Raw Data Generation Research Articles

Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning.

Deep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data. However, raw data is not always available in sufficient quantities. This study investigates synthetic data generation to complement small datasets and improve reconstruction quality. An adversarial auto-encoder was trained to generate phase and coil sensitivity maps from magnitude images, which were combined into synthetic raw data. On a fourfold accelerated MR reconstruction task, deep-learning-based reconstruction networks were trained with varying amounts of training data (20 to 160 scans). Test set performance was compared between baseline experiments and experiments that incorporated synthetic training data. Training with synthetic raw data showed decreasing reconstruction errors with increasing amounts of training data, but importantly this was magnitude-only data, rather than real raw data. For small training sets, training with synthetic data decreased the mean absolute error (MAE) by up to 7.5%, whereas for larger training sets the MAE increased by up to 2.6%. Synthetic raw data generation improved reconstruction quality in scenarios with limited training data. A major advantage of synthetic data generation is that it allows for the reuse of magnitude-only datasets, which are more readily available than raw datasets.

Disaster impact prediction in the power grid using artificial intelligence based on Texas synthetic grid data replication

Power grids are endangered yearly by disasters that could well affect our livelihood. This paper investigates an AI (Artificial Intelligence) solution to understand better and ultimately predict disasters using the data from previously observed disasters and artificially generated data. The data is fed to the AI in the form of resiliency calculations, which are handled by another sub-program and then go through stages of linear regression models, normal curving models, and decision tree models. Afterward, the main body assigns a value percentile to each of the predictions and reaches a final prediction that is reliable, fast, and accurate. The AI then reviews its decision and checks it with the actual data to see if the forecast is accurate and refines itself according to the newly gathered data. This Algorithm has massive potential implications for decision-making while facing a disaster. It can also help test out different approaches to the problems in a safe environment before the actual procedure initiates, saving time and reducing costs. This paper is an extension to this lab’s effort to simulate a real-life disaster and grid via disaster data and multi-layer analysis to predict the steps of disaster and the grid’s responses. The raw data (generator’s generations, load’s amount, lines current, etc.) is refined through the resiliency equations introduced by this lab to be evaluated via the algorithm.

GPU Accelerated Simulation of Scene Generation of 3D Photonic Mixer Device Camera

Simulation of Photonic Mixer Device (PMD) sensors have the capability to create virtual environment to test 3D camera design. This simulation comprises of multiple steps like scene generation using ray tracing, power calculation, raw data generation and raw data processing. However, each step-in situation process takes longer time to implement and they are simulation process, simulators need to be faster. In this paper, we propose parallel implementation method for scene generation using GPGPUs. The feasibility of the method is confirmed using Amdahl’s law before implementation. The method is implemented and tested on GeForce 820M, GeForce 750Ti and Volta V100.Tthe highest speed up obtained is 219.913 using Volta (GV100) GPU for block size 1024. Thus, parallel method optimizes the scene generation time as compared to serial processing and the implemented results are better than the state of the art in the literature.

A Monostatic Forward-Looking Staring Spotlight SAR Raw Data Generation and Hybrid-Domain Image Formation Modifications Based on Extended Azimuth Nonlinear Chirp Scaling Autofocusing

Forward-looking staring spotlight synthetic aperture radar (FSS-SAR) offers operational advantages over conventional squinted SAR counterparts. However, due to its complex observation configuration, pixel anomalies occur when processing raw data. For instance, in the forward-looking geometry, symmetrically distributed point scatterers along the flight path have the same Doppler history, resulting in azimuth ambiguities, whereas staring observation narrows and skews the 2D received spectrum, reducing signal orthogonality and causing spectral folding. As a result, not only is the Doppler frequency gradient too small to be precisely measured in the FSS-SAR mode, limiting the ability to achieve high azimuth angular resolution, but it also suffers from azimuth ambiguities when forming an image. To overcome such azimuth-variant characteristics known as inherent resolutional anomalies, a hybrid-domain image formation algorithm (IFA) based on azimuth scaling is modified here. As an approach, radiometric bias correction (RBC) techniques in conjunction with an extended azimuth nonlinear chirp scaling (ANCS) algorithm capable of equalizing range space variance in the azimuth direction, which benefits from an embedded autofocusing step, have been designed for raw data processing. Given the fact that the platform experiences trajectory deviations under a real-flight condition compared to an ideal trajectory, a pre-processing motion compensation (MOCO) step based on Kalman filtering is included within the IFA. The entire proposed IFA solution tackled the inherent resolutional anomalies of FSS-SAR and succeeded in reconstructing the focused image without azimuthal anomalies. To further assess the IFA, a modified Cartesian back-projection (BP) algorithm was developed along with other objective evaluation scenarios, all of which confirm that the proposed hybrid-domain extended ANCS algorithm is valid for the FSS-SAR application.

