In this paper, we report the first all-optical solution for multi-false-target range-velocity compound deception jamming based on frequency-comb-enabled photonic RF memory (PRFM). The operation frequency of current digital RF memory (DRFM) in electronic countermeasures is severely limited by low-speed digital devices. Since optical processing has a larger bandwidth and higher speed, it is valuable to develop advanced functions of PRFM. Our method skillfully applies a carrier-signal separation strategy to enhance the performance and extend the functions of PRFM. By bandwidth-optimized storage of the RF signal, the ASE noise accumulation and high-power optical self-oscillations can be effectively suppressed, contributing to a significant improvement of the signal SNR. On the other hand, by comb modulation of the optical carrier, the beaten signal contains multiple coherent false targets with different range-velocity deception information. In our experiment, the maximum storage time has exceeded$840~\mu $s with less than 10 dB SNR degradation, and the storage signal frequency has reached 16 GHz. Furthermore, more than 10 false targets have been obtained in the 10 dB bandwidth simultaneously, with the SNR consistently close to 40 dB. The outstanding storage and jamming performance allows our scheme to promote the deployment and upgrade of PRFM in electronic countermeasures.

In this paper, we propose a novel physical layer security optical transmission scheme utilizing randomized quantization noise. The proposed approach encrypts low-order plaintext signals into ultra-high-order ciphertext using principles similar to quantum noise stream cipher (QNSC). While, the ultra-dense quadrature amplitude modulation (QAM) ciphertext waveforms are masked by intrinsic quantization noise. The combination of digital delta-sigma quantization and analog chaotic random scrambling not only produces randomized quantization noise, but also naturally supports one-time processing of masking ciphertext and generating keystream. Whereby, the in-band quantization noise (IBN) conceals nearby ciphertext levels bolstering security, and the out-of-band quantization noise (OOBN) is digitized to generate keystream using the Toeplitz hashing extractor. Experimental results show that 256-QAM plaintext signals were securely transmitted over standard single-mode fiber: 163-Gbps signals over 400 km, and 79.7-Gbps signals over 1800 km. To evaluate system performance, theoretical models for signal-to-noise ratio (SNR), bit error rate (BER), and number of masked signals (NMS) are derived as functions of the oversampling ratio (OSR). Our findings reveal a trade-off between transmission performance and security performance. Our results confirm that this scrambling effectively eliminates the correlation between the masking noise components. Toeplitz hashing extractor can effectively reduce the complexity of keystream generation and obtain a source-independent random keystream.

At complex construction sites, detecting whether workers are wearing safety helmets is often affected by occlusion and sunlight change. In addition, the helmets are relatively small, which can easily lead to missed detection and false detection. This paper proposes an improved YOLOv5 model. Firstly, we added a 152×152 detection layer for scale of small objects, enhancing its feature extraction capabilities. Secondly, we added the Multi-Spectral Channel Attention (MSCA) mechanism to enhance feature learning for small objects, addressing the information loss problem caused by Global Average Pooling (GAP). Finally, different activation functions, bounding box regression loss functions, and post-processing methods were adopted for YOLOv5. The Mish function serves as the activation function to enhance the model’s generalization ability and mitigate the gradient vanishing problem associated with the Leaky Relu function. The CIoU loss was employed as the loss function to improve bounding box regression accuracy and accelerate prediction convergence. DIoU-NMS, instead of NMS, was used as the post-processing method to enhance the filtering ability for prediction boxes of occluded small objects. We tested our algorithm on a dataset with 7000 images and achieved an mAP of 95.1%, surpassing the YOLOv5 model by 2.9%. Our method significantly improves safety helmet detection performance at complex construction scenes.

Keypoints-based object detection algorithms such as CenterNet and CornerNet have been proved to be very efficient. They use inherent pixels on the feature map to predict whether a pixel is a keypoint of an object, thereby eliminating the need for anchor calculation. However, in cases where the keypoints of multiple objects overlap with each other, Keypoints-based object detection algorithms cannot effectively distinguish them. The reason for the problem is that there is a one-to-one relationship in the network, that is, one location only corresponds to one prediction box. In this paper, we mainly address the problem caused by overlapping center. We achieve our purpose by a kind of multi-correspondence relationship in the network, one location corresponding to multiple prediction boxes. In order to achieve this goal, our work mainly consists of the following two parts. First, we improved the keypoint pooling method. The existing keypoint pooling methods focus on emphasizing the keypoints, but we believe that keypoints detection should suppress the surrounding information while emphasizing the true keypoints. Second, in order to make the corner better match to the correct center, we adopt Pointing Offset to match them. Specifically, the corner will match center by its offset, in order to predict Pointing Offset better, we design a module named corner-to-center (CTC) to improve the matching effect.

