Data Reconstruction Method Research Articles

Метод реконструкції відеозображень для підвищення ефективності доставки в інфокомунікаційних системах аеросегменту

Today, the main tool of effective management at both the local, regional, and state levels is the availability of appropriate information support. This especially applies to the security and defense sector, where the availability of the necessary information support is the main requirement for the effective response of departmental bodies (relevant law enforcement agencies) to crisis situations arising both in society and the state as a whole. In connection with this, the role of video information support as a means for prompt decision-making is significantly increased. This is due to the fact that the key principles of the implementation of video information provision are timeliness (operational) and reliability. For this purpose, stationary and mobile photo and video surveillance systems are quite actively used. The use of the latter is closely related to the aviation segment — unmanned aerial vehicles and complexes, the role of which is increased by the presence of such properties as scale and mobility. However, at the same time, the following problematic factors arise related to the use of wireless communication technologies for data delivery to the final addressee: an imbalance between the ever-increasing volumes of data and the bandwidth of data transmission channels; the influence of obstacles arising in the process of video data delivery on the level of reliability of the reconstructed video image. It should be noted that the use of existing methods of interference-resistant coding to solve the above-mentioned problems leads to a significant increase in the volume of video data, which is critical in the conditions of using wireless communication technologies — video is an information resource transmitted with significant time delays. For this purpose, a method of video data reconstruction is being developed based on the use of identifiers (markers) of uneven code structures assigned to the elements of clusters formed as a result of the restructuring of the information space by structural feature. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource.

Context-Based Multiscale Unified Network for Missing Data Reconstruction in Remote Sensing Images

Missing data reconstruction is a classical yet challenging problem in remote sensing images. Most current methods based on traditional convolutional neural network require supplementary data and can only handle one specific task. To address these limitations, we propose a novel generative adversarial network-based missing data reconstruction method in this letter, which is capable of various reconstruction tasks given only single source data as input. Two auxiliary patch-based discriminators are deployed to impose additional constraints on the local and global regions, respectively. In order to better fit the nature of remote sensing images, we introduce special convolutions and attention mechanism in a two-stage generator, thereby benefiting the tradeoff between accuracy and efficiency. Combining with perceptual and multiscale adversarial losses, the proposed model can produce coherent structure with better details. Qualitative and quantitative experiments demonstrate the uncompromising performance of the proposed model against multisource methods in generating visually plausible reconstruction results. Moreover, further exploration shows a promising way for the proposed model to utilize spatio-spectral-temporal information. The codes and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Oliiveralien/Inpainting-on-RSI</uri> .

An application of in-vehicle recording technologies to analyze injury severity in crashes between taxis and two-wheelers

Conventionally, the crash data used in traffic safety analysis have been collected by the police dispatched to the crash scene. Therefore, crash information inevitably includes errors that influence traffic safety analysis. Such errors can include the crash speed, crash time, crash location, and other crash characteristics. The advances in in-vehicle video recording (IVVR) technologies have recently enabled traffic safety professionals to use more accurate crash information based on crash data reconstruction methods. Although a few studies have been conducted to identify the factors affecting the crash injury severity using such detailed crash data, there was no effort to analyze the factors affecting the injury severity in crashes between taxis and two-wheelers (TWs), including bicycles and motorcycles. Therefore, this study analyzes the injury severity of TW riders in taxi-TW crashes with the accurate crash data collected by taxis equipped with IVVR devices in Incheon, Korea. Two hundred and forty-eight crash data from two years (2010–2011) were used to perform this objective. The factors affecting the injury severity to TW riders were identified based on a partial proportional odds model for these data. Seven variables were found to affect the injury severity significantly: crash speed, second collision, third collision, Delta-V, crashes that occurred with a non-helmeted motorcycle rider, crashes where the collision type was sideswipe, and crashes under rainy or snowy weather conditions. On the other hand, two variables regarding crashes, where the taxi driver behavior helped reduce visible and severe injuries, were changing lanes and the young TW riders (<18 years).

