Communications engineering最新文献

Adaptive optics has revolutionized biological microscopy by improving resolution and signal-to-noise ratio, yet its reliance on complex hardware and phototoxic wavefront sensing limits broader adoption. Here, we introduce ∅CAO, a computational phase-based adaptive optics technique that corrects optical aberrations in three-dimensional fluorescence microscopy without requiring specialized optics or training datasets. By leveraging phase transfer functions in the frequency domain, ∅CAO enables robust post-acquisition correction across diverse imaging modalities, including wide-field and structured illumination microscopy. Our method achieves substantial improvements in image fidelity, supports subregional aberration correction, and maintains performance under noisy conditions. Demonstrated on a range of biological specimens, including Caenorhabditis elegans and plant tissues, ∅CAO offers a scalable and accessible solution for high-resolution biological imaging, facilitating the broad deployment of adaptive optics approaches across the life sciences.

Two major challenges associated with robotic catheterization are, firstly, the provision of controllable degrees of freedom (DoFs) and, secondly, accessing feedback on the shape and pose of the catheter. Miniaturizable active steering can be achieved through magnetic actuation, and Magnetic Resonance Imaging (MRI) provides high definition, radiation-free 3D imaging that can be utilized for shape-sensing. Here, we propose a structurally adaptable Coaxial Sleeve Magnetic Actuator (CoSMA), with deformation energy provided by the background field of the MRI scanner. Our approach combines the magnetic actuation principle of the easy axis of alignment with the mechanical principles of concentric tube designs. This concept allows for a materially flexible ($E=O\left(1\mathrm{MPa}\right)$), and therefore risk reduced, multi-DoF catheter. We demonstrate the CoSMA, constructed of three coaxial components with respective outer diameters of 4 mm, 1.5 mm and 0.4 mm, in an aortic arch phantom navigation within the bore of a pre-clinical MRI scanner.

Propagation control is essential for the practical use of subterahertz waves. Liquid crystal (LC) metasurfaces, which can be easily fabricated using display manufacturing technologies, can achieve the large aperture required for controlling propagation channels. Here we propose a dual-linear polarization unit cell that incorporates an LC layer with a thickness that can be determined independently of cell scaling, and that can be designed over a broad frequency range from microwave to subterahertz. Our prototype transmissive metasurface includes a 3.5-μm-thick LC layer and comprises 47,524 cells with a cell size below λ/8, and exhibits an insertion loss of 2.5 dB with a 3-dB bandwidth of 10%. We experimentally demonstrate two-dimensional beam steering up to 30 degrees and variable focusing through amplitude modulation of the aperture in the 115-GHz band. We anticipate that the development of metasurfaces with display-grade LC thickness will promote the industrial use of subterahertz bands in next-generation mobile communications.

Percutaneous nephrostomy is widely used in kidney access surgeries. Despite its prevalence in urological interventions, it presents two operational challenges: 1) precise needle placement into the renal pelvis; and 2) avoiding hemorrhage from blood vessel rupture. In this study, we developed an endoscopic optical coherence tomography probe for needle navigation. We conducted experiments on thirty-one human kidneys for two aspects: 1) tissue recognition, and 2) blood vessel detection. Experimental results indicated that renal tissues including cortex, medulla, calyx, sinus fat, and pelvis could be effectively distinguished through structural optical coherence tomography imaging, and renal blood flow could be detected through the Doppler function. Deep learning methods were utilized to automate recognition procedures. For tissue classification, an Inception model was used, achieving a recognition accuracy of 99.6%. For blood vessel detection, an nnU-net model was applied, exhibiting an intersection over union value of 0.8917 for blood vessel and 0.9916 for background.

Wireless power transfer (WPT) for stent-based neuroprosthetic devices, such as endovascular electrocorticography (endoECoG) systems, is typically constrained by the need for long lead wires to subcutaneous chest implants. This study presents a method for delivering power directly to an unmodified medical stent. The proposed system employs a subcutaneous relay that converts inductive coupling to capacitive coupling, thereby improving power transfer efficiency, reducing invasiveness, and mitigating instability in skin-contact capacitance. Experimental validation using skin, bone, and vessel tissues, combined with finite element simulations, demonstrated over 45 mW of delivered power, sufficient for endoECoG and biosignal sensing. The proposed system achieved 7.26% DC-to-DC efficiency, the highest reported for stent-based implants without custom stents or auxiliary transceivers. Measured results closely matched simulations, validating the experiment results. Safety assessments, including specific absorption rate and thermal analysis, confirmed compliance with regulatory limits. While the experimental results indicate robust performance, further theoretical analysis is required to establish a complete mechanistic understanding of the underlying coupling processes. The proposed architecture enables efficient, safe, and fully wireless power delivery to endovascular implants without requiring close skin contact, supporting long-term implantation, enhancing patient comfort, and facilitating future clinical translation.

