Active compensation for perturbed coaxial reflecting space telescope using defocus point spread function and convolutional neural network

The depth of an image system can be extended by an axicon which can generate line focus. According to physical optical theory, the diffracting patterns of the defocus point spread function (PSF) for the imaging system with axicon are analyzed through the generalize pupil function. The expressions of the PSF for the imaging system illuminated by white light are described as the superposition of the intensities in individual monochromatic patterns. Experimental results show that the central portion contains the most energy of the diffraction pattern from the PSF produced by the white light, and the contrast of secondary outside circular rings decreased rapidly. Furthermore, the central spot radius varied slowly with the increase of defocus parameter, and the depth of field of the imaging system is effectively extended with a shortcoming that the images need further processing.

Due to the disadvantages of passive filter such as big size, easy occurrence of resonance with the power and the poor compensation effect, the active harmonic compensation technology was put forward in recent years. Compared with the harmonic compensation in the independent power system, active compensation supported by embedded technology can quickly achieve the single harmonic compensation and all compensation, with good compensation effect and flexible operation. An experimental platform to solve the harmonic current of the isolated oil rig power supply system is built up in this paper. Besides the introduction of the principles and compositions of active compensation device as well as the extraction and control of the compensation current, the results of harmonic compensation of the device in three-phase three-wire power system on the experimental platform is emphasized, as well as the methods to improve the compensation effect. The platform lays a solid foundation for the ultimate application of compensate device in the oil drilling power system.

The difficulty of build-up area extraction is due to complexity of remote sensing data in terms of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the built-up area from Landsat 8-OLI images provided by Google earth engine (GEE), we propose a convolutional neural networks (CNN) utilizing spatial and spectral information synchronously, which is built in Google drive using Colaboratory-Keras. To train a CNN model with good generalization ability, we choose Beijing, Lanzhou, Chongqing, Suzhou and Guangzhou of China as the training sites, which are very different in term of natural environments. The ArcGIS-Model Builder is employed to automatically select 99 332 samples from the 38-m global built-up production of the European Space Agency (ESA) in 2014. The validate accuracy of the five experimental sites is higher than 90%. We compare the results with other existing building data products. The classification results of CNN can be very good for the details of the built-up areas, and greatly reduce the classification error and leakage error. We applied the well-trained CNN model to extract built-up areas of Chengdu, Xi’an, Zhengzhou, Harbin, Hefei, Wuhan, Kunming and Fuzhou, for the sake of evaluating the generalization ability of the CNN. The fine classification results of the eight sites indicate that the generalization ability of the well-trained CNN is pretty good. However, the extraction results of Xi’an, Zhengzhou and Hefei are poor. As for the training data, only Lanzhou is located in the northwest region, so the trained CNN has poor image classification ability in the northwest region of China.

The 60-cm diameter, f/2 DCWS telescope is a modified Ritchey Chretein configuration which collects radiation in three spectral bands, 0.4 to 0.9/micron (visible), 5.0 to 9.0/microns (MWIR), and 9.0 to 12.0 microns (LWIR). The visible and infrared radiation are separated by a dichroic beamsplitter and focused on to separate focal planes; the MWIR and LWIR radiation share a common focal plane with separate detector arrays residing side-by-side. The conic constants of the primary and secondary mirrors are that of a Ritchey Chretein telescope; to improve the image quality at the edges of the field of view, a set of zero power correcting optics have been introduced to each optical train. The Ritchey Chretein telescope is corrected to have zero third order spherical aberration and zero third order coma; the DCWS telescope is further configured such that the positive power in the primary mirror is approximately equal to the negative power in the secondary mirror, resulting in a telescope with very little field curvature.

The article substantiates the relevance of steganalysis, as a determination of the presence of a hidden channel in telecommunication systems, whose nodes exchange digital images. The article deals with the application of convolutional neural networks to solve this problem. It is assumed that the probability of correct image classification using a well-trained convolutional neural network will be comparable or even better than characteristics of statistical algorithms or the RM model. We introduce principles of construction and capabilities of convolutional neural networks in the framework of their applicability to solving the problem of steganalysis. To improve the efficiency and effectiveness of the stegocontainer recognition process, a version of the image classification model for a convolutional neural network is proposed. It is based on combination of several convolutional and fully connected layers. We have developed software for this model version with the ability to train a neural network and evaluate the quality of classification. The analysis of existing software products designed for the task of determining the fact of using steganography in digital images is carried out. The advantage of classifiers based on neural networks in comparison with statistical ones is proved. Using the developed software, an experimental study of classification model on sets of digital images contained in open sources has been carried out. The article presents the results of neural network training, as well as an analysis of the strengths and weaknesses of the selected model.

