IMPLEMENTASI METODE INTERPOLASI LINEAR UNTUK PEMBESARAN RESOLUSI CITRA

The ordinary kriging interpolation method is adopted to estimate the 2-D strain tensor elements. The results are then compared with those of the linear and spline interpolation methods. The ordinary kriging method is implemented on 12 GPS permanent stations in South California, collected during 2006 to 2012. By employing these time series, the coordinate changes in the UTM coordinate system can be obtained and the strain tensor elements at these 12 stations can be estimated by means of finite difference method. Interpolation of the strain tensor elements over the study area through these three methods (kriging, spline and linear) indicates that the ordinary kriging results, on average are 70% better than those of the spline and linear interpolation methods.

The article is devoted to the issue of definition in situ vertical effective stress in weakly filtering soils (WFS). Shown. that the calculation of the vertical effective stress in soils, taking into account the weighing action of water, is valid only for the special case of the hydrostatic distribution of pore pressure over depth. In this regard, it is proposed to determine the vertical effective stress by the difference between total and pore pressure, in accordance with the principle of effective stresses of K. Terzagi, as more universal. The existing variants of pore pressure calculations in weakly filtering soils are considered. The example shows that zeroing the pore pressure leads to an overestimation of the in situ effective stress in the WFS, and the hydrostatic distribution, as a rule, leads to its underestimation. A computational method for determining the vertical effective stress in the WFS by linear interpolation is presented. Formulas for determining pore pressure and vertical effective stress in the WFS layer are proposed. The features of the application of the interpolation method for different values of groundwater levels in aquifers in depth are considered. A necessary condition for calculating the pore pressure by interpolation is information about the level of groundwater in the underlying aquifer, which must be taken into account when performing engineering-geological surveys. Examples of comparison of the results obtained by various calculation methods with the data of direct measurements of pore pressure by the dissipation method and the method of filled wells are given. The analysis showed that the values of pore pressure in the WFS layers calculated by linear interpolation correspond to direct measurements of pore pressure to the greatest extent. Thus, the linear interpolation method is the most reliable calculation method and can be recommended for use when performing geotechnical calculations.

Sparse-view tomography has many applications such as in low-dose computed tomography (CT). Using under-sampled data, a perfect image is not expected. The goal of this paper is to obtain a tomographic image that is better than the naïve filtered backprojection (FBP) reconstruction that uses linear interpolation to complete the measurements. This paper proposes a method to estimate the un-measured projections by displacement function interpolation. Displacement function estimation is a non-linear procedure and the linear interpolation is performed on the displacement function (instead of, on the sinogram itself). As a result, the estimated measurements are not the linear transformation of the measured data. The proposed method is compared with the linear interpolation methods, and the proposed method shows superior performance.

One critical issue in network real-time kinematic (NRTK) is the interpolation of atmospheric delay for user stations. Some classic interpolation algorithms, such as linear interpolation method (LIM), ignore the strong correlation between tropospheric delay and height factors, and the interpolation accuracy is poor in areas with large height difference. To solve this problem, a troposphere modelling method based on error compensation, namely ECDIM (Error Compensation-Based DIM), is proposed, and this method can be applied to both conventional single Delaunay triangulated network (DTN) and multi-station scenarios. The results of California Real Time Network (CRTN) with large height difference show that compared with LIM, the overall modelling accuracy with ECDIM has been improved by 50.1% to 67.3%, and especially for low elevation satellites (e.g., 10–20 degree), the accuracy is increased from tens of centimetres to a few centimetres. At user end, the positioning error in up direction with LIM has an obvious systematic deviation, and the fix rate of epoch is relatively low. This situation has been improved significantly after using ECDIM. The results of Tianjin Continuously Operating Reference System (TJCORS) show that in areas with small height difference, both methods have achieved high precision interpolation accuracy, and the positioning accuracy with ECDIM in up direction is improved by 21.2% compared with LIM.

Improved linear interpolation method for the estimation of snow-covered area from optical data

In this paper, the stability and smoothness of trajectory planning and attitude control of the manipulator are studied. The DH motion model of Kinova Jaco Gen-2 manipulator was established to solve the forward and inverse kinematics equations. Firstly, the third-order Bezier curve is designed to plan the trajectory of manipulator, which has good continuity and smoothness, and can control the workspace. Secondly, an attitude interpolation method based on quaternion is proposed. And different attitude interpolation methods are compared and analyzed, and spherical linear interpolation method is used to solve the problem of uneven velocity of the linear interpolation method. Finally, MATLAB robot toolbox is used to simulate and verify that the angle of each joint changes smoothly, and the trajectory of manipulator has good smoothness and stability based on Bezier curve and quaternion interpolation. According to the experimental results, the path planning based on the third order Bezier curve and spherical linear interpolation has a continuous smooth joint trajectory curve and a continuous joint angular velocity curve. The manipulator can move smoothly to the target point under the constraint of the end posture. Therefore, the trajectory planning method can be used as an ideal planning method for manipulator.

