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Digital pulse shape discrimination (PSD) systems are widely used to extract various kinds of information from the shape of output pulses of radiation detectors. Such systems replace the traditional analog PSD circuits with numerical algorithms running on a digital processor, which makes them more flexible for implementing various PSD methods. However, the available digital PSD algorithms have been generally tailored to the characteristics of the concerned detectors, which limit their general use. Here, for the purpose of building a general-purpose digital PSD module, we present a PSD algorithm that, with minimum alterations, can be used with a wide range of radiation detectors. Our approach is based on using the cosine similarity measure to quantify the variations in the shape of detectors’ pulses with respect to the shape of a unit step pulse. The method is described in detail, and the results of the test experiments with different types of radiation detectors, including a boron tetrafluoride (BF3) proportional counter, a liquid scintillation detector, and high purity germanium (HPGe) detector are shown. The performance of the algorithm is also compared to that of the common rise time discrimination method. [ABSTRACT FROM AUTHOR]

The problem of reconstructing a sparse signal vector from magnitude-only measurements (a.k.a. compressive phase retrieval) emerges in diverse applications, but it is NP-hard in general. Building on recent advances in nonconvex optimization, this paper puts forth a new algorithm that is termed compressive reweighted amplitude flow (CRAF) and abbreviated as CRAF, for compressive phase retrieval. Specifically, CRAF operates in two stages. The first stage seeks a sparse initial guess via a new spectral procedure. In the second stage, CRAF implements a few hard thresholding based iterations using reweighted gradients. When there are sufficient measurements, CRAF provably recovers the underlying signal vector exactly with high probability under suitable conditions. Moreover, its sample complexity coincides with that of state-of-the-art procedures. Finally, numerical tests showcase improved performance of the new spectral initialization, as well as improved exact recovery relative to competing alternatives. Empirically, the number of measurements required for exact recovery by CRAF is about 40% less than those of competing alternatives in our experiments using real images. [ABSTRACT FROM AUTHOR]

There exist two issues among popular lattice reduction algorithms that should cause our concern. The first one is Korkine–Zolotarev (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms may increase the lengths of basis vectors. The other is KZ reduction suffers worse performance than Minkowski reduction in terms of providing short basis vectors, despite its superior theoretical upper bounds. To address these limitations, we improve the size reduction steps in KZ and LLL to set up two new efficient algorithms, referred to as boosted KZ and LLL, for solving the shortest basis problem with exponential and polynomial complexity, respectively. Both of them offer better actual performance than their classic counterparts, and the performance bounds for KZ are also improved. We apply them to designing integer-forcing (IF) linear receivers for multi-input multioutput communications. Our simulations confirm their rate and complexity advantages. [ABSTRACT FROM PUBLISHER]

A new robust and statistically efficient estimator for ARMA models called the bounded influence propagation $\tau$-estimator is proposed. The estimator incorporates an auxiliary model, which prevents the propagation of outliers. Strong consistency and asymptotic normality of the estimator for ARMA models that are driven by independently and identically distributed (iid) innovations with symmetric distributions are established. To analyze the infinitesimal effect of outliers on the estimator, the influence function is derived and computed explicitly for an AR(1) model with additive outliers. To obtain estimates for the AR($p$) model, a robust Durbin–Levinson type and a forward–backward algorithm are proposed. An iterative algorithm to robustly obtain ARMA($p,q$) parameter estimates is also presented. The problem of finding a robust initialization is addressed, which for orders $p+q > 2$ is a nontrivial matter. Numerical experiments are conducted to compare the finite sample performance of the proposed estimator to existing robust methodologies for different types of outliers both in terms of average and of worst case performance, as measured by the maximum bias curve. To illustrate the practical applicability of the proposed estimator, a real-data example of outlier cleaning for R–R interval plots derived from electrocardiographic data is considered. The proposed estimator is not limited to biomedical applications, but is also useful in any real-world problem whose observations can be modeled as an ARMA process disturbed by outliers or impulsive noise. [ABSTRACT FROM PUBLISHER]

This paper focuses on the development of novel greedy techniques for distributed learning under sparsity constraints. Greedy techniques have widely been used in centralized systems due to their low computational requirements and at the same time their relatively good performance in estimating sparse parameter vectors/signals. The paper reports two new algorithms in the context of sparsity-aware learning. In both cases, the goal is first to identify the support set of the unknown signal and then to estimate the nonzero values restricted to the active support set. First, an iterative greedy multistep procedure is developed, based on a neighborhood cooperation strategy, using batch processing on the observed data. Next, an extension of the algorithm to the online setting, based on the diffusion LMS rationale for adaptivity, is derived. Theoretical analysis of the algorithms is provided, where it is shown that the batch algorithm converges to the unknown vector if a Restricted Isometry Property (RIP) holds. Moreover, the online version converges in the mean to the solution vector under some general assumptions. Finally, the proposed schemes are tested against recently developed sparsity-promoting algorithms and their enhanced performance is verified via simulation examples. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated. Therefore, the full data could follow the forest structure and we call this property forest sparsity. It exploits both intra- and inter- channel correlations and enriches the family of existing model-based compressive sensing theories. The proposed theory indicates that only \cal O(Tk+\log (N/k)) measurements are required for multi-channel data with forest sparsity, where T is the number of channels, N and k are the length and sparsity number of each channel, respectively. This result is much better than \cal O(Tk+T\log (N/k)) of tree sparsity, \cal O(Tk+k\log (N/k)) of joint sparsity, and far better than \cal O(Tk+Tk\log (N/k)) of standard sparsity. In addition, we extend the forest sparsity theory to the multiple measurement vectors problem, where the measurement matrix is a block-diagonal matrix. The result shows that the required measurement bound can be the same as that for dense random measurement matrix, when the data shares equal energy in each channel. A new algorithm is developed and applied on four example applications to validate the benefit of the proposed model. Extensive experiments demonstrate the effectiveness and efficiency of the proposed theory and algorithm. [ABSTRACT FROM PUBLISHER]