Accurate 3D lane detection remains a critical challenge for autonomous vehicles, particularly in complex scenarios involving slopes and occlusions. While multimodal and temporal fusion paradigms have succeeded in general object detection, directly applying them to 3D lane detection is limited by the sparse and slender nature of lane markings. To address these limitations, we propose 3DLaneDT, a robust framework tailored for 3D lane detection. Distinct from existing depth-aware methods with standard convolutions, we propose an Adaptive Depth Network (ADN) incorporating Adaptive Area Convolution (AAConv). This design dynamically adjusts to the sparsity of lane depth signals, integrating reliable geometric priors into the Transformer attention mechanism. Furthermore, unlike attention-based temporal fusion which suffers from quadratic complexity, we introduce a State Space Model (SSM)-based Temporal Propagation Module (TPM). This module efficiently models the continuous geometric evolution of sparse lane queries. Experimental results on OpenLane and Apollo datasets demonstrate that 3DLaneDT surpasses state-of-the-art methods in both accuracy and efficiency. Code will be available after acceptance.

This paper presents SWIFT (Swarm-Wise Inference for Fast Trajectory Planning), a distributed one-stage planner designed for efficient multi-quadrotor trajectory optimization in cluttered environments. SWIFT unifies depth-based perception, interaction-aware modeling, and trajectory prediction into a single lightweight network, enabling decentralized real-time planning without reliance on global maps. Each quadrotor processes its local depth observation and asynchronously received neighbor intentions to predict multiple candidate trajectories, leveraging a confidence-guided cross-attention mechanism to selectively model critical inter-agent interactions. The proposed framework achieves sub-millisecond planning latency while ensuring collision avoidance, dynamic feasibility, and cooperative behavior under limited communication. Furthermore, a visual-inertial odometry drift correction strategy based on inter-agent visual detections enhances positioning accuracy during real-world flights. Extensive simulations and forest experiments validate that SWIFT outperforms existing baselines in trajectory smoothness, success rate, scalability, and robustness across diverse scenarios.

In this paper, we propose a combined Unscented Kalman Filter and Kalman Filter (UKF/KF) structured attitude determination method that aims to handle large initial attitude errors on a moving base at much lower computational cost. The proposed algorithm includes modified UKF that reduces initial attitude errors to a small range during coarse alignment and KF to proceed fine alignment that further diminish attitude errors. A Measurement- based Feedback algorithm is proposed to accelerate the convergent speed of UKF. Handover strategy for stable transition from UKF to KF is investigated. Moreover, block matrix multiplication is adopted, which reduces about 10.4% of the computation burden of KF. To improve system observability, position and velocity information from Global Positioning System (GPS), together with magnetic measurements are utilized. Numerical results of real-world simulated trajectory show that the proposed UKF/KF structure outperforms alternative UKF method in convergent speed as well estimation accuracy for both tactical- grade SINS and low-cost MEMS-based SINS.

Wireless Physical Layer Identification (WPLI) aims at identifying or classifying authorized devices based on the unique Radio Frequency Fingerprints (RFFs) to guarantee physical-layer security between each individual device. WPLI is a promising technique in the security area of Internet of Things (IoT), which avoids the burden of conventional software-level security in IoT on the computation capability, on-chip storage and power consumption of devices. Moreover, WPLI directly extracts the RFFs from radio frequency signals at physical layer, which breaks the software-level security barriers of heterogeneous device networks in IoT. However, in the existing works in WPLI, a general work focusing on theoretical characterization of performance WPLI systems in IoT is still in absence. In this work, we evaluate the performance of WPLI in IoT by thoroughly characterizing user capacity of WPLI system as the metric. We utilize the concept of IoT by combining multiple devices to represent one user's secure identity to increase the user capacity of WPLI system. Based on the information-theoretic tool we proposed, the changes in user capacity of WPLI is characterized over typical real-world scenarios according to different number of devices assigned for one user. Various field experiments on classification performance of a practical multi- user-multi-device WPLI system with are conducted to validate the user capacity characterization for each scenario.

