Terahertz Pulse Shaping Using Diffractive Surfaces

Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive networks merge wave-optics with deep learning to design task-specific elements to all-optically perform various tasks such as object classification and machine vision. Here, we present a diffractive network, which is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact pulse engineering system. We experimentally demonstrate the synthesis of square pulses with different temporal-widths by manufacturing passive diffractive layers that collectively control both the spectral amplitude and the phase of an input terahertz pulse. Our results constitute the first demonstration of direct pulse shaping in terahertz spectrum, where a complex-valued spectral modulation function directly acts on terahertz frequencies. Furthermore, a Lego-like physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.Comment: 27 pages, 6 figure

Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks

3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction. Here, we demonstrate the design of optical networks in the form of multiple diffractive layers that are trained using deep learning to transform and encode the spatial information of objects into the power spectrum of the diffracted light, which are used to perform optical classification of objects with a single-pixel spectroscopic detector. Using a time-domain spectroscopy setup with a plasmonic nanoantenna-based detector, we experimentally validated this machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also report the coupling of this spectral encoding achieved through a diffractive optical network with a shallow electronic neural network, separately trained to reconstruct the images of handwritten digits based on solely the spectral information encoded in these ten distinct wavelengths within the diffracted light. These reconstructed images demonstrate task-specific image decompression and can also be cycled back as new inputs to the same diffractive network to improve its optical object classification. This unique machine vision framework merges the power of deep learning with the spatial and spectral processing capabilities of diffractive networks, and can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with spectrally encoded classification tasks performed through diffractive optical networks.Comment: 21 pages, 5 figures, 1 tabl

Identification of pathogenic bacteria in complex samples using a smartphone based fluorescence microscope

Diagnostics based on fluorescence imaging of biomolecules is typically performed in well-equipped laboratories and is in general not suitable for remote and resource limited settings. Here we demonstrate the development of a compact, lightweight and cost-effective smartphone-based fluorescence microscope, capable of detecting signals from fluorescently labeled bacteria. By optimizing a peptide nucleic acid (PNA) based fluorescence in situ hybridization (FISH) assay, we demonstrate the use of the smartphone-based microscope for rapid identification of pathogenic bacteria. We evaluated the use of both a general nucleic acid stain as well as species-specific PNA probes and demonstrated that the mobile platform can detect bacteria with a sensitivity comparable to that of a conventional fluorescence microscope. The PNA-based FISH assay, in combination with the smartphone-based fluorescence microscope, allowed us to qualitatively analyze pathogenic bacteria in contaminated powdered infant formula (PIF) at initial concentrations prior to cultivation as low as 10 CFU per 30 g of PIF. Importantly, the detection can be done directly on the smartphone screen, without the need for additional image analysis. The assay should be straightforward to adapt for bacterial identification also in clinical samples. The cost-effectiveness, field-portability and simplicity of this platform will create various opportunities for its use in resource limited settings and point-of-care offices, opening up a myriad of additional applications based on other fluorescence-based diagnostic assays

Machine Learning-Enabled Optical Sensors and Devices

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/�L 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

Machine Learning-Enabled Optical Sensors and Devices

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/�L 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

KOSGEB devlet desteği alan genç girişimcilerin karşılaştığı problemler.

This Master’s thesis aims to determine the problems faced by young entrepreneurs benefiting from state support in Turkey. It describes and examines the results of a survey completed by more 1,000 young entrepreneurs benefiting from the KOSGEB Entrepreneurship Support Program. The survey participants were asked about four different subjects. The impact of gender differences, education level, regions, and sector of entrepreneurs on problems of young entrepreneurs were also investigated. The results were interpreted and analyzed statistically. The problems of young entrepreneurs who benefit from state support in Turkey are compared with those of young entrepreneurs in other countries.Thesis (M.S.) -- Graduate School of Social Sciences. Business Administration

