nils-herrmann/clean_scirepeval huggingface dataset:
Structural and electrical characteristics of Al-doped TiO2 high-k gate dielectric grown by atomic layer deposition

Al-doped TiO2 thin films grown on Si(100) by atomic layer deposition has been investigated as a potential high dielectric constant insulator in the application of microelectronics. The film thickness is determined by spectroscopy ellipsometry and transmission electron microscopy. X-ray photoelectron spectrometry is used to characterize the chemical composition and bonding states. The relative permittivity (k) determined by C-V measurement is 14.6. Moreover, ultralow hysteresis of C-V curves has been demonstrated for the Al-doped TiO2 film with few instable-trapped charges.
Constant Voltage-Based Power Delivery Scheme for 3-D ICs and Interposers

Advances in 3-D integrated circuit (3-DIC) technology have allowed for advantages in integration, system speed, and power consumption for digital systems. In systems operating at very high data rates with large data width, the effect of simultaneous switching noise (SSN) can, however, drastically affect system performance. Therefore, the effect of SSN in the power delivery network (PDN) design of a through silicon via (TSV)-based 3-D stack has been investigated in this paper, and eye diagrams have been simulated in each level of the stack to analyze power supply noise and transition jitter. Using a novel PDN design concept based on constant voltage power transmission lines, it is shown that this new PDN design can significantly improve signal quality in a 3-DIC application. The 3-D system considered here consists of a PCB, an interposer, and three IC dies. Each IC contains a TSV layer, PDN, and digital logic.
Computerized tongue diagnosis based on Bayesian networks

Tongue diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, Bayesian networks are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on a group of 455 patients affected by 13 common diseases as well as other 70 healthy volunteers, and the diagnostic results predicted by the previously trained Bayesian network classifiers are reported.
Mesh parameterization by minimizing the synthesized distortion metric with the coefficient-optimizing algorithm

The parameterization of a 3D mesh into a planar domain requires a distortion metric and a minimizing process. Most previous work has sought to minimize the average area distortion, the average angle distortion, or a combination of these. Typical distortion metrics can reflect the overall performance of parameterizations but discount high local deformations. This affects the performance of postprocessing operations such as uniform remeshing and texture mapping. This paper introduces a new metric that synthesizes the average distortions and the variances of both the area deformations and the angle deformations over an entire mesh. Experiments show that, when compared with previous work, the use of synthesized distortion metric performs satisfactorily in terms of both the average area deformation and the average angle deformation; furthermore, the area and angle deformations are distributed more uniformly. This paper also develops a new iterative process for minimizing the synthesized distortion, the coefficient-optimizing algorithm. At each iteration, rather than updating the positions immediately after the local optimization, the coefficient-optimizing algorithm first update the coefficients for the linear convex combination and then globally updates the positions by solving the Laplace system. The high performance of the coefficient-optimizing algorithm has been demonstrated in many experiments.
Pattern Classification for Doppler Ultrasonic Wrist Pulse Signals

Wrist pulse signal contains important information about the health status of a person and it has been used in Traditional Chinese Medicine for a long time. In this work, digitalized wrist pulse signals from patients with different diseases as well as healthy persons are collected by a Doppler ultrasonic device. Two methods, namely, the wavelet method and the auto regressive prediction error (ARPE) method, are proposed to analyze the pulse signals and distinguish patients from healthy persons. Distinctive features are first extracted from the pulse signals and then the support vector machine (SVM) is used for classification. The applicability of the methods is investigated using wrist pulse signals collected from 50 healthy persons and 74 patients. The results illustrate a great promise of the proposed methods for computerized pulse signal analysis.
SVR based color calibration for tongue image

Tongue color is one of the most important pathological features for computer aided tongue diagnosis system (CATDS). Color distortion of tongue image, which is often caused by the inconstancy of lighting conditions, can seriously affect the validity of diagnosis results obtained by CATDS and furthermore, impair the interchangeability among tongue images captured by different devices. To circumvent the above problems, we present a novel color calibration model based on support vector regression (SVR) method in this paper. First, a new colorchecker which provides a suitable reference for the following color calibration is produced by color clustering of 322 tongue images. Then, a comparative analysis between the SVR based color calibration method and polynomial regression based one is carried out. Results indicate that SVR based color calibration model, cooperating with the proposed colorchecker, provides an outstanding calibration effect and plays an important part in the image acquisition of CATDS.
Evolutionary Cost-Sensitive Extreme Learning Machine

