nils-herrmann/clean_scirepeval huggingface dataset:
Analytical calculation of inductance parameter of tangentially magnetized PM motor based on basic air-gap magnetic field

Because permanent magnets (PMs) exist in the d-axis magnetic circuit of tangentially magnetized PM synchronous motor, its magnetic field and calculation of inductance parameter is different from common synchronous motor. A new analytic method of calculation of PM motor inductance parameter is proposed in this paper, which is based on basic air gap magnetic field and is more convenient than the conventional method using air gap magnetic permeability. The concept of basic air gap magnetic field and how to calculate its magnetic induction are introduced, the analytic calculation procedure of this method is given and the analytic calculation formula is deduced. The inductance parameters of a 350kW twelve-phase tangential magnetized PM propulsion motor are calculated by the analytic method and Finite Element Method (FEM) and the results have shown that the two cases agree well, so the validity and accuracy of the presented method are verified.
Design of space class library for 3D geological modeling

The research of 3D geological modeling and visualization is a huge and complicated system engineering. Through the analysis, induction and abstraction about the spatial geological data, we establish a space class library of GeoSIS, in which the objects related to 3D modeling are systematically classified based on the OOP and COP. The functions, such as the integration of geological data, construction of complex 3D models, and spatial analysis and visualization, can be achieved by calling the corresponding class methods. With strong extensibility, flexibility and openness, the space class library can make GeoSIS easy to meet the demands of technology development and application in the future. The actual application shows that, the design method proposed in this paper can be widely used in hydrogeology, engineering geology, environmental geology and geological disaster, to realize 3D modeling and visualization analysis, and provide a strong support for the Digital Earth plan.
Exponential Stability of Discrete Time-Varying Purturbed Markov Switched Systems with Impulsive Jump

The exponential stability of a class of discrete time-varying impulsive jump linear systems with a finite-state Markov chain form process is studied. The given jump linear systems are composed of stable subsystems and unstable subsystems. Based on the average dwell time concept and by dividing the total activation time into the time with stable subsystems and the time with unstable subsystems, it is shown that if the average dwell time and the ratio of the expectation of the activation time with stable subsystems to the expectation of the activation time with unstable subsystems are properly large, the given jump linear system is exponentially stable with a desired stability margin even if there exists impulse jump at the switching instances
Performance of optimum transmitter power control in CDMA cellular mobile systems

Efficient power control is of great importance in the design of high-capacity cellular radio systems. Optimum power control scheme (OPCS), in the sense that it minimizes the outage probability, has been fully investigated for frequency-division multiple-access/time-division multiple-access (FDMA/TDMA) cellular systems. In this paper, optimum power control theory is introduced to code-division multiple-access (CDMA) cellular systems to derive upper performance bounds for all types of transmitter power control schemes. Simulation results indicate that the OPCS will increase the CDMA system capacity by more than 1.9 dB over the perfect average power control scheme under IS-95-defined radio condition.
A Macrobending Fiber Based Micro-Displacement Sensor

An all-fiber micron displacement sensor with a simple configuration is proposed and investigated experimentally. The proposed fiber displacement sensor consists of a half-loop structure of bare high-bend loss singlemode fiber-1060XP, employing the well-known Whispering Gallery mode (WGM) effect. A ratiometric power measurement system for interrogating the proposed sensor is also presented. By measuring the change in ratio of bend loss in the ratiometric system, a change in displacement can be measured assuming the ratiometric system is calibrated. The proposed macrobending fiber based displacement sensor achieves a higher resolution (less than 75 nm of displacement) than other conventional fiber-optic sensors and also benefits from simplicity.
Hyper-Chaos and Singular Beats in Congestive Heart Failure

Transient hyper-chaos with extraordinary high strength was observed in HRV of CHF patients by means of numerical titration method based on Volterra-Wiener series. In comparison with healthy subjects, RR series of CHF patients present sporadic impulses composed of normal, rather then premature beats. To illustrate such impulses and its relation to hyper-chaos, a sinoatrial node (SAN) model was developed. Simulations showed that high norepinephrine and low acetylcholine concentrations induced singularity in RR intervals and resulted in hyper-chaos. It is consistent with the high sympathetic and low parasympathetic tones in CHF.
Proposal for a simple polarization converter based on integrated optical ion exchanged waveguide

