Transformation Coefficients Research Articles

Developing Transformation Functions for VENμS and Sentinel-2 Surface Reflectance over Israel

Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 are both ongoing earth observation missions that provide high-resolution multispectral imagery at 10 m (VENμS) and 10–20 m (Sentinel-2), at relatively high revisit frequencies (two days for VENμS and five days for Sentinel-2). Sentinel-2 provides global coverage, whereas VENμS covers selected regions, including parts of Israel. To facilitate the combination of these sensors into a unified time-series, a transformation model between them was developed using imagery from the region of interest. For this purpose, same-day acquisitions from both sensor types covering the surface reflectance over Israel, between April 2018 and November 2018, were used in this study. Transformation coefficients from VENμS to Sentinel-2 surface reflectance were produced for their overlapping spectral bands (i.e., visible, red-edge and near-infrared). The performance of these spectral transformation functions was assessed using several methods, including orthogonal distance regression (ODR), the mean absolute difference (MAD), and spectral angle mapper (SAM). Post-transformation, the value of the ODR slopes were close to unity for the transformed VENμS reflectance with Sentinel-2 reflectance, which indicates near-identity of the two datasets following the removal of systemic bias. In addition, the transformation outputs showed better spectral similarity compared to the original images, as indicated by the decrease in SAM from 0.093 to 0.071. Similarly, the MAD was reduced post-transformation in all bands (e.g., the blue band MAD decreased from 0.0238 to 0.0186, and in the NIR it decreased from 0.0491 to 0.0386). Thus, the model helps to combine the images from Sentinel-2 and VENμS into one time-series that facilitates continuous, temporally dense vegetation monitoring.

On the modelling of membrane-coupled Helmholtz resonator and its application in acoustic metamaterial system

Abstract In recent years, the Helmholtz resonator and membrane are two popular elements that have been vastly employed in the design of energy harvesters and metamaterials. In this paper, a theoretical study of the modelling of the membrane-coupled Helmholtz resonator is presented. The membrane is first represented with lumped parameters as a single-degree-of-freedom piston/centre-mass model. The physical meaning of the effective force-bearing area is explained. The membrane-coupled Helmholtz resonator is then modelled as a multiple-degree-of-freedom system. From the acoustic-mechanical interaction perspective, transformation coefficients are derived to bridge the acoustic and mechanical domains. Inspired by the fact that the membrane-coupled Helmholtz resonator exhibits multiple resonances in the low frequency regime, an acoustic metamaterial system is proposed by integrating the membrane-coupled Helmholtz resonators. A theoretical model of the proposed acoustic metamaterial is developed and multiple band gaps are predicted from the band structure analysis. All the theoretical models presented in this paper have been verified by corresponding finite element models.

Generalized conformal transformation and inflationary attractors

We investigate the inflationary attractors in models of inflation inspired from general conformal transformation of general scalar-tensor theories to the Einstein frame. The coefficient of the conformal transformation in our study depends on both the scalar field and its kinetic term. Therefore the relevant scalar-tensor theories display the subset of the class I of the degenerate higher-order scalar-tensor theories in which both the scalar field and its kinetic term can non-minimally couple to gravity. We find that if the conformal coefficient $\Omega$ takes a multiplicative form such that $\Omega \equiv w(\phi)W(X)$ where $X$ is the kinetic term of the field $\phi$, the theoretical predictions of the proposed model can have usual universal attractor independent of any functions of $W(X)$. For the case where $\Omega$ takes an additive form, such that $\Omega \equiv w(\phi) + k(\phi) \Xi(X)$, we find that there are new $\xi$ attractors in addition to the universal ones. We analyze the inflationary observables of these models and compare them to the latest constraints from the Planck collaboration. We find that the observable quantities associated to these new $\xi$ attractors do not satisfy the constraints from Planck data at a strong coupling limit.

