The smart grid combines the classical power system with the information technology, leading to a cyber-physical system. In such an environment, the malicious injection of data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect well designed false data injection (FDI) attacks. Moreover, most of the works on FDI attack detection are based on the linearized DC model of the power system and fails to detect attacks based on the AC model. The aim of this paper is to address these problems by using the graph structure of the grid and the AC power flow model. We derive an attack detection method that is able to detect previously undetectable FDI attacks. This method is based on concepts originating from graph signal processing (GSP). The proposed detection scheme calculates the graph Fourier transform of an estimated grid state and filters the graph’s high-frequency components. By comparing the maximum norm of this outcome with a threshold, we can detect the presence of FDI attacks. Case studies on the IEEE 14-bus system demonstrate that the proposed method is able to detect a wide range of previously undetectable attacks, both on angles and on magnitudes of the voltages.

Because of their unique attributes of plasticity and accessibility, bone marrow-derived mesenchymal stem cells (MSCs) may find use for therapy of neurodegenerative disorders. Our previous studies of adult human MSCs demonstrated that these cells express an extensive assortment of neural genes at a low but clearly detectable level. Here, we report expression of 12 neural genes, 8 genes related to the neuro-dopaminergic system, and 11 transcription factors with neural significance by human MSCs. Our results suggest that, as opposed to cells that do not express neural genes, human MSCs are predisposed to differentiate to neuronal and glial lineages, given the proper conditions. Our findings add a new dimension in which to view adult stem cell plasticity, and may explain the relative ease with which MSCs, transplanted into the central nervous system (CNS) differentiate to a variety of functional neural cell types. Our results further promote the possibility that adult human MSCs are promising candidates for cell-based therapy of neurodegenerative diseases.

Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multisignal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance superresolution DoA recovery while being highly applicable in practice. MUSIC is a model-based algorithm, relying on an accurate mathematical description of the relationship between the signals and the measurements and assumptions on the signals themselves (non-coherent, narrowband sources). As such, it is sensitive to model imperfections. In this work, we propose to overcome these limitations of MUSIC by augmenting the algorithm with specifically designed neural architectures. Our proposed deep augmented MUSIC (DA-MUSIC) algorithm is thus a hybrid model-based/data-driven DoA estimator, which leverages data to improve performance and robustness while preserving the interpretable flow of the classic method. DA-MUSIC is shown to learn to overcome limitations of the purely model-based method, such as its inability to successfully localize coherent sources as well as estimate the number of coherent signal sources present. We further demonstrate the superior resolution of the DA-MUSIC algorithm in synthetic narrowband and broadband scenarios as well as with real-world data of DoA estimation from seismic signals

The Kalman filter (KF) is a widely used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe an SS model limits its applicability under complex settings, e.g., when tracking based on visual data, and the processing of high-dimensional signals often induces notable latency. These challenges can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach. By gradually tackling the challenges in handling the measurement model and the task, we develop Latent-KalmanNet, which implements tracking from high-dimensional measurements by leveraging data to jointly learn the KF along with the latent space mapping. Latent-KalmanNet combines a learned encoder with data-driven tracking in the latent space using the recently proposed-KalmanNet, while identifying the ability of each of these trainable modules to assist its counterpart via providing a suitable prior (by KalmanNet) and by learning a latent representation that facilitates data-aided tracking (by the encoder). Our empirical results demonstrate that the proposed Latent-KalmanNet achieves improved accuracy and run-time performance over both model-based and data-driven techniques by learning a surrogate latent representation that most facilitates tracking, while operating with limited complexity and latency.

In this paper, we consider the problem of blind estimation of states and topology (BEST) in power systems. We use the linearized DC model of real power measurements with unknown voltage phases (i.e. states) and an unknown admittance matrix (i.e. topology) and show that the BEST problem can be formulated as a blind source separation (BSS) problem with a weighted Laplacian mixing matrix. We develop the constrained maximum likelihood (ML) estimator of the Laplacian matrix for this graph BSS (GBSS) problem with Gaussian-distributed states. The ML-BEST is shown to be only a function of the states' second-order statistics. Since the topology recovery stage of the ML-BEST approach results in a high-complexity optimization problem, we propose two low-complexity methods to implement it: (1) Two-phase topology recovery, which is based on solving the relaxed convex optimization and then finding the closest Laplacian matrix, and (2) Augmented Lagrangian topology recovery. We derive a closed-form expression for the associated Cramer-Rao bound (CRB) on the topology matrix estimation. The performance of the proposed methods is evaluated for three case studies: the IEEE-14 bus system, the IEEE 118-bus system, and a random network, and compared with the oracle minimum mean-squared-error (MSE) state estimator and with the proposed CRB.

Inference tasks in signal processing are often characterized by the availability of reliable statistical modeling with some missing instance-specific parameters. One conventional approach uses data to estimate these missing parameters and then infers based on the estimated model. Alternatively, data can also be leveraged to directly learn the inference mapping end to end. These approaches for combining partially known statistical models and data in inference are related to the notions of generative and discriminative models used in the machine learning literature [1] , [2] , typically considered in the context of classifiers.