A simple, compact, low-cost electro-optic polarization converter based on a nematic liquid crystal (LC) sandwiched between two ion-exchanged glass channel waveguides is proposed. A three-dimensional (3D) fully-vectorial (FV) finite difference beam propagation method (FDBPM) is used to simulate this optical device. The performance of the proposed polarization converter is analyzed numerically at zero and 5 V applied voltage for the case of strong anchoring of LC molecules to the surface of the waveguide. The polarization conversion efficiency is ~65% at a wavelength of 1550 nm. The total length of the directional coupler structure based converter is only 724 µm.
Estimation of DOA in unknown noise: performance analysis of UN-MUSIC and UN-CLE, and the optimality of CCD

In a previous paper, a new approach was proposed for the consistent estimation of the directions of arrival (DOA) of signals in an unknown spatially correlated noise environment using generalized correlation decomposition (GCD). Based on the various interesting properties of the eigenspace structure obtained by GCD, two effective methods (UN-MUSIC and UNCLE) of estimating the DOA in an unknown correlated noise were developed, In this paper, the performance of the two methods are analyzed. It is shown that the performance of these two methods can be optimized by assigning optimum weighting matrices in their respective criteria. Furthermore, and more importantly, it is also shown that of all the correlation decompositions, the canonical correlation decomposition (CCD) leads to the optimum performance of the methods. Computer simulations confirm these conclusions and show that the use of CCD is robust even under variable spatially correlated noise conditions.
Effects of slow fading SIR errors on CDMA capacity

With fast close-loop power control, slowly varying power control errors, which cannot be eliminated by coding and interleaving, have been found lognormally distributed in the received SIR and may result in great performance reduction. In this paper, a closed-form formula is derived to show an important fact that 1 dB standard deviation of the received SIR corresponds to 2.3 dB reduction of the CDMA reverse link capacity.
An Improved Anytime RRTs Algorithm

Mobile robots often need to find a trajectory to another position in their environment, subject to constraints posed by obstacles and the robot’s capabilities. Several approaches have been used to address this problem each with some limitations, including state discretizations, planning efficiency, and lack of interleaved execution. Anytime RRTs Algorithm is a recently developed algorithm on which fast continuous domain path planners can be based. This paper presents a modification to the Anytime RRTs Algorithm. Optimization include a more general biased distribution, which was explored in this work in the form of a waypoint cache. Which improve replanning efficiency and the quality of generated paths. We demonstrate the effectiveness of the algorithm on single robot planning domains.
Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image

Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper proposes a new multi-view gait recognition approach which tackles the problems mentioned above. Being different from other approaches of same category, this new method creates a so called View Transformation Model (VTM) based on spatial-domain Gait Energy Image (GEI) by adopting Singular Value Decomposition (SVD) technique. To further improve the performance of the proposed VTM, Linear Discriminant Analysis (LDA) is used to optimize the obtained GEI feature vectors. When implementing SVD there are a few practical problems such as large matrix size and over-fitting. In this paper, reduced SVD is introduced to alleviate the effects caused by these problems. Using the generated VTM, the viewing angles of gallery gait data and probe gait data can be transformed into the same direction. Thus, gait signatures can be measured without difficulties. The extensive experiments show that the proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods in literature.
A MEP and IP Based Flexible Neural Tree Model for Exchange Rate Forecasting

Forecasting exchange rate is an important financial problem that is received much more attentions because of its difficulty and practical applications. The problem of prediction of foreign exchange rates by using multi expression programming and immune programming based flexible neural tree (MEPIP-FNT) is presented in this paper. This work is an extension of our previously traditional FNT model which can optimize the architectures and the weights of flexible neuron model respectively. The novel MEPIPFNT model with the underlying immune theories is capable of evolving the architectures and the weights simultaneously. To demonstrate the efficiency of the model, we conduct three different datasets in our forecasting performance analysis.
The effect of image enhancement on biomedical pattern recognition

Image enhancement has been an area of active research for decades. Most studies were aimed at improving the quality of image display for better visualization. Yet few studies have been conducted to investigate the impact of image enhancement on biomedical pattern recognition. In this paper, we examine quantitatively the effect of image enhancement on the performance of biomedical pattern recognition. We apply the wavelet-based image enhancement technique developed in our earlier work [Y. Wang et al.,  2001], to a well-known biomedical pattern recognition problem: chromosome classification. Experiments were conducted on a test set of chromosome images before and after the enhancement, using the same feature measurement and classifier methods. The test results show that our image enhancement method substantially reduces the error rate of chromosome classification. We learn from this study that proper image enhancement leads to significantly improved recognition accuracy, and the quantification of performance improvement may be used as an objective measure of success for evaluating various image enhancement techniques.
Parallel solution of large sparse matrix equations and parallel power flow