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Improved Variant Detection in Clinical Myeloid NGS Testing by Supplementing a Commercial Myeloid NGS Assay with Custom or Extended Data Filtering and Accessory Fragment Analysis.

Commercial myeloid next-generation sequencing (NGS) panels may facilitate uniform generation of raw data between laboratories. However, different strategies for data filtering and variant annotation may contribute to differences in variant detection and reporting. Here, we present how custom data filtering or the use of Oncomine extended data filtering improve detection of clinically relevant mutations with the Oncomine Myeloid Research Assay. The study included all patient samples (n = 264) analyzed during the first-year, single-site, clinical use of the Ion Torrent Oncomine Myeloid Research Assay. In data analysis, the default analysis filter was supplemented with our own data filtering algorithm in order to detect additional clinically relevant mutations. In addition, we developed a sensitive supplementary test for the ASXL1 c.1934dupG p.Gly646fs mutation by fragment analysis. Using our custom filter chain, we found 96 different reportable variants that were not detected by the default filter chain. Twenty-six of these were classified as variants of strong or potential clinical significance (tier I/tier II variants), and the custom filtering discovered otherwise undetected tier I/tier II variants in 25 of 132 patients with clinically relevant mutations (19%). The remaining 70 variants not detected by the default filter chain were classified as variants of unknown significance. Among these were several unique variants with possible pathogenic potential judged by bioinformatic predictions. The recently launched Oncomine 5.14 extended filter algorithm detects most but not all of the tier I/tier II variants that were not detected by the default filter. The supplementary fragment analysis for the ASXL1 c.1934dupG p.Gly646fs confidently detected a variant allele frequency of down to 4.8% (SD 0.83%). The assay also detected the ASXL1 c.1900_1922del23 mutation. Detection of clinically relevant variants with the Oncomine Myeloid Research NGS assay can be significantly improved by supplementing the default filter chain with custom data filtering or the recently launched Oncomine 5.14 extended filter algorithm. Our accessory fragment analysis facilitates easy testing for frequent ASXL1 mutations that are poorly or not covered by the NGS assay.

Geosynchronous SAR raw data simulator in presence of ionospheric scintillation using reverse backprojection

The future L-band geosynchronous synthetic aperture radar (GEO SAR) system will be inevitably affected by ionospheric scintillation. Due to the lack of the real system, it is necessary to achieve the scintillation-affected GEO SAR raw data for the research of influential mechanism and correction strategies. A method for the generation of scintillation-affected GEO SAR raw data is proposed that utilises the reverse backprojection (ReBP) to efficiently and accurately modulate the scintillation transfer function on GEO SAR signals. A simulation experiment for a real SAR scene from one of the ALOS-PALSAR observation is operated in case of the current inclined L-band GEO SAR system configuration. The simulation takes less time by using the ReBP, compared with the existent method, and shows the speckle-like phase error similar to the injected scintillation phase error, which validates the nice efficiency and accuracy of the proposed methodology.

Feasibility Study and Conceptual Design of Missile-Borne Synthetic Aperture Radar

In this paper, feasibility study along with conceptual design of missile-borne synthetic aperture radar (SAR) has been accomplished, which can effectively improve the detection capability and the blow precision of a missile. First, SAR echo signal modeling, geometry configuration for missile-borne SAR scenario, and an appropriate image formation algorithm are presented. Then, a system block diagram along with complete discussions about different parts and system design considerations of the SAR seeker are explained. Finally, simulation of the whole system along with raw data generation considering the imaging geometry of the missile-borne SAR seeker has been performed. Experimental results show that the proposed system is capable of imaging and targeting in the complicated geometry of missile-borne SAR.

English

High quality RNA extraction from field-grown jute plants can be difficult due to the presence of complex polyphenolic, polysaccharide and waxes. But isolation of RNA in high quality is a basic need in plant genetics, genomics, transcriptomics, molecular biology and related physiological investigations. Here, cetyl trimethyl ammonium bromide (CTAB) based modified RNA isolation protocol suitable for isolating high quality total RNA from different parts of field-grown jute plants was reported. Modifications come with two extra wash with extraction buffer, residual polysaccharides precipitation with absolute ethanol and potassium acetate during phenol:chloroform:isoamyl alcohol (PCI), intermediate pelleting with lithium chloride followed by dissolving the pellet in sodium dodecyl sulfate (SDS). The 28S/18S ratio and RIN of isolated RNA were greater than 2.0 and 7.3 to 8.8, respectively reveal RNA to be high purity. The isolated RNA can be used directly for subsequent downstream applications including cDNA library construction, reverse transcription polymerase chain reaction (RT-PCR) and RNA-Seq raw data generation without further purification. Moreover, this study showed that this method could also be successfully applied to other polyphenols and polysaccharides rich malvaceous plants. Key words: RNA isolation, Corchorus, Jute, quantitative polymerase chain reaction with reverse transcription (RT-qPCR), cDNA library construction, next-generation sequencing (NGS) data generation.