Efficient and accurate imaging spectroscopy data processing asks for perfectly consistent (i.e., ideally uniform) data in both the spectral and spatial dimensions. However, real pushbroom-type imaging spectrometers are affected by various point spread function (PSF) nonuniformity artifacts. First, individual pixels or lines may be missing in the raw data due to bad pixels originating from the detector, readout errors, or even electronic failures. Second, so-called smile and keystone optical aberrations are inherent to imaging spectrometers. Appropriate resampling strategies are required for the preprocessing of such data if emphasis is put on spatial PSF uniformity. So far, nearest neighbor interpolations have been often recommended and used for resampling. This paper shall analyze the radiometric effects if linear interpolation is used to optimize the spatial PSF uniformity. For modeling interpolation effects, an extensive library of measured surface reflectance spectra as well as real imaging spectroscopy data over various land cover types are used. The real measurements are systematically replaced by interpolated values, and the deviation between original and resampled spectra is taken as a quality measure. The effects of nearest neighbor resampling and linear interpolation methods are compared. It is found that linear interpolation methods lead to average radiometric errors below 2% for the correction of spatial PSF nonuniformity in the subpixel domain, whereas the replacement of missing pixels leads to average errors in the range of 10%-20%

Plant data are used to compare the effectiveness of wavelet-based methods with other compression techniques. The challenge is to effectively treat the data so that the maximum compression ratio is achieved while the important features are retained in the compressed data. Wavelets have properties that are desirable for data compression. They are localized in time (or space) and in frequency. This means that important short-lived high-frequency disturbances can be preserved in the compressed data, and these disturbances may be differentiated from slower, low-frequency trends. Besides discrete wavelet transforms, linear interpolation, discrete cosine transform, and vector quantization are also used to compress data. The transform-based compression algorithms perform better than the linear interpolation methods, such as swinging door, that have been used traditionally in the chemical process industries. Among these techniques, the wavelet-based one compresses the process data with excellent overall and best local accuracy.

Micrometeorological methods are ideally suited for continuous measurements of N2O fluxes, but gaps in the time series occur due to low-turbulence conditions, power failures, and adverse weather conditions. Two gap-filling methods including linear interpolation and artificial neural networks (ANN) were utilized to reconstruct missing N2O flux data from a corn–soybean–wheat rotation and evaluate the impact on annual N2O emissions from 2001 to 2006 at the Elora Research Station, ON, Canada. The single-year ANN method is recommended because this method captured flux variability better than the linear interpolation method (average R2 of 0.41 vs. 0.34). Annual N2O emission and annual bias resulting from linear and single-year ANN were compatible with each other when there were few and short gaps (i.e., percentage of missing values <30%). However, with longer gaps (>20 d), the bias error in annual fluxes varied between 0.082 and 0.344 kg N2O-N ha−1 for linear and 0.069 and 0.109 kg N2O-N ha−1 for single-year ANN. Hence, the single-year ANN with lower annual bias and stable approach over various years is recommended, if the appropriate driving inputs (i.e., soil temperature, soil water content, precipitation, N mineral content, and snow depth) needed for the ANN model are available.

— Performance of pilot-aided channel estimation techniques such as the Least Squares (LS) method depends on not only on the signal-to-noise ratio (SNR), channel conditions and pilot ratio, but also on the choice of interpolation method for deriving channel estimates at non-pilot subcarriers. This paper investigates the bit-error-rate (BER) performance of linear, spline and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation methods in LS channel estimation over the three extended ITU channel profiles defined for Long Term Evolution (LTE) testing. Simulation results show that applying the linear interpolation method produces the best BER performance over the fading channel with the smallest multipath delay spread. It is also shown that the choice of best interpolation method actually depends on the SNR in some of the fading channel profiles. Index Terms—ITU, LS, LTE, PCHIP, spline. I. INTRODUCTION Orthogonal frequency division multiplexing (OFDM) technology has been adopted in many of the next generation communication systems such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX) [1]. OFDM is preferred not only because it offers high data rate transmission but also because of its flexibility and robustness against multipath fading [1]. Multipath channel fading exists when multiple delayed copies of a transmitted signal are received at the receiver, thereby resulting in the distortion of the transmitted signal. These distortions are collectively termed „channel fading‟, causing reception errors and hence increased bit-error-rate (BER). Channel fading can be broadly classified as time-selective and/or frequency selective, depending on the relationship between the fading channel and the transmitted signal parameters [2]. To combat multipath fading and improve BER, some form of channel estimation method is required in order to estimate the channel distortion effect for each of the active subcarriers within an OFDM symbol [3].

Prototype waveform interpolation (PWI) is a practical and promising coding technique applicable to voiced speech. The waveform and duration of only one pitch cycle (the prototype) per frame is extracted and coded using LPC techniques. Segments of missing speech between the prototypes are reconstructed at the receiver by interpolation from the decoded prototype waveforms. Although waveform reconstruction may not be very accurate over the interpolated segments, suprisingly good speech quality can be achieved at bit rates in the region of 2.5 to 3.5 kb/s using frames of about 20 ms duration, provided the prototype waveforms and pitch periodicity are satisfactorily reproduced. For reasons of low complexity and bit rate, most reported work on PWI uses linear interpolation methods with linear interpolation functions, but these suffer from inherent difficulties in reproducing non-uniform variations in pitch cycle waveforms and lengths. It is shown that nonlinear techniques can improve the representation of voiced speech in interpolated segments without significantly increasing bit rates. Pitch structure is improved by using a temporal differential rate codebook for transmission of small differences in the duration of pitch cycles. Waveform fidelity is improved by deriving optimal combination coefficients (OCC) which determine the composition of each pitch cycle waveform in terms of the given prototypes at segment boundaries. The OCC vectors allow for nonlinear variation in waveform composition and are vector quantised for transmission.

