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Compressed sensing can be used to yield both compression and a limited form of security to the readings of sensors. This can be most useful when designing the low-resources sensor nodes that are the backbone of IoT applications. Here, we propose to use chaining of subsequent plaintexts to improve the robustness of CS-based encryption against ciphertext-only attacks, known-plaintext attacks and man-in-the-middle attacks.

In many signal processing tasks, one seeks to recover an r- column matrix object X ∈ Cn×r from a set of nonnegative quadratic measurements up to orthonormal transforms. Example applications include coherence retrieval in optical imaging and co- variance sketching for high-dimensional streaming data. To this end, efficient nonconvex optimization methods are quite appealing, due to their computational efficiency and scalability to large-scale problems.

Real-time nonlinear Bayesian filtering algorithms are overwhelmed by data volume, velocity and increasing complexity of computational models. In this paper, we propose a novel ensemble based nonlinear Bayesian filtering approach which only requires a small number of simulations and can be applied to high-dimensional systems in the presence of intractable likelihood functions.

Population Monte Carlo (PMC) algorithms are a family of
adaptive importance sampling (AIS) methods for approximating
integrals in Bayesian inference. In this paper, we propose
a novel PMC algorithm that combines recent advances
in the AIS and the optimization literatures. In such a way, the
proposal densities are adapted according to the past weighted
samples via a local resampling that preserves the diversity,
but we also exploit the geometry of the targeted distribution.
A scaled Langevin strategy with Newton-based scaling metric

The problem of anomaly detection among multiple processes is considered within the framework of sequential design of experiments. The objective is an active inference strategy consisting of a selection rule governing which process to probe at each time, a stopping rule on when to terminate the detection, and a decision rule on the final detection outcome. The performance measure is the Bayes risk that takes into account not only sample complexity and detection errors, but also costs associated with switching across processes.

Deep neural networks have recently demonstrated the traffic prediction capability with the time series data obtained
by sensors mounted on road segments. However, capturing spatio-temporal features of the traffic data often requires a
significant number of parameters to train, increasing computational burden. In this work we demonstrate that embedding

Deep neural networks have recently demonstrated the traffic prediction capability with the time series data obtained by sensors mounted on road segments. However, capturing spatio-temporal features of the traffic data often requires a significant number of parameters to train, increasing computational burden. In this work we demonstrate that embedding topological information of the road network improves the process of learning traffic features.

Many problems in statistical signal processing involve tracking the state of a dynamic system that evolves on a Stiefel manifold. To this aim, we introduce in this paper a novel particle filter algorithm that approximates the optimal importance function on the Stiefel manifold and is capable of handling nonlinear observation functions. To sample from the required importance function, we develop adaptations of previous MCMC algorithms.