Parallel data acquisition and reconstruction method of near-field ptychography for large samples

Near-field ptychography is an attractive modality of coherent diffraction imaging, which can provide quantitative phase of samples at sub-pixel resolution and shows low requirement on beam coherence and detector dynamic range. In the case of studying extensive samples, a large dataset would be recorded, resulting in a long data acquisition time and high requirements for computer memory and computing power. Here, we proposed a simple experimental arrangement for parallel data acquisition and the corresponding image reconstruction algorithm. The scheme can dramatically increase the overall imaging speed. The algorithm can be efficiently implemented on graphic processing units (GPUs). The feasibility and effectiveness of the method have been validated with numerical simulation and optical experiments. The proposed approach would be helpful for imaging using large-array cameras.

Integrating real world data and clinical trial results using survival data reconstruction and marginal moment-balancing weights

ABSTRACT Outcomes in electronic health records (EHR)-derived cohorts can be compared to similarly treated clinical trial cohorts to estimate the efficacy-effectiveness gap, the discrepancy in performance of an intervention in a trial compared to the real world. However, because clinical trial data may only be available in the form of published summary statistics and Kaplan-Meier curves, survival data reconstruction methods are needed to recreate individual-level survival data. Additionally, marginal moment-balancing weights can adjust for differences in the distributions of patient characteristics between the trial and EHR cohorts. We evaluated bias in hazard ratio (HR) estimates by comparing trial and EHR cohorts using survival data reconstruction and marginal moment-balancing weights through simulations and analysis of real-world data. This approach produced nearly unbiased HR estimates. In an analysis of overall survival for patients with metastatic urothelial carcinoma treated with gemcitabine-carboplatin captured in the nationwide Flatiron Health EHR-derived de-identified database and patients enrolled in a trial of the same therapy, survival was similar in the EHR and trial cohorts after using weights to balance age, sex, and performance status (HR = 0.93, 95% confidence interval (0.74, 1.18)). Overall, we conclude that this approach is feasible for comparison of trial and EHR cohorts and facilitates evaluation of outcome differences between trial and real-world populations.

Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data

An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage. This phenomenon will limit the predicative ability and performance for supporting data analyses by IoT-based platforms. Therefore, it is necessary to exploit a way to reconstruct these lost data with high accuracy. A new data reconstruction method based on spectral k-support norm minimization (DR-SKSNM) is proposed for NB-IoT data, and a relative density-based clustering algorithm is embedded into model processing for improving the accuracy of reconstruction. First, sensors are grouped by similar patterns of measurement. A relative density-based clustering, which can effectively identify clusters in data sets with different densities, is applied to separate sensors into different groups. Second, based on the correlations of sensor data and its joint low rank, an algorithm based on the matrix spectral k-support norm minimization with automatic weight is developed. Moreover, the alternating direction method of multipliers (ADMM) is used to obtain its optimal solution. Finally, the proposed method is evaluated by using two simulated and real sensor data sources from Panzhihua environmental monitoring station with random missing patterns and consecutive missing patterns. From the simulation results, it is proved that our algorithm performs well, and it can propagate through low-rank characteristics to estimate a large missing region’s value.

Data Reconstruction Method for CD Basis Weight Analysis of Paper by using Compressed Sensing Technology

The control of the cross-directional (CD) basis weight of paper is very challenging. The actuator of the system needs to control the properties of the entire sheet according to a set of restricted data measured by a scanning sensor. The scanning sensor measures the sheet in the cross direction of the paper. For the moving sheet, CD variations were those along an axis perpendicular to the motion of the sheet, while MD (machine direction) variations were those along the axis of motion. One of the problems that make the CD control difficult is data sampling, which is obtained by continuously measuring the CD basis weight of paper through multiple scanning sensors. As the paper moves vertically, the scanning sensor can only measure the “Z” shape area on the paper. When the scanning frequency was not at least twice the maximum frequency of the paper variation, it was difficult to estimate the sheet profile. According to the sparse characteristics of paper signal, a novel approach to accurately reconstruct the sheet properties by a random sampling protocol was proposed by using compressed sensing technology. Compared with the simple bandwidth based uniform sampling theory, it could accurately reconstruct the real process variations from less measurement data. Based on the reconstructed data and actual industrial process data, the CD response model was identified and the reconstruction effect was verified. The CD and MD basis weight data were separated by the predictive separation algorithm, which ensured the basis for CD control. The approach of the compressed sensing technology was found to be effective.

