Conventional Linear Interpolation Research Articles

Machine Learning-Based Soil Moisture Retrieval Using CYGNSS and Interpolated SMAP Data

Abstract. Soil moisture is a crucial component of the global terrestrial ecosystem water vapor cycle, and higher spatial-temporal soil moisture product has significant impacts on research in agriculture, hydrology, and ecology. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology which can be used to retrieve soil moisture. Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA in 2016 to observe ocean surface hurricanes, has also collected a substantial amount of data of GNSS signals reflected over the land. This paper focuses on soil moisture retrieval using CYGNSS and SMAP dataset. In this study, three machine learning methods (GSVM, BP and RF) are used for spatial interpolation of SMAP soil moisture data. Result shows RF interpolation is the best with the RMSE of 0.044 cm3/cm3 and the CC of 0.959 compared with SMAP truly SM. Then soil moisture data after RF interpolation and conventional linear interpolation are respectively used as response variables to train the retrieval model by ELM, BP and RF machine learning methods respectively. Results show that, among the six regression models, the RF-RF regression model performs the best with the RMSE of 0.012 cm3/cm3 and the CC of 0.986. Using this model, we get a global soil moisture product and have higher temporal and spatial resolution.

Real-time Physically Guided Hair Interpolation

Strand-based hair simulations have recently become increasingly popular for a range of real-time applications. However, accurately simulating the full number of hair strands remains challenging. A commonly employed technique involves simulating a subset of guide hairs to capture the overall behavior of the hairstyle. Details are then enriched by interpolation using linear skinning. Hair interpolation enables fast real-time simulations but frequently leads to various artifacts during runtime. As the skinning weights are often pre-computed, substantial variations between the initial and deformed shapes of the hair can cause severe deviations in fine hair geometry. Straight hairs may become kinked, and curly hairs may become zigzags. This work introduces a novel physical-driven hair interpolation scheme that utilizes existing simulated guide hair data. Instead of directly operating on positions, we interpolate the internal forces from the guide hairs before efficiently reconstructing the rendered hairs based on their material model. We formulate our problem as a constraint satisfaction problem for which we present an efficient solution. Further practical considerations are addressed using regularization terms that regulate penetration avoidance and drift correction. We have tested various hairstyles to illustrate that our approach can generate visually plausible rendered hairs with only a few guide hairs and minimal computational overhead, amounting to only about 20% of conventional linear hair interpolation. This efficiency underscores the practical viability of our method for real-time applications.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. This is the first study applying super-resolution in data reduction of chest radiographs.

Coarray Tensor Completion for DOA Estimation

Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed to handle multi-dimensional sub-Nyquist sampled signals. Furthermore, augmented virtual arrays can be derived for Nyquist-matched coarray tensor processing. However, the partially augmentable sparse array corresponds to a discontinuous virtual array, whereas the existing methods can only utilize its continuous part. Conventional virtual linear array interpolation techniques complete coarray covariance matrices with dispersed missing elements, but fail to complete the coarray tensor with whole missing slices. In this paper, we propose a coarray tensor completion algorithm for two-dimensional DOA estimation, where the coarray tensor statistics can be entirely exploited. In particular, in order to impose an effective low-rank regularization on the slice-missing coarray tensor, we propose shift dimensional augmenting and coarray tensor reshaping approaches to reformulate a structured coarray tensor with sufficiently dispersed missing elements. Furthermore, the shape of the reformulated coarray tensor is optimized by maximizing the dispersion-to-percentage ratio of missing elements. As such, a coarray tensor nuclear norm minimization problem can be designed to optimize the completed coarray tensor corresponding to a filled virtual array, based on which the closed-form DOA estimation is achieved. Meanwhile, the global convergence of the coarray tensor completion is theoretically proved. Simulation results demonstrate the effectiveness of the proposed algorithm compared to other matrix-based and tensor-based methods.

FPGA Implementation of Adaptive Multiplier-Based Linear Image Interpolation

This work provides an image interpolation for multimedia applications by utilizing an adaptive multiplier-based stepwise linear interpolation with clam filter. Image interpolation is also termed as image up-scaling. Generally, while enlarging an image some vacant bit positions are introduced and due to this empty pixel positions, the quality of the image is decreased. Therefore to maintain the quality of the image, new pixels are introduced and those pixels are used to fill the vacant bit positions by using interpolation techniques. In the adaptive interpolation techniques, edge pixels are identified and filtered at prior to the interpolation process. This will improve the quality of the interpolated image. However, the adaptive interpolation scheme increases the complexity of the system. To reduce the complexity, this work uses low complexity stepwise linear interpolation and to maintain the quality it uses multiplier-based linear stepwise (MBLSI) and edge enhancement technique. The experimental results demonstrate that the complexity of the proposed work is less as compared with other related work as well as the quality is also maintained. The proposed work utilizes 275 LUTs to provide the average peak signal to noise ratio (PSNR) of 20.44 dB and structural similarity index (SSIM) as 0.8250. This proposed work increases the PSNR by 0.89 dB from the conventional multiplier-based stepwise linear interpolation. Further the proposed interpolation algorithm utilizes less number of resources in field programmable gate array (FPGA) by comparing with other related interpolation techniques.

