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Reliable real-time sensing plays a vital role in ensuring the reliability and safety of industrial cyberphysical systems (CPSs) such as wireless sensor and actuator networks. For many reasons, such as harsh industrial environments, fault-prone sensors, or malicious attacks, sensor readings may be abnormal or faulty. This could lead to serious system performance degradation or even catastrophic failure. Current anomaly detection approaches are either centralized and complicated or restricted due to strict assumptions, which are not suitable for practical large-scale networked industrial sensing systems (NISSs), where sensing devices are connected via digital communications, such as wireless sensor networks or smart grid systems. In this paper, we introduce a fully distributed general anomaly detection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs. We formally prove the scalability of our GAD approach and evaluate the performance of GAD for two industrial applications: building structure monitoring and smart grids. Extensive trace-driven simulations validate our theoretical analysis and demonstrate that our approach can significantly outperform state-of-the-art approaches in terms of detection accuracy and efficiency. [ABSTRACT FROM PUBLISHER]

Smart grids are essentially electric grids that use information and communication technology to provide reliable, efficient electricity transmission and distribution. Security and trust are of paramount importance. Among various emerging security issues, FDI attacks are one of the most substantial ones, which can significantly increase the cost of the energy distribution process. However, most current research focuses on countermeasures to FDIs for traditional power grids rather smart grid infrastructures. We propose an efficient and real-time scheme to detect FDI attacks in smart grids by exploiting spatial-temporal correlations between grid components. Through realistic simulations based on the US smart grid, we demonstrate that the proposed scheme provides an accurate and reliable solution. [ABSTRACT FROM PUBLISHER]

In long term evolution-advanced (LTE-A) networks, the carrier aggregation technique is incorporated for user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs) for achieving higher transmission rate. Many research works for LTE-A systems with carrier aggregation configuration have concentrated on the radio resource management problem for downlink transmission, including mainly CC assignment and packet scheduling. Most previous studies have not considered that the assigned CCs in each UE can be changed. Furthermore, they also have not considered the modulation and coding scheme constraint, as specified in LTE-A standards. Therefore, their proposed schemes may limit the radio resource usage and are not compatible withLTE-A systems. In this paper, we assume that the scheduler can reassign CCs to each UE at each transmission time interval and formulate the downlink radio resource scheduling problem under the modulation and coding scheme constraint, which is proved to be NP-hard. Then, a novel greedy-based scheme is proposed to maximize the system throughput while maintaining proportional fairness of radio resource allocation among all UEs. We show that this scheme can guarantee at least half of the performance of the optimal solution. Simulation results show that our proposed scheme outperforms the schemes in previous studies. [ABSTRACT FROM AUTHOR]

Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise. In order to promote sparsity more strongly than convex regularization, it is also standard practice to employ non-convex optimization. In this paper, we take a third approach. We utilize a non-convex regularization term chosen such that the total cost function (consisting of data consistency and regularization terms) is convex. Therefore, sparsity is more strongly promoted than in the standard convex formulation, but without sacrificing the attractive aspects of convex optimization (unique minimum, robust algorithms, etc.). We use this idea to improve the recently developed ‘overlapping group shrinkage’ (OGS) algorithm for the denoising of group-sparse signals. The algorithm is applied to the problem of speech enhancement with favorable results in terms of both SNR and perceptual quality. [ABSTRACT FROM PUBLISHER]

Trifluoromethane-containing plasma is used to passivate the mesa surfaces and suppresses the surface leakage current of GaN p-i-n rectifiers. Reduction of surface leakage enhances the reverse blocking voltage by 25% measured at $J=1$ A/cm2. Differential forward resistances of control samples and plasma-treated ones are 0.65 and 0.49 \text{m}\Omega -cm2, respectively, and an excellent Baliga’s figure-of-merit of more than 800 MW/cm2, as compared with the conventional 545 MW/cm2, is achieved for GaN diodes fabricated on sapphire substrates. [ABSTRACT FROM AUTHOR]