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Digital security in modern systems very often uses biometric, and increasingly new implementations appear. Such applications can be found everywhere, even when picking up the package from courier, we certify its receipt through our signature on tablet. However, verification of this form is not one of the simplest elements in information processing systems. Given the different sizes, angles, or writing conditions that may affect its stability new methods to evaluate signatures are constantly needed. In this article, we propose the use of spline interpolation and two types of artificial neural networks to verify the identity of a person based on selected local and global features extracted from the image of a signature. Global features are extracted concerning interpolation and graphic processing methods, while local features are verified using Convolutional Neural Networks. Both sets of features are used in the identity verification process. The article presents the model of operation together with experiments, taking into account various parameters of the proposed extraction. We have reached an accuracy of 87.7% on the SVC2004 database.

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection -- the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this paper we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

Hard landing is a typical flight safety incident, and interpretability plays an important role in flight safety research. However, existing studies failed to provide good interpretability of the reasons for hard landing incidents and suffer from low prediction accuracy. To address the above problems, in this paper we propose CurveCluster+, a curve clustering method based on quick access recorder (QAR) data for hard landing risk evaluation. Specifically, we first conduct an in-depth analysis on hard landing flights by comparing key QAR parameter curves with the group behavior, based on which we establish a two-level hierarchical classification of hard landing incidents according to the hard landing patterns. Then we extract curve-level features from key QAR parameters through interpolation and resampling. After that we turn the classic K-means clustering into a semi-supervised algorithm by incorporating some expert experience and apply it on the curve-level features to automatically recognize the hard landing patterns. Finally, we propose a risk evaluation model based on the clustering results to discover high-risk flights from normal ones. We evaluate our method on a QAR dataset of 37,943 Airbus 320 aircraft flights. The results show that compared with other state-of-the-art data-driven methods, CurveCluster+ provides strong interpretability of hard landing incidents and exhibits good performance in recognizing hard landing patterns (the overall accuracy of our method reaches up to 92.99%). Moreover, it only requires a handful of hard landing samples to discover high-risk flights from tremendous normal landing flights, which is critical for flight safety warnings.

Image upscaling aims to increase the resolution and size of a low resolution (LR) image in order to generate a high resolution (HR) image of high frequency (HF). There are several polynomial methods for obtaining a sharpened, upscaled HR image. The interpolated pixel is measured using a weighted average of the neighboring pixels within the image grid that blur at HF regions in these methods. Edge degradation is also caused by other edge-directed and learning-based upscaling methods, which produce blurring artifacts. A novel approach is proposed to fill these gaps. Using the concept of unsharp masking (USM), the LR image is blurred adaptively based on the region’s local variance. The sharpened high pass filtered (HPF) image is then obtained by subtracting the adaptively blurred image from the LR image. According to USM, the HPF image is combined with the LR image via a gain factor optimized using the cuckoo search (CS) algorithm. To compensate for the loss caused by upscaling, this pre-processing step is performed prior to interpolation. Aside from that, the edge of the B-spline interpolated image is detected and expanded. Edge expansion of the upscaled image is performed to further restore the HF details and reduce zigzag artifacts introduced by upscaling while also preserving the edge boundary. The proposed method outperforms the Lanczos, Bicubic, and Bilinear schemes in terms of peak signal to noise ratio (PSNR) gain of 3.475, 8.3839, and 8.075 dB, respectively. In terms of performance, this method outperforms state-of-the-art techniques both objectively (PSNR and SSIM) and subjectively (visual quality).

Vehicle trajectory collection usually faces challenges such as inaccurate and incomplete trajectory data, mainly due to missing trajectories caused by Global Navigation Satellite System (GNSS) outages. In this paper, a novel ensemble transfer regression framework is proposed for urban environments with transfer learning as the primary solution for constructing a fine-grained trajectory dataset during GNSS outages. First, GNSS and motion information are fused for the training process. Then, a regression-to-classification (R2C) process is employed to implement incremental training to adapt to dynamically changing environments. Third, to account for GNSS outages, transfer learning is integrated to construct a data filtering strategy that minimizes negative sample weights during the current scenario. Finally, a more accurate classification-type loss function for ensemble learning is designed to obtain the ensemble transfer regression model. We utilize real-world datasets to verify the accuracy of the comparative methods and the proposed framework in trajectory interpolation prediction. The experimental results show that our framework is significantly superior to the comparative methods.

