While unencrypted information inspection in physical layer (e.g., open headers) can provide deep insights for optimizing wireless networks, the state-of-the-art (SOTA) methods heavily depend on full sampling rate (a.k.a Nyquist rate), and high-cost radios, due to terrestrial and non-terrestrial networks densely occupying multiple bands across large bandwidth (e.g., from 4G/5G at 0.4–7 GHz to LEO satellite at 4–40 GHz). To this end, we present SigChord, an efficient physical layer inspection system built on low-cost and sub-Nyquist sampling radios. We first design a deep and rule-based interleaving algorithm based on Transformer network to perform spectrum sensing and signal recovery under sub-Nyquist sampling rate, and second, cascade protocol identifier and decoder based on Transformer neural networks to help physical layer packets analysis. We implement SigChord using software-defined radio platforms, and extensively evaluate it on over-the-air terrestrial and non-terrestrial wireless signals. The experiments demonstrate that SigChord delivers over 99% accuracy in detecting and decoding, while still decreasing 34% sampling rate, compared with the SOTA approaches.

Traffic accidents remain a significant concern due to their potential severity and impact on society. The rapid and accurate detection of car damage is increasingly crucial. Manual assessment of car damage usually relies on multi-view car images taken at the scene, which can provide richer information for damage assessment. However, most car damage algorithms are based on single-view datasets, and it is hard to fully leverage the complementary information and alignment information between distant view and close-up images. In this paper, we propose the Multi-View Car Damage Detection model (MVA-CDD), comprising three key modules: Feature Split (FS), Feature Fusion (FF), and Image Alignment (IA). The FS module extracts global and detailed information from distant-view and close-up images separately, which are then combined by the FF module. The IA module effectively aligns car damage information in distant-view and close-up images to correct errors and biases. Meanwhile, we created the new Car Damage Detection Multi-view dataset (CDDM), which has a significant advantage in both image quantity and diversity across categories, addressing the shortcomings of existing multi-view datasets. Our proposed MVA-CDD outperforms the state-of-the-art single-view and multi-view models with the dataset. Results from ablation studies further confirm the efficiency of MVA-CDD. This study contributes to optimizing the car damage detection and claims adjudication process, leading to significant labor and material cost savings. CDDM dataset is available athttps://github.com/SCUT-CCNL/CDDM.

Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from 70 MHz to 8 GHz). Consequently, for network monitoring and spectrum sharing applications, a sniffer for extracting physical layer information, such as structure of packet, with low sampling rate (especially, sub-Nyquist sampling) can significantly improve their cost- and energy-efficiency. However, to achieve a multiband signals sniffer is really a challenge. To this end, we propose Sums, a system that can sniff and analyze multiband signals in a blind manner. Our Sums takes advantage of hardware and algorithm co-design, multi-coset sub-Nyquist sampling hardware, and a multi-task deep learning framework. The hardware component breaks the Nyquist rule to sample GHz bandwidth, but only pays for a 50 MSPS sampling rate. Our multi-task learning framework directly tackles the sampling data to perform spectrum sensing, physical layer protocol recognition, and demodulation for deep inspection from multiband signals. Extensive experiments demonstrate that Sums achieves higher accuracy than the state-of-the-art baselines in spectrum sensing, modulation classification, and demodulation. As a result, our Sums can help researchers and end-users to diagnose or troubleshoot their problems of wireless infrastructures deployments in practice.

Compressed spectrum sensing (CSS) plays a pivotal role in dynamic spectrum access within mobile cognitive radio networks by offering reduced power consumption and lower hardware costs. The multicoset sampler, a well-known implementation for periodic nonuniform sampling, has been widely studied and is considered a promising architecture for realizing CSS. This paper focuses on the design of the multicoset sampling pattern, aiming at enhancing the isometry property of the sensing matrix. Unlike previous studies which assume a noise-free setup, our work considers the problem in a real-world environment with noise. First, we propose a deterministic algorithm for sampling pattern generation, particularly for specific hardware setup parameters. This algorithm offers strict mutual-coherence control in the multicoset sensing matrix. To address more general hardware configurations, we propose two optimization algorithms. One of them searches for nearly optimal sampling patterns through a random search strategy, while the other employs a greedy pursuit strategy to find a local optimizer. Furthermore, we propose an algorithm to iteratively optimize the sampling pattern between consecutive spectrum sensing windows by minimizing a restricted version of mutual coherence. The excellent performance of our proposed algorithms has been demonstrated through numerical experiments and has been verified on a self-developed hardware platform.

The traditional vehicle damage assessment process is complicated and time-consuming, asking for intelligent methods for detecting vehicle damage. At present, most damage detection methods for vehicles require two models to detect damage and component where the damage is located separately, which is complex and inefficient. To this end, we propose an end-to-end multi-detection model named MD R-CNN, which simultaneously outputs damage detection and component recognition results by adding an extra classification branch. To improve the positioning precision of detection, the regression of the detection box adopts a self-attention convolution head (SA-Head) composed of a residual module and two SC Attention modules; Moreover, since there are few damage annotation datasets available, D-FPN is proposed to enhance the multi-scale detection performance. The experimental results show that MD R-CNN increases the Average precision (AP) by about 2.4% on vehicle damage datasets, and the precision can reach 80.22%, which has a favorable performance.