Multi-view correlation tracking with adaptive memory-improved update model

Recently, some researchers concentrate on applying multi-view learning to the correlation filter tracking to achieve both the efficiency and accuracy. However, most of them fail to effectively collaborate multiple views to deal with more complex environment. Moreover, their methods are prone to drift in case of long-term occlusion due to the memory loss. In this paper, we propose a novel multi-view correlation filters-based tracker for robust visual tracking. First, we present an adaptive multi-view collaboration strategy to highlight different contributions of different views by jointly considering the reliability and discrimination. Second, an effective memory-improved model update rule is introduced to avoid falling into a contaminated target model. Compared with the conventional linear interpolation update rule, it can effectively deal with long-term occlusion by improving the memory of historical models. Furthermore, instead of assigning a unified learning rate for all views in each frame, we design varying learning rates for different views according to their respective evaluations on the current tracking result, which can prevent the target models of all views from being contaminated at the same time. Finally, a failure-aware scale update scheme is developed to avoid noisy scale estimation in case of temporal tracking failure. Extensive experimental results on the recent benchmark demonstrate that our tracker performs favorably against other state-of-the-art trackers with a real-time performance.

Look-up table 기반 PMSM 제어시 최종 출력 데이터 선형 보간 오차 개선 방법

This paper proposes a novel linear interpolation method for improving final output data interpolation error appliable in look-up table based PMSM drive. Conventional linear interpolation method is generally 2D-Interpolation method which has two input parameters. This 2D-Interpolation method uses constant unit torque for reducing DSP processing time, it has a problem when the interpolation process is performed with final torque output, interpolated output can not follow the torque reference. In this paper, a modified conventional interpolation method is proposed to remove torque control error of final torque output data.

2.5D marine CSEM modeling in the frequency-domain based on an improved interpolation scheme at receiver positions

In the marine controlled-source electromagnetic (CSEM) survey, the receivers are usually placed at the seafloor. The resistivity contrast between the seawater and seafloor sediments is large, which can cause difficulties in numerical modeling of CSEM fields at receiver locations. In this paper, we present an improved interpolating method for calculating electric and magnetic fields at the seafloor with a resistivity contrast. This method is applied to the 2. 5 dimensional (2. 5D) frequency-domain CSEM modeling with towed transmitters and receivers located at the seafloor. Considering the discontinuity of the normal electric fields, we use the normal current electric density for interpolation. We simulate the 2. 5D marine CSEM responses by the staggered finite-difference (SFD) method with Fourier transform to the strike direction. The final SFD equations are solved by the direct solver MUMPS (MUltifrontal Massively Parallel Sparse direct Solver). To avoid the source singularities, the secondary-field approach is used and the primary fields excited by the electric dipole source can be calculated quasi-analytically for the one-dimensional (1D) layered background model. We focus on interpolating of electric and magnetic fields in the wavenumber domain to the receiver locations at the seafloor interface between the conductive seawater and resistive seafloor formation. The secondary electric and magnetic fields are used for interpolation instead of the total fields for high numerical accuracy. After performing the inverse Fourier transform to the wavenumbers, the electric and magnetic fields in the space domain are obtained. To check the accuracy of our 2. 5D marine CSEM SFD modeling algorithm with the improved receiver interpolating technique, we compare our results with both the 1D analytical results and the adaptive finite element results. The SFD numerical results are approved to be accurate. We also compare the numerical accuracy between our improved interpolation scheme and others, i.e., the conventional linear interpolation and the rigorous interpolation. The proposed interpolation only utilizes the nodes below/above the seafloor interface, and is proved to be much more accurate than the other two interpolating methods used.

High temporal resolution rainfall rate estimation from rain gauge measurements

Abstract Rainfall rates derived from tipping bucket rain gauges generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional linear interpolation method (LIM) and the cubic spline algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt observatories, UK. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash–Sutcliffe efficiency, lower root mean square error and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.