An Experimental and Analytical Study of Hybrid Accelerometers

Hybrid accelerometers comprise sensor elements from different technologies, capable of combining two output signals into a highly linear response. Typically, a piezoaccelerometer would be combined with a MEMS accelerometer to extend an otherwise AC coupled response down to DC. The complementary filter function is an effective and distortion-free method for combining a DC capacitive sensing element that is critically damped with an AC piezosensing element; however, this method does not work well with lightly damped DC sensing elements. A detailed examination was undertaken of the issues that arise when combining AC and DC sensing elements where the damping factor of the DC sensor element is < 1, as is the case with many MEMS piezoresistive accelerometers. Results include earlier work with a prototype hybrid accelerometer, as well as a later analytical model to investigate in detail the complex interaction between system parameters that lead to distortion, nonlinearity and changes in impulse response when lightly damped DC sensing elements are combined. The analysis concludes with a post-processing method investigated for the removal of spurious transformation coefficients that currently preclude the use of lightly damped DC sensors.

Transformation of Long Surface and Tsunami-Like Waves in the Ocean with a Variable Bathymetry

We consider a transformation of long linear waves in an ocean with a variable depth. We calculate the transformation coefficients (the coefficients of transmission and reflection) as the functions of frequency and the total depth drop for three typical models of bottom profile variation: (i) piecewise-linear, (ii) piecewise-quadratic, and (iii) hyperbolic tangent profiles. For all these cases, exact solutions are obtained, analysed and graphically illustrated. This allows us to derive the transformation coefficients in the analytic form for the subsequent comparison with the results of approximate, numerical, or experimental study. We show that the results obtained are in agreement with both the energy flux conservation and Lamb’s formulae in the limiting case of zero frequency. We also study wave transformation on the underwater barriers and trenches of different shapes and compare the results obtained.

Stochastic optimal transmission switching considering the correlated wind power

Optimal transmission switching (OTS) is widely used in mitigating transmission congestion and reducing power losses. However, with large-scale renewable energy integration to the grid, making a decision for the randomness characteristic of renewable energy (e.g. wind and solar power) is complicated in OTS. In this study, a novel stochastic optimal power flow-based point estimation method (PEM) is presented to model the uncertainties of wind power and load in OTS. Polynomial normal transformation is introduced to handle the correlations between random variables, and percentile matching method is employed to obtain the coefficients of polynomial normal transformation, which can avoid the integral operation. Moreover, the expected value of power flow obtained by the PEM is applied to the presented model to control the line overload risk. Two indexes, namely, population sample mean and mean of population sample standard deviation, are proposed to investigate the effect of correlations on OTS strategies. The proposed model is finally transformed into a mixed-integer second-order cone programming to consider bus voltage and reactive power, and the modified IEEE RTS 24-bus system and IEEE 118 system are presented to test the model.

Estimation of the Power Quantities Below One Signal Period Using DFT Coefficients

This paper proposes a method to estimate power quantities based on IEEE 1459–2010 using measurement window with length as close as possible to the length of the changing signal period and the nonparametric estimation approach in the frequency domain. Parameters are calculated from the largest local discrete Fourier transformation (DFT) coefficients of the particular frequency components. The improvement in reducing the influences of the harmonic components is the estimation around one signal period. When the time is shortened below the signal period, the dc coefficients of the DFT are taken into account using two Rife–Vincent windows class I with different orders. First, the frequency is estimated by the quotient of the dc coefficients on the sampling points from the measurement interval of around half of the signal period, and upon this, the fine estimation of the signal period, number of samples in this period, and belonging relative frequency with displacement can be effectively estimated. Effectiveness of estimations and tracking of the power quantities is demonstrated by simulations and on the real signals of the short-circuit conditions in the power distribution system.

