An imaging system and method evaluates non-uniformity or irregularity in reflective displays, such as assembled display modules of the type found in smartphones, tablets and the like. The system includes an incoherent light, such as a light-emitting diode (LED), which is polarized and collimated. The surface to be evaluated is perpendicular to the collimated light, such that the light impinges directly upon the surface. The polarization of the light is altered before and after reflection, and the reflected light from the surface under evaluation is received by a sensor. Non-uniformity or irregularity of the surface will appear in the sensed image as contrast variation. Because the reflection from the surface under evaluation is a 180-degree reflection, the sensed image can be in sharp focus across the entire surface to be evaluated. Optionally, the system may utilize a single collimation lens without a collection lens for efficiency and compactness.
An interferometer detection system, including a beam splitter receiving a collimated light signal and splitting the signal into a first light signal and a second light signal. The system includes a first mirror receiving and reflecting the first light signal along a first path. The system includes a second mirror receiving and reflecting the second light signal along a second path via a transparent material. The system includes a 2D photosensor array configured to receive from the beam splitter the reflected first light signal merged with the reflected second light signal double passing through the transparent material and configured to generate an interference fringe pattern. A non-sinusoidal interference fringe pattern indicates geometrical variation between a wavefront of the reflected first light signal along the first path and a wavefront of the reflected second light signal double passing through the transparent material along the second path.
Virtualized Computing Platform For Inferencing, Advanced Processing, And Machine Learning Applications
- San Jose CA, US Bojan Vukojevic - Pleasanton CA, US Risto Haukioja - Palo Alto CA, US Andrew Feng - Cupertino CA, US Yan Cheng - Great Falls VA, US Sachidanand Alle - Cambridge, GB Daguang Xu - Potomac MD, US Holger Reinhard Roth - Rockville MD, US Johnny Israeli - San Jose CA, US
In various examples, a virtualized computing platform for advanced computing operations—including image reconstruction, segmentation, processing, analysis, visualization, and deep learning—may be provided. The platform may allow for inference pipeline customization by selecting, organizing, and adapting constructs of task containers for local, on-premises implementation. Within the task containers, machine learning models generated off-premises may be leveraged and updated for location specific implementation to perform image processing operations. As a result, and using the virtualized computing platform, facilities such as hospitals and clinics may more seamlessly train, deploy, and integrate machine learning models within a production environment for providing informative and actionable medical information to practitioners.
Westchester Medical Center Oncology 100 Wood Rd STE 7S, Valhalla, NY 10595 (914)4937488 (phone), (914)4937483 (fax)
Education:
Medical School St. George's University School of Medicine, St. George's, Greneda Graduated: 2011
Languages:
English
Description:
Dr. Cheng graduated from the St. George's University School of Medicine, St. George's, Greneda in 2011. He works in Valhalla, NY and specializes in Hematology/Oncology. Dr. Cheng is affiliated with Westchester Medical Center.