Higher-Order Transonic Flutter Predictions

An order-of-accuracy convergence study is presented for a methodology to predict dynamic instabilities arising from fluid–structure coupling. The methodology models the fluid with compressible Euler equations and the structure with Timoshenko beam elements. The tightly coupled equations of the two disciplines are linearized to formulate a stability eigenvalue problem, which is rewritten in a form that is well known as the flutter solution method. The rate of convergence is measured in the norm defined by the relative error in instability speed for polynomial orders from 1 to 4. This is done for the divergence mode at Mach 0.7 and the flutter mode at Mach 1.1. The influence of relative order of basis functions for structural and fluid disciplines is investigated on the rate of convergence. The results show that, for most cases, the error converges at a rate of approximately for a th-order polynomial. A comparison of the computational cost shows that, in comparison with the conventional linear interpolation schemes, the high-order polynomials can provide the same level of accuracy with over an order-of-magnitude reduction in computational cost.

Accurate Interpolation at Receiver Positions: A Novel Method for Frequency-Domain Marine CSEM Finite-Difference Modelling

We present an accurate interpolating method for calculating electric and magnetic fields at the seafloor with a resistivity contrast. This method is applied to the three-dimensional (3D) frequency-domain marine controlled-source electromagnetic (CSEM) modeling with the towed transmitters and receivers located at the seafloor. We simulate the 3D marine CSEM responses by the staggered finite-difference method with a direct solver. The secondary-field approach is used for avoiding the source singularities and the primary fields excited by the electric dipole source could be calculated quasi-analytically for the one-dimensional layered background. Therefore, in this study, we focus on interpolating of electric and magnetic fields to the receiver locations at the seafloor interface between the conductive seawater and resistive seafloor formation. Considering the discontinuity of the normal electric fields, we use the normal current electric density for interpolation. The secondary electric and magnetic fields are also used for interpolation instead of the total fields for high numerical accuracy. The proposed interpolation only utilizes the nodes below/above the seafloor interface and is approved to be much more accurate than other tested interpolating methods, i.e., the conventional linear interpolation and the rigorous interpolation.

Evaluation of energy band offset of Si1−xSnx semiconductors by numerical calculation using density functional theory

We examined the numerical calculation for evaluating the energy band offset of Si1−xSnx semiconductors and compared our calculation results with the results of previous theoretical calculation and experimental estimation. By estimating the charge neutrality level of Si1−xSnx as a mutual basis level for comparison of the first-principles calculations for different Si1−xSnx contents, the calculated valence band offset of Si1−xSnx to Si was found to sensitively shift with upward bowing with increasing Sn content compared with that obtained using a conventional linear interpolation model. This is in good agreements with the experimental result.

An area and power efficient adder-based stepwise linear interpolation for digital signal processing

Linear interpolation is frequently used in digital signal processing applications to reconstruct signals, e.g., image, and audio processing, for which the multiplier and divider are two power- and area-hungry operators. Since most natural world phenomena are not exactly representable in linear forms, it is inefficient to use multiplier-based linear interpolation (MBLI) for accurate linear curve fitting. In this paper, area- and power-efficient adder-based stepwise linear interpolation (ABSI) is proposed to emulate the behavior of conventional linear interpolation. As a test vehicle for system demonstrations and performance evaluations, a general purpose display controller for applying an interpolationbased image/video scaling engine and overdrive for multimedia displays such as televisions, monitors, and projectors, was designed. Compared with a scaling engine using conventional MBLI, a scaling engine using the proposed ABSI has a similar scaling quality in terms of the peak signalto- noise ratio (PSNR), while reducing the area and power consumption by 45% and 44%, respectively. Furthermore, as compared with the compression/decompression of an overdrive using conventional MBLI, the data compression/decompression of the overdrive with the proposed ABSI shows a comparable compression/ decompression quality while reducing the area and power consumption by 46% and 45%, respectively.

A Novel Image Interpolation Algorithm Based on Directional Extension of AP-DCT Interpolation Kernel

In order to decrease the smoothing effect of traditional linear interpolation methods and avoid the complexity of edge-directed adaptive algorithms, a novel image interpolation algorithm based on the directional extension of linear interpolation kernel is proposed. At first, All Phase Non-subsampled Contourlet Transform is used to divide the image of low resolution into different frequencies and directional sub-band images, and then the All Phase Discrete Cosine Transform Interpolation Kernel is extended to 2-D with distinct directional priorities and used to enlarge respective sub-band images. The interpolated sub-band images are finally synthesized as the upsampled image. Simulation results demonstrate that the proposed algorithm improves both the subjective and objective quality of the interpolated images over conventional linear interpolation. The computation complexity of the proposed algorithm is moderate and it can be implemented with parallel structure so as to save more computation time.