AYDIN İLİ KÖPEKLERİNDE BULUNAN HEPATOZOON CANİS’İN TEŞHİSİNDE MİKROSKOBİK VE PCR BULGULARININ KARŞILAŞTIRILMASI

AYDIN İLİ KÖPEKLERİNDE BULUNAN HEPATOZOON CANİS’İN TEŞHİSİNDE MİKROSKOBİK VE PCR BULGULARININ KARŞILAŞTIRILMASI DEMİRBİLEK M.V. Aydın Adnan Menderes Üniversitesi Sağlık Bilimleri Enstitüsü Parazitoloji (Veteriner) Programı, Yüksek Lisans Tezi, Aydın, 2019. Hepatozoon canis köpeklerde ölümle sonuçlanabilen hastalık tablosuna neden olan kene kaynaklı protozoal bir parazittir. Bu çalışmada Aydın ilindeki sahipli köpeklerde Hepatozoon canis’in varlığının mikroskobik ve PCR ile belirlenerek iki teşhis yönteminin sonuçlarının karşılaştırılması amaçlanmıştır. Aydın Adnan Menderes Üniversitesi Veteriner Fakültesi Hayvan Hastanesi’ne gelen köpeklerden toplanan kan örneklerinden ince yayma preparatlar hazırlanarak mikroskobik olarak incelenmişlerdir. Aynı örneklerden DNA ekstraksiyonu yapılarak parazite ait DNA varlığı PCR ile incelenmiş, elde edilen sonuçlar karşılaştırılmıştır. Mikroskobik incelemede hiçbir kan örneğinde H.canis gamontuna rastlanmaz iken PCR ile üç kan örneğinde pozitiflik saptanmıştır. Ülkemizde köpeklerde hepatozoonosis’in prevalansı hakkında daha önce yapılan çalışmalar da mevcuttur. Bu çalışma ile de köpek hepatozoonozisi’nin tanı ve tedavi prosedürlerinin geliştirilmesi açısından katkı sağlanacağı düşünülmektedir.İÇİNDEKİLER KABUL VE ONAY SAYFASI i TEŞEKKÜR ii İÇİNDEKİLER iii SİMGELER VE KISALTMALAR DİZİNİ iv ŞEKİLLER DİZİNİ v RESİMLER DİZİNİ vi TABLOLAR DİZİNİ vii ÖZET viii ABSTRACT ix 1. GİRİŞ 1 2. GENEL BİLGİLER 4 2.1 Prevalans 6 2.2 Yaşam Döngüsü 11 2.3.1. Klinik Bulgular 17 2.3.2. Patolojik Bulgular 20 2.4. Teşhis 21 2.5. Tedavi 22 3.GEREÇ VE YÖNTEM 25 3.1.Mikroskobik Teşhis 25 3.2.DNA ekstraksiyonu 25 3.3.PCR 26 3.3.1. Sekans ve filogenetik analizleri 27 4. BULGULAR 29 4.1. Moleküler Bulgular 30 5. TARTIŞMA 39 6. SONUÇ VE ÖNERİLER 43 KAYNAKLAR 44 ÖZGEÇMİŞ 5

Adaptive neural-network based fuzzy logic (ANFIS) based trajectory controller design for one leg of a quadruped robot

5th International Conference on Mechatronics and Control Engineering, ICMCE 2016 -- 14 December 2016 through 17 December 2016 -- -- 126966In this paper, a hybrid learning algorithm referred to as Adaptive Neuro Fuzzy Inference System (ANFIS) is used to obtain a neural-network based fuzzy logic (NNFL) controller to ensure walking in desired trajectory of the one leg of a quadruped robot. Firstly, Computer aided model drawing (CAD) model of system is converted into the Simulink/SimMechanics and PID controllers applied to the system Then, input and output data are obtained from PID controller set up training and checking data sets of the ANFIS. After trained network in the MATLAB/Fuzzy Logic Toolbox, NNFL controllers is acquired and applied to the system. PID controls and NNFL controllers are simulated in the MATLAB/Simulink and compared with each other according their performances in the trajectory tracking. The Simulation results are presented in graphical form to investigate the controllers. © 2016 ACM