Wireless Physical Layer Identification (WPLI) system aims at identifying or classifying authorized devices based on the unique Radio Frequency Fingerprints (RFFs) extracted from their radio frequency signals at the physical layer. Current works of WPLI focus on demonstrating system feasibility based on experimental error performance of WPLI with a fixed number of users. While an important question remains to be answered: what's the user number that WPLI can accommodate using different RFFs and receiving equipment. The user capacity of the WPLI can be a major concern for practical system designers and can also be a key metric to evaluate the classification performance of WPLI. In this work, we establish a theoretical understanding on user capacity of WPLI in an information-theoretic perspective. We apply information-theoretic modeling on RFF features of WPLI. An information-theoretic approach is consequently proposed based on mutual information between RFF and user identity to characterize the user capacity of WPLI. Based on this theoretical tool, the achievable user capacity of WPLI is characterized under practical constrains of off-the-shelf receiving devices. Field experiments on classification error performance are conducted for the validation of the information-theoretic user capacity characterization.

The wireless physical-layer identification (WPLI) techniques utilize the unique features of the physical waveforms of wireless signals to identify and classify authorized devices. As the inherent physical-layer features are difficult to forge, WPLI is deemed as a promising technique for wireless security solutions. However, as of today, it still remains unclear whether the existing WPLI techniques can be applied under real-world requirements and constraints. In this paper, through both theoretical modeling and experiment validation, the reliability and the differentiability of the WPLI techniques are rigorously evaluated, especially under the constraints of the state-of-the-art wireless devices, real operation environments, as well as wireless protocols and regulations. In particular, a theoretical model is first established to systematically describe the complete procedure of the WPLI. More importantly, the proposed model is then implemented to thoroughly characterize the various WPLI techniques that utilize the spectrum features coming from the nonlinear RF front-end, under the influences from different transmitters, receivers, and wireless channels. Subsequently, the limitations of the existing WPLI techniques are revealed and evaluated in details using both the developed theoretical model and the in-lab experiments. The real-world requirements and constraints are characterized along each step in WPLI, including: 1) the signal processing at the transmitter (device to be identified); 2) the various physical-layer features that originate from circuits, antenna, and environments; 3) the signal propagation in various wireless channels; 4) the signal reception and processing at the receiver (the identifier); and 5) the fingerprint extraction and classification at the receiver.

The networks of micro-sized wireless sensors will enable the in-situ monitoring in the challenging oil reservoir environments, which can significantly increase the current recovery factor of crude oil and natural gas. However, due to the micro device size defined by the environmental structure and the harsh wireless channel in the complicated dense media, traditional wireless communication and networking solutions do not work in oil reservoirs. To address the problems, in this paper, the system architecture and operational framework of a Magnetic Induction (MI)-based wireless sensor network is proposed, where both the wireless energy transfer and the wireless communications in the oil reservoir environments are realized by the magnetic induction technique. More importantly, the wireless channel model and the energy transfer/consumption model of such networks are analytically developed to validate the feasibility of the proposed system architecture and operational framework.

In this letter, we propose a novel computational model for automatic ship detection in optical satellite images. The model first selects salient candidate regions across entire detection scene by using a bottom-up visual attention mechanism. Then, two complementary types of top-down cues are employed to discriminate the selected ship candidates. Specifically, in addition to the detailed appearance analysis of candidates, a neighborhood similarity-based method is further exploited to characterize their local context interactions. Furthermore, the framework of our model is designed in a multiscale and hierarchical manner which provides a plausible approximation to a visual search process and reasonably distributes the computational resources. Experiments over panchromatic SPOT5 data prove the effectiveness and computational efficiency of the proposed model.

This paper proposes a multi-node cooperative threshold selection maximum ratio combining (TS-MRC) system for multi-hop wireless networks. We analyze and derive the regenerative outage probability of TS-MRC system and MRC-based cooperative system over Nakagami fading channels. The numerical and simulation results show that TS-MRC achieves a tradeoff between the system performance and the process capability of the destination node. It is very helpful to reduce the power consumption and the hardware complexity of the destination node. And that MRC-based system can offer the full-route diversity over Nakagami fading channels, where the generalized diversity order of the whole system equals to the minimal sum of the possible diversities of the full routes, not the sum of all cooperative nodes.