Iterative Decomposition Method Research Articles

Hybrid modality dual-energy imaging aggregating complementary advantages of kV-CT and MV-CBCT: concept proposal and clinical validation

Megavoltage cone-beam CT (MV-CBCT) is advantageous in metal artifact reduction during Image-Guided Radiotherapy (IGRT), although it is limited by poor soft tissue contrast. This study proposed and evaluated a novel hybrid modality dual-energy (DE) imaging method combining the complementary advantages of kV-CT and MV-CBCT.
Approach: The kV-CT and MV-CBCT images were acquired on a planning CT scanner and a Halcyon linear accelerator respectively. After rigid registration, images of basis materials were generated using the iterative decomposition method in the volumetric images. The decomposition accuracy was quantitatively evaluated on a Gammex 1472 phantom. The performance of contrast enhancement and metal artifact reduction in virtual monochromatic images were evaluated on both phantom and patient studies.
Main results: Using the proposed method, the mean percentage errors for RED and SPR were 0.90% and 0.81%, outperforming the clinical single-energy mapping method with mean errors of 1.28% and 1.07%, respectively. The contrasts of soft-tissue insets were enhanced by a factor of 2~3 at 40 keV compared to kV-CT. The standard deviation in the metal artifact area was reduced by ~67%, from 42 HU (kV-CT) to 14 HU (150 keV monochromatic). The head and neck patient test showed that the percent error of soft-tissue RED in the metal artifact area was reduced from 18.1% (HU-RED conversion) to less than 1.0% (the proposed method), which was equivalent to the maximum dosimetric difference of 28.7% based on the patient-specific plan.
Significance: Without hardware modification or extra imaging dose, the proposed hybrid modality method enabled kV-MV DE imaging, providing improved accuracy of quantitative analysis, soft-tissue contrast and metal artifact suppression for more accurate IGRT.&#xD.

Incipient bearing fault detection using adaptive fast iterative filtering decomposition and modified Laplace of Gaussian filter

The impact components induced by faulty bearings can be readily concealed by environmental noise and other interferences due to their inherent weakness, especially during the incipient stages of fault development. A novel approach is presented in this study for the detection of incipient bearing faults, which combines an adaptive fast iterative filtering decomposition (FIFD) method with a modified Laplace of Gaussian filter. The first step involves proposing an adaptive FIFD (AFIFD) method employing improved sparrow search algorithm, enabling adaptive selection of the optimal parameter within the FIFD method. The AFIFD technique is able to adaptively decompose a complicated signal into a set of mono-components. Subsequently, a modified Laplace of Gaussian is used to highlight the fault-related cyclic impulse train from a sensitive mono-component decomposed by the AFIFD method. Finally, the envelope analysis performing on enhanced signals is applied to identify fault characteristic frequencies. Results from some case studies demonstrate that the proposed method is capable of extracting incipient fault signatures. The superiority of the proposed method is further validated through some comparative tests with recently developed fault detection methods.

Comparison of modelled pursuits with ESPRIT and the matrix pencil method in the modelling of medical percussion signals

The objective of this paper is to compare Modelled Pursuits (MoP), a recently developed iterative signal decomposition method, with more established matrix based subspace methods used to aid or automate medical percussion diagnoses.Medical percussion is a technique used by clinicians to aid the diagnosis of pulmonary disease. It requires considerable expertise, so it is desirable to automate this process where possible. Previous work has examined the application of modal decomposition techniques, since medical percussion signals (MPS) can be intuitively characterised as combinations of exponentially decaying sinusoidal (EDS) vibrations. Best results have typically been reported with matrix based subspace methods such as Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and the Matrix Pencil Method (MPM).Since ESPRIT and MPM are computationally expensive, this paper investigates whether an iterative method such as MoP can produce similar results with less computation and/or memory overheads. Using randomly generated synthetic signals designed to replicate typical ‘tympanic’ and ‘resonant’ percussion signals, we compared each method: MoP, ESPRIT, and MPM, for accuracy, speed and memory usage.We find that for low Signal to Noise Ratios (SNRs) MoP gives less accuracy than both ESPRIT and MPM, however for high SNRs (as would be typically encountered in a clinical setting) it is more accurate than MPM but less accurate than ESPRIT. We conclude that in embedded clinical applications where both operations-per-second and memory-usage are a factor, MoP is less computationally intensive than ESPRIT and thus is worth considering for use in those contexts.