A very efficient parallel LU factorization and substitution algorithm for solving large sparse network equations on shared memory multi-processor parallel computers is presented. By rearranging the order of the computations, better parallelism is obtained out of the traditionally sequential method. Performance results on an actual parallel computer are presented and discussed. Parallel gains for the power flow solution using Newton's and fast decoupled methods are presented to demonstrate it's effectiveness. Better speedup gains are obtained for larger systems and speedup of over 13 for Newton's power flow on a 20 processor shared memory computer has been obtained.
Network-based l2–l∞ control of systems with time-varying delays

This paper deals with the problem of l2–l∞ control for networked control systems(NCSs) with time-varying delays. By resorting to free-weighting matrix approach, a conclusion is given in the form of nonlinear matrix inequalities. In order to obtain the controller, we transform the nonlinear matrix inequalities to linear matrix inequalities based on an inequality. The new delay-dependent condition guarantees the asymptotical stability of the closed-loop system, as well as reduces the effect of the disturbance input on the controlled output to a prescribed level. Meantime, a stabilizing state feedback controller is also presented. A numerical example is provided to demonstrate the effectiveness of the proposed algorithms.
Classification of sMRI data in Alzheimer's disease based on UMPCA and Laplacian Score

Classification of structural magnetic resonance imaging (sMRI) brain scans is helpful to detect Alzheimer's disease (AD) at its early stage. In this paper we present a classification scheme that combines the uncorrelated multilinear principal component analysis (UMPCA) and Laplacian Score (LS) methods, which are known to be effective to the structural correlation preserving and redundancy reduction of the AD-related features hidden in the sMRI. In this scheme, UMPCA is first employed to extract features directly from the tensorial sMRI data of AD subjects and healthy control (HC) subjects. Then, Laplacian Score is used to select the more discriminative features by evaluating their power of locality preserving. Finally, an SVM classifier is built to distinguish AD patients from HC subjects. Experimental results demonstrate that the UMPCA-LS-based method achieves higher recognition accuracy than existing methods.
Application of computer aided analysis in the structural design of microwave components

With the development of technology, microwave components have been used in various fields widely. The performance and the reliability of the components must be improved to fulfill the requirements of the complex environment. It is a big problem for the structural designers to predict various results during the design process. In this paper, several factors, which are electromagnetic field, heat exchange, vibration and force, are discussed. They have important influence on the performance and the reliability of the microwave components. And with the aid of professional software, the designer analyzes several cases. Some problems are found and some optimizations are also taken to improve the structure projects. The results are satisfactory. These methods are applicable to all microwave components.
An optical proximity model for negative toned developing photoresists

At 14 nm technological node and beyond, negative toned developing (NTD) has become needed process for trench and contact/via photolithographic processes owning to its higher utilization of light energy compared with positive toned developing (PTD) process. However, the efficiency gain for the NTD process only happens in semi-dense to isolated pitches where more light can pass through the mask. For dense pitches, such as the pitches ranging from 76–100 nm, the NTD process has no advantage compared to the PTD process. This is because in the PTD process, the photochemical reaction is only needed to weaken the targeted photoresist volume so that the targeted area can collapse and be removed by the developer to form photoresist image. In the case of negative toned developing method, however, the targeted photoresist portion has to be fully reacted, or its dissolution properties in the solvent has to be fully changed in order to remain after solvent developing. As a confirmation of the model, after developing, the profile of the NTD resists will shrink in thickness and size. This indicates that, for the same amount of light, there will be more chemical amplification in NTD photoresist than in PTD photoresist. We will present a model based on the above idea.
Hardware-Software Co-design for Dynamic Reconfigurable Computing with Collaborative Supports of Architecture and Operating System

Nowadays it is widely adopted by reconfigurable computing system developers to obtain significant performance improvements through converting computational intensive codes from software to hardware. To program for such systems with hardware accelerators included, the programmers have to know the details of hardware accelerators, and control the configurations of hardware accelerators as well as communications between software and hardware parts. This state-of art programming style is not efficient for system development. In this paper, a transparent hardware-software co-design framework for reconfigurable computing system is proposed. It allows the programmers to access the hardware accelerators in the same way as calling software functions, leaving the configuration and communication issues to underlying operating system and assistant hardware control logic. Dynamic function calling is also supported in this model to utilize the dynamic reconfiguration ability of the modern programmable devices for reducing the requirement of hardware resources, hence increasing the efficiency of the whole system.
Appearance-based gaze block estimation via CNN classification

