In this paper, a common Lyapunov function approach to event-triggered control with self-triggered sampling for switched linear systems is proposed. A switched system is a system where the dynamics can be switched by a switching signal (mode). In the proposed method, based on the upper bound of the common Lyapunov function, the update time of the control input and the mode, and the next sampling time of the state are determined. As a control specification, it is guaranteed that the closed-loop system is uniformly ultimately bounded. Finally, the proposed method is demonstrated by a numerical example.

Deep learning based object detection methods have achieved promising performance recently. However, these methods lack sufficient capabilities to handle satellite images owing to the fact that small-sized objects in remote sensing images are difficult to detect. To address this issue, we propose a novel small object detection method based on YOLO X named Attention Cross Stage Transformers Network (ACSTNet). Specifically, a novel backbone network, Multi-scale Cross Fusion Network (MCFNet) is constructed to capture semantic dependencies between pixels over long distances and increase the depth-interaction information at different levels. Meanwhile, a new feature fusion layer is added to the upper feature output layer of dark3, allowing the model to maximize the retention of low-level features of small objects and to locate them more accurately. Furthermore, to address the problem of the inaccurate feature extraction caused by overlapping and occlusion of dense objects, we propose an efficient channel and space normalized fusion attention mechanism (ECSNFAM), which is composed of channel attention, space attention, and batch normalization attention branches, using residual structure to enhance the sensitivity of the attention mechanism for small targets. Experiments are conducted to evaluate the performance of the general remote sensing dataset, and the results show that our proposed method improves the mean Average Precision (mAP) by 1.2% and 1.4% on the DIOR and the RSOD-DATA datasets compared with the YOLO X. The source code is available at https:github.com/Wei-JL/ACSTNet.git.

Identity-based encryption with equality test (IBE-ET) allows the detection of whether two different ciphertexts encrypt the same plaintext without decryption within the conventional identity-based encryption (IBE) model. This property ensures the confidentiality of communication and reduces the storage overhead of ciphertexts in cryptosystems. However, IBE-ET schemes based on traditional assumptions, such as discrete logarithm and integer factoring, are vulnerable to quantum algorithm attacks, highlighting the importance of designing lattice-based IBE-ET schemes. To address this, researchers have proposed several lattice-based IBE-ET schemes that utilize outdated lattice IBE paradigms and are inefficient in terms of parameter size. In this work, we construct a new lattice-based IBE-ET scheme using the most compact lattice IBE framework known to date. Our new proposal significantly improves the parameters compared to previous constructions. Furthermore, we provide a security reduction in the random oracle model, along with corresponding parameter selection and the comparison between our scheme and known constructions. The results imply that our scheme is efficient.

This paper proposes a novel approach for tunnel crack identification, employing local segmentation and global fusion detection. Initially, a local segmentation network is constructed using weights from an encoding layer pre-trained on numerous non-tunnel cracks, with only a limited number of tunnel crack samples. The input image is divided, and the local segmentation network performs pixel segmentation on these sub-images, with the sub-results stitched together to ensure accurate identification of all suspicious crack pixels. Subsequently, a global fusion detector is introduced, comprising two sub-models: Sub-model 1 extracts total crack targets within the stitched results, while Sub-model 2 detects possible false alarms from regular-shaped areas. The results from both sub-models are combined to effectively reduce the false alarm rate and ensure accurate segmentation results of cracks. Experimental findings on actual tunnel images demonstrate that the “segmentation before localization” method proposed in this paper achieves superior recognition accuracy and IOU ratio compared to the Unet3+, DeeplabV3+, and “localization before segmentation” Mask-RCNN algorithms. Specifically, the proposed method yields an accuracy improvement of 3.81% over the Unet3+ network, 2.71% over the DeeplabV3++ network, and 1.93% over the Mask-RCNN network. Moreover, noise interference from bolt repair areas is effectively mitigated, enhancing the method’s engineering applicability.

Underwater image super-resolution reconstruction technologies have played a very important role in ocean resource exploration since it can significantly improve the clarity of underwater optical images. Although recent deep learning based methods have achieved promising performance in terrestrial image super-resolution, these methods lack sufficient capabilities to handle those dark, turbid and blurred underwater images. In this work, we propose a novel network, namely feature separable reconstruction network (FSRN), to separate the extraction features and the reconstruction features for better using of features in each layer, solving the problem of long-distance transmission of shallow features in the neural network. We design a depthwise separable convolutional residual block with large convolutional kernels (DWRB) to augment receptive fields, which improves the effectiveness of high-frequency feature extraction in the blur images. We further propose a channel attention mechanism based on the SE module and explore an optimal attention module insertion mode which pays more attention to the weight between reconstruction information, reducing information loss. Moreover, we also modify the convolutional kernel padding mode and propose a perceptual loss function with boundary clipping to avoid the inconsistent in feature extraction from boundary and non-boundary regions. Extensive experiments on underwater datasets demonstrate our proposed underwater super-resolution framework outperform over the state-of-the-art methods in terms of reconstruction accuracy and real-time performance.