Interrupted-sampling repeater jamming suppression based on iterative decomposition

Interrupted-sampling repeater jamming (ISRJ) is a type of intra-pulse coherent interference. The classical ISRJ model has constraints on forwarding strategies. We generalize the classical ISRJ model. The entire ISRJ is viewed as the sum of multiple sub-interference signals corresponding to different sampling sequences. Based on the general model, an iterative decomposition method is proposed. The received signal is decomposed into several sub-signals. In each iteration, the processes of extraction, estimation, smoothing, identification and cancellation are finished. First, the signal sample corresponding to the current target is extracted. Then we use the amplitude information of the main peak to establish the objective function. The sample points that contribute to the amplitude are selected during optimization and the corresponding sampling sequence can be estimated. Finally, the attribution of the current target is determined by the characteristic value, and the corresponding sub-signal is eliminated from the original echo. The processes are repeated until all sub-signals are eliminated. Simulation experiments show that the proposed method has excellent recognition and suppression performance for direct forwarding, repeated forwarding, and periodic forwarding ISRJ.

Iterative Pure Source Transfer Domain Decomposition Methods for Helmholtz Equations in Heterogeneous Media

Iterative Pure Source Transfer Domain Decomposition Methods for Helmholtz Equations in Heterogeneous Media

Research on balancing and sequencing problems of flexible mixed model assembly lines with alternative precedence relations

To tackle the balancing and sequencing problems of flexible mixed model assembly lines with alternative precedence relations, If–then rules and AND/OR graphs are adopted as modelling tools to replace precedence graphs that have limitations in representing alternative precedence relations. Mixed integer linear programming (MILP) and constraint programming (CP) models are established respectively. Moreover, an iterative decomposition method is developed to deal with large-sized problems. Computational experiments on different scales are carried out to test those methods. The computational results reveal that the performance of CP is better than MILP especially when more OR relations exist. And the iterative decomposition method outperforms MILP and CP in terms of solution quality and time.

Optimal hybrid precoder design for millimeter-wave massive MIMO systems

Millimeter-waves (mmWave) have emerged as a promising candidate for next-generation communication systems. However, they have severe degradation issues that can be overcome by multiple-input multiple-output (MIMO) technology. Still, high-power consumption and budget issues are associated with multiple antennas. Therefore, hybrid precoders are exploited to combat these inevitable issues. The prime objective of this manuscript is to develop a hybrid precoder for massive MIMO systems that can provide better performance than the existing ones in terms of bit error rate (BER) versus signal-to-noise ratio (SNR). In this context, we exploit iterative geometric mean decomposition (IGMD) method that presents asymptotically optimum performance not only in traditional MIMO systems but also in massive MIMO systems. Later, the outcomes of the proposed scheme are compared with a recent design exploiting generalized triangular decomposition (GTD) scheme. The simulation studies show that the proposed method provides more than 7 dB improvement according to other designs.

Modelling of natural growth with memory effect in economics: An application of adomian decomposition and variational iteration methods

The power-law memory effect is taken into consideration in a generalisation of the economic model of natural growth. The memory effect refers to a process's reliance on its current state and its history of previous changes. However, the study that focuses on natural growth in economics considering the memory effect with fractional order-linear differential equation model is still limited. The current investigation seeks to solve the natural growth with memory effect in the economics model and decide the best model using fractional differential equation (FDE), namely Adomian Decomposition and Variational Iteration Methods. Also, this study assumes the level of consumer loss memory during a certain time interval denoted by a parameter (α). This study showed the model of loss memory effect with 0 < α ≤ 1 given a slowdown in output growth compared to a model without memory effect. Besides that, this study also found that output Y(t) is growing faster with the Variational Iteration method compared to the Adomian decomposition method. Also, using graphical simulation, this study found the output Y(t) is closer to the exact solution with α=0.4 and α=0.9. In conclusion, this study successfully solved natural growth with memory effect in economics and decided the best model between FDE, namely Adomian decomposition and Variational iterative methods using numerical analysis.