Resolutional Analysis of Multiplicative High-Frequency Speckle Noise Based on SAR Spatial De-Speckling Filter Implementation and Selection

Due to the inherent characteristics of the electromagnetic wave scattering phenomenon, synthetic aperture radar (SAR) images are directly degraded by high-frequency multiplicative speckle (HMS) noise, which makes image de-speckling filter application and selection a challenge. In this regard, an adverse effects analysis of the HMS under implementation of seven different spatial de-speckling filters on a reference SAR image is considered in this paper. The investigation includes the formulation of the backscattered data and the HMS based on the pixel statistics and their distribution as an image noise behavioral analysis method. The resulting complex behavioral model is used for HMS power spectral density (PSD) function modeling. This paper also includes HMS system resolution effects analysis on the raw data generation (RDG) and the received frequency profile (RFP). An objective quality assessment procedure was also carried out to investigate both the de-speckled image resolution and the spatial filter evaluation in the presence of the HMS. As a result, the simulations verify that speckles are embedded within the high-frequency parts of the raw data, directly affecting the spatial resolution and the image resolution with non-specific patterns. The results also show that no spatial de-speckling filter consistently outperforms others, and their implementation is completely dependent on the texture, the system parameters, and their evaluation index. As a novel approach, HMS spectral behavioral modeling within the filtered images, as well as the proposed spatial de-speckling filter evaluation methods, are the proper techniques for optimum filter selection and specific purpose applications. The results are very helpful for remote sensing image restoration and data preservation when dealing with SAR images with a less fine resolution, such as ice-covered areas, coastal change detection, vegetation texture detection, geological structures mapping, and so forth. The SAR system resolution analysis is completed based on inversed problem solution (IPS) and with the help of a hybrid-domain image formation algorithm (IFA).

Imaging Simulation for Synthetic Aperture Radar: A Full-Wave Approach

Imaging simulation of synthetic aperture radar (SAR) is one of the potential tools in the field of remote sensing. The echo signal in imaging simulation based on the point target model cannot be linked to practical scenes due to the model being a simple mathematical form, stating only the synthetic process and lacking the physical process based on electromagnetic theory. In this paper, the full-wave method is applied to include the electromagnetic effects in raw data generation, and then a refined omega-K algorithm is used to perform image focusing. According to the proposed method, the focused images not only demonstrate the difference under dielectric constant variation but also present the diversified interaction among the targets with the spacing change. In addition, the images are simulated in different observation modes and bandwidths to provide a satisfactory reference for the design of system parameters. The simulation results from the full-wave method also compare well with chamber experiments. The simulation of SAR imaging based on a full-wave method offers more complete recovery of scattering information and is useful in designing future novel SAR systems and in speckle reduction analysis.

Kinetics and thermodynamics of swelling and dissolution of PVA gels obtained by freeze-thaw technique

PVA hydrogels obtained by freeze-thaw technique find important applications in regenerative medicine and controlled drug delivery technologies. The novelty of this contribution is the establishment of the cross-dependency of kinetics and thermodynamics parameters of swelling and dissolution of PVA hydrogels with the freeze-thaw process main variables. The work also establishes cross-correlations between the freeze-thaw process conditions, the gel microstructure and the properties of obtained hydrogel/hydrosoluble PVA materials. The conditions of the freeze-thaw process were selected by following an experimental design. The time evolution of the water up-take of PVA hydrogels was measured and used as main raw data generator. A 4-D surface response of the swelling and dissolution parameters in the space of freeze-thaw process variables was obtained and analyzed in terms of the resulting hydrogel and hydrosoluble polymeric structures. The 4-D surface response in the space of freeze-thaw process variables can predict very precisely the swelling and dissolution of PVA systems and their conditions for developing fully crosslinked hydrogel networks to fully hydrosoluble polymeric bodies.

Reverse Backprojection Algorithm for the Accurate Generation of SAR Raw Data of Natural Scenes

Future synthetic aperture radar (SAR) mission concepts often rely on locally nonlinear (e.g., high orbits and bistatic) surveys or acquisition schemes. The simulation of the raw data of natural scenes as acquired by future systems appears as one powerful tool in order to understand the particularities of these systems and assess the impact of system and propagation errors on their performance. We put forward, in this letter, a new formulation of the reverse backprojection algorithm for the accurate simulation of raw data of natural surfaces. In particular, the algorithm is perfectly suited to accommodate any kind (1-D/2-D) of temporal and spatial variation, e.g., in observation geometry, acquisition strategy, or atmospheric propagation. The algorithm is analyzed with respect to its SAR image formation sibling, and tested under different simulation scenarios. We expect the reverse backprojection algorithm to play a relevant role in the simulation of future geosynchronous and multistatic SAR missions.

