Polarization-based passive millimeter-wave imaging has been applied in several applications, including material clustering, pattern recognition, and target detection. We present here a general formulation of a metal target detection method called dual linear polarization discriminator (DLPD), utilizing passive millimeter-wave polarimetric imagery. Several potential discriminators are defined, and linear polarization difference ratio (LPDR) is selected and proposed to be a new feature discriminator that is sensitive to material composition and able to reduce ambient radiation effects when detecting target with different material and shape. Furthermore, the detection criterion is verified utilizing the threshold values determined by a statistical analysis of LPDR. Outdoor experiments demonstrate that the proposed detection method is highly effective for detecting a metal target in a complex background.

The Inertial Navigation Systems (INS) and Global Navigation Satellite Systems(GNSS) integrated navigation system is widely employed for vehicular positioning. However, obstacles in the wireless signal path often lead to GNSS outages, diminishing the reliability of positioning. Many deep learning based methods have been proposed for their high accuracy and outstanding generalization capabilities. Nevertheless, they overlook the constraints posed by limited computational resources and memory capacity within real-time, online navigation systems. To strike a balance between positioning accuracy and model complexity, this paper proposes an Online Semi-supervised Transformer (OSS-Transformer) model for GNSS/INS integrated navigation that functions effectively during GNSS signal outages and has low computational requirements. This model comprises three critical components: (1) A Transformer-based architecture for adaptive feature extraction; (2) A self-supervised Masked Language Model (MLM) task to understand contextual relationships and feature distributions within INS data; (3) An improved memory replay technique for efficient online network training. Verified by the road test, the proposed OSS-Transformer based integrated navigation system reaches a maximum error of 6.02 m during 40 s outages and 9.81 m during 100 s outages, significantly outperforming traditional deep-learning methods. Additionally, the improved memory replay technique reduces loss by 26.23% compared to conventional methods, making it feasible on navigation devices with constrained computational resources and memory.

The adoption of blockchain technology has catalyzed the expansion of decentralized finance (DeFi), leading to the harnessing of blockchain platforms. However, the decentralization of blockchain has given rise to blockchain extractable value (BEV) activities, influenced by consensus mechanisms. This study centers on BEV, unveiling a real-time discovery and mining system (RDMS) tailored for arbitrage-based DeFi activities. The system employs innovative methodologies for localized computation and execution. It establishes a comprehensive monitoring system for arbitrage and liquidation activities, contributing positively to the DeFi ecosystem. Leveraging round-the-clock on-chain data indexing and event-driven parsing methods, the RDMS enables automated and periodic analysis of BEV activities. This system provides valuable insights for BEV research, particularly in the context of arbitrage and liquidation activities. And we are able to consistently extract value using arbitrage strategies on blockchains, using RDMS that monitors the chain in real time and applies gas cost reduction mechanisms. Experimental testing and comparative analysis validate the RDMS’s effectiveness, showcasing minimal latency and remarkable gas optimization capabilities.

Passive millimeter-wave (PMMW) imaging technology is widely used in civilian and military applications. However, there are reflections similar to the optical band in PMMW images, which have negative influence on the target detection and recognition. In this paper, we present a reflection-based method to enhance the target features in PMMW images. The dividing line between target and reflection is obtained by the similarity of brightness temperature (TB). By combining the similarity and reflection principle, we propose a new method to obtain the feature points of target and reflection for registration. Then, the weighted method based on region TB is used to fusion target and reflection. Finally, to avoid interference with target detection and recognition, the reflection is removed. The experimental results show that the method can obtain higher contrast and more accurate target information.

The ability to segment moving objects from three-dimensional (3D) LiDAR scans is critical to advancing autonomous driving technology, facilitating core tasks like localization, collision avoidance, and path planning. In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. Our method processes sequential range images, employing depth pixel difference convolution (DPDC) to improve the efficacy of dilated convolutions, thus boosting spatial information extraction from range images. Additionally, we incorporate Bayesian filtering to impose posterior constraints on predictions, enhancing the accuracy of motion segmentation. To handle the issue of uneven object scales in range images, we develop a novel edge-aware loss function and use a progressive training strategy to further boost performance. Our method is validated on the SemanticKITTI-based LiDAR MOS benchmark, where it significantly outperforms current state-of-the-art (SOTA) methods, all while working directly on two-dimensional (2D) range images without requiring mapping.

Precise carrier phase positioning is a widely used technique in vehicle navigation, localization, and mapping, which utilizes pseudorange and carrier phase measurements with the extended Kalman filter (EKF) to estimate position, velocity, and ambiguities. For highly accurate vehicle positioning requirements, propagation errors of measurements need to be considered, especially the ionospheric delay, which is the most significant error source. In EKF, a priori information of different error characteristics can be fully utilized as constraints to obtain more precise state estimation results, even when no corrections are available. This paper aims to improve the positioning accuracy for vehicle navigation and proposes a spherical harmonic function (SHF)-based undifferenced and uncombined (UDUC) carrier phase positioning method for vehicles to fit the ionospheric delays of multiple satellites in a single formula and reduce the marginal effect caused by polynomials. Better positioning results are expected compared to traditional UDUC and polynomial fitting approaches. The feasibility of the proposed method is tested on a kinematic dataset from a real-world car test. The experimental results show that the proposed method outperforms the traditional UDUC and polynomial fitting methods, respectively, with a root mean square error of 0.860 m and a standard deviation of 0.579 m when using broadcast ephemeris. In summary, the results demonstrate that the proposed method is highly beneficial for precise vehicle navigation.