Comparison of different atmospheric phase screen correction models in ground-based radar interferometry for landslide and open-pit mine monitoring

ABSTRACT Although the atmospheric compensation models developed to date have been generally robust and effective for InSAR, how to choose the right atmospheric correction method for ground-based InSAR is worth studying. This paper uses different methods for atmospheric phase screen (APS) compensation based on studies of ground-based radar (GBR) to zero-baseline acquisitions over the Woda landslide in Sichuan, China. This landslide has a steep topography, thick fog, turbulent rivers and strong rains. Data were acquired at the Ku-band using a GAMMA Portable Radar Interferometer II (GPRI-II) in multiple campaigns (4 July 2019–13 July 2019). In 2019, the Miyun open-pit mine was also studied; the maximum deformation reached 4.03 mm/hour. The collected data were processed within one unit based on the small baseline set (SBAS) approach. The experimental results show the following (1) For long-distance monitoring with complex atmospheric disturbances, range- and/or height-dependent models fail. (2) The iterative decomposition (ITD) method can effectively address complex atmospheric disturbances. (3) If the threshold of wavelength is set to be large, the ITD method becomes a stratified model.

Municipal solid waste management in circular economy: A sequential optimization model

We develop a sequential optimization model to analyze a circular economy framework where waste is collected by municipalities and it is partially reused by a recycling company for the production of electricity, thermal energy and other goods.The considered municipalities collect recyclable and not-recyclable waste types, sorted by citizens, which are sold to the recycling company and sent to landfill, respectively. Each municipality aims at maximizing its profits deriving from selling the recyclable waste to the recycling company, taking into account the waste collection and the dismantling costs. On the other side, the recycling company maximizes its total profit obtained from the difference between the revenues of selling the new goods produced and the costs of purchasing and treating recyclable waste.In this closed-loop supply chain, the municipalities define the prices at which they can offer recyclable waste to the recycling company and the tax that citizens has to pay for the waste management service. In contrast, the recycling company selects the processing volumes of recyclable waste to purchase depending on the offer prices set by the municipalities.In order to find the equilibrium of the whole supply chain, we propose an iterative decomposition method, applied to waste collection in the Lombardy Region in Italy, that accounts for the sequence of the decisions taken by the municipalities and the recycling company.

A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks.

Dual-energy computed tomography (DECT) is highly promising for material characterization and identification, whereas reconstructed material-specific images are affected by magnified noise and beam-hardening artifacts. Although various DECT material decomposition methods have been proposed to solve this problem, the quality of the decomposed images is still unsatisfactory, particularly in the image edges. In this study, a data-driven approach using dual interactive Wasserstein generative adversarial networks (DIWGAN) is developed to improve DECT decomposition accuracy and perform edge-preserving images. In proposed DIWGAN, two interactive generators are used to synthesize decomposed images of two basis materials by modeling the spatial and spectral correlations from input DECT reconstructed images, and the corresponding discriminators are employed to distinguish the difference between the generated images and labels. The DECT images reconstructed from high- and low-energy bins are sent to two generators separately, and each generator synthesizes one material-specific image, thereby ensuring the specificity of the network modeling. In addition, the information from different energy bins is exploited through the feature sharing of two generators. During decomposition model training, a hybrid loss function including L1 loss, edge loss, and adversarial loss is incorporated to preserve the texture and edges in the generated images. Additionally, a selector is employed to define the generator that should be trained in each iteration, which can ensure the modeling ability of two different generators and improve the material decomposition accuracy. The performance of the proposed method is evaluated using digital phantom, XCAT phantom, and real data from a mouse. On the digital phantom, the regions of bone and soft tissue are strictly and accurately separated using the trained decomposition model. The material densities in different bone and soft-tissue regions are near the ground truth, and the error of material densities is lower than 3mg/ml. The results from XCAT phantom show that the material-specific images generated by directed matrix inversion and iterative decomposition methods have severe noise and artifacts. Regarding to the learning-based methods, the decomposed images of fully convolutional network (FCN) and butterfly network (Butterfly-Net) still contain varying degrees of artifacts, while proposed DIWGAN can yield high quality images. Compared to Butterfly-Net, the root-mean-square error (RMSE) of soft-tissue images generated by the DIWGAN decreased by 0.01g/ml, whereas the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the soft-tissue images reached 31.43dB and 0.9987, respectively. The mass densities of the decomposed materials are nearest to the ground truth when using the DIWGAN method. The noise standard deviation of the decomposition images reduced by 69%, 60%, 33%, and 21% compared with direct matrix inversion, iterative decomposition, FCN, and Butterfly-Net, respectively. Furthermore, the performance of the mouse data indicates the potential of the proposed material decomposition method in real scanned data. A DECT material decomposition method based on deep learning is proposed, and the relationship between reconstructed and material-specific images is mapped by training the DIWGAN model. Results from both the simulation phantoms and real data demonstrate the advantages of this method in suppressing noise and beam-hardening artifacts.