Appearance-based gaze estimation methods have received increasing attention in the field of human-computer interaction (HCI). These methods tried to estimate the accurate gaze point via Convolutional Neural Network (CNN) model, but the estimated accuracy can't reach the requirement of gaze-based HCI when the regression model is used in the output layer of CNN. Given the popularity of button-touch-based interaction, we propose an appearance-based gaze block estimation method, which aims to estimate the gaze block, not the gaze point. In the proposed method, we relax the estimation from point to block, so that the gaze block can be estimated by CNN-based classification instead of the previous regression model. We divide the screen into square blocks to imitate the button-touch interface, and build an eye-image dataset, which contains the eye images labelled by their corresponding gaze blocks on the screen. We train the CNN model according to this dataset to estimate the gaze block by classifying the eye images. The experiments on 6- and 54-block classifications demonstrate that the proposed method has high accuracy in gaze block estimation without any calibration, and it is promising in button-touch-based interaction.
Speed-invariant gait recognition based on Procrustes Shape Analysis using higher-order shape configuration

Walking speed change is considered a typical challenge hindering reliable human gait recognition. This paper proposes a novel method to extract speed-invariant gait feature based on Procrustes Shape Analysis (PSA). Two major components of PSA, i.e., Procrustes Mean Shape (PMS) and Procrustes Distance (PD), are adopted and adapted specifically for the purpose of speed-invariant gait recognition. One of our major contributions in this work is that, instead of using conventional Centroid Shape Configuration (CSC) which is not suitable to describe individual gait when body shape changes particularly due to change of walking speed, we propose a new descriptor named Higher-order derivative Shape Configuration (HSC) which can generate robust speed-invariant gait feature. From the first order to the higher order, derivative shape configuration contains gait shape information of different levels. Intuitively, the higher order of derivative is able to describe gait with shape change caused by the larger change of walking speed. Encouraging experimental results show that our proposed method is efficient for speed-invariant gait recognition and evidently outperforms other existing methods in the literatures.
Hardware Design of a High Performance Multi-Channel High Speed Signal Acquisition and Processing System

This paper describes the hardware design of a high speed acquisition system based on a single DSP of ADI Blackfin family. In order to realize multi-functions in a single board, CPLD and FPGA chips are integrated in this system. This study discusses several main problems during hardware design of this system, such as sampling of multi-channel analog signals, multi-channel digital signals and communication with host board by extended PCI bus, in addition talking about several points concerned with signal integrity issues. The main aim of this research is to demonstrate that as development of DSP and semiconductor techniques, a more complex electric system can be realized on a single DSP.
Circadian Rhythms of Spectral Components of Heart Rate Variability

Circadian rhythms of heart rate variability have been widely studied in recent years. However, most previous reports described such rhythms in terms of normalized units of the low- and high-frequency (LF and HF) spectral components. In this study, we analyzed circadian rhythms of spectral components in absolute units and found unexpected results in normal subjects as well as coronary heart disease (CHD) and congestive heart failure (CHF) patient groups. The results indicate that the notion of sympathovagal balance needs to be re-evaluated
Extension of ESPRIT method to unknown noise environments

With the assumption that noise correlation is spatially limited, it is proposed to use two subarrays to eliminate the effects of unknown noise (UN). To find the estimate of the signal subspace, canonical decomposition is used. Direction-of-arrival (DOA) estimation is then carried out by using the spatial invariance between the two subarrays, which is similar to the methodology of ESPRIT. The method provides a consistent estimator for all spatially band limited noise. Numerical simulations have verified that when the noise spectrum is moderately rough, UN-ESPRIT gives acceptable performance, while ESPRIT fails at even relatively high SNRs. On the other hand, for a given number of sensors, UN-ESPRIT has smaller aperture than ESPRIT.<<ETX>>
Finite-time stabilization of stochastic nonlinear systems in p-normal form

This paper investigates the problem of finite-time stabilization for a class of stochastic nonlinear systems in p-normal form. Different from the existing results, the considered systems are allowed to have the powers of both odd and even rational numbers. By delicately combining sign function with adding a power integrator technique, a continuous state feedback controller is presented to guarantee that the closed-loop system is finite-time stable in probability. Simulation results of a liquid-level system are provided to show the effectiveness and applicability of the proposed method.