In microscopy, several different phase contrast methods (e.g. Zernike contrast, differential interference contrast) have been developed to image phase objects. For these methods specialized microscopic equipment (modified microscope objectives, filters, etc.) is needed. The static elements within such microscopes are a trade-off, because phase contrast imaging depends strongly on the object and the information to be visualized. We show results of an implementation of many different phase contrast methods using a high resolution phase-only spatial light modulator (SLM) in the pupil plane. All implemented methods are realized by software. Therefore, it is not only feasible to change the different phase contrast methods in real time, but it is also possible to optimize the parameters. Images obtained from different settings are combined digitally to improve the final image quality. Furthermore, completely new phase contrast filters can be tested easily because the phase of each pixel can be changed arbitrarily. We use this method to implement a new phase contrast filter that is obtained by combining a focused and a defocused point spread function. We will present theoretical as well as experimental results of this vertical differential interference contrast filter.

Collimator is essential to evaluate and assemble the other telescopes. Its diameter should be larger than that of the target telescope for the correct use. We are currently developing the Cassegrain type collimator of which diameter is 0.9 m. The primary mirror is light-weighted so that its weight is only 70 kg. Due to its structure, the primary mirror can be supported only at the backside of the mirror. This mirror is tested with the combination of null Hartmann test and interferometer. The secondary mirror is tested with a Hindle method. This method requires 600 mm high quality spherical mirror. The distance between the primary and secondary mirror is maintained by the Carbon composite material. The assembly of two mirrors is carried out by the computer aided alignment method. The whole structure is designed to maintain the performance of the collimator under +/-5 degrees of temperature variation.

Late gadolinium-enhanced (LGE) MRI has become a widely used technique to non-invasively image the left atrium prior to catheter ablation. However, LGE-MRI images are prone to variable image quality, with quality metrics that do not necessarily correlate to the image's diagnostic quality. In this study, we aimed to define consistent clinically relevant metrics for image and diagnostic quality in 3D LGE-MRI images of the left atrium, have multiple observers assess LGE-MRI image quality to identify key features that measure quality and intra/inter-observer variabilities, and train and test a CNN to assess image quality automatically. We identified four image quality categories that impact fibrosis assessment in LGE-MRI images and trained individuals to score 50 consecutive pre-ablation atrial fibrillation LGE-MRI scans from the University of Utah hospital image database. The trained individuals then scored 146 additional scans, which were used to train a convolutional neural network (CNN) to assess diagnostic quality. There was excellent agreement among trained observers when scoring LGE-MRI scans, with inter-rater reliability scores ranging from 0.65 to 0.76 for each category. When the quality scores were converted to a binary diagnostic/non-diagnostic, the CNN achieved a sensitivity of and a specificity of . The use of a training document with reference examples helped raters achieve excellent agreement in their quality scores. The CNN gave a reasonably accurate classification of diagnostic or non-diagnostic 3D LGE-MRI images of the left atrium, despite the use of a relatively small training set.

This is part six of a series describing the ongoing optical modeling activities for the James Webb Space Telescope (JWST). The first two discussed modeling JWST on-orbit performance using wavefront sensitivities to predict line of sight motion induced blur and stability during thermal transients. The third investigates the aberrations resulting from alignment and figure errors after compensation of the controllable degrees of freedom of the observatory (i.e. primary and secondary mirrors). This paper follows the theme of the third, but considers the highly unlikely case where a gross figure error occurs on the secondary mirror. We then compensate for this error using figure control on the primary mirror. The level of compensation as well as the total motions required are evaluated and reported. Impact to image quality over the full field of the observatory is also discussed.

Spectrum sensing for the Digital Television Terrestrial Multimedia Broadcasting (DTMB) system is considered in this paper. A novel method, which contains a preprocessing method of partition and averaging, a convolutional neural network (CNN) and a threshold chosen scheme, is proposed to solve this problem. In the spectrum sensing process, the received signal is firstly preprocessed by a scheme of partition and averaging. Then it is fed into the well-trained CNN. The posterior probability of the signal being noise is output. Based on the Neyman-Pearson theorem, a threshold can be chosen by the Monte Carlo method. The result of spectrum sensing can be obtained by comparing the posterior probability with the threshold and the complexity of the proposed method is also analyzed. Through computer simulations, the advantage of the proposed method is demonstrated. At a low signal-to-noise ratio (SNR), the proposed method can achieve a satisfactory detection probability. Besides, the proposed method is robust to different SNRs and has good generalization performance under different noise assumptions. When the proposed method is transferred to sense DTMB signal under time dispersive channels, the performance does not deteriorate and even can be better, which shows the robustness of the proposed method to the time dispersive channels.

A well-trained Convolutional Neural Network can easily be pruned without significant loss of performance. This is because of unnecessary overlap in the features captured by the network's filters. Innovations in network architecture such as skip/dense connections and Inception units have mitigated this problem to some extent, but these improvements come with increased computation and memory requirements at run-time. We attempt to address this problem from another angle - not by changing the network structure but by altering the training method. We show that by temporarily pruning and then restoring a subset of the model's filters, and repeating this process cyclically, overlap in the learned features is reduced, producing improved generalization. We show that the existing model-pruning criteria are not optimal for selecting filters to prune in this context and introduce inter-filter orthogonality as the ranking criteria to determine under-expressive filters. Our method is applicable both to vanilla convolutional networks and more complex modern architectures, and improves the performance across a variety of tasks, especially when applied to smaller networks.