The objects in remote sensing images are usually small and dense with complex background. The most important feature is non-axial symmetry and arbitrary alignment. Therefore, HBB (horizontal bounding box) is not suitable to represent the objects in remote sensing images. We propose a detection framework named Oriented CenterNet, which can detect arbitrary orientation objects efficiently. We adopt the light weight backbone called Swin-Tiny Transformer. Compared with CNN-based backbone, it can obtain the global receptive field and establish connections with other pixel points. And then, we propose a new and simple six-parameter representation, named Cornerpoint Offset Representation, to represent rotated objects, which can convert HBB to RBB (rotated bounding box) easily. For small objects in remote sensing images, the network adopts an effective feature fusion and sampling method, Center Pooling is integrated into the prediction module, to enhances the features of small objects and weakens the background noise. In addition, we redefine the loss function by dividing the regression loss into horizontal and rotated parts, in order to provide better supervision in the training process of the network. Experiments illustrate the effectiveness of our method. Our Oriented CenterNet with Swin-Tiny-FPN achieving 70.21 % mAP on DOTA dataset.

In recent years, many rotated object detection (ROD) methods have been proposed and have attracted wide attention in many fields. Most of them use anchor-based or Gaussian heatmaps for label assignment (LA), which cannot capture the shape and orientation characteristics of the rotated object and introduce a large number of hyper parameters. At the same time, most methods only add angle regression or use enclosing rectangles to realize ROD, which cannot express the object well. In this paper, we propose a new method for ROD, named GRDet, which is keypoint-based Anchor-free algorithm. GRDet can adaptively learn and represent an object with point sets, discarding the limitation of anchor on the size and orientation of the object. Specifically, we introduce a conversion function that is able to transform the point set into a rotated bounding box (RBB) for precise localization and classification. In addition, we propose a Gaussian-based dynamic label assignment (GDLA) strategy to realize the assignment of positive and negative (P&N) samples, which can adaptively learn according to the size and orientation characteristics of any rotated object. Moreover, we define an intersection over union (IoU) suitable for ROD, called Gaussian-IoU, which simulates the calculation of IoU by Gaussian distribution and solves the case that some points are not differentiable. Furthermore, we design a dynamic spatial quality constraint (DSQC) for RBB, which can dynamically evaluate the quality of the predicted RBB, and adaptively select high quality RBB. We use KFIoU loss and introduce Gaussian center loss to supervise the training of the network. Extensive experiments with DOTA dataset demonstrate the effectiveness of our proposed method.

In recent years, object tracking techniques based on Siamese networks have shown excellent tracking performance. However, in the tracking process, there will be many similar objects, and it is easy to track the wrong object due to the weak discriminative ability of the network. At the same time, the classification and regression of SiamRPN ++ are usually optimized independently, which will cause a mismatch problem, that is, the location with the highest classification confidence is not necessarily the object. To address these problems, we proposed a Siamese network tracker by attention module and relation detector module (SiamAR). First, we introduce a multi-scale attention mechanism in SiamRPN++ to capture information at different scales, and fuse spatial attention and channel attention to improving the ability to learn feature information. Not only different receptive fields are obtained, but also useful features are selectively focused and less useful features are suppressed. In order not to affect the computational efficiency, the method of grouping parallel computing is used. Secondly, we add a relation detector module to our tracker to filter out distractors from the background and distinguish the object in the cluttered background. Experiment results show that our algorithm out-performs several well-known tracking algorithms in terms of tracking accuracy and robustness.

With the rapid development and popularization of IoT devices in our daily lives, secure data transmission has become a critical and challenging issue in resource-constrained devices. Simple chaotic circuits can provide highly efficient data encryption and transmission simultaneously. Time delay feedback system is considered as a promising solution to meet the demand for generating highly complex chaotic signal. The time delay is an important parameter that significantly influences the security of such systems. Although there exist many methods that can extract this parameter by adopting the leaked chaotic time series and certain information of the system model, they more or less face some restrictions, especially when the system nonlinearity is quite strong. To break through these limitations, we propose a novel method that can extract the key (i.e., time delay signature, TDS) of time delay chaotic systems by machine learning strategy. The 1D time series transmitted from a time delay chaotic system is embedded in 2D planes to generate training images. A convolutional neural network (CNN) is explored to learn from these images to identify common and different features for TDS extraction. Simulation results verify the feasibility and efficiency of the proposed scheme.

In this study, the authors use a non‐symmetric partition to generate N ‐phase pseudorandom sequences using the logistic maps. The theoretical analysis shows that this N ‐phase logistic sequence is with uniform distribution, as well as independent and identically distributed. Numerical experiments show that these N ‐phase logistic sequences have high complexity and are with good randomness. A fast algorithm with its time complexity be O ( M ) is proposed to generate these N ‐phase sequences. Furthermore, they propose a new image encryption algorithm based on the N ‐phase logistic sequence, which is combined with both shuffling and substitution algorithms. Several security tests are carried out to demonstrate that the authors’ new algorithm is with a high security level, and can resist various attacks, which can be competitive with some other recently proposed image encryption algorithms.