In the process industry, it is essential to establish a data-driven soft sensor to predict the key variable that is difficult to online measure directly. The accuracy performance of data-driven soft sensors relies heavily on data. Unfortunately, it is hard to acquire sufficient and informative data from the samples with limited number, which is called as the small sample problem. For handling the small sample problem, it is a good solution to generating virtual samples according to the distribution of original data. This paper proposes an enhanced method of virtual sample generation utilizing manifold features to develop soft sensors using small data. First, T-Distribution Stochastic Neighbor Embedding (t-SNE) is utilized to extract the features of input data. The main idea of generating virtual samples is to use the interpolation algorithm to obtain virtual t-SNE input features and then the random forest algorithm is utilized to get the virtual outputs using virtual t-SNE input features. Finally, virtual samples using the proposed t-SNE based virtual sample generation (t-SNE-VSG) can be achieved. For the sake of confirming the effectiveness and feasibility of the presented t-SNE-VSG, a standard data set is first used. What is more, a small data set from an actual industrial process of Purified Terephthalic Acid is used to establish a soft sensor model. The results from simulations show that the accuracy performance of the soft sensor established with small data can be effectively improved by adding the virtual samples generated by t-SNE-VSG. In addition, t-SNE-VSG achieves superior accuracy to state-of-the-art virtual sample generation methods.

MixUp is an effective data augmentation method to regularize deep neural networks via random linear interpolations between pairs of samples and their labels. It plays an important role in model regularization, semisupervised learning (SSL), and domain adaption. However, despite its empirical success, its deficiency of randomly mixing samples has poorly been studied. Since deep networks are capable of memorizing the entire data set, the corrupted samples generated by vanilla MixUp with a badly chosen interpolation policy will degrade the performance of networks. To overcome overfitting to corrupted samples, inspired by metalearning (learning to learn), we propose a novel technique of learning to a mixup in this work, namely, MetaMixUp. Unlike the vanilla MixUp that samples interpolation policy from a predefined distribution, this article introduces a metalearning-based online optimization approach to dynamically learn the interpolation policy in a data-adaptive way (learning to learn better). The validation set performance via metalearning captures the noisy degree, which provides optimal directions for interpolation policy learning. Furthermore, we adapt our method for pseudolabel-based SSL along with a refined pseudolabeling strategy. In our experiments, our method achieves better performance than vanilla MixUp and its variants under SL configuration. In particular, extensive experiments show that our MetaMixUp adapted SSL greatly outperforms MixUp and many state-of-the-art methods on CIFAR-10 and SVHN benchmarks under the SSL configuration.

A robust algorithm to solve the low-thrust fuel-optimal trajectory optimization problem for interplanetary spacecraft is developed in this paper. The original nonlinear optimal control problem is convexified and transformed into a parameter optimization problem using an arbitrary-order Gauss-Lobatto discretization scheme with nonlinear control interpolation. A homotopic approach that considers the energy-to-fuel smoothing path is combined with an adaptive second-order trust-region mechanism to increase the convergence rate. The overall robustness is assessed in several fuel-optimal transfers with poor initial guesses. The results show a superior performance in terms of convergence compared to standard convex programming approaches in the literature.

Establishing correct correspondences between two images should consider both local and global spatial context. Given putative correspondences of feature points in two views, in this paper, we propose Order-Aware Network, which infers the probabilities of correspondences being inliers and regresses the relative pose encoded by the essential or fundamental matrix. Specifically, this proposed network is built hierarchically and comprises three operations. First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix. These clusters are in canonical order and invariant to input permutations. Next, the clusters are spatially correlated to encode the global context of correspondences. After that, the context-encoded clusters are interpolated back to the original size and position to build a hierarchical architecture. We intensively experiment on both outdoor and indoor datasets. The accuracy of the two-view geometry and correspondences are significantly improved over the state-of-the-arts. Besides, based on the proposed method and advanced local feature, we won the first place in CVPR 2019 image matching workshop challenge and also achieve state-of-the-art results in the Visual Localization benchmark. Code is available at https://github.com/zjhthu/OANet.

We propose a generic and scalable method for computing global solutions of nonlinear, high-dimensional dynamic stochastic economic models. First, within an MPI–TBB parallel time-iteration framework, we approximate economic policy functions using an adaptive, high-dimensional model representation scheme, combined with adaptive sparse grids. With increasing dimensions, the number of points in this efficiently-chosen combination of low-dimensional grids grows much more slowly than standard tensor product grids, sparse grids, or even adaptive sparse grids. Moreover, the adaptivity within the individual component functions adds an additional layer of sparsity, since grid points are added only where they are most needed — that is to say, in regions of the computational domain with steep gradients or at non-differentiabilities. Second, we introduce a performant vectorization scheme of the interpolation compute kernel. Third, we validate our claims with numerical experiments conducted on “Piz Daint" (Cray XC50) at the Swiss National Super-computing Center. We observe significant speedups over the state-of-the-art techniques, and almost ideal strong scaling up to at least 1, 000 compute nodes. Fourth, to demonstrate the broad applicability of our method, we compute global solutions to two different versions of a dynamic stochastic economic model: a high-dimensional international real business cycle model with capital adjustment costs, and with or without irreversible investment. We solve these models up to 300 continuous state variables globally.

