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In multi-heterodyne interferometry, the non-ambiguous range (NAR) and measurement accuracy are limited by the generation of synthetic wavelengths. In this paper, we propose a multi-heterodyne interferometric absolute distance measurement based on dual dynamic electro-optic frequency combs (EOCs) to realize high-accuracy distance measurement with large scale. The modulation frequencies of the EOCs are synchronously and quickly controlled to perform dynamic frequency hopping with the same frequency variation. Therefore, variable synthetic wavelengths range from tens of kilometer to millimeter can be flexibly constructed, and traced to an atomic frequency standard. Besides, a phase-parallel demodulation method of multi-heterodyne interference signal is implemented based on FPGA. Experimental setup was constructed and absolute distance measurements were performed. Comparison experiments with He-Ne interferometers demonstrate an agreement within 8.6 µm for a range up to 45 m, with a standard deviation of 0.8 µm and a resolution better than 2 µm at 45 m. The proposed method can provide sufficient precision with large scale for many science and industrial applications, such as precision equipment manufacturing, space mission, length metrology.
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http://dx.doi.org/10.1364/OE.487340 | DOI Listing |
Int J Comput Assist Radiol Surg
September 2025
Division of Plastic and Reconstructive Surgery, Neonatal and Pediatric Craniofacial Airway Orthodontics, Department of Surgery, Stanford University School of Medicine, 770 Welch Road, Palo Alto, CA, 94394, USA.
Background: Alveolar molding plate treatment (AMPT) plays a critical role in preparing neonates with cleft lip and palate (CLP) for the first reconstruction surgery (cleft lip repair). However, determining the number of adjustments to AMPT in near-normalizing cleft deformity prior to surgery is a challenging task, often affecting the treatment duration. This study explores the use of machine learning in predicting treatment duration based on three-dimensional (3D) assessments of the pre-treatment maxillary cleft deformity as part of individualized treatment planning.
View Article and Find Full Text PDFJ Cardiovasc Magn Reson
September 2025
Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts.
Introduction: Mitral annular disjunction (MAD) is a pathologic fibrous separation of the mitral valve hinge point from the ventricular myocardium. The aims of this study were to describe the range of MAD distance by cardiac magnetic resonance (CMR) in children and young adults with connective tissue disorders (CTDs) versus a healthy control sample, and to assess the MAD distance as a predictor of adverse cardiovascular outcomes.
Methods: This was a retrospective, single-center study of healthy subjects and patients with Marfan syndrome, Loeys-Dietz syndrome, Ehlers-Danlos syndrome, or nonspecific CTD who underwent CMR between 01/01/2000 and 01/01/2020.
Ophthalmol Sci
July 2025
Illinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
Purpose: To validate a custom FIJI (ImageJ) program for more reproducible, faster curvilinear periorbital measurements, as compared with 2 custom artificial intelligence-based tools.
Design: Combined technical validation and method comparison study.
Subjects: Front-facing photographs of 45 cleft palate syndromic patients.
Ophthalmol Sci
July 2025
Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio.
Purpose: To determine the proximity between the thinnest corneal point (TCP) and focal corneal weakening in normal, subclinical keratoconus (SKC), and manifest keratoconus (KC) eyes using motion-tracking Brillouin microscopy.
Design: Prospective cross-sectional study.
Participants: Ninety-five eyes from 95 patients were evaluated: 40 from bilaterally normal patients (controls), 40 from patients with SKC, and 15 from patients with manifest KC.
Poult Sci
August 2025
Department of Agricultural Education, Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand. Electronic address:
Bruising chicken broiler is caused by physical stress and injury to the skin and underlying tissues is a major problem in poultry production, affecting both animal welfare and economic outcomes. The aim of this study was to classify the bruising class (low or high percentage of carcass showing bruise at slaughterhouse) per truckload comparing the predictive performance of six machine learning (ML) models- Least Absolute Shrinkage and Selection Operator (LASSO), Classification Tree (CT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB)- and using a data set including information about season, time of transport, sex of the flock, flock size, chicken age, chicken mean body weight, housing stocking density, on farm mortality and culling rate, and feed withdrawal time. The general objective was to offer tools for the early detection of flocks with a higher likelihood of bruising and to highlight how ML can support decision-making, strengthen welfare monitoring programs, and reduce economic losses in commercial broiler production.
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