Conventional extreme learning machines (ELMs) solve a Moore–Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition-based access control system, where misclassifying a stranger as a family member may result in more serious disaster than misclassifying a family member as a stranger. Though recent cost-sensitive learning can reduce the total loss with a given cost matrix that quantifies how severe one type of mistake against another, in many realistic cases, the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive ELM, with the following merits: 1) to the best of our knowledge, it is the first proposal of ELM in evolutionary cost-sensitive classification scenario; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; and 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Experiments in a variety of cost-sensitive tasks well demonstrate the effectiveness of the proposed approaches, with about 5%–10% improvements.
Nonideality of Negative Capacitance Ge Field-Effect Transistors Without Internal Metal Gate

We demonstrate negative capacitance (NC) Ge field-effect transistors (FETs) utilizing the TaN/HfZrO<sub><italic>x</italic></sub>/SiO<sub>2</sub> gate stack. Typical characteristics of NC transistors, including the sub-60 mV/decade subthreshold swing (SS), the gate capacitance <inline-formula> <tex-math notation="LaTeX">${C} _{G}$ </tex-math></inline-formula> peak, and the negative differential resistance effect are achieved in NC Ge FETs without internal metal gate. Significant <inline-formula> <tex-math notation="LaTeX">${C} _{\textit {G}}$ </tex-math></inline-formula> peak, as the evidence of NC effect is obtained in NC transistors at the frequency up to MHz. Ferroelectric Ge FETs with counterclockwise <inline-formula> <tex-math notation="LaTeX">${I} _{\text {DS}} - {V} _{\text {GS}}$ </tex-math></inline-formula> loops due to trapping/detrapping, opposite hysteresis to the NC switching are also observed. The NC transistor has much steeper SS compared to the device dominated by the trapping/detrapping process. Statistical results show that only 10% of devices are dominated by the NC effect, and the density of defects in ferroelectric needs to be reduced to improve the yield of NC transistors.
Erasing-Modes Dependent Performance of a-IGZO TFT Memory With Atomic-Layer-Deposited Ni Nanocrystal Charge Storage Layer

Amorphous indium–gallium–zinc–oxide (a-IGZO) thin-film transistor (TFT) memory devices are fabricated with plasma-assisted atomic-layer-deposition (ALD) charge storage medium of high-density Ni nanocrystals (NCs). The effect of Al2O3 tunneling layer thickness (i.e., 8 and 12 nm) on the memory characteristics is investigated. The results indicate that the 8-nm tunneling layer device shows a programming window as large as 4.7 V after 5-ms programming at 18 V, reflecting that the ALD Ni NCs have a high charge storage capacity. To achieve a high erasing efficiency, different erasing modes are explored, including electrical erasing, monochromatic light (ML) erasing, and ML-assisted electrical erasing. It is demonstrated that the ML-assisted electrical erasing can attain the highest erasing efficiency, e.g., an erasing window as large as 9.11 V is obtained after 100-s ML (300 nm)-assisted electrical erasing at −20 V for the 8-nm tunneling layer. Based on the above programming and erasing conditions, the ten-year memory window is extrapolated to be 4.7 V at room temperature. Though increasing the tunneling layer thickness may enhance the data retention, it also degrades the programming and erasing efficiencies slightly.
Approximate Orthogonal Sparse Embedding for Dimensionality Reduction

Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1 -norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.
The multiscale competitive code via sparse representation for palmprint verification

Palm lines are the most important features for palmprint recognition. They are best considered as typical multiscale features, where the principal lines can be represented at a larger scale while the wrinkles at a smaller scale. Motivated by the success of coding-based palmprint recognition methods, this paper investigates a compact representation of multiscale palm line orientation features, and proposes a novel method called the sparse multiscale competitive code (SMCC). The SMCC method first defines a filter bank of second derivatives of Gaussians with different orientations and scales, and then uses the l1-norm sparse coding to obtain a robust estimation of the multiscale orientation field. Finally, a generalized competitive code is used to encode the dominant orientation. Experimental results show that the SMCC achieves higher verification accuracy than state-of-the-art palmprint recognition methods, yet uses a smaller template size than other multiscale methods.
Moving Vehicle Detection for Automatic Traffic Monitoring