The absence of effective reinforcement has hindered the widespread use of 3D-printed concrete. In this study, we propose an innovative in-process embedding technology that enables the simultaneous printing of concrete and flexible fiber-reinforced polymer grids. We investigate the performance of grids as reinforcement in 3D-printed concrete through pull-out and splitting tensile tests to assess the bonding properties between the grids and concrete. Additionally, three-point bending tests were conducted to evaluate the impact of grids on the flexural performance of 3D-printed concrete plates. Test results show that the load-bearing capacity and deflection of 3D-printed concrete plates reinforced with grids increased by approximately 41% and 552%, respectively. However, the voids and reduced contact area caused by the simultaneous printing of the grids weakened the bonding between adjacent concrete layers. Overall, the findings confirm the feasibility of the proposed in-process embedding technology and demonstrate its protential to advance 3D-printed concrete structures through the effective integration of in-processfiber-reinforced polymer reinforcement.

This research reports the synthesis of a wave altering non-contact design. Resonators enable vibration and wave modulation, but typically require mechanical attachments that modify host properties and limit retrofit. Inspired by remote field technologies, we introduce a tunable non-contact resonator that traps propagating wavefronts and suppresses targeted modes in elastic structures. Eddy-current interactions provide remote, bidirectional coupling between structural vibrations and coil voltage. Analog impedance converters tune the shunt impedance, establishing LC-resonance at select frequencies while compensating for dissipative loss. In this manner, local resonance is induced on electrically conductive media, facilitating tunable dispersion and modal suppression. Intrinsic host properties are preserved, facilitating non-intrusive elastodynamic control for in-service retrofit, delicate structures, and challenging environments. Analytical modeling predicts underlying electromagnetics and structural dynamics, informing electrical tunings while highlighting functional dependencies. Experiments demonstrate tunable suppression and wave-blocking, unveiling constraints and improvement paths. The results establish a foundation for adaptive, non-contact elastic metamaterials.

Magnetic manipulation is increasingly used in medical applications for its potential in remote control. However, precise magnetic field generation in large workspaces remains challenging. This paper introduces an adaptive robotic end effector, the tunable magnetic end effector (TME), capable of generating spatially controllable magnetic fields. By integrating permanent magnets, the TME enables accurate magnetic control for wireless manipulation of miniaturized medical devices. Compared to standard switchable permanent magnets, TME offers enhanced field control suited for delicate operations. Finite element (FEM) simulations and experiments confirm reliable ON/OFF field switching, showing a 7.2% average error. Key design parameters (magnet size, material, and arrangement) were optimized via simulation. An artificial neural network (ANN), trained on spatial, rotational, and magnetic data, enables adaptive control. Proof-of-concept demos include steering millimeter-scale magnetic carriers, shaping magnetic soft robots, and directing magnetic nanoparticle swarms. The dual-TME configuration further expands the effective manipulation workspace and enables dynamic switching of magnetic field directions across different regions, thereby enhancing the system's applicability.

Efficient identification of cathode chemistry in end-of-life lithium-ion batteries is essential for enabling effective battery recycling. Current approaches often rely on battery disassembly or time-consuming testing, limiting their practical use at scale. Here we report a rapid classification strategy based on X-ray fluorescence spectroscopy combined with statistical analysis. A reference dataset was established from high-quality elemental spectra collected from more than 100 end-of-life lithium-ion batteries. Statistical grouping was used to define cathode categories, which were validated by selective disassembly and complementary chemical analysis. The trained classification model was then applied to newly acquired spectra collected within seconds per battery, enabling fast identification without additional disassembly. The approach achieves high prediction accuracy across the studied dataset and demonstrates the feasibility of rapid cathode identification for battery recycling applications.