Conventional Decomposition Method Research Articles

Optimized topological quantum compilation of three-qubit controlled gates in the Fibonacci anyon model: A controlled-injection approach

A method, termed controlled injection, is proposed for compiling three-qubit controlled gates within the non-Abelian Fibonacci anyon model. Building on single-qubit compilation techniques with three Fibonacci anyons, the approach showcases enhanced accuracy and reduced braid length compared to the conventional decomposition method for the controlled three-qubit gates. This method necessitates only four two-qubit gates for decomposition, a notable reduction from the conventional five. In conjunction, the study introduces a class of controlled three-qubit gates and conducts a numerical simulation of the topological iToffoli gate to validate the approach. In addition, we propose an optimization method for single-qubit gate approximation using algebraic relations and numerical methods, including distributed computing. Published by the American Physical Society 2024

A Novel Polarization Scattering Decomposition Model and Its Application to Ship Detection

In polarimetric synthetic aperture radar (POLSAR), it is of great significance for civil and military applications to find novel model-based decomposition methods suitable for ship detection in different detection backgrounds. Based on the physical interpretation of polarimetric decomposition theory and the Lasso rule for sparse features, we propose a four-component decomposition model, which is composed of surface scattering (Odd), double-bounce scattering (Dbl), volume scattering (Vol), and ±45° oriented dipole (Od). In principle, the Od component can describe the compounded scattering structure of a ship consisting of odd-bounce and even-bounce reflectors. Moreover, the pocket perceptron learning algorithm (PPLA) and support vector machine (SVM) are utilized to solve the linear inseparable problems in this study. Using large amounts of RADARSAT-2 (RS-2) fully polarized SAR data and AIRSAR data, our experimental results show that the Od component can make a great contribution to ship detection. Compared with other conventional decomposition methods used in the experiments, the proposed four-component decomposition method has better performance and is more effective and feasible to detect ships.

Mueller matrix polarization imaging and quantitative parameters analysis method.

Mueller matrix polarization imaging is a new biomedical optical imaging method that can generate both polarization and isotropic intensity images of structures of the biological tissue sample surface. In this paper, a Mueller polarization imaging system in the reflection mode is described for obtaining the Mueller matrix of the specimens. Diattenuation, phase retardation, and depolarization of the specimens are derived by using the conventional Mueller matrix polarization decomposition method and a newly proposed direct method. The results show that the direct method is more convenient and faster than the conventional decomposition method. The polarization parameter combination method is then presented in which any two of the diattenuation, phase retardation, and depolarization parameters are combined, and three new quantitative parameters are defined in order to reveal more detailed anisotropic structures. The images of in vitro samples are presented to demonstrate the capability of the parameters introduced.

Full-waveform LiDAR echo decomposition method based on deep learning and sparrow search algorithm

For the full-waveform LiDAR echo, the conventional decomposition method is to judge the number of waveform components in the echo through the IP (inflection point) method or RL (Richardson-Lucy) deconvolution method after filtering and obtain the initial estimation of their parameters, and then use the intelligent optimization algorithm to fit the waveform. However, this preprocessing method has poor accuracy in judging the echo components when the distance between targets is small. In this paper, a highly accurate decomposition method is proposed based on LSTM with SSA (Sparrow Search Algorithm). Firstly, the LSTM network trained by the simulation data sets under three kinds of background noise is used to judge the number of Gaussian components in the full-waveform LiDAR echo, and then SSA is used for waveform fitting. The accuracy rate of each LSTM network is more than 95%. This method is compared with IP, RL and MGD (multi-Gaussian decomposition) method. Within the accuracy of 0.1 m, the minimum decomposition distance of LiDAR echo is shorten from 0.7 m to 0.45 m compared with IP method and from 0.55 m to 0.45 m compared with RL and MGD method. The minimum ranging distance of LiDAR echo is shorten from 0.75 m to 0.65 m compared with IP and RL method and from 0.85 m to 0.65 m compared with MGD method. With proposed method, the accuracy of full-waveform LiDAR echo decomposition is improved and distance limit of decomposition is reduced.

