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We propose a rapid and precise scheme for characterizing the full-field frequency response of a thin-film lithium niobate-based intensity modulator (TFLN-IM) via a specially designed multi-tone microwave signal. Our proposed scheme remains insensitive to the bias-drift of IM. Experimental verification is implemented with a self-packaged TFLN-IM with a 3 dB bandwidth of 30 GHz. In comparison with the vector network analyzer (VNA) characterization results, the deviation values of the amplitude-frequency response (AFR) and phase-frequency response (PFR) within the 50 GHz bandwidth are below 0.3 dB and 0.15 rad, respectively. When the bias is drifted within 90% of the V range, the deviation fluctuation values of AFR and PFR are less than 0.3 dB and 0.05 rad, respectively. With the help of the full-field response results, we can pre-compensate the TFLN-IM for the 64 Gbaud PAM-4 signals under the back-to-back (B2B) transmission, achieving a received optical power (ROP) gain of 2.3 dB. The versatility of our proposed full-field response characterization scheme can extend to various optical transceivers, offering the advantage of low cost, robust operation, and flexible implementation.
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http://dx.doi.org/10.1364/OL.519329 | DOI Listing |
Sensors (Basel)
August 2025
School of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.
Due to the slender geometry and low-amplitude vibrations of stayed cables, existing vision-based methods often fail to accurately identify their full-field dynamic parameters, especially the higher-order modes. This paper proposes a novel holographic vision-based method to accurately identify the high-order full-field dynamic parameters and estimate the tension of the stayed cables. Particularly, a full-field optical flow tracking algorithm is proposed to obtain the full-field dynamic displacement information of the stayed cable by tracking the changes in the optical flow field of the continuous motion signal spectral components of holographic feature points.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Mechanical Science and Engineering, University of Illinois, Urbana, IL, USA.
We introduce an advanced turbulence spectrum model developed from mathematical foundations from a covariance function class and empirically validated using extensive field data. This model captures the complex dynamics of long-range dependence, and fractal characteristics prevalent in riverine and atmospheric boundary layer (ABL) flows that are ignored by classical spectrum models, such as IEC (International Electrotechnical Commission) von Kármán and Kaimal model. The model delineates scaling behaviors across distinct frequency bands and offers substantial flexibility through five well-defined parameters each characterizing a distinct physical aspect of the velocity time series.
View Article and Find Full Text PDFBr J Ophthalmol
August 2025
Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
Purpose: -associated retinitis pigmentosa (RP) is a rare inherited retinal disease leading to severe vision loss and blindness, with no available treatment. This study assessed the safety and vision outcomes of a gene therapy using an adeno-associated virus (AAV) vector encoding PDE6A (AAV8.hPDE6A).
View Article and Find Full Text PDFWith the advancement of spectral technologies, fiber spectrometers have found widespread applications in scientific research, agriculture, biomedicine, and industrial production. Currently, conventional fiber spectrometers typically employ dispersive elements such as gratings or prisms in combination with array detectors to capture spectral information in a single exposure. However, the fixed detector size imposes a trade-off between spectral range and spectral resolution, thereby limiting instrument performance.
View Article and Find Full Text PDFThis paper presents two end-to-end digital image correlation (DIC) models-D-ST and S-ST-that leverage the Swin Transformer architecture to accurately predict full-field displacement and strain distributions. Unlike conventional DIC methods and existing CNN-based approaches, our models integrate local and global information via window-based and shifted window-based multi-head self-attention mechanisms, enabling robust and precise measurement of high-frequency deformation features. Utilizing a U-Net-like encoder-decoder framework with a multiscale feature fusion strategy, the proposed models address longstanding challenges in capturing complex strain gradients and nonlinear deformation patterns.
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