Standard Errors of IRT Parameter Scale Transformation Coefficients: Comparison of Bootstrap Method, Delta Method, and Multiple Imputation Method

AbstractThe present study evaluated the multiple imputation method, a procedure that is similar to the one suggested by Li and Lissitz (2004), and compared the performance of this method with that of the bootstrap method and the delta method in obtaining the standard errors for the estimates of the parameter scale transformation coefficients in item response theory (IRT) equating in the context of the common‐item nonequivalent groups design. Two different estimation procedures for the variance‐covariance matrix of the IRT item parameter estimates, which were used in both the delta method and the multiple imputation method, were considered: empirical cross‐product (XPD) and supplemented expectation maximization (SEM). The results of the analyses with simulated and real data indicate that the multiple imputation method generally produced very similar results to the bootstrap method and the delta method in most of the conditions. The differences between the estimated standard errors obtained by the methods using the XPD matrices and the SEM matrices were very small when the sample size was reasonably large. When the sample size was small, the methods using the XPD matrices appeared to yield slight upward bias for the standard errors of the IRT parameter scale transformation coefficients.

Repetitive Iteration CORDIC based Energy Efficient Image Compression for Portable Devices

The rapid advancements in technology in recent years have led to a massive increase in the exchange of data (images, videos, audio, etc.) between portable devices. This has invoked the necessity for building algorithms which consume low power with no compromise in the performance. In this paper, the above captioned issue is taken into account and accordingly an image compression technique using Repetitive Iteration CORDIC (RICO) architecture has been proposed. The proposed method is power efficient as it uses RICO for Discrete Cosine Transform (DCT) coefficient generation, and performs equally well when compared to Joint Photographic Experts Group (JPEG) standard. Results have been obtained via Matrix Laboratory (MATLAB) and they show that the proposed technique performs equally well and consumes less power in comparison with the other techniques.

An adaptive steganographic scheme for H.264/AVC video with distortion optimization

With the popularity of streaming media, data hiding in H.264/AVC video streams has attracted more and more attention. However, how to achieve high secure video steganography while preventing intra-frame distortion drift is a challenging task. In this paper, based on Syndrome–Trellis Code(STC), we propose a secure steganographic scheme using Quantized Discrete Cosine Transform (QDCT) coefficients of I-frames. We first present a theoretical analysis of the block distortion, which consists of inter-block distortion caused by data embedding in reference pixels of adjacent blocks and inner-block distortion caused by data embedding in local pixels of current block. Based on block distortion analysis, we propose a novel distortion optimization strategy by avoiding inter-block distortion and decreasing inner-block distortion. The cost function is constructed based on block distortion, bit-rate change and texture complexity of DCT block. Compared with distortion compensation methods, the proposed method not only considers the distortion optimization but also introduces secure steganographic scheme. Experimental results show that the proposed scheme can resist the state-of-the-art steganalytic method while achieving high visual quality.

Reversible video data hiding using zero QDCT coefficient-pairs

H.264/Advanced Video Coding (AVC) is one of the most commonly used video compression standard currently. In this paper, we propose a Reversible Data Hiding (RDH) method based on H.264/AVC videos. In the proposed method, the macroblocks with intra-frame $4\times 4$ prediction modes in intra frames are first selected as embeddable blocks. Then, the last zero Quantized Discrete Cosine Transform (QDCT) coefficients in all $4\times 4$ blocks of the embeddable macroblocks are paired. In the following, a modification mapping rule based on making full use of modification directions are given. Finally, each zero coefficient-pair is changed by combining the given mapping rule with the to-be-embedded information bits. Since most of last QDCT coefficients in all $4\times 4$ blocks are zero and they are located in high frequency area. Therefore, the proposed method can obtain high embedding capacity and low distortion.

Interaction of EEG Rhythms in a Set to a Facial Expression

Interactions of the θ, α, and β EEG rhythms were studied in a set to an emotional facial expression in 30 adult subjects. Electroencephalogram (EEG) data wee analyzed by wavelet transformation in the range 1–35 Hz. The mean and maximum wavelet transformation coefficients (WTC) were computed. Relationships between the β2/α1 and β2/α2 subranges were seen in subjects with erroneous recognition at the set testing stage, as detected using the mean WTC level. The β2/θ rhythm pair showed significant correlation coefficients both for the mean WTC level and the maximum WTC level, and for maximum WTC values in subjects without errors at the set formation stage. New data were obtained supporting the view that the thalamocortical and corticohippocampal systems are involved in supporting the process of recognizing facial expression during set formation and testing, while their interaction may provide a predictor for the success of visual recognition.

