Peng Han

Accurate measurement of the Zeta potential is crucial for evaluating the dispersion and aggregation behavior of colloidal systems. Currently, the prevailing Electrophoretic Light Scattering (ELS) method is an indirect average measurement technique that lacks visual intuitiveness. Meanwhile, traditional micro-electrophoresis is constrained by the optical diffraction limit and fails to account for measurement deviations induced by the axial motion of particles. To enhance the measurement accuracy and visualization characterization capability, this paper proposes a method for visualizing the Zeta potential of nanoparticles. By integrating the correlation operation of dark-field microscopic images and the axial motion speed compensation strategy, the three-dimensional velocity of the particles is measured, and the electrophoretic mobility is calculated using the Henry's equation. Comparative experiments utilizing particles of various sizes demonstrate that the measurement results of the proposed system are highly consistent with those of commercial ELS instruments (relative error < 7%). Furthermore, this method exhibits better applicability in the measurement of extremely low-concentration samples and accurately reflects the variation trends of the Zeta potential in response to pH changes. This strategy combines visualization, high precision and stability, providing a universal dark-field microscopic imaging and measurement platform for the characterization of nanoparticle Zeta potential.

Traditional Visual-Inertial Odometry (VIO) systems degrade in weakly textured and dynamically changing indoor environments, limiting reliable robot localization. To address this issue, we propose Dynamic Structural Visual-Inertial Odometry (Dynamic-SVIO), an indoor Red-Green-Blue and Depth (RGB-D) VIO system that integrates point-line-plane features with the Atlanta World (AW) constraint to enhance geometric observability. Dynamic-SVIO further introduces a dynamic feature identification and removal module that combines object detection and epipolar constraints to suppress misleading observations caused by human motion. A tightly coupled sliding-window backend fuses visual, depth, and inertial measurements for accurate state estimation. On the OpenLORIS dataset, Dynamic-SVIO achieves an average Relative Pose Error (RPE) of 0.030 and an average Absolute Trajectory Error (ATE) of 0.187, corresponding to approximately 25 percent and 19 percent improvements over representative baselines, respectively, and more than 60 percent ATE reduction compared with VINS-Mono. These results demonstrate that jointly leveraging structural priors and dynamic feature suppression significantly improves indoor ego-motion estimation, supporting robust embodied perception in human-robot coexisting environments.

On-chip non-Hermitian dynamic systems based on dissipative engineering have achieved remarkable results, but intrinsic losses and Langevin noise limit the further development of on-chip non-Hermitian devices. In this article, we propose a simple and feasible on-chip gain-gain coupling fully quantum non-Hermitian sensing model based on quasi-phase-matched second-order degenerate parametric downconversion in periodically poled LiNbO ₃ waveguides and demonstrate the feasibility of the theory through finite element simulations. Our theory suggests that this model exhibits hidden quadrature pseudo-PT (parity-time) symmetry behavior and gives rise to the phenomenon of competitive amplification between downconverted photons. Additionally, based on quantum Fisher information, our proposed scheme can achieve quantum-sensing enhancement in the full PT-symmetric domain and super-Heisenberg scaling close to the exceptional point (EP). For detuning disturbances, our system exhibits excellent robustness in the nonspecial EP region. These findings will introduce a new perspective for the development of on-chip non-Hermitian technologies and promote the development of non-Hermitian quantum-sensing theories.

Existing 3D Gaussian Splatting (3DGS)-based Simultaneous Localization and Mapping (SLAM) methods improve tracking accuracy and rendering quality by incorporating additional components such as loop closure. However, this improvement comes at the cost of reduced speed, and these methods have limited the use of image information. In this paper, we propose EAGS-SLAM, an edge-assisted RGB-D Gaussian Splatting SLAM that leverages edge information to improve system performance. EAGS-SLAM combines edge-based visual odometry and gaussian tracking, which reduces camera tracking time by up to 50%. We propose a novel gaussian seeding method that directly utilizes edge information to capture geometric details in images. A decoupled submap design that can run loop closure in parallel, ensuring globally consistent mapping while improving system speed. The proposed system is evaluated on real-world datasets Scan-Net, TUM RGB-D, and synthetic dataset Replica. Compared with existing state-of-the-art systems, the proposed system demonstrates competitive or superior performance in tracking, rendering, and runtime. The code and visualization results are available at https://endermands.github.io/EAGSSLAM.

In electromagnetic compatibility analysis and non-destructive fault diagnosis of circuits, accurately measuring the radio-frequency current distribution in printed circuit board (PCB) is of critical importance. However, for circuit modules without dedicated test points, non-destructive and direct measurement of in-situ current remains a formidable challenge. Currently, a common alternative approach involves measuring the magnetic field directly above the PCB with a magnetic field probe, and then reconstructing the current distribution on the PCB surface using the Biot-Savart law. Nevertheless, this inversion algorithm typically assumes quasi-static current conditions, and its inversion parameters are often empirically selected. These limitations significantly constrain its applicability in complex radio-frequency (RF) environments. To address this challenge, this work proposes an enhanced Magnetic Current Imaging method specifically designed for reconstructing the RF current distribution on PCBs surfaces. This method leverages the current-magnetic field relationship and employs a regularization technique to constrain current distribution. Additionally, it utilizes a mask-based approach and an extended measurement window to mitigate current artifacts. Finally, its feasibility and accuracy are comprehensively validated through simulations and near-field scanning experiments, demonstrating its robustness and effectiveness even under high-noise conditions.

Reconfigurable metasurfaces, as planar devices with powerful capabilities to regulate, have attracted extensive attention. Conventional phase-continuous reconfigurable transmissive metasurfaces have air gaps, which make the design of metasurfaces more complex. Although several studies have explored metasurfaces without air gaps, their phase-tunable resolution is still not optimal. We present a phase-continuous reconfigurable transmissive metasurface without air gaps based on Huygens' surface theory. The metasurface comprises a multilayer interconnected meta-unit with integrated varactors that can control magnetic resonance characteristics for Huygens' resonance. It achieves 224° continuous phase modulation at 6.7 GHz, the average transmittance of 0.7 at 6.7-7 GHz, and the phase-tunable resolution of approximately 1°/0.1 V. To validate metasurface functional characteristics, beam steering, real-time dynamic focusing, and simultaneous dual focusing are experimentally demonstrated. Both the simulation and experimental results confirm that the proposed metasurface has excellent phase-continuous dynamic reconfigurable capabilities. This advancement will drive the development of metasurface technology, providing support for the next generation of wireless communication and intelligent imaging.

Vehicle-to-vehicle communication enables capturing sensor information from diverse perspectives, greatly aiding in semantic scene completion in autonomous driving. However, the misalignment of features between ego vehicle and cooperative vehicles leads to ambiguity problems, affecting accuracy and semantic information. In this paper, we propose a Two-Stream Multi-Vehicle collaboration approach (TSMV), which divides the features of collaborative vehicles into two streams and regresses interactively. To overcome the problems caused by feature misalignment, the Neighborhood Self-Cross Attention Transformer (NSCAT) module is designed to enable the ego vehicle to query the most similar local features from collaborative vehicles through cross-attention, rather than assuming spatial-temporal synchronization. A 3D occupancy map is finally generated from the features of collaborative vehicle aggregation. Experimental results on both V2VSSC and SemanticOPV2V datasets demonstrate TSMV outpace state-of-the-art collaborative semantic scene completion techniques.