In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

With the increased proliferation of smart devices, the transit passengers of today expect a higher quality of service in the form of real-time traffic updates, accurate expected time-of-arrival (ETA) predictions. Providing these services requires public transit agencies and private transportation players to maintain full situational awareness of the city-wide traffic. However, most such agencies and companies are resource-constrained and do not have access to city-wide traffic data. The availability of sparsely sampled and outlier-corrupted traffic data renders the resulting traffic maps patchy and unreliable and necessitates the use of sophisticated real-time traffic interpolation and prediction algorithms. Moreover, since the traffic data is measured and collected in a sequential manner, the estimations must also be generated online. Thankfully, the traffic matrices are spatially and temporally structured, allowing the use of time-series and matrix/tensor completion algorithms. This work puts forth a generative model for the traffic density and subsequently uses a variational Bayesian formalism to learn the parameters of the model. Specifically, we consider low-rank traffic matrices whose subspace evolves according to a state-space model with possible sparse outliers. Unlike most matrix/tensor completion algorithms, the proposed model is equipped with automatic relevance determination priors that allow it to learn the parameters in an entirely data-driven manner. A forward-backward algorithm is proposed that enables the updates to be carried out at low-complexity. Simulations carried out on real traffic speed data demonstrate that the proposed algorithm better predicts the future traffic densities as compared to the state-of-the-art matrix/tensor completion algorithms.

This article presents a 9-bit 500-MS/s 2-bit/cycle successive approximation register (SAR) analog-to-digital converter (ADC) with an error-tolerant interpolation technique. The proposed interpolation technique uses flip-flops to implement a 2-bit/cycle operation in the SAR ADC. By taking advantage of the metastable region of the flip-flop, the proposed interpolator can defer the bit decision when a decision error occurs with a high probability. Because the SAR ADC approximates the signal range step by step, the deferred decisions proceed to the next conversion cycles without any increase in quantization noise. The deferring-decision characteristic increases the error tolerance in the presence of comparator mismatches and increases the inherent linearity of the interpolation technique compared to conventional latch interpolation. A prototype ADC was designed using the 28-nm CMOS technology to verify the effectiveness of the proposed interpolation technique. The measured signal-to-noise-and-distortion ratio (SNDR) and spurious-free dynamic range (SFDR) at the Nyquist rate are 50.6 and 61.4 dB, respectively. The power consumption is 1.87 mW at a sampling frequency of 500 MS/s. The proposed ADC achieves a Walden figure of merit (FoM) of 13.5 fJ/conversion-step.

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution.

Assembly interfaces, the joint surfaces between the vertical tail and rear fuselage of a large aircraft, are thin-wall components. Their machining quality are seriously restricted by the machining vibration. To address this problem, an in-process adaptive milling method is proposed for the large-scale assembly interface driven by real-time machining vibration data. Within this context, the milling operation is first divided into several process steps, and the machining vibration data in each process step is separated into some data segments. Second, based on the real-time machining vibration data in each data segment, a finite-element-unit-force approach and an optimized space–time domain method are adopted to estimate the time-varying in-operation frequency response functions of the assembly interface. These FRFs are in turn employed to calculate stability lobe diagrams. Thus, the three-dimensional stability lobe diagram considering material removal is acquired via interpolation of all stability lobe diagrams. Third, to restrain milling chatter and resonance, the cutting parameters for next process step, e.g., spindle speed and axial cutting depth, are optimized by genetic algorithm. Finally, the proposed method is validated by a milling test of the assembly interface on a vertical tail, and the experimental results demonstrate that the proposed method can improve the machining quality and efficiency of the assembly interface, i.e., the surface roughness reduced from 3.2 μm to 1.6 μm and the machining efficiency improved by 33%.

Traditionally, reduced hair simulation methods are either restricted to heuristic approximations or bound to specific hairstyles. We introduce the first CNN-integrated framework for simulating various hairstyles. The approach produces visually realistic hairs with an interactive speed. To address the technical challenges, our hair simulation pipeline is designed as a two-stage process. First, we present a fully-convolutional neural interpolator as the backbone generator to compute dynamic weights for guide hair interpolation. Then, we adopt a second generator to produce fine-scale displacements to enhance the hair details. We train the neural interpolator with a dedicated loss function and the displacement generator with an adversarial discriminator. Experimental results demonstrate that our method is effective, efficient, and superior to the state-of-the-art on a wide variety of hairstyles. We further propose a performance-driven digital avatar system and an interactive hairstyle editing tool to illustrate the practical applications.

This paper proposes an open-loop based demodulation technique for the instantaneous phase angle estimation of three phase voltage systems. The proposed demodulation-based method relies on the Clarke transform, finite-impulse-response filters and algorithms for phase angle and frequency deviation estimation. When compared to a frequency-locked loop (FLL) based on dual second-order generalized integrators and a phase-locked loop (PLL) relying on adaptive delayed signal cancellation operator, the proposed one does not use any integrators, recursive signals and feedback of frequency or phase angle, thus unconditionally stable and relatively simple tuning is achieved. In addition, the proposed method is computationally efficient due to avoiding interpolation, square-root, trigonometric functions or their inverse operations. Moreover, under equal dynamic condition, the proposed one generates improved accuracy at steady-state in a range of the frequency deviation specified by standard and grid code. Simulated and experimental results are presented to verify the efficacy of the proposed method.