Application of compressed sensing in the guided wave structural health monitoring of switch rails

Switch rails are weak but essential components of high-speed rail (HSR) systems. In the condition-based maintenance of HSR, ultrasonic guided wave (UGW) on-line monitoring technology is widely used in judging real-time operating conditions; however, it always generates large amounts of data. Too much data bring significant challenges, such as too many unnecessary costs of energy, storage, and network bandwidth in the structural health monitoring of switch rails, making it challenging to realize embedded sensor networks with high durability and low power consumption. Furthermore, the structural damage occurs relatively less over the long-term, indicating that these measurements are inherently sparse. The sparseness of structural damage makes them attractive for the compressed sensing technique. This study proposes a novel data compression and reconstruction method to meet the challenges and reduce the amount of data transmitted by sensor networks and maintain their accuracies simultaneously. First, a lightweight data dictionary is constructed to perform a sparse decomposition of UGW signals according to the characteristics of UGW propagation. Second, an effective sampling method based on a sparse random matrix is designed for sub-Nyquist sampling and compressing UGW signals. Third, a novel block adaptive matching pursuing algorithm is proposed to reconstruct UGW signals from compressed data. Finally, numerical signals, finite element simulation, and several actual monitoring experiments on the foot of a switch rail are conducted to verify the effectiveness and accuracy of the proposed method. The influence of different compression ratios and block sizes on the reconstruction performance of guided wave signals is investigated. The results indicate that the proposed method can sample UGW signals with much lower requirements than the Nyquist sampling theorem and is superior to other novel algorithms.

Experimental velocity data estimation for imperfect particle images using machine learning

We propose a method using supervised machine learning to estimate velocity fields from particle images having missing regions due to experimental limitations. As a first example, a velocity field around a square cylinder at the Reynolds number of ReD=300 is considered. To train machine learning models, we utilize artificial particle images (APIs) as the input data, which mimic the images of the particle image velocimetry (PIV). The output data are the velocity fields, and the correct answers for them are given by a direct numerical simulation (DNS). We examine two types of the input data: APIs without missing regions (i.e., full APIs) and APIs with missing regions (lacked APIs). The missing regions in the lacked APIs are assumed following the exact experimental situation in our wind tunnel setup. The velocity fields estimated from both full and lacked APIs are in great agreement with the reference DNS data in terms of various statistical assessments. We further apply these machine learned models trained with the DNS data to experimental particle images so that their applicability to the exact experimental situation can be investigated. The velocity fields estimated by the machine learned models contain approximately 40 fold denser data than that with the conventional cross-correlation method. This finding suggests that we may be able to obtain finer and hidden structures of the flow field, which cannot be resolved with the conventional cross-correlation method. We also find that even the complex flow structures are hidden due to the alignment of two square cylinders, the machine learned model is able to estimate the field in the missing region reasonably well. The present results indicate a great potential of the proposed machine learning-based method as a new data reconstruction method for PIV.

Hyperpolarized Metabolic MRI-Acquisition, Reconstruction, and Analysis Methods.