A video-based traffic monitoring system must be capable of working in various weather and illumination conditions. In this paper, we will propose an example-based algorithm for moving vehicle detection. Different from previous works, this algorithm learns from examples and does not rely on any a priori model for vehicles. First, a novel scheme for adaptive background estimation is introduced. Then, the image is divided into many small nonoverlapped blocks. The candidates of the vehicle part can be found from the blocks if there is some change in gray level between the current image and the background. A low-dimensional feature is produced by applying principal component analysis to two histograms of each candidate, and a classifier based on a support vector machine is designed to classify it as a part of a real vehicle or not. Finally, all classified results are combined, and a parallelogram is built to represent the shape of each vehicle. Experimental results show that our algorithm has a satisfying performance under varied conditions, which can robustly and effectively eliminate the influence of casting shadows, headlights, or bad illumination
Biometrics Computing

Many biometrics requirements have led to the extensive study of biometric technologies and the development of numerous algorithms, applications, and systems, which could be defined as Biometrics Computing. This presentation will systematically explain this new research trend. As case studies, a new biometrics technology (palmprint recognition) and two new biometrics applications (medical biometrics and aesthetical biometrics), are introduces. Based on over ten years' experiences, many useful results could be given to illustrate their effectiveness.
Step acceleration based training algorithm for feedforward neural networks

This paper presents a very fast step acceleration based training algorithm (SATA) for multilayer feedforward neural network training. The most outstanding virtue of this algorithm is that it does not need to calculate the gradient of the target function. In each iteration step, the computation only concentrates on the corresponding varied part. The proposed algorithm has attributes in simplicity, flexibility and feasibility, as well as high speed of convergence. Compared with the other methods, including the conventional backpropagation (BP), conjugate gradient, and weight extrapolation based BP, many simulations confirmed the superiority of this algorithm in terms of converging speed and computation time required.
On Robust Fuzzy Rough Set Models

Rough sets, especially fuzzy rough sets, are supposedly a powerful mathematical tool to deal with uncertainty in data analysis. This theory has been applied to feature selection, dimensionality reduction, and rule learning. However, it is pointed out that the classical model of fuzzy rough sets is sensitive to noisy information, which is considered as a main source of uncertainty in applications. This disadvantage limits the applicability of fuzzy rough sets. In this paper, we reveal why the classical fuzzy rough set model is sensitive to noise and how noisy samples impose influence on fuzzy rough computation. Based on this discussion, we study the properties of some current fuzzy rough models in dealing with noisy data and introduce several new robust models. The properties of the proposed models are also discussed. Finally, a robust classification algorithm is designed based on fuzzy lower approximations. Some numerical experiments are given to illustrate the effectiveness of the models. The classifiers that are developed with the proposed models achieve good generalization performance.
Fusion of phase and orientation information for palmprint authentication

This paper presents a novel approach of palmprint authentication based on the fusion of the phase and orientation information. This approach is an improvement of a previous palmprint recognition method - fusioncode method (A. Kong and D. Zhang, 2004). In the proposed approach, the phase information (fusion-code) of a palmprint is extracted by using four 2-D Gabor filters with different orientations, and at the same time, the orientation information (called orientationcode) of the palmprint is also extracted. The fusioncode and the orientationcode are fused to make a new feature, called the palmprint phase orientation code (PPOC). At the matching stage, a modified Hamming distance is defined to measure the similarity of two PPOCs. This approach is tested on a palmprint database containing 7605 samples and the experimental results show that the PPOC approach greatly improves the performance of the fusioncode method.
A novel breath analysis system for diabetes diagnosis

A noninvasive breath analysis system for diabetes diagnosis is proposed in this paper. It utilizes commercial chemical sensors to detect acetone in human breath. The device is portable, fast and easy to operate. Experiments with real breath samples from both inpatient and outpatient diabetics validated the accuracy of this system. An optimal sensor array for diabetes diagnosis is decided according to the classification accuracy. We hope this system could contribute a step toward a practical system that could be used for diabetes screening.
Median LDA: A Robust Feature Extraction Method for Face Recognition

