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| 1 | PhaseStain:the digital staining of label-free quantitative phase microscopy images using deep learning显示文摘Using a deep neural network,we demonstrate a digital staining technique,which we term PhaseStain,to transform the quantitative phase images(QPI)of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained.Through pairs of image data(QPI and the corresponding brightfield images,acquired after staining),we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin,kidney,and liver tissue,matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin,Jones’stain,and Masson’s trichrome stain,respectively.This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general,by eliminating the need for histological staining,reducing sample preparation related costs and saving time.Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning. | Yair Rivenson Tairan Liu Zhensong Wei Yibo Zhang Kevin de Haan Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 19 |
| 2 | Deep learning in holography and coherent imaging显示文摘Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance.Through data-driven approaches,these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography.These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications. | Yair Rivenson Yichen Wu Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 16 |
| 3 | Computational imaging without a computer:seeing through random diffusers at the speed of light显示文摘Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase diffusers.After the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others. | Yi Luo Yifan Zhao Jingxi Li Ege Çetintaş Yair Rivenson Mona Jarrahi Aydogan Ozcan | 2022 | eLight2022，2，1： | 16 |
| 4 | A deep learning-enabled portable imaging flow cytometer for cost-effective, highthroughput, and label-free analysis of natural water samples显示文摘We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h.The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel.These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling.Motion blur is eliminated by simultaneously illuminating the sample with red,green,and blue light-emitting diodes that are pulsed.Operated by a laptop computer,this portable device measures 15.5 cm×15 cm×12.5 cm,weighs 1 kg,and compared to standard imaging flow cytometers,it provides extreme reductions of cost,size and weight while also providing a high volumetric throughput over a large object size range.We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro-and nanoplankton composition.Furthermore,we measured the concentration of a potentially toxic alga(Pseudo-nitzschia)in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health.The cost-effectiveness,compactness,and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for largescale and continuous monitoring of the ocean microbiome,including its plankton composition. | Zoltán Gӧrӧcs Miu Tamamitsu Vittorio Bianco Patrick Wolf Shounak Roy Koyoshi Shindo Kyrollos Yanny Yichen Wu Hatice Ceylan Koydemir Yair Rivenson Aydogan Ozcan | 2018 | Light(Science & Applications)2018，7，1： | 14 |
| 5 | Bright-field holography:cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram显示文摘Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram.However,unlike a conventional bright-field microscopy image,the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects.Here,we demonstrate that cross-modality deep learning using a generative adversarial network(GAN)can endow holographic images of a sample volume with bright-field microscopy contrast,combining the volumetric imaging capability of holography with the speckle-and artifact-free image contrast of incoherent bright-field microscopy.We illustrate the performance of this“bright-field holography”method through the snapshot imaging of bioaerosols distributed in 3D,matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope.This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging,and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram,benefiting from the wave-propagation framework of holography. | Yichen Wu Yilin Luo Gunvant Chaudhari Yair Rivenson Ayfer Calis Kevin de Haan Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 12 |
| 6 | Design of task-specific optical systems using broadband diffractive neural networks显示文摘Deep learning has been transformative in many fields,motivating the emergence of various optical computing architectures.Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks.Diffraction-based all-optical object recognition systems,designed through this framework and fabricated by 3D printing,have been reported to recognize handwritten digits and fashion products,demonstrating all-optical inference and generalization to sub-classes of data.These previous diffractive approaches employed monochromatic coherent light as the illumination source.Here,we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning.We experimentally validated the success of this broadband diffractive neural network architecture by designing,fabricating and testing seven different multi-layer,diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize(1)a series of tuneable,single-passband and dual-passband spectral filters and(2)spatially controlled wavelength de-multiplexing.Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy,broadband diffractive neural networks help us engineer the light–matter interaction in 3D,diverging from intuitive and analytical design methods to create taskspecific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning. | Yi Luo Deniz Mengu Nezih T.Yardimci Yair Rivenson Muhammed Veli Mona Jarrahi Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 9 |
