Multi-vehicle collaborative sensing has emerged as a new paradigm to boost the safety of autonomous vehicles. The cornerstone of this vision is the real-time and accurate registration of mmWave radar point clouds among multiple vehicles. To accomplish this, we designmmReg, an innovative system capable of achieving centimeter-level and real-time sensing fusion between vehicles.mmRegconsists of three major components: (i) aSAR imaging-driven point cloud generationcomponent leverages SAR imaging to image sparse and disordered radar point clouds to generate high-quality point clouds; (ii) amotion-aware frame synchronizationcomponent can achieve the spatio-temporal alignment of point clouds between vehicles for effectively mitigating the impact of asynchronous radar frames; (iii) ashared object-based registrationcomponent can capture and understand the unique global position of shared objects, supporting real-time and accurate registration. We implement and evaluatemmRegon CARLA and real-world campus datasets. The results demonstrate thatmmRegcan improve the vehicle’s sensing range by 117% in an average of 99.91 ms, achieving a 4.82x improvement in accuracy.

Current WiFi imaging approaches focus on monitoring dynamic targets to facilitate easy object distinction and capture rich signal reflections for image construction. In static object imaging, massive antenna array or emulated antenna array is often necessary. We proposeWiCamera, a novel WiFi imaging prototype that utilizes vortex electromagnetic waves (VEMWs) to monitor stationary human postures using commodity WiFi, by generating human silhouettes with only$3 \times 3$MIMO. VEMWs possess a helical wavefront with different phase variations, enabling the imaging of stationary objects through different OAM (Orbital Angular Momentum) modes with time-division multiplexing.WiCameraemits three OAM modes waves from WiFi devices and utilizes their phase variations for imaging. By ray tracing the received signals to a target image plane,WiCameragenerates a wavefront image. A generative adversarial network (GAN)-based model is further utilized to refine the wavefront image and create a high-resolution human silhouette. The system's output images are evaluated using metrics such as structural similarity index measure (SSIM) and Szymkiewicz-Simpson coefficient (SSC), comparing them to ground truth images captured by cameras. The evaluation shows thatWiCameraperforms consistently well in various environments and with different users, with an SSIM reaching up to 0.89 and an SSC reaching up to 0.93.

WiFi imaging is an emerging technology that can overcome camera limitations like occlusion and poor lighting. Imaging static objects without antenna arrays or mobile platforms is challenging. In this paper, we use a metasurface with a single pair of WiFi transceiver to achieve high-resolution WiFi imaging for static objects. We analyze the WiFi multipath propagation model with a metasurface and propose an imaging system. This system employs diverse radiation patterns by directing the WiFi beam reflected from the metasurface to illuminate a target. We utilize an image-guided diffusion model for high resolution imaging. We prototype MAWI with commodity WiFi and evaluate its performance in real environments. Experimental results show MAWI performs well on four typical target shapes.

With the widespread adoption of deep learning models in wireless sensing, substantial efforts have been made to develop sophisticated models that improve the accuracy and performance of sensing applications. However, the exploration of potential vulnerabilities in deep learning models has been limited, with existing studies primarily focusing on evaluating wireless adversarial performance in communication or sensing alone. Moreover, there is a lack of a comprehensive definition for attack imperceptibility. In this article, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our objective is to create an adversarial perturbation capable of degrading WiFi sensing performance while preserving WiFi communication integrity. To achieve this, we propose WiCAM2.0 to reveal the temporal and spatial attention of a deep neural network, capturing the crucial portions of its input. Then, we design a mask to confine adversarial perturbations in the attended parts only, minimizing the impact on WiFi communication. WiCAM2.0 is a general adversarial framework that integrates adversarial methods such as the Fast Gradient Sign Method and Projected Gradient Descent to generate perturbations, capable of initiating both non-targeted and targeted attacks. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets.

In the past decade, object detection tasks are defined mostly by large public datasets. However, building object detection datasets is not scalable due to inefficient image collecting and labeling. Furthermore, most labels are still in the form of bounding boxes, which provide much less information than the real human visual system. In this paper, we present a method to synthesize object-in-scene images, which can preserve the objects' detailed features without bringing irrelevant information. In brief, given a set of images containing a target object, our algorithm first trains a model to find an approximate center of the object as an anchor, then makes an outline regression to estimate its boundary, and finally blends the object into a new scene. Our result shows that in the synthesized image, the boundaries of objects blend very well with the background. Experiments also show that SOTA segmentation models work well with our synthesized data.

Recognizing new action categories from a few reference samples is an encouraging research field because the cost of labeling data is expensive. This work presents a method for few-shot (or one-shot) skeleton-based action recognition by fusing temporal and spatial features of actions. Trajectory primitives are proposed to characterize the temporal features, which can be obtained by segmenting and clustering the trajectories of joints. After that, we modify the original dynamic time warping (DTW) algorithm and use it to measure the similarity between trajectory primitive sequences. Besides, we compute the joint angles as spatial feature vectors. Support vector machines (SVM) are used to classify the joint angle vectors. In this way, the temporal distance matrix can be calculated by modified DTW, and the spatial distance matrix can be obtained by trained SVM. Finally, we fuse temporal and spatial distance matrices by adjusting a parameter to improve recognition accuracy. Furthermore, extensive experiments are conducted on three small-scale datasets to verify the effectiveness of our proposed method.

Human action recognition is a quite hugely investigated area where most remarkable action recognition networks usually use large-scale coarse-grained action datasets of daily human actions as inputs to state the superiority of their networks. We intend to recognize our small-scale fine-grained Tai Chi action dataset using neural networks and propose a transfer-learning method using NTU RGB+D dataset to pre-train our network. More specifically, the proposed method first uses a large-scale NTU RGB+D dataset to pre-train the Transformer-based network for action recognition to extract common features among human motion. Then we freeze the network weights except for the fully connected (FC) layer and take our Tai Chi actions as inputs only to train the initialized FC weights. Experimental results show that our general model pipeline can reach a high accuracy of small-scale fine-grained Tai Chi action recognition with even few inputs and demonstrate that our method achieves the state-of-the-art performance compared with previous Tai Chi action recognition methods.

With the popularization of deep learning models in wireless sensing, researchers have made considerable efforts to construct sophisticated models to improve the accuracy of related applications. But very few studies have addressed the potential vulnerabilities of deep models, and existing works evaluate wireless adversarial performance only in communication or sensing. None of them has a comprehensive definition of attack imperceptibility. In this paper, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our goal is to craft an adversarial perturbation, which can degrade the performance of WiFi sensing without compromising WiFi communication. To achieve this goal, we propose WiCAM to reveal the temporal and spatial attention of a DNN, capturing the crucial portions of its input. Then we design a mask to limit adversarial perturbation in the attended parts only, and thus the impact of the attack on WiFi communication is minimized. WiCAM is a general adversarial framework that can integrate existing adversarial methods such as FGSM and PGD to generate perturbations. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets. The results show that when declining the accuracy of a target model below 50%, WiCAM can reduce the impact on communication in terms of BER by up to 77.78% in QAM-64, compared to the common adversarial methods.