Hyperpolarized metabolic MRI with C-labeled agents has emerged as a powerful technique for in vivo assessments of real-time metabolism that can be used across scales of cells, tissue slices, animal models, and human subjects. Hyperpolarized contrast agents have unique properties compared to conventional MRI scanning and MRI contrast agents that require specialized imaging methods. Hyperpolarized contrast agents have a limited amount of available signal, irreversible decay back to thermal equilibrium, bolus injection and perfusion kinetics, cellular uptake and metabolic conversion kinetics, and frequency shifts between metabolites. This article describes state-of-the-art methods for hyperpolarized metabolic MRI, summarizing data acquisition, reconstruction, and analysis methods in order to guide the design and execution of studies.

Optimal Control Method for Reconstruction of Geometric Optical Transmission Characteristic Data

Geometrical optical projection three-dimensional profile measurement has the advantages of non-contact, non-destructive, and fast data acquisition. Its measurement system is the most promising kind of macro-optical profiler. The thesis analyzes the laser light source image acquisition and image processing, designs the tomographic image reconstruction and three-dimensional image display experimental system of the imaging system, and conducts theoretical analysis of its imaging theory, imaging quality and imaging error. At the same time, the paper uses compressed sensing algorithm (CS) to reconstruct and optimize the transmission data, and realize the analysis of the transmission characteristics of geometric optics.

High-Resolution Reconstruction for Multidimensional Laplace NMR.

As a perfect complement to conventional NMR that aims for chemical structure elucidation, Laplace NMR constitutes a powerful technique to study spin relaxation and diffusion, revealing information on molecular motions and spin interactions. Different from conventional NMR adopting Fourier transform to deal with the acquired data, Laplace NMR relies on specially designed signal processing and reconstruction algorithms resembling the inverse Laplace transform, and it generally faces severe challenges in cases where high spectral resolution and high spectral dimensionality are required. Herein, based on the tensor technique for high-dimensional problems and the sparsity assumption, we propose a general method for high-resolution reconstruction of multidimensional Laplace NMR data. We show that the proposed method can reconstruct multidimensional Laplace NMR spectra in a high-resolution manner for exponentially decaying relaxation and diffusion data acquired by commercial NMR instruments. Therefore, it would broaden the scope of multidimensional Laplace NMR applications.

A PV generation data reconstruction method based on improved super-resolution generative adversarial network

With the growing penetration of solar photovoltaic (PV) generation, advanced data analysis methods have been applied to the smart grid operation. However, the low-temporal-resolution PV generation data limits the utilization of the data analysis methods, because the low-temporal-resolution PV generation data contains too little information. On the other hand, the existing data reconstruction methods are less than satisfactory in reconstructing high-temporal-resolution PV generation data from low-temporal-resolution data, since most of them cannot fully capture the characteristics of PV generation data. To address this issue, a PV generation data reconstruction method based on improved super-resolution generative adversarial network is proposed in this paper. First, a data-image construction method is proposed to encode the PV generation data into the so-called data-images. Furthermore, we develop a data-image super-resolution generative adversarial network (DISRGAN) model, and the data-images are used to train the DISRGAN model. Finally, based on the trained DISRGAN model, a general framework is developed to reconstruct high-temporal-resolution PV generation data from low-temporal-resolution data. Numerical experiments have been carried out based on PV generation data from the State Grid Corporation of China, to reconstruct the high-temporal-resolution data from low-temporal-resolution data. The results demonstrate the superior performance of the proposed framework compared with a series of state-of-the-art methods.

Quality Parameter Index Estimation for Compressive Sensing Based Sparse Audio Signal Reconstruction

As we, all know that the size of data is increasing tremendously day by day. In a recent project, several petabytes were used to save an image of the Black Hole. Therefore, it is very crucial to develop a method that can reduce the size of data for transmission & storage purposes. The Traditional method for data compression & reconstruction requires so much data space, due to this problem another technique is proposed for the compression and recovery purpose. This method is termed Compressive Sensing (CS). As per the Nyquist sampling theorem, for proper reconstruction of the signal, we have to do sampling at the double rate of maximum data rate available in the signal. As a result, the storage requirement increased as well as the cost of the system was also enhanced. While on the other hand in Compressive Sensing, little samples are required for the reconstruction of the signal. So here in this paper, we have considered three music signals which are single tone, instrumental and vocal song. Values of Mean Square Error, Root Mean Square Error and Signal to Noise Ratio for different compression ratios mentioned in the tables and plots. By analyzing these values we can easily investigate the effectiveness of compressive sensing.