A Quadratic-Interpolated LUT-Based Digital Predistortion Technique for Cellular Power Amplifiers

Baseband digital predistortion (DPD) is an efficient and low-cost method for linearizing a power amplifier (PA) in a wireless system using a modulation scheme with nonconstant envelope. The polynomial and the lookup table (LUT) predistortion schemes are two commonly used approaches. Both approaches require time to find the inverse characteristics of the PA to be predistorted. However, for DPD to be actually implemented in real-world scenarios, fast convergence is highly desirable. A low-complexity joint-polynomial/LUT predistortion PA linearizer is proposed to obtain fast linearization. The algorithm utilizes quadratic interpolation of the LUT, which reduces the required LUT size and hence reduces convergence time without degradation of linearity. Simulation result shows that the proposed method can achieve up to 500% improvement in convergence speed compared with the conventional linear interpolation approach for the wideband code-division multiple access application.

A new wave scheme for trajectory simulations of stratospheric water vapor

AbstractWe have developed a new wave scheme particularly aiming to provide better temperature fields with realistic variability for trajectory modeling of dehydration processes in the tropical tropopause layer (TTL). The new scheme includes amplitude‐phase interpolation and amplification of waves in reanalysis data. Amplification factors are based on statistical variability differences between reanalysis data and radiosonde observations at 24 tropical locations during 1997–2013 boreal winters. We show that conventional linear interpolation of temperatures in the vertical coordinate degrades wave amplitudes and variability. Amplitude‐phase interpolation in Fourier space greatly mitigates the problem found in linear interpolation. Furthermore, amplitudes of existing waves in reanalyses were amplified to generate realistic variability. In addition to improvements in variability, the scheme lowers cold point temperatures and raises cold point tropopause heights. Having realistic variability with the new approach will reduce uncertainties in simulations of TTL cirrus clouds and stratospheric water vapor.

Image Prediction Method with Nonlinear Control Lines Derived from Kriging Method with Extracted Feature Points Based on Morphing

Method for image prediction with nonlinear control lines which are derived from extracted feature points from the previously acquired imagery data based on Kriging method and morphing method is proposed. Through comparisons between the proposed method and the conventional linear interpolation and widely used Cubic Spline interpolation methods, it is found that the proposed method is superior to the conventional methods in terms of prediction accuracy.

Colorimetric characterization models based on colorimetric characteristics evaluation for active matrix organic light emitting diode panels

The colorimetric characterization of active matrix organic light emitting diode (AMOLED) panels suffers from their poor channel independence. Based on the colorimetric characteristics evaluation of channel independence and chromaticity constancy, an accurate colorimetric characterization method, namely, the polynomial compensation model (PC model) considering channel interactions was proposed for AMOLED panels. In this model, polynomial expressions are employed to calculate the relationship between the prediction errors of XYZ tristimulus values and the digital inputs to compensate the XYZ prediction errors of the conventional piecewise linear interpolation assuming the variable chromaticity coordinates (PLVC) model. The experimental results indicated that the proposed PC model outperformed other typical characterization models for the two tested AMOLED smart-phone displays and for the professional liquid crystal display monitor as well.

Hierarchical patch-based sparse representation--a new approach for resolution enhancement of 4D-CT lung data.

4D-CT plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior-inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior-inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.

Adaptive Polar-Linear Interpolation Aided Channel Estimation for Wireless Communication Systems

Channel Estimation (CE) is one of the key components in wireless communication systems employing coherent demodulation and interference cancellation. Among the numerous CE techniques in the literature, Pilot-Assisted Channel Estimation (PACE) has been widely used in many practical systems, thanks to its simplicity and efficiency for implementation. In order to minimise pilot overhead, interpolation is typically mandatory in PACE. Conventional linear and Polar-Linear Interpolation (PLI) methods are easy to implement, but naturally result in residual interpolation errors that can not be mitigated even at high Signal-to-Noise Ratios (SNRs). In this paper, we propose a CE scheme equipped with a novel interpolation method referred to as Adaptive Polar-Linear Interpolation (APLI). The proposed APLI-aided CE scheme can achieve a good performance, thanks to its capability of adaptively tracking the instantaneous variation of channel's fading coefficients, while maintaining a low complexity that is similar to the simple linear interpolation. The benefits of our proposal are demonstrated by a range of numerical illustrations and simulation results.