A Quadratic Convergent Iteration-Variable-Correction-Based Method for Optimal Power Flow of Transmission-Distribution-Coupled Systems

An iteration-variable-correction (IVC) based method is proposed for solving optimal power flow (OPF) of transmission-distribution-coupled (TDC) systems. First, a TDC system can be divided into a single coordinator and multiple agents. Then, the basic formulation, subproblem construction, and selection of iteration variables are given based on the conventional heterogeneous decomposition algorithm (HGD). Considering the limitations of the conventional HGD, the IVC is proposed. According to the general theory of the IVC, when it is applied to solve TDC OPF, the voltage magnitude of boundary buses and the multipliers corresponding to boundary power injections will be corrected every four iterations with first-order and second-order differential operations on historical results in the previous four iterations. The quadratic convergence of the IVC is proved under several weak assumptions. The numerical experiments demonstrate that the proposed IVC has the same accuracy as the method based on the centralized OPF model. Also, better convergence and efficiency can be achieved under the IVC compared with some conventional decomposition methods, including alternating direction method of multipliers, auxiliary problem principle method, analytical target cascading method, and the HGD.

End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

Different from conventional decomposition methods which utilize several steps to obtain the final result, a self-attention based neural network, Attention Full-waveform Decomposition Network (AFD-Net), is discussed in this paper for end-to-end full-waveform LiDAR signal decomposition. In existing LiDAR waveform decomposition methods, complicate functional models are used to fit echo components. Thus, the echo decomposition problem can be translated into a function approximation task. Recent studies present great progress in estimating the parameters of fitting models, hence in the final decomposition results. However, the shape of received echo components are always irregular. None of the parametric functional models can fit the received echo components perfectly, which leads to unavoidable errors in the initial step of echo decomposition. In this paper, we propose an end-to-end net work AFD-Net to solve the echo decomposition problem without assuming any parametric functional models. AFD-Net is consisted with two modules, the classification module and the decomposition module. The former module is used to determine the number of echo components in a received waveform. Then the decomposition module is used to output the echo components. By experiments, we have a classification accuracy 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> using the first module. The average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> coefficient for the decomposed echo components is 0.9799. In addition, there are no public datasets for the waveform decomposition task available. Thus, another contribution of our work is to develop a tool to generate synthetic full-waveform LiDAR signals, which can help researchers to construct their own dataset for related works. All of our source codes are available in the following site: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZedFm/AFD-Net</uri>

A Novel Median-Point Mode Decomposition Algorithm for Motor Rolling Bearing Fault Recognition

Precise fault recognition of motor rolling bearing fault is playing a significant role in any machinery and equipment. However, conventional decomposition methods fail to completely reveal the fault signal information of motor rolling bearing due to mixed modes problem. To solve the problem, the median-point mode decomposition (MMD) method is presented. The MMD method uses sort-based inversion to sort out each variation of the same time interval for better and specific mode decomposition, with the assistance of the advanced envelope curve formed by the median points between adjacent extreme points. It certainly alleviates the mixed mode during the iteration of intrinsic mode functions (IMFs). Therefore, comparison results are simulated in the proposed MMD method with conventional methods. Experiment of motor rolling bearing fault is operated for fault recognition in order to demonstrate the MMD algorithm.

Imaging conditions for elastic reverse time migration

Elastic reverse time migration (RTM) has the ability to retrieve accurately migrated images of complex subsurface structures by imaging the multicomponent seismic data. However, the imaging condition applied in elastic RTM significantly influences the quality of the migrated images. We evaluated three kinds of imaging conditions in elastic RTM. The first kind of imaging condition involves the crosscorrelation between the Cartesian components of the particle-velocity wavefields to yield migrated images of subsurface structures. An alternative crosscorrelation imaging condition between the separated pure wave modes obtained by a Helmholtz-like decomposition method could produce reflectivity images with explicit physical meaning and fewer crosstalk artifacts. A drawback of this approach, though, was that the polarity reversal of the separated S-wave could cause destructive interference in the converted-wave image after stacking over multiple shots. Unlike the conventional decomposition method, the elastic wavefields can also be decomposed in the vector domain using the decoupled elastic wave equation, which preserves the amplitude and phase information of the original elastic wavefields. We have developed an inner-product imaging condition to match the vector-separated P- and S-wave modes to obtain scalar reflectivity images of the subsurface. Moreover, an auxiliary P-wave stress image can supplement the elastic imaging. Using synthetic examples with a layered model, the Marmousi 2 model, and a fault model, we determined that the inner-product imaging condition has prominent advantages over the other two imaging conditions and generates images with preserved amplitude and phase attributes.

Analytic Quantification of Shilnikov Chaos in Epileptic EEG Data

Dynamical Systems Based Modeling (DSBM) is a method to decompose a multivariate signal leading to both a dimensionality reduction and parameter estimation describing the dynamics of the signal. We present this method and its application to EEG data sets of Petit-Mal epilepsies considering Shilnikov chaos as the underlying dynamic interaction. We demonstrate the power of this method compared to conventional decomposition methods like PCA and ICA. Since the fitting quality showed a strong correlation to the ictal phases of the signal, we performed a cross validation on seizure detection with a resulting specifity of 84% and sensitivity of 75%. By applying DSBM in a moving window setup we investigated the comparability of the obtained dynamic models and tested the hypothesis of Shilnikov chaos in terms of linear stability analysis for each of the investigated windows. Thereby we could corroborate the Shilnikov hypothesis for approx. 50% of the relevant windows.

A Low-Cost Comparator-Based Method for Accurate Decomposition of Deterministic Jitter in High-Speed Links

Jitter decomposition is a key tool to identify root causes of jitters in a high-speed digital communication system. It is a huge challenge in balancing the test cost and precision for conventional decomposition methods implemented in instruments where the time interval error (TIE) data are necessary. In this paper, we propose a deterministic jitter decomposition method using Boolean output from a network of simple low-cost comparators to identify the deviation of current sampling position from the ideal sampling position instead of TIE data. The new method simultaneously separates intersymbol interference (ISI), periodic jitter (PJ), and duty cycle distortion (DCD). Simulation and measurement results demonstrate that the proposed method can estimate the ISI, PJ, and DCD with sufficient accuracy using significantly fewer data samples than the state-of-the-art instrument test, and thus, reduce test cost greatly. Furthermore, the comparators have extremely relaxed design requirements, offering potential for possible on-chip implementation for built-in self-test or background test.

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

Data decomposition method for parallel polygon rasterization considering load balancing

It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. In parallel rasterization, it is difficult to design an effective data decomposition method. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. First, four factors that possibly affect the rasterization efficiency were investigated. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. In addition, the results showed that DMPC exhibited consistently better performance for different spatial distributions of polygons.

In2O3 nanoporous nanosphere: A highly efficient photocatalyst for decomposition of perfluorooctanoic acid

Perfluorooctanoic acid (PFOA) is an emerging persistent organic pollutant and receives increasing concerns due to its global occurrence and resistance to most conventional decomposition methods. In2O3 nanoporous nanospheres (NPNSs) with uniform size (∼100nm) were obtained by calcining nanoporous InOOH/In(OH)3 nanospheres and its photocatalytic activity for PFOA decomposition were evaluated. The InOOH/In(OH)3 precursor was self-assembled from ultrathin nanoplates (∼2nm) in an ethylenediamine-assisted alcohol-thermal system. The In2O3 NPNSs possessed a high value of specific surface area (39.0m2g−1) and a large number of nanopores (∼6nm). Compared with commercial In2O3 nanocrystals (NCs) and TiO2 (Degussa P25), In2O3 NPNSs showed a remarkable photocatalytic activity to decompose PFOA with the half-life of 7.1min. The first-order rate constant of PFOA by In2O3 NPNSs was nearly 9 and 54.6 times higher than that by In2O3 NCs and TiO2 respectively. The superior activity of In2O3 NPNSs for PFOA decomposition can be attributed to its nanoporous structure, large specific surface area, and direct reaction between the photogenerated hole and PFOA.

Evaluating the Ability of Modified Adomian Decomposition Method to Simulate the Instability of Freestanding Carbon Nanotube: Comparison with Conventional Decomposition Method

Evaluating the Ability of Modified Adomian Decomposition Method to Simulate the Instability of Freestanding Carbon Nanotube: Comparison with Conventional Decomposition Method

Multiple calibration decomposition analysis: Energy use and carbon dioxide emissions in the Japanese economy, 1970–1995

The purpose of this paper is to present a new approach to evaluating structural change of the economy in a multisector general equilibrium framework. The multiple calibration technique is applied to an ex post decomposition analysis of structural change between periods, enabling the distinction between price substitution and technological change to be made for each sector. This approach has the advantage of sounder microtheoretical underpinnings when compared with conventional decomposition methods. The proposed technique is empirically applied to changes in energy use and carbon dioxide (CO 2) emissions in the Japanese economy from 1970 to 1995. The results show that technological change is of great importance for curtailing energy use and CO 2 emissions in Japan. Total CO 2 emissions increased during this period primarily because of economic growth, which is represented by final demand effects. On the other hand, the effects such as technological change for labor or energy mitigated the increase in CO 2 emissions.

