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This paper presents an efficient optimization technique for gridless 2-D line spectrum estimation, named decoupled atomic norm minimization (D-ANM). The framework of atomic norm minimization (ANM) is considered, which has been successfully applied in 1-D problems to allow super-resolution frequency estimation for correlated sources even when the number of snapshots is highly limited. The state-of-the-art 2-D ANM approach vectorizes the 2-D measurements to their 1-D equivalence, which incurs huge computational cost and may become too costly for practical applications. We develop a novel decoupled approach of 2-D ANM via semi-definite programming (SDP), which introduces a new matrix-form atom set to naturally decouple the joint observations in both dimensions without loss of optimality. Accordingly, the original large-scale 2-D problem is equivalently reformulated via two decoupled one-level Toeplitz matrices, which can be solved by simple 1-D frequency estimation with pairing. Compared with the conventional vectorized approach, the proposed D-ANM technique reduces the computational complexity by several orders of magnitude with respect to the problem size. It also retains the benefits of ANM in terms of precise signal recovery, small number of required measurements, and robustness to source correlation. The complexity benefits are particularly attractive for large-scale antenna systems such as massive MIMO, radar signal processing and radio astronomy.

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Featuring high efficiency, low harmonic distortion, high modularity and scalability, the modular multilevel converter (MMC) is particularly suitable for high voltage direct current (HVDC) transmission applications. As an advanced control strategy, model predictive control (MPC) has the advantage of direct modeling and fast dynamic response. It can simultaneously control multiple variables through an appropriate cost function. The conventional MPC can achieve an optimal control objective by evaluating all the candidate switching states for the MMC; however, with increasing number of submodules, there is an increasing number of candidate switching states that place an enormous burden on the control. In this paper, a grouping-sorting-optimized MPC (GSOMPC) strategy is proposed for the MMC with the number of submodules for each arm increases to hundreds. It divides all submodules of each arm into M groups, with each containing X submodules. By the implementation of the first level and second level optimized MPC (OMPC) between groups and submodules respectively, the computational load of each phase decreases from C N 2N to 2X+M+3(N=M×X). In addition, to reduce the strict requirements of control hardware for sorting and calculation, the proposed strategy is able to simultaneously control the submodule voltage, ac current, circulating current and switching frequency. Applied to a 2.7 kV/60 kW MMC back-to-back dynamic test system, experimental results verify the feasibility and effectiveness of the proposed GSOMPC strategy.

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State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multigrained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channelwise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency or energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such trade-offs compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines.