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Automatic modulation recognition (AMR) serves as a crucial component in domains such as cognitive radio and electromagnetic countermeasures, acting as a significant prerequisite for the efficient signal processing of receivers. Deep neural networks (DNNs), despite their effectiveness, are known to be vulnerable to adversarial attacks. This vulnerability has inspired the introduction of subtle interference to wireless communication signals—interference so minuscule that it is difficult for the human eye to discern. Such interference can mislead eavesdroppers into erroneous modulation pattern recognition when using DNNs, thereby camouflaging communication signal modulation patterns. Nonetheless, the majority of current camouflage methods used for electromagnetic signal modulation recognition rely on a global perturbation of the signal. They fail to consider the local agility of signal disturbance and the concealment requirements for bait signals that are intercepted by the interceptor. This paper presents a generator framework designed to produce perturbations with sparse properties. Furthermore, we introduce a method to reduce spectral loss, which minimizes the spectral difference between adversarial perturbation and the original signal. This method makes perturbation more challenging to monitor, thereby deceiving enemy electromagnetic signal modulation recognition systems. The experimental results validated that the proposed method significantly outperformed existing methods in terms of generation time. Moreover, it can generate adversarial signals characterized by high deceivability and transferability even under extremely sparse conditions. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments. [ABSTRACT FROM AUTHOR]

Defect detection is an important part of the manufacturing process of mechanical products. In order to detect the appearance defects quickly and accurately, a method of defect detection for the metal base of TO-can packaged laser diode (metal TO-base) based on the improved You Only Look Once (YOLO) algorithm named YOLO-SO is proposed in this study. Firstly, convolutional block attention mechanism (CBAM) module was added to the convolutional layer of the backbone network. Then, a random-paste-mosaic (RPM) small object data augmentation module was proposed on the basis of Mosaic algorithm in YOLO-V5. Finally, the K-means++ clustering algorithm was applied to reduce the sensitivity to the initial clustering center, making the positioning more accurate and reducing the network loss. The proposed YOLO-SO model was compared with other object detection algorithms such as YOLO-V3, YOLO-V4, and Faster R-CNN. Experimental results demonstrated that the YOLO-SO model reaches 84.0% mAP, 5.5% higher than the original YOLO-V5 algorithm. Moreover, the YOLO-SO model had clear advantages in terms of the smallest weight size and detection speed of 25 FPS. These advantages make the YOLO-SO model more suitable for the real-time detection of metal TO-base appearance defects. [ABSTRACT FROM AUTHOR]

Metamaterials (MMs) with dual electromagnetically induced transparency (EIT)-like windows possess more powerful capacity in Terahertz filtering than their single-EIT counterparts, but the design difficulty normally increases as the MM structure becomes more complicated. This work proposes a deep-learning method using a long short-term memory (LSTM) assisted convolutional neural network (LSTM-CNN) to simplify the design of dual-EIT MMs. Compared to the traditional CNN, the LSTM-CNN exhibits higher prediction accuracy for the geometric parameters of the MM according to the input spectrum, which can be ascribed to enhanced capability of extracting the spectral characteristics after adding the LSTM network. Further adding a feature-transforming network before the LSTM-CNN model to build a link between the MM performance and structure, a customizable MM design is realized. Two different dual-EIT metamaterials are quickly designed with this strategy, and the design accuracy has been fully demonstrated by simulations and experiments. The proposed deep learning method can achieve the rapid and efficient design of dual-EIT MMs, which offers a new pathway for the on-demand design of complex MM-based devices. • A long short-term memory assisted convolutional neural network (LSTM-CNN) is used to simplify the design of metamaterials. • The LSTM-CNN shows higher inverse design accuracy compared to the traditional CNN. • The design efficiency of the proposed deep learning method is much higher than the traditional method. [ABSTRACT FROM AUTHOR]

• Stochastic co-optimization of kinematic states and EMS for CAEVs is developed. • Dynamic probabilistic constraints ensuring driving safety are formulated. • A deep learning model predicting the PDF of preceding vehicle speed is constructed. • An efficient hierarchical solver for the co-optimization problem is designed. Ameliorating energy efficiency and enhancing driving safety are both extremely concerning issues for connected and automated electric vehicles (CAEVs) driving in a random traffic environment. To enhance driving safety and fully coordinate the potential conflict between driving safety and energy efficiency, an adaptive co-optimization method of speed planning and energy management strategy (EMS) with dynamic probabilistic constraints is proposed under the framework of stochastic model predictive control. The dynamic probabilistic constraints are enabled by the proposed composite sequence generation model, which predicts the future speed distribution of the preceding vehicle according to the probability relationship among future speed sequence, historical speed sequence, and macroscopic traffic state of downstream road segments, effectively modeling the macro and micro disturbance from random traffic environment and improving the prediction accuracy by about 10% (along with an over 57% decrease in distribution divergence) compared with pure sequence generation model. Comparison with existing co-optimization methods under the same car-following tasks validates the promising performance of the proposed adaptive co-optimization method, which produces dynamic feasible regions for kinematic states according to downstream traffic state and the driving state of the preceding vehicle, raising the driving safety by 14.81% and retaining the relatively high energy efficiency. [ABSTRACT FROM AUTHOR]

Due to their success at node classification, Graph Convolutional Networks (GCN) have raised a research upsurge of deep learning on graph-structured data. For the semi-supervised classification, graph convolution essentially acts as a low-pass filter on graph spectral domain. According to Graph Signal Processing theory, the low-pass filter in GCN is a finite impulse response (FIR) graph filter. However, compared with FIR graph filters, infinite impulse response (IIR) graph filters exhibit more powerful representation ability and flexibility. Intuitively, it is feasible to replace FIR filter in GCN with IIR graph filter to improve GCN. Therefore, inspired by the direct implementation of IIR graph filters, we propose a Bi-filtering Graph Convolutional Network (BGCN) which can be realized by simply cascading two sub filtering modules. Experimental results demonstrate that BGCN works well in node classification task and achieves comparable performance to GCN and its variants. The improvement of BGCN, however, is at the expense of a time-complexity increase. To simplify the proposed BGCN, we construct a Simple Bi-filtering Graph Convolution framework (SBGC) from the perspective of Graph Signal Processing. Furthermore, for the implementations of BGCN and SBGC, we design a novel low-pass graph filter to capture the low-frequency components that are beneficial to data representation for the task of node classification. Extensive experiments show that SBGC not only outperforms other baseline methods in performance, but also keeps a high level in computational efficiency. Moreover, it is particularly worth noting that both BGCN and SBGC are robust to feature noise and exhibit high label efficiency. [ABSTRACT FROM AUTHOR]