One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems

Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods.

Ultrafast Reaction between Li3N and LiNH2 To Prepare the Effective Hydrogen Storage Material Li2NH

Li2NH is a promising candidate for hydrogen storage. However, its performance for hydrogen absorption is strongly dependent on its preparation method. In this paper, we report that an ultrafast solid reaction between Li3N and LiNH2 provides a new synthesis approach to an effective Li2NH material, which can reversibly store 6.8 wt % hydrogen with fast kinetics and excellent stability. In contrast, Li2NH prepared via the conventional LiNH2 decomposition method absorbs less than 2 wt % hydrogen in 500 min. Scanning electron microscopy (SEM) and BET measurements demonstrated that the poor performance of Li2NH prepared via the conventional decomposition method is caused by sintering. In addition, the hydrogen capacity of Li2NH, which was prepared via the reaction between LiH and LiNH2, reached a value of only about 4 wt % after 500 min.

Resolving feature convolution in middleware systems

Middleware provides simplicity and uniformity for the development of distributed applications. However, the modularity of the architecture of middleware is starting to disintegrate and to become complicated due to the interaction of too many orthogonal concerns imposed from a wide range of application requirements. This is not due to bad design but rather due to the limitations of the conventional architectural decomposition methodologies. We introduce the principles of horizontal decomposition (HD) which addresses this problem with a mixed-paradigm middleware architecture. HD provides guidance for the use of conventional decomposition methods to implement the core functionalities of middleware and the use of aspect orientation to address its orthogonal properties. Our evaluation of the horizontal decomposition principles focuses on refactoring major middleware functionalities into aspects in order to modularize and isolate them from the core architecture. New versions of the middleware platform can be created through combining the core and the flexible selection of middleware aspects such as IDL data types, the oneway invocation style, the dynamic messaging style, and additional character encoding schemes. As a result, the primary functionality of the middleware is supported with a much simpler architecture and enhanced performance. Moreover, customization and configuration of the middleware for a wide-range of requirements becomes possible.

ANALYSIS OF SYMMETRIC MULTIWAVELETS AND ITS APPLICATION FOR IMAGE COMPRESSION

Multiwavelets are the new addition to the body of wavelet theory. It is also based on the idea of multiresolution analysis (MRA). An MRA is usually generated by one scaling function. However, such wavelets cannot possess the properties of compact support, linear phase, and orthogonality simultaneously. Realized as matrix-valued filterbanks leading to wavelet bases, multiwavelets can offer these three properties simultaneously which are strongly desired in many applications, such as a fast multiresolution pyramid decomposition algorithm of image with the 2 ґ 2 matrix. To succeed using the EZW algorithms, we wish there still are some of the relationships between ancestors and their offspring. But the relationship is not very clear after the conventional iteration of multiwavelet decomposition which is similar to the scalar wavelet decomposition, and the encoding effect and efficiency are not very good. Therefore, this paper presents full new decomposition and quantization methods adapt to the symmetric property of multiwavelets. Extensive experimental results demonstrate that our techniques exhibit performance equal or superior to the conventional decomposition methods.

Methodological issues in cross-country/region decomposition of energy and environment indicators

A recent development in energy and environment decomposition analysis is the application of the technique to cross-country/region comparisons. Cross-country/region decomposition gives rise to several problems that do not normally occur in chronological decomposition of changes in a specific country. These include large variations in explanatory factors in the data, the measure of economic output and structural comparability. We examine these problems using energy-related CO 2 emission data of three world regions. It is concluded that the conventional decomposition methods are not effective in cross-country/region studies as they tend to leave a large residual. Instead, perfect decomposition methods should be adopted. The choice of economic output measure can affect decomposition results greatly and the impacts vary from one decomposition method to another.