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1. Compressive Sensing-Based Reconstruction of Lissajous-Like Nodding Lidar Data

   https://doi.org/10.1115/DSCC2019-9201  

   Benson, Michael T. ; Sathishchandra, Harish ; Clayton, Garrett M. ; Andersson, Sean B. ( October 2019 , 2019 ASME Dynamic Systems and Control Conference)

   In this article, a compressive sensing (CS) reconstruction algorithm is applied to data acquired from a nodding multi-beam Lidar system following a Lissajous-like trajectory. Multi-beam Lidar systems provide 3D depth information of the environment for applications in robotics, but the vertical resolution of these devices may be insufficient to identify objects, especially when the object is small and/or far from the robot. In order to overcome this issue, the Lidar can be nodded in order to obtain higher vertical resolution with the side-effect of increased scan time, especially when raster scan patterns are used. Such systems, especially when combined with nodding, also yield large volumes of data which may be difficult to store and mange on resource constrained systems. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally sub-sample the imaged area and the data can be further reduced by simply not collecting each data point along the trajectory. The final depth image must then be reconstructed from the sub-sampled data. In this article, a CS reconstruction algorithm is applied to data collected during a fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare its results to images produced from simple nearest-neighbor interpolation. 

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2. On the detection of upper mantle discontinuities with radon-transformed receiver functions (CRISP-RF)

   https://doi.org/10.1093/gji/ggad447  

   Olugboji, Tolulope ; Zhang, Ziqi ; Carr, Steve ; Ekmekci, Canberk ; Cetin, Mujdat ( November 2023 , Geophysical Journal International)

   Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities can be obtained with P-to-S receiver function (Ps-RF) where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower resolution S-to-P receiver function (Sp-RF). In this study, a new method called CRISP-RF (Clean Receiver-function Imaging using SParse Radon Filters) is proposed, which incorporates ideas from compressive sensing and model-based image reconstruction. The central idea involves applying a sparse Radon transform to effectively decompose the Ps-RF into its underlying wavefield contributions, that is direct conversions, multiples, and noise, based on the phase moveout and coherence. A masking filter is then designed and applied to create a multiple-free and denoised Ps-RF. We demonstrate, using synthetic experiment, that our implementation of the Radon transform using a sparsity-promoting regularization outperforms the conventional least-squares methods and can effectively isolate direct Ps conversions. We further apply the CRISP-RF workflow on real data, including single station data on cratons, common-conversion-point stack at continental margins and seismic data from ocean islands. The application of CRISP-RF to global data sets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous mid-lithospheric discontinuity and the elusive X-discontinuity.

    

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3. On the Detection of Sharp Upper Mantle Discontinuities with Radon-Transformed Ps Receiver Functions (CRISP-RF)

   Tolulope Olugboji, Ziqi Zhang ( June 2023 , Geophysical journal international)

   Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities (depth, amplitude, sharpness) can be obtained with Ps receiver functions where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower-resolution Sp-RFs which are not affected by these multiples. Here, we present a novel method for obtaining ‘Clean Receiver-function Imaging using SParse Radon Filters’ (CRISP-RF). The central idea involves the transformation of Ps-RF-data into a Radon-transformed Ps-RF. This approach results in the decomposition of the signal into its underlying wavefield contributions: direct conversions, multiple reflections, and noise. A selective filter is then applied to create multiple-free, denoised, source-deconvolved seismograms. The Radon transform is implemented using a sparsity-promoting regularization, common in disciplines such as compressive sensing and model-based image reconstruction, e.g., optical and microscopic imaging, magnetic resonance imaging, and radar astronomy. We review different algorithms for solving this optimization problem and, based on synthetic and real data, show that our approach outperforms the conventional Tikhonov-regularized least-squares methods. The application of CRISP-RF to global datasets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous Mid-Lithospheric Discontinuity (MLD), and the elusive X-discontinuity. 

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4. Automated plankton classification from holographic imagery with deep convolutional neural networks

   https://doi.org/10.1002/lom3.10402  

   Guo, Buyu ; Nyman, Lisa ; Nayak, Aditya R. ; Milmore, David ; McFarland, Malcolm ; Twardowski, Michael S. ; Sullivan, James M. ; Yu, Jia ; Hong, Jiarong ( December 2020 , Limnology and Oceanography: Methods)

   In situ digital inline holography is a technique which can be used to acquire high‐resolution imagery of plankton and examine their spatial and temporal distributions within the water column in a nonintrusive manner. However, for effective expert identification of an organism from digital holographic imagery, it is necessary to apply a computationally expensive numerical reconstruction algorithm. This lengthy process inhibits real‐time monitoring of plankton distributions. Deep learning methods, such as convolutional neural networks, applied to interference patterns of different organisms from minimally processed holograms can eliminate the need for reconstruction and accomplish real‐time computation. In this article, we integrate deep learning methods with digital inline holography to create a rapid and accurate plankton classification network for 10 classes of organisms that are commonly seen in our data sets. We describe the procedure from preprocessing to classification. Our network achieves 93.8% accuracy when applied to a manually classified testing data set. Upon further application of a probability filter to eliminate false classification, the average precision and recall are 96.8% and 95.0%, respectively. Furthermore, the network was applied to 7500 in situ holograms collected at East Sound in Washington during a vertical profile to characterize depth distribution of the local diatoms. The results are in agreement with simultaneously recorded independent chlorophyll concentration depth profiles. This lightweight network exemplifies its capability for real‐time, high‐accuracy plankton classification and it has the potential to be deployed on imaging instruments for long‐term in situ plankton monitoring.

    

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5. Real-time Lissajous imaging with a low-voltage 2-axis MEMS scanner based on electrothermal actuation

   https://doi.org/10.1364/OE.380690  

   Tanguy, Quentin A. A. ; Gaiffe, Olivier ; Passilly, Nicolas ; Cote, Jean-Marc ; Cabodevila, Gonzalo ; Bargiel, Sylwester ; Lutz, Philippe ; Xie, Huikai ; Gorecki, Christophe ( March 2020 , Optics Express)

   Laser scanning based on Micro-Electro-Mechanical Systems (MEMS) scanners has become very attractive for biomedical endoscopic imaging, such as confocal microscopy or Optical Coherence Tomography (OCT). These scanners are required to be fast to achieve real-time image reconstruction while working at low actuation voltage to comply with medical standards. In this context, we report a 2-axis Micro-Electro-Mechanical Systems (MEMS) electrothermal micro-scannercapable of imaging large fields of view at high frame rates, e.g. from 10 to 80 frames per second. For this purpose, Lissajous scan parameters are chosen to provide the optimal image quality within the scanner capabilities and the sampling rate limit, resulting from the limited A-scan rate of typical swept-sources used for OCT. Images of 233 px × 203 px and 53 px × 53 px at 10 fps and 61 fps, respectively, are experimentally obtained and demonstrate the potential of this micro-scannerfor high definition and high frame rate endoscopic Lissajous imaging.

    

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