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Machine Learning-Enabled Optical Sen...

Veli, Muhammed,

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Machine Learning-Enabled Optical Sensors and Devices /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning-Enabled Optical Sensors and Devices // Muhammed Veli.
作者:	Veli, Muhammed,
面頁冊數:	1 electronic resource (140 pages)
附註:	Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:	Dissertations Abstracts International82-06B.
標題:	Optics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28260680
ISBN:	9798557004299

Machine Learning-Enabled Optical Sensors and Devices /
Veli, Muhammed,

Machine Learning-Enabled Optical Sensors and Devices /Muhammed Veli. - 1 electronic resource (140 pages)

Source: Dissertations Abstracts International, Volume: 82-06, Section: B.

Machine learning has been transforming many fields including optics by creating a new avenue for designing optical sensors and devices. This new paradigm takes a data driven approach, without focusing on underlying physics of the design. This new alternative and yet powerful method brings new advancements to traditional design tools and opens up numerous opportunities.This dissertation introduces machine learning-enabled optical sensors and devices in which computational imaging and deep learning based design of devices tackle various challenges. First, a cost-effective and portable platform is presented to non-invasively detect and monitor a bacteria that resides in human ocular microbiome, Staphylococcus aureus. Contact lenses are designed to capture S. aureus using surface chemistry protocol, and sandwich immunoassay with polystyrene microbeads is performed to tag captured bacteria. Lens-free on-chip microscope is used to obtain a single hologram of the contact lens surface and 3D surface of it is computationally reconstructed. Support vector machine based machine learning algorithm is employed to detect and count the amount of bacteria on contact lens surface. This platform, which only weighs 77 g, is controlled by laptop and provides ~16 bacteria/µL detection limit. This wearable sensor platform can be used to analyze and monitor other viruses and bacteria in tear with the appropriate modification to its surface chemistry protocol. Second, a novel physical mechanism is introduced, diffractive optical networks, to perform all-optical machine learning using passive diffractive layers that work together to implement various functions. This framework merges wave-optics with deep learning to all optically perform different tasks. A classification of handwritten digits and fashion products were demonstrated with 3D-printed diffractive optical networks. Moreover, a diffractive optical network is designed to function as an imaging lens in terahertz spectrum. This scalable platform can execute various functions at the speed of light with low power and help us to design exotic optical components. Third, terahertz pulse shaping architecture using diffractive optical surfaces is introduced. This platform engineers arbitrary broadband input pulse into desired waveform. Synthesis of various pulses has been demonstrated by designing and fabricating diffractive layers. This works constitutes the first demonstration of direct pulse shaping in terahertz spectrum with precise control of amplitude and phase of input broadband light over a wide frequency range. This approach can also find applications in other fields like optical communications, spectroscopy and ultra-fast imaging.

English

ISBN: 9798557004299Subjects--Topical Terms:

517925
Optics.
Subjects--Index Terms:

Computational imaging

Machine Learning-Enabled Optical Sensors and Devices /
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