With the widespread application of unmanned aerial vehicles (UAVs), intrusion detection based on wireless communication traffic has become increasingly critical. While deep learning (DL) models have significantly enhanced anomaly detection accuracy, recent research indicates that they remain vulnerable to adversarial sample attacks, which poses severe threats to the open communication networks used by UAV systems. To address this challenge, an enhanced UAV intrusion detection framework is proposed for defending against adversarial samples, integrating generating adversarial samples with adversarial networks (AdvGAN) and decision boundary reconstruction. Specifically, a convolutional neural network (CNN) is first developed as the baseline intrusion detection model to detect anomalous traffic in UAV wireless communication. Subsequently, to overcome the gradient-dependent perturbation limitations of traditional adversarial attack methods, AdvGAN is employed to analyze real monitored traffic data and generate semantically consistent adversarial samples that retain the intrinsic characteristics of authentic network flow. Finally, a hybrid adversarial-real data training strategy is proposed to dynamically reconstruct the model’s intrusion decision boundaries. This strategy augments the training dataset with AdvGAN-generated adversarial samples, enabling the model to adaptively adjust decision thresholds through iterative backpropagation. On a real-world dataset, the superior performance of proposed method is verified: it maintains high detection accuracy for legitimate traffic while substantially reducing the success rate of adversarial attacks. Experimental results confirm the stealthiness of generated adversarial samples and the robustness of the enhanced detection model, validating the effectiveness of the proposed framework.

Unmanned Aerial Vehicles (UAVs) equipped with onboard processors enable edge intelligence for smart-grid Internet of Things (IoT) inspection, yet insulator defect detection from aerial imagery remains challenging due to cluttered backgrounds, small targets, and strict latency constraints. Compared with handcrafted methods that rely on manual feature design and parameter tuning, this paper presents an end-to-end improved Faster R-CNN detector whose feature extractor is redesigned via convolutional feature reuse and a two-layer multi-scale fusion scheme with a deeper extraction path, preserving fine-grained cues while limiting model complexity. Experiments on the Chinese Power Line Insulator Dataset (CPLID) and a newly constructed Glass Insulator Dataset (GID) achieve [email protected] values of 97.75% and 96.79%, respectively, which is 7.1% higher than a standard Faster R-CNN (VGG16) baseline on CPLID while reducing the model size to 58 MB. On a UAV system (DJI M300 + H20T + Jetson Xavier NX), TensorRT acceleration runs at 8.5 FPS, outperforming Faster R-CNN (3.2 FPS) and YOLOv11 (5.3 FPS) under the same edge setting. Outdoor flight tests on 330 kV transmission lines further validate field readiness, supporting low-latency and bandwidth-efficient UAV inspection for smart-grid IoT maintenance.

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search.

Robust visual tracking for outdoor vehicle is still a challenging problem due to large appearance variations caused by illumination variation, occlusion and scale variation, etc. In this paper, a deep-learning-based approach for robust outdoor vehicle tracking is proposed. Firstly, a stacked denoising auto-encoder is pre-trained to learn the feature representation way of images. Then, a k-sparse constraint is added to the stacked denoising auto-encoder and the encoder of k-sparse stacked denoising auto-encoder (kSSDAE) is connected with a classification layer to construct a classification neural network. After fine-tuning, the classification neural network is applied to online tracking under particle filter framework. Extensive tracking experiments are conducted on a challenging single object online tracking evaluation platform benchmark to verify the effectiveness of our tracker. Experiments show that our tracker outperforms most state-of-the-art trackers.

BACKGROUND: Dystrophinopathy is one of the most common human monogenic diseases which results in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD). Mutations in the dystrophin gene are responsible for both DMD and BMD. However, the clinical phenotypes and treatments are quite different in these two muscular dystrophies. Since early diagnosis and treatment results in better clinical outcome in DMD it is essential to establish accurate early diagnosis of DMD to allow efficient management. Previously, the reading-frame rule was used to predict DMD versus BMD. However, there are limitations using this traditional tool. Here, we report a novel molecular method to improve the accuracy of predicting clinical phenotypes in dystrophinopathy. We utilized several additional molecular genetic rules or patterns such as "ambush hypothesis", "hidden stop codons" and "exonic splicing enhancer (ESE)" to predict the expressed clinical phenotypes as DMD versus BMD. RESULTS: A computer software "DMDtoolkit" was developed to visualize the structure and to predict the functional changes of mutated dystrophin protein. It also assists statistical prediction for clinical phenotypes. Using the DMDtoolkit we showed that the accuracy of predicting DMD versus BMD raised about 3% in all types of dystrophin mutations when compared with previous methods. We performed statistical analyses using correlation coefficients, regression coefficients, pedigree graphs, histograms, scatter plots with trend lines, and stem and leaf plots. CONCLUSIONS: We present a novel DMDtoolkit, to improve the accuracy of clinical diagnosis for DMD/BMD. This computer program allows automatic and comprehensive identification of clinical risk and allowing them the benefit of early medication treatments. DMDtoolkit is implemented in Perl and R under the GNU license. This resource is freely available at http://github.com/zhoujp111/DMDtoolkit , and http://www.dmd-registry.com .

Circuit-level timing speculation has been proposed as a technique to reduce dependence on design margins and eliminating power/performance overheads. Recent work has proposed microarchitectural methods to dynamically detect and recover from timing errors in processor logic. To a large extent existing work has relied on statistical error models and has not evaluated potential disparity of error rates at the level of static instructions. In this paper, we analyze gate-level hardware models for an execution pipeline and demonstrate pronounced locality in instruction-level error rates due to value locality and data dependences. We propose timing error prediction to dynamically anticipate timing errors at the instruction-level and error padding techniques to avoid the full recovery cost of timing errors. We show that with simple prediction strategies our mechanism can reduce 80% of the performance penalty incurred by error recovery on average. This allows us to alleviate some limitations of timing speculation and improves energy-efficiency by 21% when compared to baseline timing speculation techniques using the same dynamic adaptive tuning mechanism.