The automatic sorting system for sea cucumbers in food processing plants faces challenges such as high false detection rates, slow processing speeds, and sensitivity to light intensity variations. This paper presents a high-precision, high-efficiency real-time recognition and sorting method for sea cucumbers, based on YOLOv9 and the RepViT network. We improved the YOLOv9 model by introducing auxiliary training modules to help the model better understand the characteristics of sea cucumbers. Additionally, we used the lightweight RepViT network as the backbone to enhance the model’s expressive power and computational efficiency while maintaining a low weight. We replaced the original CIoU loss function with the EIoU loss function to accelerate convergence. Experimental results show that our improved model achieves an accuracy of 98.33% in sea cucumber sorting, with an inference speed of 92.71 fps and a model size of only 42.53 MB, outperforming most detection models. Moreover, the average sorting speed for a single sea cucumber is just 0.92 seconds, meeting the production needs of food processing plants.

This research proposes a sliding mode variable structure model reference adaptive system based on the modified super-twisting algorithm (MST-SM-MRAS) to estimate the position and speed of the generator in a permanent magnet synchronous wind power system. Firstly, the reference model and the adjustable model are designed according to the mathematical model of PMSG; secondly, the MST-SMC is constructed according to the output error between the two models, which makes the output error converge in finite time and improves the system’s anti-disturbance capability. A smooth sigmoid function is used instead of the sign function to suppress the sliding mode chattering and improve the control accuracy of the system. The proposed strategy’s effectiveness is verified through simulation.

Cross Z-complementary pairs (CZCPs), characterized by two symmetric zero autocorrelation zones (ZACZs) and one tail-end zero cross-correlation zone (ZCCZ), play an instrumental role in the design of training sequences for broadband spatial modulation systems. In this letter, we propose a systematic construction of CZCPs with large cross Z-complementary ratio (CZCR) by employing Turyn’s method to some seed CZCPs and Golay complementary pairs (GCPs). By appropriately selecting the seed CZCPs, we can extend the CZCPs with parameters (18, 7) and (22, 9) to new (18N, 8N - 1)-CZCPs and (22N, 9N + Z₁)-CZCPs, where Z₁ signifies the zero correlation zone width achievable by a binary GCP. Additionally, we introduce new CZCPs with parameters (34, 14) and (38, 14), which were not previously reported in the literature, and extend them to (34N, 14N + Z₁)-CZCPs and (38N, 15N - 1)-CZCPs.

In this letter, we investigate the problem of multiple-input multiple-output (MIMO) mmWave channel estimation in a hybrid analog-digital architecture by exploiting both sparsity and the structure of the channel. To gain noise robustness, we first introduce a method that applies the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm to a distributed setting, where subsystems over sub-carriers share the same support set. To further enhance the accuracy of the estimation, we propose a novel algorithm to estimate the number of paths present in the channel. This technique leverages a modified Silhouette method to determine the exact support for the mmWave sparse system, thereby reducing the ambiguity of the estimate returned by the Distributed StOMP (DStOMP) algorithm. Simulation results demonstrate that our proposed method outperforms the standard OMP method and achieves nearly the same recovery accuracy compared to the Simultaneous OMP method, even without prior knowledge of signal sparsity.

This study evaluates the Bluetooth low-energy (BLE) positioning and direction-finding system. The evaluation conditions are set closer to real usage; considering the number of devices, type of devices, and time interval between measurements of the database and measurements of evaluation. Subsequently, positioning and direction-finding performance evaluation experiments are performed in a real environment. The results of the comparison with a previous method show that the hybrid method including both positioning and direction-finding performs better under the conditions considering real usage. The analysis using confusion matrices reveals the trends of the direction errors. Furthermore, the hybrid method maintains the positioning and direction-finding performance and reduces the number of BLE beacon installations. Consequently, the effectiveness of the hybrid method under the evaluation conditions considering real usage is demonstrated and the importance of performance evaluation closer to real usage is shown.

Global contextual information and spatial structural details are pivotal elements in the context of super-resolution (SR) reconstruction for remote sensing images. Therefore how to generate rich contextual semantic information and accurate spatial structure information simultaneously is a key challenge for remote sensing image SR. In this paper, we propose a novel progressive multi-scale learning strategy based on residual prior to solve the remote sensing image SR problem. In particular, we propose a novel progressive up-down mapping unit (PUMU) that asymptotically maps the input low-dimensional vectors into a high-dimensional space to learn global context information, which avoids loss of global information. Subsequently, we suggest introducing a novel method of explicitly mining spatial structure information, called residual prior (RP), which can help the proposed model to achieve spatial-structure-preserving SR. We have conducted extensive experiments on two public datasets including UCMerced and PatternNet, and the experimental results demonstrate the effectiveness of the proposed method.

In this paper, a feature-enhanced deep source channel joint coding scheme called DJSCC-E is proposed, considering the varying importance of transmitted data in DJSCC schemes and the diversity of target scales for encoding and decoding images. The scheme is designed with a Weight Adjustment Module (WAM) that adjusts the weight values of the feature images based on varying Signal-to-Noise Ratio (SNR). Furthermore, a multi-scale encoder-decoder structure is proposed to optimize the encoding and decoding of target information at different scales. The experimental results demonstrate that the DJSCC-E scheme enhances the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image by approximately 0.7 dB compared to the current DJSCC scheme when the SNR of the AWGN channel falls within the range of 0 to 20 dB, while reducing the number of parameters.