A Robust Approach to Optimal Matched Filter Design in Ultrasonic Non-Destructive Evaluation (NDE)

The matched filter was demonstrated to be a powerful yet efficient technique to enhance defect detection and imaging in ultrasonic non-destructive evaluation (NDE) of coarse grain materials, provided that the filter was properly designed and optimized. In the literature, in order to accurately approximate the defect echoes, the design utilized the real excitation signals, which made it time consuming and less straightforward to implement in practice. In this paper, we present a more robust and flexible approach to optimal matched filter design using the simulated excitation signals, and the control parameters are chosen and optimized based on the real scenario of array transducer, transmitter-receiver system response, and the test sample, as a result, the filter response is optimized and depends on the material characteristics. Experiments on industrial samples are conducted and the results confirm the great benefits of the method

Real-time multiframe blind deconvolution of solar images

The quality of images of the Sun obtained from the ground are severely limited by the perturbing effect of the turbulent Earth's atmosphere. The post-facto correction of the images to compensate for the presence of the atmosphere require the combination of high-order adaptive optics techniques, fast measurements to freeze the turbulent atmosphere and very time consuming blind deconvolution algorithms. Under mild seeing conditions, blind deconvolution algorithms can produce images of astonishing quality. They can be very competitive with those obtained from space, with the huge advantage of the flexibility of the instrumentation thanks to the direct access to the telescope. In this contribution we leverage deep learning techniques to significantly accelerate the blind deconvolution process and produce corrected images at a peak rate of ~100 images per second. We present two different architectures that produce excellent image corrections with noise suppression while maintaining the photometric properties of the images. As a consequence, polarimetric signals can be obtained with standard polarimetric modulation without any significant artifact. With the expected improvements in computer hardware and algorithms, we anticipate that on-site real-time correction of solar images will be possible in the near future.Comment: 16 pages, 12 figures, accepted for publication in A&

Linking Visual Cortical Development to Visual Perception

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

Video-rate volumetric neuronal imaging using 3D targeted illumination

Fast volumetric microscopy is required to monitor large-scale neural ensembles with high spatio-temporal resolution. Widefield fluorescence microscopy can image large 2D fields of view at high resolution and speed while remaining simple and costeffective. A focal sweep add-on can further extend the capacity of widefield microscopy by enabling extended-depth-of-field (EDOF) imaging, but suffers from an inability to reject out-of-focus fluorescence background. Here, by using a digital micromirror device to target only in-focus sample features, we perform EDOF imaging with greatly enhanced contrast and signal-to-noise ratio, while reducing the light dosage delivered to the sample. Image quality is further improved by the application of a robust deconvolution algorithm. We demonstrate the advantages of our technique for in vivo calcium imaging in the mouse brain.This work was funded by the National Institutes of Health (R21EY026310) and the National Science Foundation (CBET-1508988). The authors wish to thank E. McCarthy and Prof. M.J. Baum for providing mouse brain slices used in this manuscript, and A. I. Mohammed for providing in vivo mouse brain samples in the early stages of this work. (R21EY026310 - National Institutes of Health; CBET-1508988 - National Science Foundation)Published versio

Comparison of solar photospheric bright points between SUNRISE observations and MHD simulations

Bright points (BPs) in the solar photosphere are radiative signatures of magnetic elements described by slender flux tubes located in the darker intergranular lanes. They contribute to the ultraviolet (UV) flux variations over the solar cycle and hence may influence the Earth's climate. Here we combine high-resolution UV and spectro-polarimetric observations of BPs by the SUNRISE observatory with 3D radiation MHD simulations. Full spectral line syntheses are performed with the MHD data and a careful degradation is applied to take into account all relevant instrumental effects of the observations. It is demonstrated that the MHD simulations reproduce the measured distributions of intensity at multiple wavelengths, line-of-sight velocity, spectral line width, and polarization degree rather well. Furthermore, the properties of observed BPs are compared with synthetic ones. These match also relatively well, except that the observations display a tail of large and strongly polarized BPs not found in the simulations. The higher spatial resolution of the simulations has a significant effect, leading to smaller and more numerous BPs. The observation that most BPs are weakly polarized is explained mainly by the spatial degradation, the stray light contamination, and the temperature sensitivity of the Fe I line at 5250.2 \AA{}. The Stokes $V$ asymmetries of the BPs increase with the distance to their center in both observations and simulations, consistent with the classical picture of a production of the asymmetry in the canopy. This is the first time that this has been found also in the internetwork. Almost vertical kilo-Gauss fields are found for 98 % of the synthetic BPs. At the continuum formation height, the simulated BPs are on average 190 K hotter than the mean quiet Sun, their mean BP field strength is 1750 G, supporting the flux-tube paradigm to describe BPs.Comment: Accepted for publication in Astronomy & Astrophysics on May 30 201

Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference

This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the interference is considered to be Gaussian-distributed and spatially white. However, in practice, this assumption is not guaranteed, which results in degraded DOA estimation performance. Maximum likelihood DOA estimation in the presence of non-Gaussian and spatially colored interference is computationally complex and not practical. Therefore, this work proposes a neural network (NN) based DOA estimation approach for spatial spectrum estimation in multi-source scenarios with a-priori unknown number of sources in the presence of non-Gaussian spatially-colored interference. The proposed approach utilizes a single NN instance for simultaneous source enumeration and DOA estimation. It is shown via simulations that the proposed approach significantly outperforms conventional and NN-based approaches in terms of probability of resolution, estimation accuracy, and source enumeration accuracy in conditions of low SIR, small sample support, and when the angular separation between the source DOAs and the spatially-colored interference is small.Comment: Submitted to IEEE Transactions on Aerospace and Electronic System