architecture of the AI-based detection framework by including learning and evolving capabilities. The proposed framework was assessed with a fully controlled experiment in simulation that consisted of numerous traffic and incident scenarios. The results indicated that the proposed AI-based framework achieved higher detection rates, lower false alarm rates, and shorter time to detect the incidents in the studied scenarios than conventional approaches. Some extensions of the proposed framework are also discussed

Study of J/ψJ/\psi and ψ(3686)→Σ(1385)0Σˉ(1385)0\psi(3686)\rightarrow\Sigma(1385)^{0}\bar\Sigma(1385)^{0} and Ξ0Ξˉ0\Xi^0\bar\Xi^{0}

We study the decays of $J/\psi$ and $\psi(3686)$ to the final states $\Sigma(1385)^{0}\bar\Sigma(1385)^{0}$ and $\Xi^0\bar\Xi^{0}$ based on a single baryon tag method using data samples of $(1310.6 \pm 7.0) \times 10^{6}$ $J/\psi$ and $(447.9 \pm 2.9) \times 10^{6}$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider. The decays to $\Sigma(1385)^{0}\bar\Sigma(1385)^{0}$ are observed for the first time. The measured branching fractions of $J/\psi$ and $\psi(3686)\rightarrow\Xi^0\bar\Xi^{0}$ are in good agreement with, and much more precise, than the previously published results. The angular parameters for these decays are also measured for the first time. The measured angular decay parameter for $J/\psi\rightarrow\Sigma(1385)^{0}\bar\Sigma(1385)^{0}$, $\alpha =-0.64 \pm 0.03 \pm 0.10$, is found to be negative, different to the other decay processes in this measurement. In addition, the "12\% rule" and isospin symmetry in the $J/\psi$ and $\psi(3686)\rightarrow\Xi\bar\Xi$ and $\Sigma(1385)\bar{\Sigma}(1385)$ systems are tested.Comment: 11 pages, 7 figures. This version is consistent with paper published in Phys.Lett. B770 (2017) 217-22

Observation of Ds+→pnˉD^+_s\rightarrow p\bar{n} and confirmation of its large branching fraction

The baryonic decay $D^+_s\rightarrow p\bar{n}$ is observed, and the corresponding branching fraction is measured to be $(1.21\pm0.10\pm0.05)\times10^{-3}$, where the first uncertainty is statistical and second systematic. The data sample used in this analysis was collected with the BESIII detector operating at the BEPCII $e^+e^-$ double-ring collider with a center-of-mass energy of 4.178~GeV and an integrated luminosity of 3.19~fb$^{-1}$. The result confirms the previous measurement by the CLEO Collaboration and is of greatly improved precision, which may deepen our understanding of the dynamical enhancement of the W-annihilation topology in the charmed meson decays

Observation and study of the decay J/ψ→ϕηη′J/\psi\rightarrow\phi\eta\eta'

We report the observation and study of the decay $J/\psi\rightarrow\phi\eta\eta'$ using $1.3\times{10^9}$ $J/\psi$ events collected with the BESIII detector. Its branching fraction, including all possible intermediate states, is measured to be $(2.32\pm0.06\pm0.16)\times{10^{-4}}$. We also report evidence for a structure, denoted as $X$, in the $\phi\eta'$ mass spectrum in the $2.0-2.1$ GeV/$c^2$ region. Using two decay modes of the $\eta'$ meson ($\gamma\pi^+\pi^-$ and $\eta\pi^+\pi^-$), a simultaneous fit to the $\phi\eta'$ mass spectra is performed. Assuming the quantum numbers of the $X$ to be $J^P = 1^-$, its significance is found to be 4.4$\sigma$, with a mass and width of $(2002.1 \pm 27.5 \pm 21.4)$ MeV/$c^2$ and $(129 \pm 17 \pm 9)$ MeV, respectively, and a product branching fraction $\mathcal{B}(J/\psi\rightarrow\eta{}X)\times{}\mathcal{B}(X\rightarrow\phi\eta')=(9.8 \pm 1.2 \pm 1.7)\times10^{-5}$. Alternatively, assuming $J^P = 1^+$, the significance is 3.8$\sigma$, with a mass and width of $(2062.8 \pm 13.1 \pm 7.2)$ MeV/$c^2$ and $(177 \pm 36 \pm 35)$ MeV, respectively, and a product branching fraction $\mathcal{B}(J/\psi\rightarrow\eta{}X)\times{}\mathcal{B}(X\rightarrow\phi\eta')=(9.6 \pm 1.4 \pm 2.0)\times10^{-5}$. The angular distribution of $J/\psi\rightarrow\eta{}X$ is studied and the two $J^P$ assumptions of the $X$ cannot be clearly distinguished due to the limited statistics. In all measurements the first uncertainties are statistical and the second systematic.Comment: 10 pages, 6 figures and 4 table