nils-herrmann/clean_scirepeval huggingface dataset:
A New Model for Designing Multiwindow Multipermeability Nonlinear LTCC Inductors

Nonlinear inductors have wide applications in dc/dc converters. The multiwindow multipermeability nonlinear inductor based on low-temperature co-fired ceramic (LTCC) technology has been proven to have a gradually changing wide-range inductance value curve while requiring no extra dc bias, which makes them suitable in various high-efficiency dc/dc converters. This paper focuses on the design of such a multiwindow multipermeability inductor based on an innovative module. Currently, the design of the LTCC inductors requires complicated simulation when coupling matrices among all the windows are taken into consideration. A simplified model for calculating the inductance value is proposed in this paper to reduce complexity. In the proposed model, the complicated inductance simulation is simplified to calculating inductance of three basic units in a nine-window inductor. By simulating the nine-window inductor, inductance of a rectangle-shaped inductor with any number of evenly distributed windows on it can be calculated. Based on the proposed model, the design guideline of the nonlinear inductor is also summarized. A 16-window two-permeability prototype is presented to demonstrate the design process according to the new model. The complete simulation and the test results are also provided to verify the model. The proposed model correlates with the simulation results very well.
A digital control algorithm for DC-DC converters under input voltage changes

In this paper, a new optimal two-switching cycle compensation algorithm is proposed to achieve optimal transient performance for DC-DC converters under an input voltage change. Using the principle of capacitor charge balance, the proposed algorithm predicts the optimized two-switching cycle duty cycle series to drive the output voltage back to the steady state when the input voltage changes. Experiments are performed in a 2.5 V, 10 A, synchronous buck converter to verify the effectiveness of the proposed algorithm. The results show that using the proposed algorithm, good dynamic performance, including as small overshoot/undershoot and short recovery time is achieved.
A new adaptive morphological algorithm for contour extraction on ultrasound image

This paper describes a new adaptive algorithm for contour extraction employing fuzzy sets theory together with morphological image processing techniques. Advantage of this algorithm is that its parameter can be adjusted adaptively to extract the best contour. The contour extraction is accomplished in two stages. The first stage employs fuzzy sets theory to enhance image. The second stage extracts the image contour adaptively and effectively employing morphological theory. The validity of the approach is demonstrated using ultrasound image.
A Flicker-Free Single-Stage Offline LED Driver With High Power Factor

A conventional offline single-stage light-emitting diode (LED) driver with a high power factor usually produces a significant twice-line-frequency ripple LED current, where the ripple LED current is presented as flickering to human eye. This paper introduces a ripple cancellation method to remove the twice-line-frequency voltage ripple for an offline single-stage LED driver with a power factor correction. Consequently, a dc LED current can be produced to achieve flicker-free LED driving performance. At the same time, the required storage capacitor for the proposed LED driver can be greatly reduced, and the circuit implementation to achieve ripple cancellation is simple. Thus, the overall cost of the proposed LED driver is low. The proposed LED driver also features high efficiency because of its power structure. A 35-W Flyback experimental prototype and a 10-W Buck-Boost experimental prototype have been built to validate the proposed design and demonstrate its optimal performance.
An integrated system of detecting end-effector motion states and wafer stick-slip on a wafer transfer robot

The online detection of end-effector motion states and wafer stick-slip on a wafer transfer robot is crucial in accessing the quality of trajectory planning and improving the performance of robotic control system. This paper presents a new integrated system for detecting end-effector motion states (position and velocity) and wafer stick-slip on a wafer transfer robot. The system mainly contains a custom-built position-sensing-detector (PSD) camera, an inertial module and a tactile sensor. The end-effector position measurement is achieved by the PSD camera, and the end-effector velocity is estimated by using a kinematic Kalman filter (KKF). The KKF fuses the outputs from the PSD camera and the inertial sensor with a tri-axis accelerator and a tri-axis gyroscope. The wafer stick-slip detection is built on the measurements of the electromagnetic-based tactile sensor. The experimental results show that the integrated system is capable of measuring the position with a repeatability of ± 0.023mm along the X-axis and ± 0.017mm along the F-axis, remarkably improving the accuracy of velocity measurement, and online detecting wafer stick-slip on the wafer transfer robot.
A comparative study of double gate MOSFET with asymmetric barrier heights at source/drain and the symmetric DG-SBFET

Double gate SBFET with asymmetric barrier heights at source/drain and the symmetric DG-SBFET are simulated. A comparative study between them is made. We have found that the DG-ASBFET is more appropriate for LOP and LSPT applications. Furthermore, the DG-ASBFET shows a better scale ability and better immunity to the short channel effects.
An action layout system in the smart virtual human

