Browsing by Author "Chen, Jianbo"

Now showing 1 - 6 of 6
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    Active dielectric antenna on chip for spatial light modulation
    (Nature Publishing Group, 2012-11-14) Qiu, Ciyuan; Chen, Jianbo; Xia, Yang; Xu, Qianfan
    Integrated photonic resonators are widely used to manipulate light propagation in an evanescently-coupled waveguide. While the evanescent coupling scheme works well for planar optical systems that are naturally waveguide based, many optical applications are free-space based, such as imaging, display, holographics, metrology and remote sensing. Here we demonstrate an active dielectric antenna as the interface device that allows the large-scale integration capability of silicon photonics to serve the free-space applications. We show a novel perturbation-base diffractive coupling scheme that allows a high-Q planer resonator to directly interact with and manipulate free-space waves. Using a silicon-based photonic crystal cavity whose resonance can be rapidly tuned with a p-i-n junction, a compact spatial light modulator with an extinction ratio of 9.5 dB and a modulation speed of 150 MHz is demonstrated. Method to improve the modulation speed is discussed.
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    Compressive imaging systems and algorithms to extend machine vision beyond the visible spectrum
    (2018-06-21) Chen, Jianbo; Kelly, Kevin F
    Machine vision finds its importance in today’s most revolutionary technologies from artificial intelligence that surpasses humans in playing Go and chess to automobiles that drive themselves. For many of these tasks the key component that makes superior machine vision possible is the image sensor technology development that has paralleled the equally rapid development of processing power. However there is still a dilemma between the pursuit of higher resolution images that require a focal plane array (FPA) with more pixels on the front end, and the demands on acquisition for embedded systems restrained by power, transmission bandwidth, and storage. To overcome these challenges, the works presented in this thesis aim to seek solutions in solving particular machine vision tasks with compressive imaging system and advanced algorithms. The first strategy focused on achieving more robust infrared object classification utilizing measurements directly from the single-pixel camera without reconstruction with a multiscale compressive matched filter algorithm. Secondly, a multi-pixel hybrid optical convolutional neural network machine vision system was designed and validated to perform high-speed infrared object detection. Lastly, an approach to accomplish super-resolution beyond the resolutions of both the spatial light modulator and FPA in a compressive imaging system will be demonstrated by exploiting a coded point spread function to obtain sub-pixel information. Both simulation and experiment results were presented and analyzed to demonstrate the result of super-resolving an image with 4 times more of it original resolution. Resolving images beyond 4 times of their original resolutions is also possible by extending the idea of this work.
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    Exploiting compressive matrices for dynamic infrared object tracking
    (2016-04-22) Chen, Jianbo; Kelly, Kevin
    Recent development on compressive sensing (CS) presents a great potential for this technique to be used in broader applications from hyper-spectroscopy microscopy to homeland security. And the new mathematics of CS has drastically benefited this field especially in imaging and video applications. Based on novel theoretical principles and experiments, it has been demonstrated that an image can be reconstruct with only K << N measurements from an N-dimensional basis, which is much less than the sampling rate required by the Shannon-Nyquist sampling theorem. The compressive single pixel camera is one embodiment of such an imaging system and has proven capable of capturing both static images and dynamic scenes using fewer measurements than the current schemes. In this thesis we will explore compressive dynamic scene acquisition with prior information or models, incorporating with different sensing matrixes. We demonstrate through simulations and experiments the effectiveness of knowledge-enhanced patterns over unbiased compressive measurements in a variety of applications including motion tracking and object recognition. We also present using a SPC like system for high-speed anomaly detection. Despite its importance in a wide variety of machine vision applications, extending anomaly detection and tracking beyond the visible spectrum in a cost-effective manner presents a significant technological challenge. As a step in this direction, we present a compressive imaging system, specially designed patterns, and a set of metrics to identify the existence of short durance anomalies against a complex background. Our novel measurement design is chosen to be most sensitive to singular anomalies based on the Walsh-Hadamard transform. We illustrate the utility of our approach via a series of simulations and experiments on the compressive single-pixel camera system.
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    Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions
    (Springer Nature, 2016) Shuang, Bo; Wang, Wenxiao; Shen, Hao; Tauzin, Lawrence J.; Flatebo, Charlotte; Chen, Jianbo; Moringo, Nicholas A.; Bishop, Logan D.C.; Kelly, Kevin F.; Landes, Christy F.
    Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.
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    A high-throughput three-dimensional cell migration assay for toxicity screening with mobile device-based macroscopic image analysis
    (Nature Publishing Group, 2013) Timm, David M.; Chen, Jianbo; Sing, David; Gage, Jacob A.; Haisler, William L.; Neeley, Shane K.; Raphael, Robert M.; Dehghani, Mehdi; Rosenblatt, Kevin P.; Killian, T.C.; Tseng, Hubert; Souza, Glauco R.; Bioengineering; Physics and Astronomy
    There is a growing demand for in vitro assays for toxicity screening in three-dimensional (3D) environments. In this study, 3D cell culture using magnetic levitation was used to create an assay in which cells were patterned into 3D rings that close over time. The rate of closure was determined from time-lapse images taken with a mobile device and related to drug concentration. Rings of human embryonic kidney cells (HEK293) and tracheal smooth muscle cells (SMCs) were tested with ibuprofen and sodium dodecyl sulfate (SDS). Ring closure correlated with the viability and migration of cells in two dimensions (2D). Images taken using a mobile device were similar in analysis to images taken with a microscope. Ring closure may serve as a promising label-free and quantitative assay for high-throughput in vivo toxicity in 3D cultures.
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    A spheroid toxicity assay using magnetic 3D bioprinting and real-time mobile device-based imaging
    (Springer Nature, 2015) Tseng, Hubert; Gage, Jacob A.; Shen, Tsaiwei; Haisler, William L.; Neeley, Shane K.; Shiao, Sue; Chen, Jianbo; Desai, Pujan K.; Liao, Angela; Hebel, Chris; Raphael, Robert M.; Becker, Jeanne L.; Souza, Glauco R.; Bioengineering
    An ongoing challenge in biomedical research is the search for simple, yet robust assays using 3D cell cultures for toxicity screening. This study addresses that challenge with a novel spheroid assay, wherein spheroids, formed by magnetic 3D bioprinting, contract immediately as cells rearrange and compact the spheroid in relation to viability and cytoskeletal organization. Thus, spheroid size can be used as a simple metric for toxicity. The goal of this study was to validate spheroid contraction as a cytotoxic endpoint using 3T3 fibroblasts in response to 5 toxic compounds (all-trans retinoic acid, dexamethasone, doxorubicin, 5′-fluorouracil, forskolin), sodium dodecyl sulfate (+control), and penicillin-G (−control). Real-time imaging was performed with a mobile device to increase throughput and efficiency. All compounds but penicillin-G significantly slowed contraction in a dose-dependent manner (Z’ = 0.88). Cells in 3D were more resistant to toxicity than cells in 2D, whose toxicity was measured by the MTT assay. Fluorescent staining and gene expression profiling of spheroids confirmed these findings. The results of this study validate spheroid contraction within this assay as an easy, biologically relevant endpoint for high-throughput compound screening in representative 3D environments.