False data injection (FDI) attacks, as a new class of cyberattacks, bring a sever threat to the security and reliable operation of smart grid by damaging the state estimation of the power system. To address this issue, an extreme learning machine (ELM)-based One-Class-One-Network (OCON) framework is proposed for detecting the FDI attacks in this work. Under this framework, to effectively detect bus-based FDI attacks and identify the bus node being attacked, the subnets of state identification layer in OCON adopt the ELM algorithm to accurately divide the false data and the normal data. After that, a global layer is employed to analyze whether the bus node associated with its corresponding subnet is attacked by false data utilizing the results from the state identification layer. Finally, in order to improve the resilience of the power system, a prediction recovery strategy is proposed to remedy the detected false data by exploiting the spatial correlation of power data. The proposed framework is tested on IEEE 14 bus system using real load data from New York independent system operator (NYISO). Simulation results demonstrate that the proposed framework not only accurately recognizes the multiple bus nodes under the FDI attacks, but also efficiently recovers the data injected by false data.

Since only the spatial feature information of ship target is utilized, the current deep learning-based synthetic aperture radar (SAR) ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale or rotations, and the complex background. To overcome these issues, a novel multidimensional domain deep learning network for SAR ship detection is developed in this work to exploit the spatial and frequency-domain complementary features. The proposed method consists of the following main three steps. First, to learn hierarchical spatial features, the feature pyramid network (FPN) is adopted to produce ship target spatial multiscale characteristics with a top-down structure. Second, with a polar Fourier transform, the rotation-invariant features of SAR ship targets are obtained in the frequency domain. After that, a novel spatial-frequency characteristics fusion network is then presented, which seeks to learn more compact feature representations across different domains by updating the parameters of sub-networks interactively. The detection results are obtained due to utilizing the multidimensional domain information, and we evaluate the effectiveness of the proposed method using the existing SAR ship detection data set (SSDD). The results of the proposed method outperform other convolutional neural network (CNN)-based algorithms, especially for multiscale and rotation ship targets under complex backgrounds.

This paper addresses the problem of recovering a synthetic aperture radar (SAR) signal that is corrupted by both radio frequency interference (RFI) and wideband interference (WBI). The time-frequency domain is utilized for both the SAR signal and interference in the form of sparse representations. By doing so, a unified framework that allows one to suppress both the RFI and WBI while recovering the SAR signal can be developed. The resulting framework is an optimization problem that is efficiently solved using a customized alternating direction method of multipliers approach. Finally, simulation results are provided to demonstrate that the performance of the joint estimation algorithm is superior to the performances of other methods in terms of both subjective and objective evaluation standards.

The last decade has seen increased interest in environmental sound classification (ESC) due to the increased complexity and rich information of ambient sounds. The state-of-the-art methods for ESC are based on transfer learning paradigms that often utilize learned representations from common image-classification problems. This paper aims to determine the effectiveness of employing pre-trained convolutional neural networks (CNNs) for audio categorization and the feasibility of retraining. This study investigated various hyper-parameters and optimizers, such as optimal learning rate, epochs, and Adam, Adamax, and RMSprop optimizers for several pre-trained models, such as Inception, and VGG, ResNet, etc. Firstly, the raw sound signals were transferred into an image format (log-Mel spectrogram). Then, the selected pre-trained models were applied to the obtained spectrogram data. In addition, the effect of essential retraining factors on classification accuracy and processing time was investigated during CNN training. Various optimizers (such as Adam, Adamax, and RMSprop) and hyperparameters were utilized for evaluating the proposed method on the publicly accessible sound dataset UrbanSound8K. The proposed method achieves 97.25% and 95.5% accuracy on the provided dataset using the pre-trained DenseNet201 and the ResNet50V2 CNN models, respectively.

Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and inland facilities in SAR images increase the difficulty for SAR ship detection. To increase the detection performance, in this paper, a novel deep learning network for SAR ship detection, termed as attention-guided balanced feature pyramid network (A-BFPN), is proposed to better exploit semantic and multilevel complementary features, which consists of the following two main steps. First, in order to reduce interferences from complex backgrounds, the enhanced refinement module (ERM) is developed to enable BFPN to learn the dependency features from the channel and space dimensions, respectively, which enhances the representation of ship objects. Second, the channel attention-guided fusion network (CAFN) model is designed to obtain optimized multi-scale features and reduce serious aliasing effects in hybrid feature maps. Finally, we illustrate the effectiveness of the proposed method, adopting the existing SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0). Experimental results show that the proposed method is superior to the existing algorithms, especially for multi-scale small ship targets under complex background.

Recently, there is an increased interest in the study of modulo analog to digital converters (ADCs). These new systems can reconstruct a signal whose amplitude is much higher than the conventional ADC's dynamic range. Modulo ADCs are characterized by their modulo threshold and in the current literature, all existing works are limited to real-valued moduli. In this paper, we propose multi-channel modulo samplers with complex-valued moduli to sample a band-limited complex signal. Specifically, we discuss the construction of complex divisors from Gaussian integers and propose their efficient implementations. A memory-efficient, closed-form recovery algorithm is also proposed. Simulation results demonstrate that the proposed systems can provide stable reconstruction of a high dynamic range complex-valued signal at low sampling rates.