| 7 | Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue显示文摘Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections,rendering the tissue constituents visible for microscopic analysis by medical experts.However,this process is time consuming,labour intensive,expensive and destructive to the specimen.Recently,the ability to virtually stain unlabelled tissue sections,entirely avoiding the histochemical staining step,has been demonstrated using tissue-stain-specific deep neural networks.Here,we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images,in which different stains are merged following a micro-structure map defined by the user.This approach uses a single deep neural network that receives two different sources of information as its input:(1)autofluorescence images of the label-free tissue sample and(2)a“digital staining matrix”,which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section.This digital staining matrix is also used to virtually blend existing stains,digitally synthesizing new histological stains.We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin(H&E),Jones’silver stain,and Masson’s trichrome stain.Using a single network,this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section,which is currently not feasible with standard histochemical staining methods. | Yijie Zhang Kevin de Haan Yair Rivenson Jingxi Li Apostolos Delis Aydogan Ozcan | 2020 | Light(Science & Applications)2020，9，1： | 7 |
| 8 | Class-specific differential detection in diffractive optical neural networks improves inference accuracy显示文摘Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine learning.Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference,achieving promising performance for object classification and imaging.We demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity.In this differential detection scheme,each class is assigned to a separate pair of detectors,behind a diffractive optical network,and the class inference is made by maximizing the normalized signal difference between the photodetector pairs.Using this differential detection scheme,involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons,we numerically achieved blind testing accuracies of 98.54%,90.54%,and 48.51%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Moreover,by utilizing the inherent parallelization capability of optical systems,we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in parallel.We further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural networks.Using this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel,our simulations achieved blind testing accuracies of 98.52%,91.48%,and 50.82%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively,coming close to the performance of some of the earlier generations of all-electronic deep neural networks,e.g.,LeNet,which achieves classification accuracies of 98.77%,90.27%,and 55.21%corresponding to the same datasets,respectively.In addition to these jointly optimized diffractive neural networks,we also independently optimized multiple diffractive networks and utilized them in a way that is similar to ensemble methods practiced in machine learning;using 3 independently optimized differential diffractive neural networks that optically project their light onto a common output/detector plane,we numerically achieved blind testing accuracies of 98.59%,91.06%,and 51.44%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Through these systematic advances in designing diffractive neural networks,the reported classification accuracies set the state of the art for all-optical neural network design.The presented framework might be useful to bring optical neural network-based low power solutions for various machine learning applications and help us design new computational cameras that are task-specific. | Jingxi Li Deniz Mengu Yi Luo Yair Rivenson Aydogan Ozcan | 2019 | Advanced Photonics2019，1，4： | 6 |
| 9 | Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning显示文摘Parasitic infections constitute a major global public health issue.Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis.Here,we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism.Based on this principle,a cost-effective and mobile instrument,which rapidly screens~3.2 mL of fluid sample in three dimensions,was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns.We demonstrate the capabilities of our platform by detecting trypanosomes,which are motile protozoan parasites,with various species that cause deadly diseases affecting millions of people worldwide.Using a holographic speckle analysis algorithm combined with deep learningbased classification,we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid(CSF)samples,achieving a limit of detection of ten trypanosomes per mL of whole blood(~five-fold better than the current state-of-the-art parasitological method)and three trypanosomes per mL of CSF.We further demonstrate that this platform can be applied to detect other motile parasites by imaging Trichomonas vaginalis,the causative agent of trichomoniasis,which affects 275 million people worldwide.With its costeffective,portable design and rapid screening time,this unique platform has the potential to be applied for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions. | Yibo Zhang Hatice Ceylan Koydemir Michelle M.Shimogawa Sener Yalcin Alexander Guziak Tairan Liu Ilker Oguz Yujia Huang Bijie Bai Yilin Luo Yi Luo Zhensong Wei Hongda Wang Vittorio Bianco Bohan Zhang Rohan Nadkarni Kent Hill Aydogan Ozcan | 2018 | Light(Science & Applications)2018，7，1： | 6 |