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a small portion of blood is labeled compared to the whole tissue volume, conventional ASL suffers from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. In this paper, we proposed a super-resolution method based on a multi-scale generative adversarial network (GAN) through unsupervised training. The network only needs the low-resolution (LR) ASL image itself for training and the T1-weighted image as the anatomical prior. No training pairs or pre-training are needed. A low-pass filter guided item was added as an additional loss to suppress the noise interference from the LR ASL image. After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer. Performance of the proposed method was evaluated by comparing the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) using normal-resolution (NR) ASL image (5.5 min acquisition) and high-resolution (HR) ASL image (44 min acquisition) as the ground truth. Compared to the nearest, linear, and spline interpolation methods, the proposed method recovers more detailed structure information, reduces the image noise visually, and achieves the highest PSNR and SSIM when using HR ASL image as the ground-truth.

The purpose of this study is to evaluate 3D dose reconstruction of pretreatment verification plans using multiple 2D planes acquired from the OCTAVIUS phantom and the Seven29 detector array. Eight VMAT patient treatment plans of different sites were delivered onto the OCTAVIUS phantom. The plans span a variety of tumor site locations from low to high plan complexity. A patient specific quality assurance (QA) plan was created and delivered for each of the 8 patients using the OCTAVIUS phantom in which the Seven29 detector array was placed. Each plan was delivered four times by rotating the phantom in 45° increments along its longitudinal axis. The treatment plans were delivered using a Novalis Tx with the HD120 MLC. Each of the four corresponding planar doses was exported as a text file for further analysis. An in-house MATLAB code was used to process the planar dose information. A cylindrical geometry-based, linear interpolation method was utilized to generate the measured 3D dose reconstruction. The TPS calculated volumetric dose was exported and compared against the measured reconstructed volumetric dose. Dose difference, dose area histograms (DAH), isodose lines, profiles, 2D and 3D gamma were used for evaluation. The interpolation method shows good agreement (<2%) between the planned dose distributions in the high dose region but shows discrepancies in the low dose region. Horizontal profiles, dose area histograms and isodose lines show good agreement for the sagittal and coronal planes but demonstrate slight discrepancies in the transverse plane. The 3D gamma index average was 92.4% for all patients when a 5%/5 mm gamma passing rate criteria was employed but dropped to <80.1% on average when parameters were reduced to 2%/2 mm. A simple cylindrical geometry-based, linear interpolation method is able to predict good agreement in the high dose region between the reconstructed volumetric dose and the planned volumetric dose. It is important to mention that the interpolation algorithm introduces dose discrepancies in small regions within the high dose gradients due to the interpolation itself. However, the work presented serves as a good starting point to establish a benchmark for the level of manipulation necessary to obtain 3D dose delivery quality assurance using current technology.

Three-dimensional (3D) dynamic contrast-enhanced magnetic resonance imaging (3D DCE-MRI) has been proposed for the assessment of regional perfusion. The aim of this work was the implementation of an algorithm for a 3D parametric visualization of lung perfusion using different cutting planes and volume rendering. Our implementation was based on 3D DCE-MRI data of the lungs of five patients and five healthy volunteers. Using the indicator dilution theory, the regional perfusion parameters, tissue blood flow, blood volume and mean transit time were calculated. Due to the required temporal resolution, the volume elements of dynamic MR data sets show a reduced spatial resolution in the z-direction. Therefore, perfusion parameter volumes were interpolated. Linear interpolation and a combination of linear and nearest-neighbor interpolation were evaluated. Additionally, ray tracing was applied for 3D visualization. The linear interpolation algorithm caused interpolation errors at the lung borders. Using the combined interpolation, visualization of perfusion information in arbitrary cutting planes and in 3D using volume rendering was possible. This facilitated the localization of perfusion deficits compared with the coronal orientated source data. The 3D visualization of perfusion parameters using a combined interpolation algorithm is feasible. Further studies are required to evaluate the additional benefit from the 3D visualization.

Right now, there is no agreed definition and measurement method for rating concentrator photovoltaic. This paper discusses present situation of the ratings of CPV modules and systems. First, it was analyzed which factors have influence to the performance to CPV modules and systems. Next, the linear interpolation method that is known as a robust estimator especially to multi-junction thin film modules is discussed. The test data was provided from Toyohashi University of Technology where a 550 X CPV module (20 cells connected in series and the total area is 5,445 cm2) was tested independently. The relative standard deviation was 0.89 %, whereas that of the linear interpolation method was 1.2 %. Different from the linear interpolation method, this stratified averaging method is unbiased.