A scalable algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, which consist of a number of sensors transmitting a summary information of their observations to the fusion center for a final decision. The proposed algorithm is directly minimizing the overall error probability of the network without resorting to minimizing pseudo objective functions such as distances between probability distributions.

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We summarise previous work showing that the basic sigmoid activation function arises as an instance of Bayes’s theorem, and that recurrence follows from the prior. We derive a layer- wise recurrence without the assumptions of previous work, and show that it leads to a standard recurrence with modest modifications to reflect use of log-probabilities. The resulting architecture closely resembles the Li-GRU which is the current state of the art for ASR. Although the contribution is mainly theoretical, we show that it is able to outperform the state of the art on the TIMIT and AMI datasets.

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- Read more about Statistical Properties of a Modified Welch Method That Uses Sample Percentiles
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We present and analyze an alternative, more robust approach to the Welch’s overlapped segment averaging (WOSA) spectral estimator. Our method computes sample percentiles instead of averaging over multiple periodograms to estimate power spectral densities (PSDs). Bias and variance of the proposed estimator are derived for varying sample sizes and arbitrary percentiles. We have found excellent agreement between our expressions and data sampled from a white Gaussian noise process.

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- Read more about Statistical Properties of a Modified Welch Method That Uses Sample Percentiles
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We present and analyze an alternative, more robust approach to the Welch’s overlapped segment averaging (WOSA) spectral estimator. Our method computes sample percentiles instead of averaging over multiple periodograms to estimate power spectral densities (PSDs). Bias and variance of the proposed estimator are derived for varying sample sizes and arbitrary percentiles. We have found excellent agreement between our expressions and data sampled from a white Gaussian noise process.

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- Read more about Robust graph-filter identification with graph-denoising regularization
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When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known. However, in many practical applications, the observed (inferred) network is prone to perturbations which, if ignored, will hinder performance. Tailored to those setups, this paper presents a robust formulation for the problem of graph-filter identification from input-output observations. Different from existing works, our approach consists in addressing the robust identification by formulating a joint graph denoising and graph-filter identification problem.

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- Read more about Radar Clutter Classification Using Expectation-Maximization Method
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In this paper, the problem of classifying radar clutter returns into statistically homogeneous subsets is addressed. To this end, latent variables, which represent the classes to which the tested range cells belong, in conjunction with the expectation-maximization method are jointly exploited to devise the classification architecture. Moreover, two different models for the structure of the clutter covariance matrix are considered.

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- Read more about Resolution Limits of 20 Questions Search Strategies for Moving Targets
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We establish fundamental limits of tracking a moving target over the unit cube under the framework of 20 questions with measurement-dependent noise. In this problem, there is an oracle who knows the instantaneous location of a target. Our task is to query the oracle as few times as possible to accurately estimate the trajectory of the moving target, whose initial location and velocity is unknown. We study the case where the oracle's answer to each query is corrupted by random noise with query-dependent discrete distribution.

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- Read more about DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION
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- Read more about On design of optimal smart meter privacy control strategy against adversarial MAP detection
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We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control.

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- Read more about ICASSP 2020
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We address the problem of detection, in the frequency domain, of a M-dimensional time series modeled as the output of a M × K MIMO filter driven by a K-dimensional Gaussian white noise, and disturbed by an additive M-dimensional Gaussian col- ored noise. We consider the study of test statistics based of the Spectral Coherence Matrix (SCM) obtained as renormalization of the smoothed periodogram matrix of the observed time series over N samples, and with smoothing span B.

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