The description of a physical problem through a model necessarily involves the introduction of parameters. Hence, one wishes to have a solution of the problem that is a function of all these parameters: a parametric solution. However, the construction of such parametric solutions exhibiting localization in space is only ensured by costly and time-consuming tests, which can be both numerical or experimental. Numerical methodologies used classically imply enormous computational efforts for exploring the design space. Therefore, parametric solutions obtained using advanced nonlinear regressions are an essential tool to address this challenge. However, classical regression techniques, even the most advanced ones, can lead to non physical interpolation in some fields such as fluid dynamics, where the solution localizes in different regions depending on the problem parameters choice. In this context, Optimal Transport (OT) offers a mathematical approach to measure distances and interpolate between general objects in a, sometimes, more physical way than the classical interpolation approach. Thus, OT has become fundamental in some fields such as statistics or computer vision, and it is being increasingly used in fields such as computational mechanics. However, the OT problem is usually computationally costly to solve and not adapted to be accessed in an online manner. Therefore, the aim of this paper is combining advanced nonlinear regressions with Optimal Transport in order to implement a parametric real-time model based on OT. To this purpose, a parametric model is built offline relying on Model Order Reduction and OT, leading to a real-time interpolation tool following Optimal Transport theory. Such a tool is of major interest in design processes, but also within the digital twin rationale.

Due to recent developments in the additive manufacturing industry, topology optimization is nowadays a powerful computational tool that allows to design feasible lightweight components. Although the Finite Element Method (FEM) is the most applied discretization technique, meshless methods are currently established as accurate numerical methods with relevant advantages in several engineering fields. Nonetheless, the state-of-the-art of meshless methods in topology optimization is still scarce. This work develops the combination of a bi-direction structural optimization (BESO) algorithm with the Natural Neighbour Radial Point Interpolation Method (NNRPIM), a meshless method combining the natural neighbours geometric concept with the RPI shape functions. First, several benchmark examples are solved to evaluate the algorithm capability under several algorithm parameters. The proposed methodology is then implemented to design new automotive lightweight components. The results from the numerical applications demonstrate that the NNRPIM is a solid technique to be incorporated in optimization algorithms. Additionally, innovative automotive industry designs for additive manufacturing can be obtained using the presented approach.

Adaptive-Network-based Fuzzy Inference System, ANFIS, offers a popular and powerful fuzzy inference mechanism. As with many other advanced data driven techniques, developing an effective ANFIS typically requires sufficient training data. However, in many real-world applications it is not always straightforward to obtain a large amount of representative data that covers the entire problem space to accomplish the required training, seriously restricting the performance of a learned ANFIS. This paper introduces a new ANFIS learning approach through an evolutionary process, which is able to generate an ANFIS with only a small number of training data in a certain problem region, by interpolating well trained ANFISs in the neighbouring regions. Such a process works by firstly producing an initial population of candidate fuzzy rules in the region of data shortage, through interpolating a rule dictionary constructed from trained ANFISs in the neighbourhood regions. The crossover and mutation operations over these candidate rules are then executed in an effort to attain candidates of improved performance. When this genetic learning process terminates the chromosomes in the final population either collectively form or each individually represent a learned ANFIS, depending on whether a single fuzzy rule or a set of fuzzy rules representing an entire ANFIS is implemented with a chromosome within the evolving population. Comparative experimental evaluations on both synthetic and real world datasets are carried out, demonstrating that in spite of data shortage, the proposed interpolation approach is able to produce ANFIS models that significantly outperform those trained using existing learning mechanisms.

While the traditional trajectory planning methods are used in robotic belt grinding of blades with an uneven machining allowance distribution, it is hard to obtain the preferable profile accuracy and surface quality to meet the high-performance requirements of aero-engine. To solve this problem, a novel trajectory planning method is proposed in this paper by considering the developed interpolation algorithm and the machining allowance threshold. The residual height error obtained from grinding experiments of titanium alloy sample was compensated to modify the calculation model of row spacing, and a new geometric algorithm was presented to dynamically calculate the cutter contact points based on this revised calculation model and the dichotomy method. Subsequently, the off-line machining program is generated based on a double-vector controlling method to obtain an optimal contact posture. On this basis, three sets of robotic grinding tests of titanium alloy blades were conducted to investigate the advantages of the proposed method. The comparative experimental results revealed that the presented algorithm had improved the surface profile accuracy of blade by 34.2% and 55.1%, respectively. Moreover, the average machined surface roughness was achieved to 0.3 µm and the machining efficiency was obviously promoted. It is concluded that this research work is beneficial to comprehensively improve the machined quality of blades in robotic belt grinding.