Band structure and thermoelectric performances of antimony under trigonal transformation

Ab initio calculation and Boltzmann transport equation have been integrated to find the fundamental influences of trigonal transformation on band structures and thermoelectric performances of antimony. Calculations reveal that antimony could keep its semimetal feature within the c/a range of 2.27–2.82 and that two transitions of band structures of antimony under trigonal transformation are revealed for the first time. Moreover, trigonal transformation has a significant influence on the thermoelectric performances of antimony, and the Seebeck coefficients for the electrons and holes of antimony reach the peaks at the c/a points of 2.72 and 2.57, respectively. The calculated results are in good agreement with the values from experiments in the literature and could deepen the comprehension of the intrinsic relationship between trigonal transformation, band structures, and Seebeck coefficients of antimony.

Partial Image Encryption Based on Using Discrete Cosine Transform Coefficients and Lightweight Stream Algorithm

Partial Image Encryption Based on Using Discrete Cosine Transform Coefficients and Lightweight Stream Algorithm

Improving accuracy of derived 12-lead electrocardiography by waveform segmentation

A number of methods have been proposed to reduce number of leads for electrocardiography (ECG) measurement without decreasing the signal quality. Some limited sets of leads that are nearly orthogonal, such as EASI, have been used to reconstruct the standard 12-lead ECG by various transformation techniques including linear, nonlinear, generic, and patient-specific. Those existing techniques, however, employed a full-cycle ECG waveform to calculate the transformation coefficients. Instead of calculating the transformation coefficients using a full-cycle waveform, we propose a new approach that segments the waveform into three segments: PR, QRS complex, and ST, hence the transformation coefficients were segment-specific. For testing, our new segment-specific approach was applied to six existing methods: Dower’s method with generic coefficients, Dower’s method with individual (patient-specific) coefficients, Linear Regression (LR), 2nd degree Polynomial Regression (PR), 3rd degree PR, and Artificial Neural Network (ANN). The results showed that the new approach outperformed the conventional full-cycle approach. It was able to significantly reduce the derivation error up to 74.50% as well as improve the correlation coefficient up to 0.66%.

Improving accuracy of derived 12-lead electrocardiography by waveform segmentation

A number of methods have been proposed to reduce number of leads for electrocardiography (ECG) measurement without decreasing the signal quality. Some limited sets of leads that are nearly orthogonal, such as EASI, have been used to reconstruct the standard 12-lead ECG by various transformation techniques including linear, nonlinear, generic, and patient-specific. Those existing techniques, however, employed a full-cycle ECG waveform to calculate the transformation coefficients. Instead of calculating the transformation coefficients using a full-cycle waveform, we propose a new approach that segments the waveform into three segments: PR, QRS complex, and ST, hence the transformation coefficients were segment-specific. For testing, our new segment-specific approach was applied to six existing methods: Dower’s method with generic coefficients, Dower’s method with individual (patient-specific) coefficients, Linear Regression (LR), 2nd degree Polynomial Regression (PR), 3rd degree PR, and Artificial Neural Network (ANN). The results showed that the new approach outperformed the conventional full-cycle approach. It was able to significantly reduce the derivation error up to 74.50% as well as improve the correlation coefficient up to 0.66%.

Performance analysis of metal hydride based simultaneous cooling and heat transformation system

The performance of metal hydrides based simultaneous cooling and heat transformation system (MHCHT) using a combination of La0.9Ce0.1Ni5–MmNi4.4Al0.6–MmNi3.7Co0.7Mn0.3Al0.3 hydrides is evaluated. The MHCHT is thermodynamically analysed using statically and dynamically measured PCIs and thermodynamic properties. In addition, a set of governing equations is solved in order to study the heat and hydrogen transfer between the reaction beds. The experimental PCI measurement data are compared with the numerical results and a reasonably good agreement is observed between them. From the results, the slope and hysteresis factors are determined for further thermal analyses. It is observed that the performance parameters i.e. cooling capacity, heat transformation capacity and coefficient of performance (COP) of MHCHT are significantly decreased by 42.4%, 26.7% and 19.1% respectively when dynamic property data are considered compared to static property data. In addition, the thermodynamic cycle is analysed by considering the variation in pressure during hydrogen transfer process between the metal hydride beds.