| 10 | Handheld high-throughput plasmonic biosensor using computational on-chip imaging显示文摘We demonstrate a handheld on-chip biosensing technology that employs plasmonic microarrays coupled with a lens-free computational imaging system towards multiplexed and high-throughput screening of biomolecular interactions for point-of-care applications and resource-limited settings.This lightweight and field-portable biosensing device,weighing 60 g and 7.5 cm tall,utilizes a compact optoelectronic sensor array to record the diffraction patterns of plasmonic nanostructures under uniform illumination by a single-light emitting diode tuned to the plasmonic mode of the nanoapertures.Employing a sensitive plasmonic array design that is combined with lens-free computational imaging,we demonstrate label-free and quantitative detection of biomolecules with a protein layer thickness down to 3 nm.Integrating large-scale plasmonic microarrays,our on-chip imaging platform enables simultaneous detection of protein mono-and bilayers on the same platform over a wide range of biomolecule concentrations.In this handheld device,we also employ an iterative phase retrieval-based image reconstruction method,which offers the ability to digitally image a highly multiplexed array of sensors on the same plasmonic chip,making this approach especially suitable for high-throughput diagnostic applications in field settings. | Arif E Cetin Ahmet F Coskun Betty C Galarreta Min Huang David Herman Aydogan Ozcan Hatice Altug | 2014 | Light(Science & Applications)2014，3，1： | 6 |
| 11 | Phase recovery and holographic image reconstruction using deep learning in neural networks显示文摘Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography.In this study,we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training.This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference-related spatial artifacts.This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram,requiring fewer measurements in addition to being computationally faster.We validated this method by reconstructing the phase and amplitude images of various samples,including blood and Pap smears and tissue sections.These results highlight that challenging problems in imaging science can be overcome through machine learning,providing new avenues to design powerful computational imaging systems. | Yair Rivenson Yibo Zhang Harun Günaydın Da Teng Aydogan Ozcan | 2017 | Light(Science & Applications)2017，6，1： | 5 |
| 12 | Air quality monitoring using mobile microscopy and machine learning显示文摘Rapid,accurate and high-throughput sizing and quantification of particulate matter(PM)in air is crucial for monitoring and improving air quality.In fact,particles in air with a diameter of≤2.5μm have been classified as carcinogenic by the World Health Organization.Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning.This platform,termed c-Air,is also integrated with a smartphone application for device control and display of results.This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air.It provides statistics of the particle size and density distribution with a sizing accuracy of~93%.We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times,and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring,which showed strong correlation to c-Air measurements.Furthermore,we used c-Air to map the air quality around Los Angeles International Airport(LAX)over 24 h to confirm that the impact of LAX on increased PM concentration was present even at 47 km away from the airport,especially along the direction of landing flights.With its machinelearning-based computational microscopy interface,c-Air can be adaptively tailored to detect specific particles in air,for example,various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality. | Yi-Chen Wu Ashutosh Shiledar Yi-Cheng Li Jeffrey Wong Steve Feng Xuan Chen Christine Chen Kevin Jin Saba Janamian Zhe Yang Zachary Scott Ballard Zoltán Göröcs Alborz Feizi Aydogan Ozcan | 2017 | Light(Science & Applications)2017，6，1： | 5 |
| 13 | Emerging Advances to Transform Histopathology Using Virtual Staining显示文摘In an age where digitization is widespread in clinical and preclinical workflows,pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides.Over the last decade,new high throughput digital scanning microscopes have ushered in the era of digital pathology that,along with recent advances in machine vision,have opened up new possibilities for Computer-Aided-Diagnoses.Despite these advances,the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption.Here,we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology. | Yair Rivenson Kevin de Haan W.Dean Wallace Aydogan Ozcan | 2020 | Biomedical Engineering Frontiers2020，1，1： | 4 |