An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multi-base-station (multi-BS) network deployment and maintenance. It can be conducted by a network operator or its served consumers. However, the number of measurement points and their locations may not be known in advance for an efficient and accurate evaluation. The main goal of this study is to propose a new methodology for understanding the selection of measurement points during coverage and signal quality assessment. It is particularly tailored to multi-BS low-power wide-area network (LPWAN) deployments without explicit knowledge of BS locations. To this aim, we first conduct a large-scale measurement campaign for three popular LPWAN technologies, namely, NB-IoT, Sigfox, and LoRaWAN. Utilizing this baseline data, we develop a procedure for identifying the minimum set of measurement points for the coverage assessment with a given accuracy as well as study which interpolation algorithms produce the lowest approximation error. Our results demonstrate that a random choice of measurement points is on par with their deterministic selection. Out of the candidate interpolation algorithms, Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. By contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as Natural-neighbor, Linear interpolation, or Inverse-Distance Weighting, can achieve comparable (and occasionally even better) accuracy levels. acceptedVersion

Trajectory similarity computation is a fundamental problem for many intelligent applications such as trip trajectory mining to find the most popular routes and road anomaly detection. Existing similarity computation methods for vehicle trajectory, such as Dynamic Time Wrapping(DTW) and Frechet distance, are dynamic programming problems with a quadratic time complexity in all cases and need to handle local time shifts when computing the distance between trajectories, thus limiting the scalability of these methods. In addition, GPS-based vehicle trajectories usually contain errors such as noise and outliers due to poor satellite visibility in urban regions and non-uniform sampling rates. To this end, we propose an embedding-based method for trajectory similarity computation with linear time complexity, which encodes a trajectory as a vector via deep representation learning and learns the similarity between trajectories with an attention-based learning to rank model. We use an interpolation-based approach to reduce noise and outliers by considering vehicle trajectory is physically constrained to the road network. Experiments on a massive vehicle trajectory dataset show that the proposed approach outperforms state-of-the-art baselines consistently and significantly.

Information sensor can realize the ubiquitous connection between objects and humans by distributed fiber-optic sensor array, i.e., emerging infrastructure for Internet of Things, in which optical frequency domain reflectometry (OFDR) with its high spatial resolution plays an important role in achieving intelligent perception and identification of the objects. However, given a certain fiber length, there are tradeoffs among the processing speed, the spatial resolution, and the strain measurement range. In this paper, a time optimization interpolation method and a distance domain compensation method are reported and experimentally verified to break the aforementioned tradeoffs for the first time. First, a full theoretical analysis on how to reduce the interpolation number and the reason why it is difficult to achieve high spatial resolution and large strain measurement and how to resolve it is made. Second in the proof-of-concept experiment, measurements of large strains up to 10000le are realized along the sensing fiber with a spatial resolution of 2mm using a conventional OFDR system. Also, the processing time is shortened by 76 times compared with the traditional processing method. This paper makes a significant step toward high-performance OFDR system with fast processing, high spatial resolution and wide strain measurement.

Harmonic measured foliation has demonstrated its usefulness for many geometric problems, including conformal parameterization and mesh quadrangulation. Due to the non-linearity and hard constraints for its computation, existing iterative solvers converge very slowly, and so impractical for large meshes. Though the multigrid approach is well-known for speeding up iterative solvers, a general multigrid solver cannot be applied here in a plug-and-play fashion, because the constraints for computing the harmonic measured foliations would be broken. In this article, we design a novel multigrid solver for this problem, where we propose specific multi-resolution mesh hierarchies and interpolation schemes to fulfill the requirements of the harmonic measured foliation. Experimental results show that our multigrid solver converges much faster than the original algorithm on meshes ranging from a few thousands to over one million edges, even by over a hundred times. This would benefit scalable geometry processing using harmonic measured foliations.

A united-reference fractional counter (URFC) is proposed for multi-level time-to-digital converters (TDCs) used in direct time-of-flight (dToF) light detection and ranging (LiDAR) applications. The URFC combines the integer counter and the second-level interpolation in one functional block that derives both the integer part and the fractional part from the same synchronization result. Thus the proposed TDC has a more robust structure to deal with misalignment errors, and measurement results from different levels can be perfectly matched. High linearity and high precision can be guaranteed because of the united reference without calibration. The response time of the URFC decreases to hundreds of picoseconds, which makes it suitable for all output pulses from the avalanche photodiode. A TDC with the proposed URFC was implemented in 65nm standard CMOS technology with a detection range of 2.5μs (~375m) and a single shot precision of 27.63ps (~4.0mm). The power consumption is 51.4mW with a reference clock at 200MHz and a repetition rate at 999kHz. The INL and DNL are ±2.751 LSB and ±1.679 LSB, respectively.

Providing a depth-rich Virtual Reality (VR) experience to users without causing discomfort remains to be a challenge with today's commercially available head-mounted displays (HMDs), which enforce strict measures on stereoscopic rendering parameters for the sake of keeping visual discomfort to a minimum. However, these measures often lead to an unimpressive VR experience with shallow depth feeling. Using radial basis function interpolation and projection matrix manipulations, we propose a novel method for dynamic tuning of stereoscopic rendering parameters toward enhanced VR experience and demonstrate that it is ready to be used with existing consumer HMDs for automated stereoscopic camera control. We present our method with an approach that is primarily intended for guided VR experiences such as VR walkthroughs or VR rides, assuming a depth layout defined by a path of depth-authored key points in the virtual environment. For this purpose, we also introduce a novel immersive interface for authoring unique 3D stereoscopic cinematographies for VR HMD experiences. The results of the user study indicate that our approach, with proper depth authoring, is able to enhance user experience in terms of overall perceived depth and picture quality while maintaining visual comfort on a par with the HMD's default arrangement. We also investigate the effects of using depth-of-field blurring in combination with our approach and observe that the quality of the outcome varies based on the overall scene depth. © 2021 Elsevier Ltd