New Imaging Frontiers in Cardiology: Fast and Quantitative Maps from Raw Data

Among the novelties in the field of cardiovascular imaging, the construction of quantitative maps in a fast and efficient way is one of the most interesting aspects of the clinical research. Quantitative parametric maps are typically obtained by post processing dynamic images, that is, sets of images usually acquired in different temporal intervals, where several images with different contrasts are obtained. Magnetic resonance imaging, and emission tomography (positron emission and single photon emission) are the imaging techniques best suited for the formation of quantitative maps. In this review article we present several methods that can be used for obtaining parametric maps, in a fast way, starting from the acquired raw data. We describe both methods commonly used in clinical research, and more innovative methods that build maps directly from the raw data, without going through the image reconstruction. We briefly described recently developed methods in magnetic resonance imaging that accelerate further the MR raw data generation, based on appropriate sub-sampling of k-space; then, we described recently developed methods for generating MR parametric maps. With regard to the emission tomography techniques, we gave an overview of both conventional methods, and more recently developed direct estimation algorithms for parametric image reconstruction from dynamic positron emission tomography data. We have provided an overview of the possible approaches that can be followed to realize useful parametric maps from imaging raw data. We moved from the conventional approaches to more recent and efficient methods for accelerating the raw data generation and the of parametric maps formation.

Efficient stripmap-mode synthetic aperture radar raw data generation accounting for trajectory deviation with a nonzero squint angle

We present a stripmap-mode raw data generator that accounts for trajectory deviations with a nonzero squint angle for an extended scene. The approach is attempted under the acquisition Doppler (AD) geometry, rather than a standard cylindrical reference system. It essentially utilizes one-dimensional azimuth Fourier domain processing, followed by range time-domain integration. As a result, the proposed algorithm is more computationally efficient compared to the time-domain method and yet maintains high accuracy. The effectiveness and efficiency of the proposed algorithm are demonstrated by numerical simulations.

Efficient TOPS mode raw data simulator of extended scenes

The terrain observation by progressive scans (TOPS) mode is a novel wide-swath imaging scheme, and it reduces the major drawbacks existing in conventional ScanSAR. An accurate and efficient synthetic aperture radar (SAR) raw data generator of distributed extended scenes is of considerable value for testing system parameters and verifying the imaging algorithms, particularly when the real raw data of recently proposed imaging mode are not available yet. The TOPS mode raw data simulation can only be exactly and directly realised in the time domain because an azimuth window function is not be suitable for each point target echo in the scene, however it is not efficient especially when simulating an extended scene. In this study, a novel TOPS mode raw data simulation method is presented to solve above issues. In this approach, an azimuth linear frequency modulated signal is introduced to form the azimuth window function and the echo is obtained in two-dimensional frequency domain by chirp-z transformation, which improves the efficiency greatly while retaining the simulation accuracy. Simulation results for point targets and a real SAR image demonstrate its validity and effectiveness.

A fast multichannel SAR raw data simulator of clutter and moving targets

Accessibility of a fast and accurate multichannel synthetic aperture radar raw data generator of stationary clutter and moving targets has high importance, especially in the application of ground moving target indication. In this paper, a fast four-stage algorithm for generating the raw data of each channel stationary clutter and moving targets, has been proposed respectively in the frequency and the hybrid time–frequency domain. Using this simulator, in different conditions in terms of target motion speed, acceleration and direction, for each of the channels, after generating the raw data, its final image has been extracted by the range-Doppler algorithm. Then, using clutter suppression techniques such as DPCA, ATI and hybrid DPCA–ATI, the multichannel SAR final image has been obtained in ideal and nonideal conditions. Finally, the obtained images of the first channel have been studied using the extracted formulas for predicting the effects of target motion parameters on the SAR images as well as analyzing the multichannel SAR final image. The results show that the proposed algorithm for generating the raw data of each channel stationary clutter and moving targets has better performance in terms of speed and accuracy than the other existing simulators and the proposed multichannel SAR simulation method has high quality.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Efficient one-stationary bistatic synthetic aperture radar raw data generation based on Fourier analysis

Raw data generation for synthetic aperture radar (SAR) is very powerful for designing systems and testing imaging algorithms. In this paper, a raw data generation method based on Fourier analysis for one-stationary bistatic SAR is presented. In this mode, two-dimensional (2-D) spatial variation is the major problem faced by the fast Fourier transform–based raw data generation. To deal with this problem, a 2-D linearization followed by a 2-D frequency transformation is employed in this method. This frequency transformation can reflect the 2-D spatial variation. Residual phase compensation is also discussed. Numerical simulation verifies the method.