Imaging through single multimode fiber prevails its counterpart using single mode fiber bundle on spatial resolution limit and minimum radius. Current multimode fiber imaging suffers from fussy calibration, which can be reduced by recent developed compressive sensing scheme [ [L. V. Amitonova, Opt. Lett. 43, 5427 (2018)]. Experiments demonstrate improvement on depth of field by more than three orders of magnitude, together with robustness against macro fiber bending, which is vital to endoscopic applications.

Cognitive impairment is common but often overlooked in patients with inflammatory demyelinating diseases such as multiple sclerosis and neuromyelitis optica spectrum disorder. The conventional assessments may fail to detect subtle deficits and require substantial time and expertise. We collected neuropsychological scores and real-time handwriting data across nine drawing tasks and tasks from the Symbol Digit Modalities Test in 93 patients. Temporal, pressure, and kinematic features were extracted, and machine learning classifiers were trained using five-fold cross-validation with bootstrap confidence intervals. The response timing and pen pressure metrics correlated significantly with global cognitive scores (|r| = 0.30–0.37, p < 0.01). A support vector machine using eight selected features achieved an area under the receiver-operating characteristic curve (AUC) of 0.910, and a streamlined five-feature variant maintained an equivalent performance (AUC = 0.921) while reducing the assessment time by 35%. These results indicate that digital handwriting metrics can complement the standard screening by capturing fine motor and temporal characteristics overlooked in conventional testing. Validation in larger, disease-balanced, and longitudinal cohorts is needed to confirm their clinical utility.

Truncated singular value decomposition (TSVD) is a popular recovery algorithm for multimode fiber single-pixel imaging (MMF-SPI), and it uses truncation thresholds to suppress noise influences. However, due to the sensitivity of MMF relative to stochastic disturbances, the threshold requires frequent re-determination as noise levels dynamically fluctuate. In response, we design an adaptive truncation threshold determination (ATTD) method for TSVD-based MMF-SPI in disturbed environments. Simulations and experiments reveal that ATTD approaches the performance of ideal clairvoyant benchmarks, and it corresponds to the best possible image recovery under certain noise levels and surpasses both traditional truncation threshold determination methods with less computation—fixed threshold and Stein’s unbiased risk estimator (SURE)—specifically under high noise levels. Moreover, target insensitivity is demonstrated via numerical simulations, and the robustness of the self-contained parameters is explored. Finally, we also compare and discuss the performance of TSVD-based MMF-SPI, which uses ATTD, and machine learning-based MMF-SPI, which uses diffusion models, to provide a comprehensive understanding of ATTD.

Polarization-maintaining rectangular core fiber is proposed. Eight modes are hold simultaneously under regular cladding, with extreme small dispersion over entire C and L bands. It’s suitable for long-distance, high-capacity MIMO-free spatial division multiplexing.

Numerical simulation recommends coherently illuminating multiple cores, other than increasing core number, as the way to get enough projecting patterns for high resolution single pixel imaging endoscopy using multicore fiber and photonic lantern.

A real-valued neural network overwhelmingly outyerforms its complex-valued counterpart in natural scene reconstruction through multimode fiber, suggesting that knowledge of intensity rather than phase information is enough, therefore brings much robustness and convenience in practice.