We present an asynchronous phase-shifting demodulation approach based on the principal component analysis demodulation method that is robust to typical problems as turbulence, vibrations, and temporal instabilities of the optical setup. The method brings together a two-step and a phase-shifting asynchronous demodulation method to share their benefits while reducing their intrinsic limitations. Thus, the proposed approach is based on a two-fold process. First, the modulating phase is estimated from a two-step demodulation approach. Second, this information is used to compute weights to each phase-shifted pattern of the interferogram sequence, which are used in a novel weighted principal component demodulation approach. The proposed technique has been tested with simulated and real interferograms affected by turbulence and vibrations providing very satisfactory results in challenging cases.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/OE.416344 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A recent advancement in distributed sensing known as the time-expanded phase-sensitive optical time-domain reflectometry (TE Φ-OTDR) addresses the trade-off between spatial resolution and detection bandwidth, enabling centimeter-scale resolution alongside RF detection bandwidth in the order of MHz. To date, TE Φ-OTDR approaches extract the fiber response from the first Nyquist zone (NZ). In this Letter, we propose a post-processing strategy to enhance the SNR by spectrally averaging different NZs.
View Article and Find Full Text PDFUltrasound imaging with flexible transducers based on real-time and high-accuracy shape estimation.
Ultrasonics
December 2024
Department of Biomedical Engineering, Fudan University, Shanghai 200438, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200032, China. Electronic address:
Article Synopsis
- Ultrasound imaging with flexible transducers needs precise shape geometry for effective beamforming, but traditional methods for shape estimation are costly in terms of computation time and often lack accuracy.
- A new deep-learning method, FlexSANet, offers a fast and accurate approach by demodulating I/Q data and using a sparse processing mechanism to reduce estimation time.
- Evaluations show that FlexSANet achieves high-quality imaging with minimal error in shape estimation (less than 1/8 wavelengths), and demonstrates promising clinical application potential with an estimation time of just 0.12 seconds.
Multiple-input multiple-output (MIMO) technology, a core component of 6G, has been widely adopted in optical wireless communication (OWC) systems. Accurate recognition of different MIMO types is essential for MIMO selection and demodulation. In this Letter, we propose an open-set MIMO recognition method for OWC systems using a Siamese neural network (SNN).
View Article and Find Full Text PDFIn optical communication, the transmitter encodes information into a set of light states defined by the modulation format, selected to accommodate specific channel conditions and to remain sufficiently distinguishable at the output. Various receiver architectures have been designed to improve the demodulation performance, ultimately limited by quantum theory. In this work, I introduce a new receiver based on a locally optimal greedy algorithm and apply it to pulse position modulation.
View Article and Find Full Text PDFCryogenic trapped-ion systems (CTISs) have emerged as indispensable platforms for the advancement of quantum computation and precision measurement techniques. However, the sensitivity of these systems to vibrational noise, especially during the compression and expansion cycles of the cold head in a Gifford-McMahon cycle refrigerator (GMCR), poses a significant challenge. To mitigate this, we have crafted an innovative methodology for characterizing low-frequency residual vibrational noise in closed-cycle cryogenic trapped-ion systems.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!