To achieve the smart behaviors of a virtual human, an interruptible and quickly responsive action plan is needed. Such an action layout system is presented, which focuses on the virtual human's automatic posture transition in the virtual environment. The production system of the AI (artificial intelligent) is introduced to create the executable action sequence. In it, we develop an improved Markov algorithm as the control strategy to select the appropriate action to display. By a simple experiment of the virtual human's posture transition, this system is proved to be quick and sensitive to the change in the virtual environment.
A high-performance 2×27 MVA machine test bench based on multilevel IGCT converters

In this paper, a high-performance 22 MW/27 MVA machine test bench using pumpback topology is introduced. It proposes a cost-effective concept that works reliably even with a weak grid. A paralleling concept of two three-level IGCT converters is presented. It generates a five-level pulse pattern with low harmonic distortion at 600 Hz carrier frequency and 80 Hz load frequency. The selected magnetic device minimizes the circulating current, which ensures the full power utilization of IGCT converters. Moreover, vector control concepts for machines are presented that work reliably at 7.5 times of carrier-to-load frequency ratio, while there is a sine filter between the converter and load machines. This test bench has been experimentally validated at the customer site.
A new model-based fractional order differentiator with application to fractional order PID controllers

This paper aims at designing a fractional order differentiator based on a integer order linear system with zero initial conditions, where the fractional derivatives of the output are estimated using the output observation corrupted by a non zero-mean noise. Firstly, an integral algebraic formula for the fractional derivatives of the output is exactly obtained in continuous noise free case, using an appropriated modulating function. Unlike the improper integrals in the definitions of the fractional derivatives, the obtained formula is given by a proper integral. Then, an additional condition is added to the used modulating function in order to deal with the non zero-mean noise. After constructing the needed modulating function, a digital fractional order differentiator is proposed in discrete noisy case with some error analysis. Finally, the proposed fractional order differentiator is applied to design a fractional order PID controller for an integer order linear system.
A New Transformer-Based Non-Isolated Topology Optimized for VRM Application

The duty cycle of conventional multi-phase buck converters becomes extremely small as the output voltage becomes lower and lower. This is a severe challenge with the switching frequency goes up. This paper introduces a non-isolated half bridge converter which can extend the duty cycle to a favorable range. Thus, the converter will have symmetrical dynamic response ability. The switching loss will reduce dramatically. The primary current is directly transferred to the output. The energy transferring is more effectively then that of conventional isolated half bridge converter. In addition, the voltage stress of the primary MOSFETs equals to the input voltage, which is much lower than that of non-isolated forward or push-pull topologies. A 12 V input, 0.8 V/30 A output, 500 kHz prototype was built to demonstrate the advantages
A citizen trust model for e-government

Trust in e-government is an important idea that should be critically investigated to help citizen favorably share information and make online transaction with government. This study aims to establish an e-government trust model from the behavioral perspectives of citizen. Based on the Technology Acceptance Model (TAM) and Customer Relationship Management (CRM), the study proposes an intergraded model of e-government citizen trust. An online survey is undertaken. The target population is citizens who have experienced with browsing and searching for the information in e-government web portals. Using Partial Least Squares approach, a highly statistically significant goodness-of-fit is found for the model. The findings indicate that perceived usefulness, perceived ease of use, perceived security and perceived risk, directly enhance the citizen trust in e-Government.
Three-dimensional regional left ventricular deformation from digital sonomicrometry

Understanding how the left ventricle deforms in 3D and how this deformation is altered with coronary occlusion may lead to the development of non-invasive imaging techniques to determine the extent of permanent injury. To determine regional 3D strains in the left ventricle of the heart the authors employed digital sonomicrometry, with high temporal and spatial resolution. Two cubic arrays of 8 omnidirectional transceiver crystals were implanted in two regions of the left ventricle in an open chest canine preparation (n=6). Additional crystals were used to define a fixed external reference space and the long axis of the ventricle. Using ultrasound transit time the distances between all the crystals were recorded. A multidimensional scaling technique was then applied to transform the distances to 3D crystal coordinates. A least squares fit of the displacement field was applied to calculate homogeneous strains for each cube. Cardiac specific directions were determined and strains rotated into the local coordinate space. This technique was applied pre- and post- coronary occlusion. Alterations in strain patterns were evident in the ischemic region and subtle temporal changes in the control region. Thus, digital sonomicrometry, with high temporal and spatial resolution, enhances one's ability to analyze regional left ventricular 3D strain patterns.
Cardiac Motion Analysis Using Nonlinear Biomechanical Constraints

