75,075 research outputs found
Index to NASA Tech Briefs, 1975
This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs
Deep learning approach to Fourier ptychographic microscopy
Convolutional neural networks (CNNs) have gained tremendous success in
solving complex inverse problems. The aim of this work is to develop a novel
CNN framework to reconstruct video sequence of dynamic live cells captured
using a computational microscopy technique, Fourier ptychographic microscopy
(FPM). The unique feature of the FPM is its capability to reconstruct images
with both wide field-of-view (FOV) and high resolution, i.e. a large
space-bandwidth-product (SBP), by taking a series of low resolution intensity
images. For live cell imaging, a single FPM frame contains thousands of cell
samples with different morphological features. Our idea is to fully exploit the
statistical information provided by this large spatial ensemble so as to make
predictions in a sequential measurement, without using any additional temporal
dataset. Specifically, we show that it is possible to reconstruct high-SBP
dynamic cell videos by a CNN trained only on the first FPM dataset captured at
the beginning of a time-series experiment. Our CNN approach reconstructs a
12800X10800 pixels phase image using only ~25 seconds, a 50X speedup compared
to the model-based FPM algorithm. In addition, the CNN further reduces the
required number of images in each time frame by ~6X. Overall, this
significantly improves the imaging throughput by reducing both the acquisition
and computational times. The proposed CNN is based on the conditional
generative adversarial network (cGAN) framework. Additionally, we also exploit
transfer learning so that our pre-trained CNN can be further optimized to image
other cell types. Our technique demonstrates a promising deep learning approach
to continuously monitor large live-cell populations over an extended time and
gather useful spatial and temporal information with sub-cellular resolution
An Iteratively Decodable Tensor Product Code with Application to Data Storage
The error pattern correcting code (EPCC) can be constructed to provide a
syndrome decoding table targeting the dominant error events of an inter-symbol
interference channel at the output of the Viterbi detector. For the size of the
syndrome table to be manageable and the list of possible error events to be
reasonable in size, the codeword length of EPCC needs to be short enough.
However, the rate of such a short length code will be too low for hard drive
applications. To accommodate the required large redundancy, it is possible to
record only a highly compressed function of the parity bits of EPCC's tensor
product with a symbol correcting code. In this paper, we show that the proposed
tensor error-pattern correcting code (T-EPCC) is linear time encodable and also
devise a low-complexity soft iterative decoding algorithm for EPCC's tensor
product with q-ary LDPC (T-EPCC-qLDPC). Simulation results show that
T-EPCC-qLDPC achieves almost similar performance to single-level qLDPC with a
1/2 KB sector at 50% reduction in decoding complexity. Moreover, 1 KB
T-EPCC-qLDPC surpasses the performance of 1/2 KB single-level qLDPC at the same
decoder complexity.Comment: Hakim Alhussien, Jaekyun Moon, "An Iteratively Decodable Tensor
Product Code with Application to Data Storage
Deep learning approach to Fourier ptychographic microscopy
Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequences of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by these large spatial ensembles so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800×10800 pixel phase image using only ∼25 seconds, a 50× speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ∼ 6×. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. We further propose a mixed loss function that combines the standard image domain loss and a weighted Fourier domain loss, which leads to improved reconstruction of the high frequency information. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.We would like to thank NVIDIA Corporation for supporting us with the GeForce Titan Xp through the GPU Grant Program. (NVIDIA Corporation; GeForce Titan Xp through the GPU Grant Program)First author draf
Particle Swarm Optimization Framework for Low Power Testing of VLSI Circuits
Power dissipation in sequential circuits is due to increased toggling count
of Circuit under Test, which depends upon test vectors applied. If successive
test vectors sequences have more toggling nature then it is sure that toggling
rate of flip flops is higher. Higher toggling for flip flops results more power
dissipation. To overcome this problem, one method is to use GA to have test
vectors of high fault coverage in short interval, followed by Hamming distance
management on test patterns. This approach is time consuming and needs more
efforts. Another method which is purposed in this paper is a PSO based Frame
Work to optimize power dissipation. Here target is to set the entire test
vector in a frame for time period 'T', so that the frame consists of all those
vectors strings which not only provide high fault coverage but also arrange
vectors in frame to produce minimum toggling
Thermal barrier coating on high temperature industrial gas turbine engines
The thermal barrier coating used was a yttria stabilized zirconia material with a NiCrAlY undercoat, and the base engine used to establish improvements was the P&WA FT50A-4 industrial gas turbine engine. The design benefits of thermal barrier coatings include simplified cooling schemes and the use of conventional alloys in the engine hot section. Cooling flow reductions and improved heating rates achieved with thermal barrier coating result in improved performance. Economic benefits include reduced power production costs and reduced fuel consumption. Over the 30,000 hour life of the thermal barrier coated parts, fuel savings equivalent to $5 million are projected and specific power (megawatts/mass of engine airflow) improvements on the order of 13% are estimated
Flow Visualization Techniques for Flight Research
In-flight flow visualization techniques used at the Dryden Flight Research Facility of NASA Ames Research Center (Ames-Dryden) and its predecessor organizations are described. Results from flight tests which visualized surface flows using flow cones, tufts, oil flows, liquid crystals, sublimating chemicals, and emitted fluids have been obtained. Off-surface flow visualization of vortical flow has been obtained from natural condensation and two methods using smoke generator systems. Recent results from flight tests at NASA Langley Research Center using a propylene glycol smoker and an infrared imager are also included. Results from photo-chase aircraft, onboard and postflight photography are presented
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