A two-stage classification model integrating feature fusion for coronary artery disease detection and classification

According to the World Health Organization, Coronary Artery Disease (CAD) is a leading cause of death globally. CAD is categorized into three types, namely Single Vessel Coronary Artery Disease (SVCAD), Double Vessel Coronary Artery Disease (DVCAD), and Triple Vessel Coronary Artery Disease (TVCAD). At present, angiography is the most popular technique to detect CAD that is quite expensive and invasive. Phonocardiogram (PCG), being economical and non-invasive, is a crucial modality towards the detection of cardiac disorders, but only trained medical professionals can interpret heart auscultations in clinical environments. This research aims to detect CAD and its types from PCG signatures through feature fusion and a two-stage classification strategy. The self-developed low-cost stethoscope was used to collect PCG data from a local hospital. The PCG signals were preprocessed through an iterative signal decomposition method known as Empirical Mode Decomposition (EMD). EMD decomposes the raw PCG signal into its constituent components called Intrinsic Mode Functions (IMFs). Preprocessed PCG signal was generated exclusively through combining those signal components that contain high discriminative characteristics and less redundancy. Next, Mel Frequency Cepstral Coefficients (MFCCs), spectral and statistical features were extracted. A two-stage classification framework was devised to identify healthy and CAD types. The first stage framework relies on the fusion of MFCC and statistical features with the K-nearest neighbor classifier to predict normal and CAD cases. The second stage is activated only when the first stage detects CAD. The fusion of spectral, statistical, and MFCC features was employed with Support Vector Machines classifier to categorize PCG signatures into DVCAD, SVCAD, and TVCAD classes in the second stage. The proposed method yields mean accuracy values of 88.0%, 89.2%, 91.1%, and 85.3% for normal, DVCAD, SVCAD, and TVCAD, respectively, through 10-fold cross-validation. Comparative analysis with existing approaches confirmed the reliability of the proposed method for categorizing CAD in general clinical environments. The proposed model enhances the diagnosis performance by providing a second opinion during the medical examination.

The Fractional View Analysis of Polytropic Gas, Unsteady Flow System

Generally, the differential equations of integer order do not properly model various phenomena in different areas of science and engineering as compared to differential equations of fractional order. The fractional-order differential equations provide the useful dynamics of the physical system and thus provide the innovative and effective information about the given physical system. Keeping in view the above properties of fractional calculus, the present article is related to the analytical solution of the time-fractional system of equations which describe the unsteady flow of polytropic gas dynamics. The present method provides the series form solution with easily computable components and a higher rate of convergence towards the targeted problem’s exact solution. The present techniques are straightforward and effective for dealing with the solutions of fractional-order problems. The fractional derivatives are expressed in terms of the Caputo operator. The targeted problems’ solutions are calculated using the Adomian decomposition method and variational iteration methods along with Shehu transformation. In the current procedures, we first applied the Shehu transform to reduce the problems into a more straightforward form and then implemented the decomposition and variational iteration methods to achieve the problems’ comprehensive results. The solution of the nonlinear equations of unsteady flow of a polytropic gas at various fractional orders of the derivative is the core point of the present study. The solution of the proposed fractional model is plotted via two- and three-dimensional graphs. It is investigated that each problem’s solution-graphs are best fitted with each other and with the exact solution. The convergence of fractional-order problems can be observed towards the solution of integer-order problems. Less computational time is the major attraction of the suggested methods. The present work will be considered a useful tool to handle the solution of fractional partial differential equations.

From topological map to local cognitive map: a new opportunity of local path planning

To solve the consistency problem in local path planning, the traditional definition of topological maps is extended in this paper, by introducing a new concept of local cognitive maps (LCMs). Principal structures of a local environment including all possible routes, key obstacles, and mutual relationships among them are incorporated. Based on the LCMs, the consistency of local path planning can be guaranteed, by keeping the relationship between the chosen route and key obstacles in sequential planning cycles as consistent as possible. To generate the LCMs, an iterative decomposition method is designed . Furthermore, to evaluate candidate routes in the LCMs, the model predictive control (MPC) based on a vehicle–road evaluation method and vehicle dynamics is incorporated. The optimal route is chosen based on the MPC simulation results with criterions, such as the spending time and route width. The final path for vehicles to follow is also achieved with the simulation results. To verify the performance of the proposed method, experiments in various kinds of environments were carried out. Experimental results illustrate the effectiveness and advantages of the proposed method.

Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method

The paper presents a multiaspect analysis of multivalues and the broadband nature of system oscillation. By analyzing the ambient signal caused by random small disturbances during the normal operation of interconnected power grids, many system operation characteristics can be obtained. The traditional signal processing method cannot extract the information from ambient signals effectively. Aiming at the problem of broadband oscillation mode superposition and the difficulty of extracting information from ambient signals, an iterative adaptive variational mode decomposition (IA-VMD) method is proposed based on frequency domain analysis and signal energy. Additionally, the IA-VMD method, combined with a bandpass filter and the Prony algorithm, is used to realize the modal identification of broadband oscillation and ambient signals. Simulation experiments show that the IA-VMD method has good adaptability, antinoise characteristics, and a certain significant engineering application value as well.

Applying Numerical Simulation for the Investigation of Memristor Structures Based on Oxides and Chalcogenides

Models that describe bipolar resistive switching (BRS) in planar microstructures based on oxide compounds (Bi2Sr2CaCu2O8 + x and Nd2 – xCexCuO4 – y) and bismuth selenide are considered. Metal/insulator/metal planar-type memristor heterostructures in which the microsize is formed by an electrode with a diameter much smaller than the total size of the structure (which can be both Sharvin-type microcontacts and film electric electrodes) are investigated. Another important feature of these heterostructures is the presence of a surface layer several tens of nanometers thick with the specific conductivity significantly reduced relative to the bulk conductivity. The change in the resistive properties of such heterostructures is caused by the formation or destruction of a conducting channel through the mentioned layer. Numerical simulation has shown that the BRS is significantly affected by the topology of the electric field’s distribution. To describe the experimentally observed memristor effects in the investigated heterostructures, a model of a critical field is proposed. In this model it is assumed that the change in the specific conductivity occurs in those parts of the surface layer where the electric field strength exceeds some critical value. The critical field model is based on the numerical calculation of the electrical potential distribution from the distribution of the specific conductivity in the structure. In addition, a model that enables analysis of the influence of the electrodiffusion of oxygen ions on resistive switching in the heterostructures based on Bi2Sr2CaCu2O8 + x is considered. In the numerical realization of the models, a combination of the integrodifferential approximation of differential equations, the multigrid approach for localization of heterogeneities in physical characteristics, the iterative decomposition method, and the composite adaptive meshes are used. This allows tracking the investigated processes with the necessary accuracy. The simulation results are compared with the experimental data.