Three-Dimensional Characterization of a Coastal Mode-Water Eddy from Multiplatform Observations and a Data Reconstruction Method

Coastal mesoscale eddies are important oceanic structures partially responsible for regulating ocean-shelf exchanges. However, their description and characterization are challenging; observations are often too scarce for studying their physical properties and environmental impacts at the required spatio-temporal resolution. Therefore, models and data extrapolation methods are key tools for this purpose. Observations from high-frequency radar, one satellite and two gliders, are used here to better characterize the three-dimensional structure of a coastal mode-water eddy from a multiplatform approach in the southeastern Bay of Biscay in spring 2018. After the joint analysis of the observations, a three-dimensional data reconstruction method is applied to reconstruct the eddy current velocity field and estimate the associated water volume transport. The target eddy is detected by surface observations (high-frequency radar and satellite) for two weeks and presents similar dimensions and lifetimes as other eddies studied previously in the same location. However, this is the first time that the water column properties are also observed for this region, which depicts a mode-water eddy behavior, i.e., an uplift of the isopycnals in the near-surface and a downlift deeper in the water column. The reconstructed upper water column (1–100 m) eddy dynamics agree with the geostrophic dynamics observed by one of the gliders and result in cross-shelf inshore (offshore) volume transports between 0.04 (−0.01) and 0.15 (−0.11) Sv. The multiplatform data approach and the data reconstruction method are here highlighted as useful tools to characterize and three-dimensionally reconstruct coastal mesoscale processes in coastal areas.

Incomplete PET projection data reconstruction method and simulation experiment

In positron emission tomography (PET) imaging, the whole body reconstruction is not required if the region of interest (ROI) is only a part of the object. This paper researched a local image reconstruction algorithm based on the PI-lines, and proposed the derivative-backprojection-Hilbert (DBH) algorithm which can use the incomplete PET projection data to achieve accurate reconstruction of ROI. We designed simulation experiments to evaluate the performance of the DBH algorithm with thorax model as the object of PET image reconstruction, and compared root mean square error (RMSE) of DBH reconstruction and RMSE of filtered backprojection (FBP) reconstruction from simulations data sets with and without noise. The result shows that DBH algorithm has advantages over traditional FBP algorithm, the RMSE decreases by 49.07% and 51.19% with the coincidence events number of 20 million and 40 million, respectively. Compared with traditional maximum-likelihood expectation-maximization (ML-EM), the local iteration algorithm which uses DBH reconstruction as the initial image produces a better reconstructed image at fewer iterations with the coincidence events number of 60 million. DBH algorithm is expected to be applied to fast two-dimensional local image reconstruction and optimize the local iterative reconstruction algorithm in clinical diagnosis.

Deep reconstruction of 1D ISOMAP representations

This paper proposes a deep learning priors-based data reconstruction method of 1D isometric feature mapping (ISOMAP) representations. ISOMAP is a classical algorithm of nonlinear dimensionality reduction (NLDR) or manifold leaning (ML), which is devoted to questing for the low dimensional structure of high dimensional data. The reconstruction of ISOMAP representations, or the inverse problem of ISOMAP, reestablishes the high dimensional data from its low dimensional ISOMAP representations, and owns a bright future in data representation, generation, compression and visualization. Due to the fact that the dimension of ISOMAP representations is far less than that of the original high dimensional data, the reconstruction of ISOMAP representations is ill-posed or undetermined. Hence, the residual learning of deep convolutional neural network (CNN) is employed to boost reconstruction performance, via achieving the priors between the low-quality result of general ISOMAP reconstruction method and its residual relative to the original data. In the situation of 1D representations, it is evaluated by the experimental results that the proposed method outbalances the state-of-the-art methods, such as nearest neighbor (NN), discrete cosine transformation (DCT) and sparse representation (SR), in reconstruction performance of video data. In summary, the proposed method is suitable for low-bitrate and high-performance applications of data reconstruction.

Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM

The fluctuation and intermission of large-scale wind power integration is a serious threat to the stability and security of the power system. Accurate prediction of wind power is of great significance to the safety of wind power grid connection. This study proposes a novel spatio-temporal correlation model (STCM) for ultra-short-term wind power prediction based on convolutional neural networks-long short-term memory (CNN-LSTM). The original meteorological factors at multi-historical time points of different sites throughout the target wind farm can be reconstructed into the input window of the model, and thus a new data reconstruction method is represented. CNN is used to extract the spatial correlation feature vectors of meteorological factors of different sites and the temporal correlation vectors of the meteorological features in ultra-short term, which are reconstructed in time series and used as the input data of LSTM. Then, LSTM extracts the temporal feature relationship between the historical time points for multi-step wind power forecasting. The STCM based on CNN-LSTM proposed in this study is suitable for wind farms that can collect meteorological factors at different locations. Taking the measured meteorological factors and wind power dataset of a wind farm in China as an example, four evaluation metrics of the CNN-LSTM model, CNN and LSTM individually used for multi-step wind power prediction, are obtained. The results show that the proposed STCM based on CNN-LSTM has better spatial and temporal characteristics extraction ability than the traditional structure model and can forecast the power of wind farm more accurately.

Robust damped rank-reduction method for simultaneous denoising and reconstruction of 5D seismic data

We have developed a new method for simultaneous denoising and reconstruction of 5D seismic data corrupted by random noise and missing traces. Several algorithms have been developed for seismic data restoration based on rank-reduction (RR) methods. More recently, a damping operator has been introduced into the conventional truncated singular-value decomposition (TSVD) formula to further remove residual noise, the presence of which disturbs the quality of the seismic results. Despite the success of the damped RR (DRR) method when the observed data have an extremely low signal-to-noise ratio (S/N), random noise is still a limiting factor for obtaining a perfect quality of the result. Therefore, how to accurately solve the simultaneous denoising and reconstruction problem with high fidelity is still challenging. We assume that introducing only the damping operator into the TSVD formula is not enough to remove the random noise and restore the useful signal well. By combining the soft thresholding (ST) operator and the moving-average (MA) filter, we have developed a new operator, which we call the STMA operator. Then, by introducing the STMA operator into the DRR framework, we have developed a new algorithm known as the robust DRR method, which aims at mixing the advantages of the STMA operator and the damping operator. The STMA operator is applied to the Hankel matrix after damped TSVD to better remove residual noise. Examples of our approach on synthetic and field 5D seismic data demonstrate the better performance in terms of the visual examination and numerical test compared with the DRR approach. Our method aims at producing an effective low-rank filter and, thus, can perfectly enhance the S/N of the simultaneously denoised and reconstructed results with higher accuracy.

Reconstructive Mapping from Sparsely-Sampled Groundwater Data Using Compressive Sensing

Compressive sensing is a powerful method for reconstruction of sparsely-sampled data, based on statistical optimization. It can be applied to a range of flow measurement and visualization data, and in this work we show the usage in groundwater mapping. Due to scarcity of water in many regions of the world, including southwestern United States, monitoring and management of groundwater is of utmost importance. A complete mapping of groundwater is difficult since the monitored sites are far from one another, and thus the data sets are considered extremely “sparse”. To overcome this difficulty in complete mapping of groundwater, compressive sensing is an ideal tool, as it bypasses the classical Nyquist criterion. We show that compressive sensing can effectively be used for reconstructions of groundwater level maps, by validating against data. This approach can have an impact on geographical sensing and information, as effective monitoring and management are enabled without constructing numerous or expensive measurement sites for groundwater.