We have designed a 30 cm cryogenic space infrared telescope for astronomical observation. The telescope is designed to observe in the wavelength range of 0.5~2.1 <TEX>${\mu}m$</TEX>, when it is cooled down to 77 K. The result of the preliminary design of the support structure and support method of the mirror of a 30 cm cryogenic space infrared telescope is shown in this paper. As a Cassegrain prescription, the optical system of a 30 cm cryogenic space infrared telescope has a focal ratio of f/3.1 with a 300 mm primary mirror (M-1) and 113 mm secondary mirror (M-2). The material of the whole structure including mirrors is aluminum alloy (Al6061-T6). Flexures that can withstand random vibration were designed, and it was validated through opto-mechanical analysis that both primary and secondary mirrors, which are assembled in the support structure, meet the requirement of root mean square wavefront error < <TEX>${\lambda}/8$</TEX> for all gravity direction. Additionally, when the M-1 and flexures are assembled by bolts, the effect of thermal stress occurring from a stainless steel bolt when cooled and bolt torque on the M-1 was analyzed.

We present our results from training and evaluating a convolutional neural network (CNN) to predict galaxy shapes from wide-field survey images of the first data release of the Dark Energy Survey (DES DR1). We use conventional shape measurements as ‘ground truth’ from an overlapping, deeper survey with less sky coverage, the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS). We demonstrate that CNN predictions from single band DES images reproduce the results of CFHTLenS at bright magnitudes and show higher correlation with CFHTLenS at fainter magnitudes than maximum likelihood model fitting estimates in the DES Y1 im3shape catalogue. Prediction of shape parameters with a CNN is also extremely fast, it takes only 0.2 ms per galaxy, improving more than 4 orders of magnitudes over forward model fitting. The CNN can also accurately predict shapes when using multiple images of the same galaxy, even in different colour bands, with no additional computational overhead. The CNN is again more precise for faint objects, and the advantage of the CNN is more pronounced for blue galaxies than red ones when compared to the DES Y1 metacalibration catalogue, which fits a single Gaussian profile using riz band images. We demonstrate that CNN shape predictions within the metacalibration self-calibrating framework yield shear estimates with negligible multiplicative bias, m &lt; 10−3, and no significant point spread function (PSF) leakage. Our proposed set-up is applicable to current and next-generation weak lensing surveys where higher quality ‘ground truth’ shapes can be measured in dedicated deep fields.

It is important to measure the spatial performance of an in-flight satellite sensor to determine if any spatial resolution degradations occur over time; then some compensation methods can be adopted to improve the image quality. In satellite on-orbit measurements of spatial resolution, the performance of the sensors can be objectively evaluated through the point spread function (PSF) or modulation transfer function (MTF). The well-known image-based PSF derivation method of sampling along a slanted linear step structure from the degraded image is widely used in the satellite image restoration area. Due to the non-uniformity of the linear ground objects, generally, the operators have to pick out good profiles from all the profiles along the edge in order to increase the estimation accuracy. However, only some qualitative criteria have been proposed in the literature to instruct researchers how to pick out good profiles. In this article, we propose a method to quantitatively measure the homogeneity and contrast of the candidate linear structures in order to extract the optimal profiles. This method first automatically detects and locates the slanted linear step edge object in the image. Then it measures the homogeneity and the contrast at each location along the edge by using two adjacent square regions on both sides of the linear step structure. The locations that have high measurement values are regarded as the optimal locations. Finally, the optimal profiles can be extracted at these edge locations to derive an accurate PSF estimation by differentiation. This method makes an automatic optimal step edge detection method for PSF derivation to be practical. Experiment results on simulated and real images are provided. This article shows that using only the ‘optimal’ profiles can play a real and important role and can achieve a good PSF estimation result.

Recent world events have underscored the need for a satellite based persistent global surveillance capability. To be useful, the satellite must be able to continuously monitor objects the size of a person anywhere on the globe and do so at a low cost. One way to satisfy these requirements involves a constellation of satellites in low earth orbit capable of resolving a spot on the order of 20 cm. To reduce cost of deployment, such a system must be dramatically lighter than a traditional satellite surveillance system with a high spatial resolution. The key to meeting this requirement is a lightweight optics system with a deformable primary and secondary mirrors and an adaptive optic subsystem correction of wavefront distortion. This proposal is concerned with development of MEMS micromirrors for correction of aberrations in the primary mirror and improvement of image quality, thus reducing the optical requirements on the deployable mirrors. To meet this challenge, MEMS micromirrors must meet stringent criteria on their performance in terms of flatness, roughness and resolution of position. Using Sandia's SUMMIT foundry which provides the world's most sophisticated surface MEMS technology as well as novel designs optimized by finite element analysis will meet severe requirements on mirror travel range and accuracy.