Modal Identification of damped vibrating systems by iterative smooth orthogonal decomposition method

The smooth orthogonal decomposition method (SOD) is an efficient algorithm that can be used to extract modal matrix and frequencies of lightly damped vibrating systems. It uses the covariance matrices of output-only displacement and velocity responses to form a generalized eigenvalues problem (EVP). The mode shape vectors are estimated by the eigenvectors of the EVP. It is stated in this work that the accuracy of the SOD method is mainly affected by the correlation characteristic of modal coordinate responses. For the damped vibration systems, biases will be contained in the results of using the SOD. Therefore, an iterative smooth orthogonal decomposition (ISOD) method is proposed to identify modal parameters of the damped system from the covariance matrices of the displacement, velocity, and acceleration responses. The modal matrix given by the SOD method is updated in each iteration step using a transformation matrix. The transformation matrix can be efficiently computed using a set of analytical formulations. Meanwhile, natural frequencies and damping ratios are obtained by using a simple search method. The performance of the proposed ISOD method is verified by numerical and experimental studies. The results demonstrate that, by considering the correlation of modal responses, the ISOD method can be used to extract accurately the modal information of vibration systems with coupled modes.

Target Detection and Localization Methods Using Compartmental Model for Internet of Things

This paper analyses the performance of target detection and localization methods in heterogeneous sensor networks using compartmental model, which is an attenuation model expressing the variation of received signal strength (RSS) with propagation distance. First, we compute the threshold for the proposed target detection scheme, based on the decision fusion of different sensors and without requiring a priori probability. We also derive the bound on the threshold and subsequently the lower and upper bounds on the detection and false-alarm probabilities. Next, the location of the detected target is estimated using iterative mini-batch Singular Value Decomposition (SVD) methods in the presence of sensor location uncertainty. We highlight that the method for localization has low computational complexity which is suitable for Internet of Things (IoT) networks. The effectiveness of the compartmental model is demonstrated using both simulation study and real experiments. The model parameters are estimated using WiFi signal strength received on the mobile phones from the access points in an indoor environment.

Two domain decomposition methods, SDIM and CBFM, for scattering from a two-dimensional perfectly conducting rough surface: comparison and parametric study.

This paper focuses on the two domain decomposition methods, the subdomain decomposition iterative method (SDIM) and the characteristics basis function method (CBFM), combined with adaptive cross approximation (ACA) to compute the normalized radar cross section (NRCS) from a perfectly conducting two-dimensional (2D) randomly rough surface. The 3D electromagnetic problem is solved from the electric field integral equation discretized by the Galerkin method of moments with the Rao-Wilton-Glisson basis functions. In addition, a parametric study versus the number of blocks, the number of overlapping edges, the thresholds of recompressed ACA (RACA; ACA combined with two QR decompositions and truncated by a SVD procedure, also named ACA-SVD or ACA-TSVD), and the parameters inherent to the CBFM is investigated. The complexity of the two methods is also addressed.

Estimation of the foetal heart rate baseline based on singular spectrum analysis and empirical mode decomposition

In this paper, we propose a novel automatic baseline estimation algorithm for foetal heart rate (FHR), and we verify the correctness and effectiveness of the algorithm through clinical trials. First, Singular Spectrum Analysis (SSA) is used to improve the denoising algorithm during the pre-processing of FHR. Compared with the traditional denoising method that simply uses the sliding average method, the use of the SSA method to denoise, in terms of the overall aspect, not only maintains the signal trend that is consistent with the traditional method, but it also produces no additional signal decay and distortion, which verifies the correctness of the algorithm. The SSA method could ensure that the processed signal remains smooth, unlike the sliding average method that is susceptible to the influence of oscillatory noise, causing the signal to have still abruptly changed the noise after being filtered. Subsequently, we propose the Empirical Mode Decomposition (EMD) iterative pruning method for the extraction of the FHR baseline. This algorithm combines the characteristics of the two classical algorithms and includes the EMD to make the algorithm more adaptive. This algorithm overcomes the difficulties in the classical algorithms, and the extracted baseline can more accurately reflect the real baseline and improve the effect of acceleration and deceleration detection.