Quantitative assessment of the motion and deformation variability of the heart has important implications for the understanding, diagnosis, and treatment of cardiac diseases. Various existing image-based approaches typically rely on linear constraining models which are however physically implausible. In this paper, we present a biomechanically constrained framework for the estimation of left ventricle deformation from medical image sequences using more realistic nonlinear geometry and material models. Once the myocardial boundaries and the sparse corresponding boundary points are derived from an active region model, we construct the cardiac dynamics where the left ventricle is modelled as an Mooney-Rivlin material undergoing large deformation. We then rely on the Newmark scheme to perform frame-to-frame estimation of the cardiac motion/deformation parameters. Experiments have been performed with 3D cardiac MR image sequences with very encouraging results
Continuous-Discrete Filtering for Cardiac Kinematics Estimation under Spatio-Temporal Biomechanical Constrains

A continuous-discrete filtering strategy is proposed for cardiac kinematics estimation from periodic medical image sequences. Stochastic multi-frame filtering frameworks are constructed to deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data coordinately. For the system with continuous dynamics and discrete measurements, the state estimates are predicted according to the continuous-time state equation between observation time points, and updated with the new measurements obtained at discrete time instants, yielding physically more meaningful and more accurate estimation results for the continuously evolving cardiac dynamics. The strategy is validated through synthetic data experiments to illustrate its advantages and on canine MR phase contrast images to show its clinical relevance
NONINVASIVE IMAGING OF 3D CARDIAC ELECTROPHYSIOLOGY

Noninvasive imaging of cardiac electrophysiology has been an active area with increasing clinical significance. Instead of equivalent-source-based approaches, however, noninvasive 3D cardiac transmembrane potentials (TMP) mapping remains a formidable goal due to the remote and integrative nature of body surface potential recordings. We recognized the incorporation of a priori physiological knowledge as one desired paradigm and presented a model-based general Bayesian framework for 3D TMP mapping from body surface potential maps (BSPMs), based on optimal integration of a priori physiological models and patient-specific observations within their respective uncertainties. An alternative being the use of additional observations for volumetric cardiac electrophysiological information, we also worked on recovering cardiac electrical excitation pattern from tomographic medical image sequences. Furthermore, aiming for a model-based data fusion framework wherein information from BSPMs and medical image sequences are integrated, we presented the dynamic image guided mapping of 3D cardiac electrophysiology.
Low dose PET reconstruction with total variation regularization.

Low dose positron emission tomography(PET) reconstruction remains a challenging issue for statistical PET reconstruction methods due to the low SNR of data. Due to the ill-conditioning of image reconstruction, proper prior knowledge should be incorporated to constrain the reconstruction. Since PET images are piecewise smoothing, we propose the total variational (TV) minimization based algorithm for low dose PET imaging. The fundamental power of this strategy rests with the edge locations of important image features tend to be preserved thanks to TV regularization. In addition, a new computational method have been employed with improved computational speed and robustness. Experimental results on Monte Carlo simulations demonstrate its superior performance.
A Simultaneous Framework for Recovering Three Dimensional Shape and Nonrigid Motion from Cardiac Image Sequences

Quantitative assessment of the shape and motion variability of the heart has important implications for the diagnosis and treatment of cardiac diseases. In this paper, we present a unified methodology which simultaneously recovers the shape and motion of the left ventricle, including the endo-, epi-, and mid-wall myocardium. The left ventricle is modeled as an isotropic linear elastic material, and represented by volumetric mesh constructed from the Delaunay triangulation of the sampling points. Specifically, the evolution forces imposed on the myocardium are individually constructed for each nodal point through the integration of the data-driven edginess measures, the prior spatial distributions of the myocardial tissues, the temporal coherence of the image-derived salient features, and the cyclic motion characteristics of the heart. The dense displacement field can then be estimated when the total energy of the elastic body is minimized at equilibrium. Experiments on 3D human magnetic resonance image sequences of healthy and pathological subjects show the accuracy and robustness of the strategy
Robust filtering strategies for soft tissue Young's modulus characterization

Accurate and robust quantification of soft tissue elasticity has significant clinical implications for disease diagnosis. For imaging-based strategies, the aim is to recover the material parameters of the assumed tissue constitutive model from noisy image-derived measurements on the kinematic states. In this paper, we develop a general material parameter identification formulation based on soft tissue continuum mechanics models and state space representation. Within this unifying formulation, we analyze the widely-used least-square (LS) solution, which does not perform well under reasonably realistic levels of disturbances, and the popular extended Kalman filtering (EKF) strategy, which is also far from optimal and subject to divergence if either the initializations are poor or the noises are not Gaussian. We then present a robust estimation paradigm derived and extended from the /spl Hscr//sub /spl infin// filtering principles. It is particularly powerful for real-world situations where the types and levels of the disturbances are unknown. Experimental results on synthetic and real images demonstrate its superior performance.
Stochastic Inverse Consistent Registration

