Reliable anti-jamming capability is a critical requirement for Internet of Things (IoT) devices operating in dense and interference-prone wireless environments, where spectrum congestion and co-channel interference can severely degrade communication quality. This paper presents a compact time-modulated phase-center reconfigurable antenna (PCRA) that achieves high-precision null steering while maintaining a small form factor suitable for resource-constrained IoT applications, by employing a periodic time-modulation technique to dynamically switch among multiple radiation states with distinct phase centers but nearly identical amplitude patterns. This mechanism enables angular resolution of 1° in null steering and mitigates the limitations of conventional reconfigurable antennas and beamforming arrays, which often suffer from hardware nonlinearity and restricted discrete states, thereby providing enhanced flexibility in spatial interference control. A prototype was fabricated and experimentally validated under quadrature phase-shift keying (QPSK) modulation, demonstrating an interference rejection ratio exceeding 20 dB, constellation and eye diagrams, together with bit error rate and error vector magnitude measurements, further confirmed the improvement in communication robustness. With its compact footprint, low implementation complexity, and effective anti-jamming capability, the proposed PCRA offers a promising hardware solution for emerging IoT scenarios, including smart homes, UAV-enabled networks, and intelligent transportation systems.

3D object detection in point clouds is critical in 3D computer vision, autonomous driving, and robotics. Existing point-based detectors, tailored to handle unstructured raw point clouds, often rely on simplistic sampling strategies to select a subset of points for local representation learning and detection. However, the diverse patterns exhibited by multiple types of point cloud data present a significant challenge to the universality of current detectors, particularly those captured by varied sensors (e.g., LiDAR and 4D Imaging Radar). In response to this challenge, we introduce an adaptable point-based single-stage 3D detector, AS-Det, engineered to excel on both LiDAR and 4D Radar point clouds. Specifically, we propose a novel active sampling strategy that actively mines object-related information to achieve efficient sampling and representation across different types of point clouds through end-to-end training. Additionally, we introduce a lightweight multi-scale center feature aggregation module to exploit multi-scale object context for precise and low-cost detection. By integrating the abovementioned modules, AS-Det achieves highly adaptive detection on various point clouds, encompassing different sensors and scales. Experimental results demonstrate the superior performance and adaptability of AS-Det on both LiDAR and 4D Radar point clouds.

LiDAR-based Semantic Scene Completion (SSC) is crucial for enhancing environmental perception and ensuring safety in autonomous driving. However, current methods face challenges in balancing accuracy and computational efficiency. On the one hand, projection-based methods reduce complexity but often suffer from spatial information loss. On the other hand, voxel-based methods preserve 3D structures but are computationally expensive. To address these limitations, we introduce SplitOcc, a novel multi-resolution approach that utilizes low-resolution voxels to represent large structures (e.g., road) and high-resolution voxels for detailed objects (e.g., bicycle). By employing multi-resolution sparse semantic voxels, SplitOcc can understand and represent the environment efficiently and accurately. Furthermore, the proposed Multi-Label Loss and Delayed-Drop strategies improve accuracy by preserving key semantic details during reconstruction. Extensive experiments demonstrate that our SplitOcc outperforms existing state-of-the-art methods across multiple evaluation metrics, showing notable improvements in both perception accuracy and detail preservation.

Online object detection and tracking are crucial for embodied intelligence systems, including autonomous vehicles and robotics. Traditional approaches employ a pipeline structure to perform detection and tracking separately, which can not fully leverage information from the detector. Moreover, most prior tracking methods rely on motion models such as constant velocity for state updates, which can lead to incorrect associations when the velocity estimates are inaccurate. To address these limitations, we propose ConTrack3D, an online tracking approach that jointly performs detection and tracking in an end-to-end manner. Specifically, ConTrack3D incorporates a Joint Encoder module to capture detection embeddings and a Temporal Extender module for data-driven state updates. By employing contrastive learning, ConTrack3D learns discriminative tracking representation for more accurate association. ConTrack3D is evaluated on the nuScenes benchmark, and the experimental results demonstrate its significant improvements in tracking performance.

Point clouds have shown significant potential in various domains, including Simultaneous Localization and Mapping (SLAM). However, existing approaches either rely on dense point clouds to achieve high localization accuracy or use generalized descriptors to reduce map size. Unfortunately, these two aspects seem to conflict with each other. To address this limitation, we propose an unified architecture, DeepPointMap, achieving excellent preference on both aspects. We utilize neural network to extract highly representative and sparse neural descriptors from point clouds, enabling memory-efficient map representation and accurate multi-scale localization tasks (e.g., odometry and loop-closure). Moreover, we showcase the versatility of our framework by extending it to more challenging multi-agent collaborative SLAM. The promising results obtained in these scenarios further emphasize the effectiveness and potential of our approach.

Simultaneous Localization and Mapping (SLAM) plays a pivotal role in autonomous driving and robotics. Existing methods often rely on hand-craft feature extraction and cross-modal fusion techniques, resulting in limited feature representation capability and reduced robustness. To address this challenge, we introduce DeepPointMap2, a novel learning-based LiDAR-Visual SLAM architecture that leverages neural descriptors to tackle multiple SLAM sub-tasks in a unified manner. Our approach employs neural networks to extract multi-modal tokens, which are then adaptively fused by the Visual-Point Fusion Module to generate sparse 3D neural descriptors, ensuring precise and robust performance. As a pioneering work, our method achieves state-of-the-art localization performance among various Visual-, LiDAR-, and Visual-LiDAR-based methods in widely-used benchmarks, as shown in the experiment results. Furthermore, the approach proves to be robust in scenarios involving camera failure and LiDAR obstruction.

The advanced Deep Learning (DL) techniques have enabled the development of semantic communication systems by their remarkable information processing and end - to-end optimization capabilities. However, the lack of performance guarantee of these DL-based methods also brings concerns on the reliability of semantic communication systems. To address this issue, we propose a semantic communication scheme with reliability-driven retransmission requests in order to improve the transmission efficiency and guarantee the reliability of the inference result at the same time. In particular, the transmitter first sends a basic amount of the information, and the receiver infers with this information, and assesses the reliability of the results. If the derived reliability is below a given threshold, a retransmission request is sent to the sender for more information to be transmitted. We exploit the entropy of the output logits to quantify the reliability of the classification results. More specifically, lower entropy corresponds to higher confidence, indicating higher reliability. Experimental results validate the effectiveness of the proposed scheme in terms of improving transmission efficiency and computational efficiency while maintaining the reliability of the inference results.