Experimental Investigation and Modeling of the Transformation of Illicit Drugs in a Pilot-Scale Sewer System.

In-sewer stability of illicit drug biomarkers has been evaluated by several reactor-based studies, but less has been done in sewer pipes. Experiments conducted in sewer pipes have advantages over lab-scale reactors in providing more realistic biomarker stability due to the flow and biological dynamics. This study assessed the transportation and transformation of seven illicit drug biomarker compounds in a pilot-scale rising main and a gravity sewer pipe. Biomarkers presented diverse stability patterns in the pilot sewers, based on which a drug transformation model was calibrated. This model was subsequently validated using transformation data sets from the literature, aiming to demonstrate the predictability of the pilot-based transformation coefficients under varying sewer conditions. Furthermore, transformation coefficients for five investigated biomarkers were generated from four studies, and their prediction capabilities under the pilot-sewer conditions were jointly assessed using performance statistics. The transformation model was successful in simulating the in-sewer stability for most illicit drugs. However, further study is required to delineate the sources and pathways for those compounds with potential formations to be simulated in the transformation model. Overall, the transformation model calibrated using the pilot-sewer data is a credible tool for the application of wastewater-based epidemiology.

Toward adaptive BDCT feature representation based image splicing measurement in smart cities

In smart cities, digital image splicing measurement is very important to ensure the security and safety of city monitoring, environment data fusion, cognitive decisions, etc. However, due to images obtained from various environments of cities usually face malevolence splicing, it is hard to perform the authenticity of a legitimate image from smart cities. In this paper, a novel block Discrete Cosine Transform (BDCT) coefficients feature distribution based statistical approach is proposed to discover image forgeries for image splicing measurement. In the proposed feature, all the BDCT neighbouring modes are categorized into a number of groups following the maximum likelihood (ML) criterion to ensure the modes in the same group having similar distributions. For each group, the transition probability matrix (TPM) or the joint probability matrix (JPM) is extracted from the BDCT coefficient pairs in the image. Moreover, the proposed scheme is constructed by concatenating all the TPM/JPM features for each group. Experimental results demonstrate that our feature outperforms two state-of-the-art approaches when taking both the measurement accuracy and feature dimension into consideration.

Enhanced Median Flow Tracker Based on Photometric Correction for Videos with Abrupt Changing Illumination

Object tracking is a fundamental task in video surveillance, human-computer interaction and activity analysis. One of the common challenges in visual object tracking is illumination variation. A large number of methods for tracking have been proposed over the recent years, and median flow tracker is one of them which can handle various challenges. Median flow tracker is designed to track an object using Lucas-Kanade optical flow method which is sensitive to illumination variation, hence fails when sudden illumination changes occur between the frames. In this paper, we propose an enhanced median flow tracker to achieve an illumination invariance to abruptly varying lighting conditions. In this approach, illumination variation is compensated by modifying the Discrete Cosine Transform (DCT) coefficients of an image in the logarithmic domain. The illumination variations are mainly reflected in the low-frequency coefficients of an image. Therefore, a fixed number of DCT coefficients are ignored. Moreover, the Discrete Cosine (DC) coefficient is maintained almost constant all through the video based on entropy difference to minimize the sudden variations of lighting impacts. In addition, each video frame is enhanced by employing pixel transformation technique that improves the contrast of dull images based on probability distribution of pixels. The proposed scheme can effectively handle the gradual and abrupt changes in the illumination of the object. The experiments are conducted on fast-changing illumination videos, and results show that the proposed method improves median flow tracker with outperforming accuracy compared to the state-of-the-art trackers