| 14 | Recurrent neural network-based volumetric fluorescence microscopy显示文摘Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical,medical and life sciences.Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume.Through a recurrent convolutional neural network,which we term as Recurrent-MZ,2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field.Using experiments on C.elegans and nanobead samples,Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens,also providing a 30-fold reduction in the number of axial scans required to image the same sample volume.We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions,including e.g.,different sequences of input images,covering various axial permutations and unknown axial positioning errors.We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input,matching confocal microscopy images of the same sample volume.Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework,overcoming the limitations of current 3D scanning microscopy tools. | Luzhe Huang Hanlong Chen Yilin Luo Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，4： | 4 |
| 15 | Synthetic aperture-based on-chip microscopy显示文摘Wide field-of-view(FOV)and high-resolution imaging requires microscopy modalities to have large space-bandwidth products.Lensfree on-chip microscopy decouples resolution from FOV and can achieve a space-bandwidth product greater than one billion under unit magnification using state-of-the-art opto-electronic sensor chips and pixel super-resolution techniques.However,using vertical illumination,the effective numerical aperture(NA)that can be achieved with an on-chip microscope is limited by a poor signal-to-noise ratio(SNR)at high spatial frequencies and imaging artifacts that arise as a result of the relatively narrow acceptance angles of the sensor’s pixels.Here,we report,for the first time,a synthetic aperture-based on-chip microscope in which the illumination angle is scanned across the surface of a dome to increase the effective NA of the reconstructed lensfree image to 1.4,achieving e.g.,,250-nm resolution at 700-nm wavelength under unit magnification.This synthetic aperture approach not only represents the largest NA achieved to date using an on-chip microscope but also enables color imaging of connected tissue samples,such as pathology slides,by achieving robust phase recovery without the need for multi-height scanning or any prior information about the sample.To validate the effectiveness of this synthetic aperture-based,partially coherent,holographic on-chip microscope,we have successfully imaged color-stained cancer tissue slides as well as unstained Papanicolaou smears across a very large FOV of 20.5 mm^(2).This compact on-chip microscope based on a synthetic aperture approach could be useful for various applications in medicine,physical sciences and engineering that demand high-resolution wide-field imaging. | Wei Luo Alon Greenbaum Yibo Zhang Aydogan Ozcan | 2015 | Light(Science & Applications)2015，4，1： | 4 |
| 16 | To image,or not to image:class-specific diffractive cameras with all-optical erasure of undesired objects显示文摘Privacy protection is a growing concern in the digital era,with machine vision techniques widely used throughout public and private settings.Existing methods address this growing problem by,e.g.,encrypting camera images or obscuring/blurring the imaged information through digital algorithms.Here,we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view.After their fabrication,the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes,while instantaneously erasing objects of the other data classes at the output field-of-view.Using the same framework,we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras,where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption,while the other classes of objects are irreversibly erased from the output image.The success of class-specific diffractive cameras was experimentally demonstrated using terahertz(THz)waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset,all-optically erasing the other handwritten digits.This diffractive camera design can be scaled to different parts of the electromagnetic spectrum,including,e.g.,the visible and infrared wavelengths,to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging. | Bijie Bai Yi Luo Tianyi Gan Jingtian Hu Yuhang Li Yifan Zhao Deniz Mengu Mona Jarrahi Aydogan Ozcan | 2022 | eLight2022，2，1： | 4 |