In the existing LDA models, class mean vector is always estimated by the class sample average. In small sample size problems such as face recognition, however, the class sample average does not suffice to provide an accurate estimate of the class mean based on a few of given samples, particularly when there are outliers in the sample set. To overcome this weakness, we use the class median vector to estimate the class mean vector in LDA modeling. The class median vector has two advantages over the class sample average: (1) the class median (image) vector preserves useful details in the sample images and (2) the class median vector is robust to outliers that exist in training sample set. The proposed median LDA model is evaluated using three popular face image databases. All experiment results indicate that median LDA is more effective than the common LDA and PCA.
A CGA-MRF Hybrid Method for Iris Texture Analysis and Modeling

This paper proposes a novel framework for iris image processing based on conformal geometric algebra (CGA) and Markov random field (MRF). Texture complexity and individual differences are two unique features of iris image, which bring many difficulties to automatic analysis and diagnosis. We propose a circle detection algorithm based on CGA for iris image segmentation. The algorithm is simple and has a wide scope of application. What's more, it can detect the inside and outside boundaries of iris simultaneously without any denoising. Then we propose a novel scheme for texture representation of iris image based on MRF. By learning the statistical texture differences of different pathological features, such as holes, cracks, a MRF based texture representation method shows different pathological regions in iris. Experimental results demonstrated that the proposed framework is very practical, provides a great help for subsequent diagnosis as well.
Local Linear Discriminant Analysis Framework Using Sample Neighbors

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach. The algorithms of LDA usually perform well under the following two assumptions. The first assumption is that the global data structure is consistent with the local data structure. The second assumption is that the input data classes are Gaussian distributions. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose an improved LDA framework, the local LDA (LLDA), which can perform well without needing to satisfy the above two assumptions. Our LLDA framework can effectively capture the local structure of samples. According to different types of local data structure, our LLDA framework incorporates several different forms of linear feature extraction approaches, such as the classical LDA and principal component analysis. The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to image recognition, such as face recognition. Our algorithms need to train only a small portion of the whole training set before testing a sample. They are suitable for learning large-scale databases especially when the input data dimensions are very high and can achieve high classification accuracy. Extensive experiments show that the proposed algorithms can obtain good classification results.
TDS and Its Application in Computer Aided TCM Diagnostics Education

In Traditional Chinese Medicine (TCM) education field, the teaching of tongue diagnostics is facing the problem of using abstract or non-quantitative notions. In this paper, a computer aided education tool for TCM, tongue diagnosis system (TDS) is described. By using TDS, the diagnosis features are processed in quantitative manner, and the diagnosis reasoning is objective. The system enables performing virtual experiments by students themselves. Moreover, the proposed TDS helps the TCM students to develop a computing thinking model from the interdisciplinary view point. Focusing on the system design, the paper puts forward systematic architecture and design modules of TDS as well as its applications in tongue diagnosis teaching and learning.
Wavelet Based Analysis of Doppler Ultrasonic Wrist-pulse Signals

Traditional Chinese pulse diagnosis (TCPD) is one of the most important diagnostic techniques in Traditional Chinese Medicine (TCM) and computerized analysis of pulse signals is a crucial step in objectifying and standardizing TCPD. In this work, we use Doppler ultrasonic device to collect wrist-pulse signals from patients with gastritis and cholecystitis as well as healthy persons. After extracting the envelopes of ultrasonic pulse contour, wavelet (packet) transforms are applied to decompose the pulse signals and extract the wavelet features. Together with some Doppler ultrasonic diagnostic parameters, such as STI, RI, etc., a two-category classifier is employed to distinguish the unhealthy persons from healthy ones, and tell the patients from different diseases. 12 gastritis sufferers (Group G), 15 cholecystitis sufferers (Group C) and 19 healthy persons (Group H) were involved in the experiment. An accuracy of 80. 77% and an accuracy of 86.21% are achieved in discriminating Group G and Group C from Group H, respectively, and the classification accuracy between Group G and Group C can reach 100%.