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Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the
flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our

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An efficient algorithm for overcomplete dictionary learning with l_p-norm as sparsity constraint to achieve sparse representation from a set of known signals is presented in this paper. The special importance of the l_p-norm (0<p<1) has been recognized in recent studies on sparse modeling, which can lead to stronger sparsity-promoting solutions than the l_1-norm. The l_p-norm, however, leads to a nonconvex optimization problem that is difficult to solve efficiently.

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This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

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We consider the problem of estimating discrete self- exciting point process models from limited binary observations, where the history of the process serves as the covariate. We analyze the performance of two classes of estimators: l1-regularized maximum likelihood and greedy estimation for a discrete version of the Hawkes process and characterize the sampling tradeoffs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear and generalized linear models with i.i.d.

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Both the articles I referenced in this document play the role of tutorials that introduce some feature extraction (FE) approaches based on signal energy and zero-crossing rates (ZCRs). They offer cutting-edge algorithms in which the feasibility of a balance among creativity, simplicity, and accuracy constitutes the main motivation. The theory presented, smoothly shown and accompanied by numerical examples, is complemented with source-codes in C/C++ programming language and interesting applications on neurophysiological signal processing, speech processing and image processing.

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Parameter estimation using quantized observations is of importance in many practical applications. Under a symmetric 1-bit setup, consisting of a zero-threshold hard limiter, it is well known that the large sample performance loss for low signal-to-noise ratios (SNRs) is moderate (2/pi or -1.96dB). This makes low-complexity analog-to-digital converters (ADCs) with 1-bit resolution a promising solution for future wireless communications and signal processing devices.

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Internet traffic monitoring is a crucial task for network security. Self-similarity, a key property for a relevant description of internet traffic statistics, has already been massively and successfully involved in anomaly detection. Self-similar analysis was however so far applied either to byte or Packet count time series independently, while both signals are jointly collected and technically deeply related. The present contribution elaborates on a recently proposed multivariate self-similar model, Operator fractional Brownian Motion (OfBm), to

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This work is a part of our research on scalable and/or distributed fusion and sensor calibration. We address parameter estimation in multi-sensor state space models which underpins surveillance applications with sensor networks. The parameter likelihood of the problem involves centralised Bayesian filtering of multi-sensor data, which lacks scalability with the number of sensors and induces a large communication load. We propose separable likelihoods which approximate the centralised likelihood with single sensor filtering terms.

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