An essential goal in medical image registration is, the forward and reverse mapping matrices should be inverse to each other, i.e., inverse consistent. Conventional approaches enforce such consistency in deterministic fashions, either through incorporation of sub-objective cost function to impose consistent property during the registration process or by construction of consistent mapping on predetermined landmarks sets. Assuming that the initial forward and reverse matching matrices have been computed and used as the inputs to our system, this paper presents a stochastic framework which yields perfect consistent registration. During the optimization process to reach the perfect consistency, we model the errors of the registration matrices and the imperfectness of the consistent constraint as stochastic processes. An iterative generalized total least square (GTLS) strategy has been developed so that consistency is optimally imposed
PET image reconstruction based on particle filter framework

PET measured data in nature follows Poisson distribution, which leads to iterative statistical methods being the primary efforts in image reconstruction. In contrast, physiological model provides a predictive tool of the imaged biological processes. To date, most of the existing efforts do not attempt to tackle the reconstruction problem by combining measured data statistics and physiological modeling constraints in a joint fashion. In this paper, we propose a novel approach that combine the statistical model and physiological parameter together during static reconstruction with the aid of particle filter. Experiments on Monte Carlo simulations, real physical phantom data demonstrate the power of the framework.
A Meshfree Representation for Cardiac Medical Image Computing

The prominent advantage of meshfree method, is the way to build the representation of computational domain, based on the nodal points without any explicit meshing connectivity. Therefore, meshfree method can conveniently process the numerical computation inside interested domains with large deformation or inhomogeneity. In this paper, we adopt the idea of meshfree representation into cardiac medical image analysis in order to overcome the difficulties caused by large deformation and inhomogeneous materials of the heart. In our implementation, as element-free Galerkin method can efficiently build a meshfree representation using its shape function with moving least square fitting, we apply this meshfree method to handle large deformation or inhomogeneity for solving cardiac segmentation and motion tracking problems. We evaluate the performance of meshfree representation on a synthetic heart data and an in-vivo cardiac MRI image sequence. Results showed that the error of our framework against the ground truth was 0.1189 ± 0.0672 while the error of the traditional FEM was 0.1793 ± 0.1166. The proposed framework has minimal consistency constraints, handling large deformation and material discontinuities are simple and efficient, and it provides a way to avoid the complicated meshing procedures while preserving the accuracy with a relatively small number of nodes.
Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences

Proposes and validates the hypothesis that one can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three-dimensional (3-D) image sequences (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the point correspondence maps. The noninvasive, algorithm-derived results are validated on two levels. First, the motion trajectories are compared to those of implanted imaging-opaque markers of a canine model in two imaging modalities, where subpixel accuracy is achieved. Second, the validity of using motion parameters (path length and thickness changes) for detecting myocardial injury area is tested by comparing algorithms derived results to postmortem analysis TTC staining of myocardial tissue, where the achieved Pearson product-moment correlation value is 0.968.
VOLUMETRIC MESHFREE FRAMEWORK FOR JOINT SEGMENTATION AND MOTION TRACKING OF THE LEFT VENTRICLE

Meshfree particle method (MPM) exhibits improved flexibility and accuracy in dealing with problems with large deformation, complex geometry and material discontinuities. In this paper, we present a MPM based framework for the simultaneous shape recovery and motion tracking of the left ventricle. The myocardium is modeled as an anisotropic elastic body accounting for the fiber directions, represented by sampling nodes bounded by endocardial and epicardial boundaries. Cardiac dynamics is then driven by external force constructed individually for each node through integration of measures of image edgeness, image derived salient feature coherence, prior tissue distribution model, and temporal motion model. The displacement field throughout the cardiac cycle is obtained when the total energy of the elastic body is minimized to reach equilibrium state. Experiments with 3D canine MR images illustrate the benefits and potentials of such effort.
Multiframe nonrigid motion analysis with anisotropic spatial constraints: applications to cardiac image analysis

Proper spatial and temporal constraints are essential for image-based motion recovery of deforming objects. Since biological organs, such as the heart, are typically composed of fibrous tissues of anisotropic nature, one must adopt realistic spatial models, in addition to those important considerations for temporal modeling, in order to properly regularize the object behavior for kinematics recovery. We present a biomechanically constrained state space analysis framework for the multiframe estimation of the heart motion and deformation. While the anisotropic physical constraints enforce spatial regulations on the myocardial behavior and spatial filtering of the image data measurements, statistical filtering techniques impose temporal constraints to incorporate multiframe information. Implemented within a mesh-free particle representation and computation framework, excellent experimental results are achieved for both synthetic data with known ground truth and canine magnetic resonance image sequences with known clinical gold standard.