Range/Doppler migration and velocity ambiguity are two well-known problems encountered in high-speed moving target detection using linear frequency-modulated continuous wave (LFMCW) automotive radar. To mitigate the problems, we introduce a simple Doppler-Range Processing (DRP) algorithm by first performing Doppler processing via fast Fourier transform (FFT) across slow-time samples, followed by a simple interpolation step, and then range processing via FFT along Doppler migration lines over fast-time samples. The proposed DRP algorithm can achieve full range and velocity resolutions, as well as full coherent integration gains. It attains a computational complexity comparable to that of the conventional 2D-FFT based Range-Doppler Processing (RDP) approach, computationally much more efficient than existing approaches. The proposed DRP algorithm can automatically resolve the velocity ambiguity problems. We analyze its velocity ambiguity mitigation capability in relation to the radar bandwidth and the number of slow-time samples within a Coherent Processing Interval (CPI). The effectiveness and the computational efficiency of the proposed algorithm are demonstrated by numerical examples.

Synthetic aperture radar raw data simulation (SAR-RDS) is beneficial to the SAR system design, signal processing method verification, and radar parameter optimization. Most SAR-RDS methods are based on the flat terrain assumption. However, the fluctuant terrain in real scene will induce severe SAR beam occlusion effect and produce radar shadow, leading to incorrect RDS results. Thus, a dynamic elevation angle interpolation (DEAI) algorithm is proposed for SAR shadow judgment by considering the actual SAR working process. The key of the proposed DEAI algorithm is the 1-D EAI and shadow visualization update, which avoids the problem that the existing methods cannot judge the shadow of partial areas due to the insufficiently refined mesh grid or the mismatch of the judgment model. Moreover, an evaluation framework named as joint image and signal criteria (JISC) is proposed from the perspectives of SAR imaging and signal processing results to objectively evaluate the SAR-RDS results and solve the problem that the existing evaluation methods cannot be compatible with fluctuant terrain. Finally, the numerical experiment verified our theoretical analyses.

Face aging and rejuvenation is applied to predict what a person looks like at different ages. While prior work brought about a significant progress in this topic, there are two central problems remaining to be solved : 1) most prior works require sequential data during training, while it is very rare in existing datasets; and 2) how to render an aging face and preserve personality at the same time. To deal with these problems, we develop a novel dual conditional GANs mechanism, thus aging faces can be trained with multiple sets of unlabeled facial images of different ages. Our basic architecture is AgeGAN, in which the primal conditional GAN converts input faces to other ages based on relevant age conditions, and the dual conditional GAN learns to invert the task. We further improve our networks, termed AgeGAN++, in which we share the weights between the primal part and the dual part to ensure a more stable training process. Moreover, in order to get more sensible results, a representation disentanglement component is integrated with the latent facial representation, and an enhanced discriminator is applied on the generated process. In addition, we firstly perform an interpolation experiment to demonstrate that our generators are powerful and effective for face aging and rejuvenation. Experimental results on four public datasets demonstrate the appealing performance of the proposed methods by comparing with the state-of-the-art methods. Our code and a demo are released at https://github.com/Sherry-JQ/AgeGAN.

The modified numerical inversion of the Laplace transform (dubbed NILT) has been recently proposed as a fast and provably stable numerical simulation for general circuits. Although it has enabled increasing the simulation time step, it highlighted the need for robust approach that can recover the full waveform in between the time points generated by NILT. This paper presents a new approach that addresses this challenge. The proposed approach leverages the NILT framework from a high-order approximation paradigm that computes points on the circuit waveforms to a methodology that computes high order derivatives of the waveforms at the same points. Using those derivatives, an interpolation approach based on Hermite interpolation is used to construct the circuit waveforms on dense points. Numerical experiments are presented to demonstrate the accuracy of both approaches.

Missing trace reconstruction is an essential step in the seismic data processing. Various interpolation methods have been proposed for handling this issue. In recent years, deep learning-based interpolation techniques, especially convolutional neural networks (CNNs), have been widely studied. Typically, these studies target regularly/randomly missing cases, leaving consecutively missing situations not handled properly. In this article, we propose a hybrid loss function SSIM+L₁, based on structural similarity (SSIM) and L₁ norm, for network training and attention mechanism as a network component that explicitly utilizes global information. We further design a CNN equipped with the hybrid loss and attention mechanism for successively missing trace reconstruction. Experiments on synthetic and field data demonstrate that our network can reconstruct more reasonable results than networks without attention mechanism in large gap situation and SSIM+L₁ loss promotes interpolation results. We also discuss the setup of key hyperparameters of the network by a thorough ablation study.

