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Saniya Patil

Publications and source records attributed to Saniya Patil.

2 recordsLinked to original sources

Metalens-transparent ultrasound transducer module for extended depth photoacoustic microscopy

Optical-resolution photoacoustic microscopy (ORPAM) provides label-free optical-absorption contrast at millimeter-scale tissue depths with micrometer-scale lateral resolution. However, its volumetric coverage is limited by the short depth of focus of conventional optical lenses, whereas system miniaturization is constrained by bulky optics and ultrasound transducers. Here, we present an extended-depth ORPAM system that combines an ultra-thin metalens for optical excitation with a planar transparent ultrasound transducer (TUT) for photoacoustic detection. Fabricated from piezoelectric lithium niobate, the chip-scale TUT provides ~80% optical transparency and enables coaxial optical excitation with simultaneous acoustic detection at 14.4 MHz center frequency. This architecture eliminates the need for bulky acousto-optic combiners and large-volume acoustic coupling arrangements between the transducer and imaging target. We evaluate three ultra-thin metalens designs as hyperbolic, quadratic, and extended depth-of-focus (EDOF), which provide effective axial ranges of 0.3, 0.8, and 1.3 mm, respectively, with lateral resolutions of 1.1 micron for hyperbolic, 1.2 micron quadratic lenses, and 1.5 micron for EDOF across the full axial range. Phantom experiments demonstrate improved visualization of multilayered, inclined, volumetrically distributed targets with the EDOF design compared with the two other metalenses. Label-free imaging in awake, head-fixed mice further demonstrates that EDOF provides extended-depth visualization of subcortical microvasculature without being subjected to anesthesia-induced confounds. This chip-scale metalens-TUT architecture provides a foundation for developing compact photoacoustic microscopy systems.

physics.optics↗

Prediction of Maneuvering Status for Aerial Vehicles using Supervised Learning Methods

Aerial Vehicles follow a guided approach based on Latitude, Longitude and Altitude. This information can be used for calculating the status of maneuvering for the aerial vehicles along the line of trajectory. This is a binary classification problem and Machine Learning can be leveraged for solving such problem. In this paper we present a methodology for deriving maneuvering status and its prediction using Linear, Distance Metric, Discriminant Analysis and Boosting Ensemble supervised learning methods. We provide various metrics along the line in the results section that give condensed comparison of the appropriate algorithm for prediction of the maneuvering status.

cs.RO↗