Smartphone users are susceptible to a privacy leakage attack called App Fingerprinting (AF), where traffic analysis is used to infer the apps in use. Despite packet encryption, AF attacks leverage packet size and timing information to identify apps, posing a privacy threat. However, existing attacks fail when a few apps are used concurrently, causing unsegmented traffic with app multiplexing and overlapping. The key reason is that they cannot accurately identify active time boundaries for the apps. This paper presents a novel AF attack, FOADA, the first to accurately predict both the location and label of a target app in traffic. FOADA approaches AF as an object detection problem, training a deep learning model to estimate boundary positions and classify traffic segments. Accurate boundary predictions help the model focus on the most relevant traffic segment, enhancing its classification performance. FOADA excels in handling noisy app traffic. With app multiplexing, it achieves an F1-score of 0.96 for predicting only app labels and an F1-score of 0.92 for predicting both app labels and their locations. FOADA surpasses the state-of-the-art attack PacketPrint, which achieves F1-scores of 0.80 and 0.48 in these two scenarios, respectively. The inference time of FOADA is 2,000 times faster than PacketPrint.

Existing video super-resolution methods often utilize a few neighboring frames to generate a higher-resolution image for each frame. However, the abundant information in distant frames has not been fully exploited in these methods: corresponding patches of the same instance appear across distant frames at different scales. Based on this observation, we propose to improve the video super-resolution quality with long-term cross-scale aggregation that leverages similar patches (self-exemplars) across distant frames. Our method can be implemented as post-processing for any super-resolution methods to improve performance. Our model consists of a multi-reference alignment module to fuse the features derived from similar patches: we fuse the features of distant references to perform high-quality super-resolution. We also propose a novel and practical training strategy for reference-based super-resolution. To evaluate the performance of our proposed method, we conduct extensive experiments on our collected CarCam dataset, the Waymo Open dataset, and the REDS dataset, and the results demonstrate our method outperforms state-of-the-art methods.

Decentralized state estimation is one of the most fundamental components of autonomous aerial swarm systems in GPS-denied areas; yet, it remains a highly challenging research topic. Omni-swarm, a decentralized omnidirectional visual–inertial–ultrawideband (UWB) state estimation system for aerial swarms, is proposed in this article to address this research niche. To solve the issues of observability, complicated initialization, insufficient accuracy, and lack of global consistency, we introduce an omnidirectional perception front end in Omni-swarm. It consists of stereo wide-field-of-view cameras and UWB sensors, visual–inertial odometry, multidrone map-based localization, and visual drone tracking algorithms. The measurements from the front end are fused with graph-based optimization in the back end. The proposed method achieves centimeter-level relative state estimation accuracy while guaranteeing global consistency in the aerial swarm, as evidenced by the experimental results. Moreover, supported by Omni-swarm, interdrone collision avoidance can be accomplished without any external devices, demonstrating the potential of Omni-swarm as the foundation of autonomous aerial swarms.

We propose a novel approach for embedding novel views in a single JPEG image while preserving the perceptual fidelity of the modified JPEG image and the restored novel views. We adopt the popular novel view synthesis representation of multiplane images (MPIs). Our model first encodes 32 MPI layers (totally 128 channels) into a 3-channel JPEG image that can be decoded for MPIs to render novel views, with an embedding capacity of 1024 bits per pixel. We conducted experiments on public datasets with different novel view synthesis methods, and the results show that the proposed method can restore high-fidelity novel views from a slightly modified JPEG image. Furthermore, our method is robust to JPEG compression, color adjusting, and cropping. Our source code will be publicly available.

In most current intelligent diagnosis methods, fault classifiers of electric machine are built based on complex handcrafted features extractor from raw signals, which depend on prior knowledge and is difficult to implement intelligentization authentically. In addition, the increasingly complicated industrial structures and data make handcrafted features extractors less suited. Convolutional neural network (CNN) provides an efficient method to act on raw signals directly by weight sharing and local connections without feature extractors. However, effective as CNN works on image recognition, it does not work well in industrial applications due to the differences between image and industrial signals. Inspired by the idea of CNN, we develop a novel diagnosis framework based on the characteristics of industrial vibration signals, which is called dislocated time series CNN (DTS-CNN). The DTS-CNN architecture is composed of dislocate layer, convolutional layer, sub-sampling layer and fully connected layer. By adding a dislocate layer, this model can extract the relationship between signals with different intervals in periodic mechanical signals, thereby overcome the weaknesses of traditional CNNs and is more applicable for modern electric machines, especially under nonstationary conditions. Experiments under constant and nonstationary conditions are performed on a machine fault simulator to validate the proposed framework. The results and comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed method in industrial applications.