Due to the constraints of natural environments, acquired prestack seismic data is usually not complete, which seriously affects subsequent seismic data processing. With the progress of deep learning, many neural networks with different structures have been applied to missing seismic data interpolation. Among them, U-net can efficiently interpolate the regularly and irregularly missing seismic traces with small gap. While for consecutively missing seismic traces with big gap, the interpolation results for low amplitude missing components need to be further improved. In this letter, we analyze the variation of interpolation results for consecutively missing seismic traces during the traditional U-net training process, and find that U-net tends to only interpolate the high amplitude missing components. Meanwhile, due to the distribution difference between low and high amplitude seismic data, one U-net model is insufficient to interpolate both high and low amplitude missing components with a wide amplitude range. To improve the interpolation results of single U-net, we propose a multistage training process to train multiple U-net models. Each U-net model focuses on interpolating different missing components with a small amplitude range. In this way, more accurate interpolation results for low amplitude missing components can be obtained. Comparison experiments conducted on synthetic and field seismic data show that, under the same number of training epochs, the proposed training process can produce more accurate interpolation results comparing with traditional single U-net.

This letter proposes a modified polar format algorithm (PFA) applied to the highly squinted synthetic aperture radar (SAR) onboard an accelerating hypersonic platform. In conventional PFA, the range wavenumber is orthogonally decomposed to obtain the 2-D wavenumbers corresponding to the imaging Cartesian coordinates. This operation, however, is inadequate when facing with more complex motion trajectory. In this letter, the essence of the proposed method is to homogenize the spatially nonuniformly sampled echoes by combining interpolation with the generalized wavenumber definition. The imaging polar coordinate system in the slant plane provides new space-wavenumber Fourier transform pair, and the 1-D interpolation compensates for the range-variant phase errors. The proposed algorithm, thus, enables fast and effective imaging of hypersonic SAR in large squint angles and high maneuverability dive mode. Its performance is discussed and analyzed in this letter, including the resolution and the size of the imaging scene. The effectiveness, superiority, and application value of the proposed algorithm are verified by simulation.

For solar concentrating photovoltaic applications, line-focusing collectors, such as parabolic trough collectors, can accommodate greater numbers of PV cells at their focus compared to point focusing collectors. However, there is a dearth of research related to homogenizing the flux distribution in such collectors. Therefore, this paper aimed to develop a method for optimizing the shape of a concentrator that has the same geometrical characteristics as a parabolic trough concentrator (PTC) but high flux homogeneity. The customization of the curve was controlled by introducing guide points and forming a curve passing through these points using the Newton Interpolation Polynomial. The ray tracing was then used to obtain the concentration ratio ( ) and the solar flux distribution uniformity factor ( ) for the customized concentrator as performance factors. Then, the location of guide points was optimized using the Particle Swarm Optimization algorithm to achieve the greatest value of . The results showed that the shape of a PTC having and can be optimized to produce a concentrator having nearly the same value of but , which is ~10 times better. An online computer tool for the evaluation purpose was also developed and has been made available to use for free.

Currently, a non-geocentric geodetic coordinate system introduced in 1942 (CS42), based on the Krasovsky ellipsoid and the Baltic system of normal heights introduced in 1977, is used for geodetic works in the Republic of Uzbekistan. The development of the GNSS network proposes a task of transition to a new geocentric system based on the World Geodetic System 1984 (WGS84) ellipsoid. Many software products adopt 3- or 7-parameter conversions, which can cause errors up to several meters and are not suitable for many applications. In this case, local transformations using a grid of differences between observation points with known coordinates in both datums would give the best accuracy. In this paper, we discuss various interpolation methods (Kriging, Minimum Curvature, Inverse Distance to a Power and Radial Basis Function) to solve the distortion modeling between CS42 and WGS84 systems for national datum improvement. The results show that the distortion models share a common tendency for all interpolation methods: the maximum horizontal displacements are concentrated along the West Tien Shan lineament, which is the boundary of the relief lowering. The discrepancies between the grid-based (calculated) and GPS-measured coordinates are evaluated. Statistical and spatial analysis has confirmed that for the coordinate transformation from CS42 to WGS84 and vice versa, grid-based transformation with Radial Basis Function interpolation has a high accuracy transformation. Analysis of the available data across the eastern part of the country shows that some positional distortions existed between the CS42 and WGS84 datums. The magnitude of these distortions for the best RBF method is about 0.019 m–0.755 m with a standard deviation of 0.015 m.

