NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Neural Network Prediction of Ultimate Compression After Impact Loads in Graphite-Epoxy Coupons from Ultrasonic C-Scan Images

The purpose of this project was to investigate how accurately an artificial neural network could predict the ultimate compressive loads of impact damaged 24-ply graphite-epoxy coupons from ultrasonic C-scan images. The 24-ply graphite-epoxy coupons were manufactured with bidirectional preimpregnated tape and cut into 21 coupons, 4 inches by 6 inches each. The coupons were impacted at known impact energies of 10, 12, 14, 16, 18, and 20 Joules in order to create barely visible impact damage (BVID). The coupons were then scanned with an ultrasonic C-scan system to create an image of the damaged area. Each coupon was then compressed to failure to determine its ultimate compressive load. Numeric values for each pixel were determined from the C-scan image. Since the image was represented as a red-green-blue (RGB) map, each pixel had three numbers associated with it, one for each of the three colors. To make the image readable to the artificial neural network the columns of the resulting matrix were then summed, and these numbers were used as inputs for a backpropagation neural network (BPNN) to generate accurate predictions of the ultimate compressive loads. The BPNN was trained and optimized on 15 of the 21 sample data sets and tested on the remaining 6 sample data sets. The optimized BPNN was able to produce ultimate compression after impact (CAI) load predictions for the BVID composite coupons with a worst case error of -8.98%. This was within the ±10% goal for this research and comfortably within the B-basis allowables commonly applied to composite structures. The ultrasonic C-scan images were then preprocessed using Fast Fourier Transforms (FFTs) in an effort to remove any image noise present. The results of the BPNN that was trained and tested on the green color data only were then compared to the results yielded by the BPNN trained and tested on the images that were processed through the FFT. It was found that the FFT processed images had a worst case BPNN prediction error of 8.65%, which was only slightly lower than the -8.98% error that was generated by the unprocessed green layer only C-scan image data. This improvement suggested that the added work involved in FFT preprocessing of the worst case error was not as productive as had been hoped, leading to a few suggestions for future noise removal research. This also reinforced the notion that BPNNs, being an iterative optimization scheme, can provide accurate predictions in the presence of at least small amounts of noise. Thus, image filtering methods coupled with the iterative optimization technique that comprises a BPNN have demonstrated the ability to generate accurate CAI load predictions in composite coupons that have experienced BVID

Microengineered Hollow Graphene Tube Systems Generate Conductive Hydrogels with Extremely Low Filler Concentration

The fabrication of electrically conductive hydrogels is challenging as the introduction of an electrically conductive filler often changes mechanical hydrogel matrix properties. Here, we present an approach for the preparation of hydrogel composites with outstanding electrical conductivity at extremely low filler loadings (0.34 S m-1, 0.16 vol %). Exfoliated graphene and polyacrylamide are microengineered to 3D composites such that conductive graphene pathways pervade the hydrogel matrix similar to an artificial nervous system. This makes it possible to combine both the exceptional conductivity of exfoliated graphene and the adaptable mechanical properties of polyacrylamide. The demonstrated approach is highly versatile regarding porosity, filler material, as well as hydrogel system. The important difference to other approaches is that we keep the original properties of the matrix, while ensuring conductivity through graphene-coated microchannels. This novel approach of generating conductive hydrogels is very promising, with particular applications in the fields of bioelectronics and biohybrid robotics

Accelerating Deep Learning Applications in Space

Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of computer vision have led to a rise in demand for fast and reliable deep learning applications. In recent years, the industry has introduced devices with impressive processing power to perform various object detection tasks. However, with real-time detection, devices are constrained in memory, computational capacity, and power, which may compromise the overall performance. This could be solved either by optimizing the object detector or modifying the images. In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques. We examine the capabilities of a NVIDIA Jetson Nano; a low-power, high-performance computer, with an integrated GPU, small enough to fit on-board a CubeSat. We take a closer look at the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) that are pre-trained on DOTA – a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of inference time, memory consumption, and accuracy. By applying image compression techniques, we are able to optimize performance. The two techniques applied, lossless compression and image scaling, improves speed and memory consumption with no or little change in accuracy. The image scaling technique achieves a 100% runnable dataset and we suggest combining both techniques in order to optimize the speed/memory/accuracy trade-off

Deep learning optimized single-pixel LiDAR

Interest in autonomous transport has led to a demand for 3D imaging technologies capable of resolving fine details at long range. Light detection and ranging (LiDAR) systems have become a key technology in this area, with depth information typically gained through time-of-flight photon-counting measurements of a scanned laser spot. Single-pixel imaging methods offer an alternative approach to spot-scanning, which allows a choice of sampling basis. In this work, we present a prototype LiDAR system, which compressively samples the scene using a deep learning optimized sampling basis and reconstruction algorithms. We demonstrate that this approach improves scene reconstruction quality compared to an orthogonal sampling method, with reflectivity and depth accuracy improvements of 57% and 16%, respectively, for one frame per second acquisition rates. This method may pave the way for improved scan-free LiDAR systems for driverless cars and for fully optimized sampling to decision-making pipelines

Modified Distributive Arithmetic based 2D-DWT for Hybrid (Neural Network-DWT) Image Compression

Artificial Neural Networks ANN is significantly used in signal and image processing techniques for pattern recognition and template matching Discrete Wavelet Transform DWT is combined with neural network to achieve higher compression if 2D data such as image Image compression using neural network and DWT have shown superior results over classical techniques with 70 higher compression and 20 improvement in Mean Square Error MSE Hardware complexity and power issipation are the major challenges that have been addressed in this work for VLSI implementation In this work modified distributive arithmetic DWT and multiplexer based DWT architecture are designed to reduce the computation complexity of hybrid architecture for image compression A 2D DWT architecture is designed with 1D DWT architecture and is implemented on FPGA that operates at 268 MHz consuming power less than 1