| 17 | All-optical synthesis of an arbitrary linear transformation using diffractive surfaces显示文摘Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical components.Here,we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input(Ni)and output(No),where Ni and No represent the number of pixels at the input and output fields-of-view(FOVs),respectively.First,we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation.In addition to this data-free design approach,we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation.We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven(deep learning-based)diffractive designs to all-optically perform(i)arbitrarily-chosen complex-valued transformations including unitary,nonunitary,and noninvertible transforms,(ii)2D discrete Fourier transformation,(iii)arbitrary 2D permutation operations,and(iv)high-pass filtered coherent imaging.Our analyses reveal that if the total number(N)of spatially-engineered diffractive features/neurons is≥Ni×No,both design methods succeed in all-optical implementation of the target transformation,achieving negligible error.However,compared to data-free designs,deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N | Onur Kulce Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，10： | 3 |
| 18 | Polarization multiplexed diffractive computing:all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network显示文摘Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning.Among different approaches,diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive,free-space optical layers.Here,we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple,arbitrarily-selected linear transformations through a single diffractive network trained using deep learning.In this framework,an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic,and different target linear transformations(complex-valued)are uniquely assigned to different combinations of input/output polarization states.The transmission layers of this polarization-multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to diffferent input/output polarization combinations.Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons(N)approaches N_(p)N_(i)N_(o),where Ni and N_(o) represent the number of pixels at the input and output fields-of-view,respectively,and N_(p) refers to the number of unique linear transformations assigned to different input/output polarization combinations.This polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks. | Jingxi Li Yi-Chun Hung Onur Kulce Deniz Mengu Aydogan Ozcan | 2022 | Light(Science & Applications)2022，11，7： | 3 |
| 19 | All-optical information-processing capacity of diffractive surfaces显示文摘The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics.These advances related to the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine-learning tasks through light-matter interactions and diffraction.Here,we analyze the information-processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output field-of-view.We show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network,up to a limit that is dictated by the extent of the input and output fields-of-view.Deeper diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher-dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view and exhibit depth advantages in terms of their statistical inference,learning,and generalization capabilities for different image classification tasks when compared with a single trainable diffractive surface.These analyses and conclusions are broadly applicable to various forms of diffractive surfaces,including,e.g.,plasmomc and/or dielectric-based metasurfaces and flat optics,which can be used to form all-optical processors. | Onur Kulce Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，2： | 3 |
| 20 | Influence of acrylamide monomer addition to the acrylic denture-base resins on mechanical and physical properties显示文摘The aim of the study was to evaluate the effect of adding acrylamide monomer(AAm) on the characterization, flexural strength, flexural modulus and thermal degradation temperature of poly(methyl methacrylate)(PMMA) denture-base resins. Specimens(n510) were fabricated from a conventional heat-activated QC-20(Qc-) and a microwave heat-activated Acron MC(Ac-) PMMA resins. Powder/ liquid ratio followed the manufacturer's instructions for the control groups(Qc-c and Ac-c) and for the copolymer groups, the resins were prepared with 5%(25), 10%(210), 15%(215) and 20%(220) acrylamide contents, according to the molecular weight ratio, respectively. The flexural strength and flexural modulus were measured by a three-point bending test. The data obtained were statistically analyzed by Kruskal–Wallis test(a50.05) to determine significant differences between the groups. The chemical structures of the resins were characterized by the nuclear magnetic resonance spectroscopy. Thermal stabilities were determined by thermogravimetric analysis(TGA) with a heating rate of 10 6C?min21from 356C to 600 6C. Control groups from both acrylic resins showed the lowest flexural strength values. Qc-15 showed significant increase in the flexural strength when compared to Qc-c(P,0.01). Ac-10 and Ac-15 showed significance when compared to Ac-c(P,0.01). Acrylamide incorporation increased the elastic modulus in Qc-10, Qc-15 and Qc-20 when compared to Qc-c(P,0.01). Also significant increase was observed in Ac-10, Ac-15 and Ac-20 copolymer groups when compared to Ac-c(P,0.01). According to the1H-nuclear magnetic resonance(NMR) results, acrylamide copolymerization was confirmed in the experimental groups. TGA results showed that the thermal stability of PMMA is increased by the insertion of AAm. | Elif Aydogan Ayaz Rukiye Durkan | 2013 | International Journal of Oral Science2013，5，4： | 3 |
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