Hyperspectral (HS) image fusion aims at integrating a panchromatic (PAN) image and an HS image, featuring the fused image with the spatial quality of the former and the spectral diversity of the latter. The classic fusion algorithm generally includes three consecutive procedures that are upsampling, detail extraction, and detail injection. In this article, we propose an HS and PAN image fusion method based on generative adversarial network and local estimation of injection gain. Instead of upsampling the HS image by classical interpolation techniques, a generative adversarial super-resolution network (GASN) is designed to obtain the interpolated HS image in the fusion framework. GASN establishes a spectral-information-based discriminator to conduct adversarial learning with the generator, so as to preserve the spectral information of the low-resolution HS image. An image segmentation-based injection gain estimation (ISGE) algorithm is subsequently proposed for HS and PAN images fusion. The injection gain is estimated over image segments obtained by a binary partition tree approach to improve the fusion performance. The proposed GASN and ISGE are implemented into two credible global estimation pansharpening methods, and experimental results prove the performance improvement of the proposed method. The proposed method is also compared with existing state-of-the-art methods, and experiments on several public databases demonstrate that the proposed method is competitive or superior to the state-of-the-art fusion methods.

Strain measurements by gauges can play an important role in analyzing the fatigue damage of the hydroelectric turbine runner. However, these measurements cannot cover every steady-state operating condition due to the experimental limitations. Thus, the aim of this research is to predict the strain signal on runner over every possible steady operating condition using available experimental measurements. The strain signal measured during steady state involves several components (such as periodicity, vortex rope component and stochastic components) which can generate difficulties during the prediction. This paper proposes a solution to predict the runner strain signals by independently interpolating each physical phenomenon over different turbine operating conditions. These components are extracted using cyclostationary decomposition operators. A case study is performed on a Francis hydroelectric turbine to verify the interpolation performance. The proposed methodology can contribute to the fatigue assessment and help to reduce the requirements of infield measurements.

The meshless numerical manifold method (MNMM) inherits two covers of the numerical manifold method. A mathematical cover is composed of nodes' influence domains and a physical cover consists of physical patches, which are produced through cutting mathematical cover by physical boundaries. Because two covers are adopted, MNMM can naturally and uniquely solve both the continuous and discontinuous problems under Galerkin's variational framework. However, Galerkin's meshless method needs background integration grids to realize solving, which often does not match the nodes' influence domains, so the accuracy of numerical integration is reduced. Consider that MNMM allows even distribution of nodes and the physical cover contains the characteristics of the boundary and nodes' influence domains, the study presents a new numerical integration strategy to ensure that the background integration grids match the nodes' influence domains. The method can be applied to continuous and discontinuous problems, and is proved to be equivalent to the influence domain integration. At the same time, a reasonable arrangement of mathematical nodes is made to assure the background integration grid that it is accordant and straightforward. In this way, the number of physical patches of each integral point is the same, which improves the accuracy of interpolation calculation. The effectiveness of the proposed method is verified by numerical examples of both continuous and discontinuous problems.

Various deep learning-based segmentation models have been developed to segment buildings in aerial images. However, the segmentation maps predicted by the conventional convolutional neural network-based methods cannot accurately determine the shapes and boundaries of segmented buildings. In this article, to improve the prediction accuracy for the boundaries and shapes of segmented buildings in aerial images, we propose the boundary-oriented binary building segmentation model (B3SM). To construct the B3SM for boundary-enhanced semantic segmentation, we present two-scheme learning (Schemes I and II), which uses the upsampling interpolation method (USIM) as a new operator and a boundary-oriented loss function (B-Loss). In Scheme I, a raw input image is processed and transformed into a presegmented map. In Scheme II, the presegmented map from Scheme I is transformed into a more fine-grained representation. To connect these two schemes, we use the USIM operator. In addition, the novel B-Loss function is implemented in B3SM to extract the features of the boundaries of buildings effectively. To perform quantitative evaluation of the shapes and boundaries of segmented buildings generated by B3SM, we develop a new metric called the boundary-oriented intersection over union (B-IoU). After evaluating the effectiveness of two-scheme learning, USIM, and B-Loss for building segmentation, we compare the performance of B3SM to those of other state-of-the-art methods using public and custom datasets. The experimental results demonstrate that the B3SM outperforms other state-of-the-art models, resulting in more accurate shapes and boundaries for segmented buildings in aerial images.

The core challenge of seismic data interpolation is how to capture latent spatial-temporal relationships between unknown and known traces in 3-D space. The prevailing tensor-based interpolation schemes seek a globally low-rank approximation to mine the high-dimensional relationships hidden in 3-D seismic data. However, when the low-rank assumption is violated for data involving complex geological structures, the existing interpolation schemes fail to precisely capture the trace relationships, which may influence the interpolation results. As an alternative, this article presents a basic deep tensor autoencoder (DTAE) and two variants to implicitly learn a data-driven, nonlinear, and high-dimensional mapping to explore the complicated relationship among traces without the need for any underlying assumption. Then, tensor backpropagation (TBP), which can be essentially viewed as a tensor version of traditional backpropagation (BP), is introduced to solve for the new model parameters. For ease of implementation, a mathematical relationship between tensor and matrix autoencoders is constructed by taking advantage of the properties of a tensor-tensor product. Based on the derived relationship, the DTAE weight parameters are inferred by applying a matrix autoencoder to each frontal slice in the discrete cosine transform (DCT) domain, and this process is further summarized into a general theoretical and practical framework. Finally, the performance benefits of the proposed DTAE-based method are demonstrated in experiments with both synthetic and real field seismic data.