nils-herrmann/clean_scirepeval huggingface dataset:
Exploratory tongue color analysis with manifold learning

With the increasing of tongue image data, exploratory data analysis would provide an effective means to obtain knowledge and to verify the correctness of several traditional tongue diagnosis findings. This paper proposes an exploratory tongue color analysis method by using the manifold learning technique. We further present an evaluation criterion to determine the optimal parameters. With the help of the evaluation criterion, we choose the value of the manifold learning algorithm and determine the appropriate color space for exploratory tongue color analysis. Experimental results show that the proposed method is effective in visualizing typical tongue color types and the trend of normal tongue color to abnormal color.
Low-Temperature Ohmic Contact Formation in GaN High Electron Mobility Transistors Using Microwave Annealing

In this letter, a low-temperature microwave annealing (MWA) method is first reported for the formation of ohmic contact to AlGaN/GaN high electron mobility transistors (HEMTs). Compared with the traditional GaN devices annealed by rapid thermal annealing (RTA), MWA-HEMTs can achieve a low ohmic contact resistance with much smoother surface of ohmic contacts. The spike mechanism is observed in our ohmic contact annealed by microwave under a low-temperature condition. Besides, MWA-HEMTs have higher ION/IOFF ratio and lower gate leakage current than the fabricated RTA-HEMTs in this letter.
Estimation and adaptive observation of environmental plumes

The estimation and forecasting environmental plume movement based on information from mobile sensors recently received renewed attention due to the Gulf coast oil and Icelandic ash problems, and remains of sustained interest today in homeland security settings (plant explosions, dirty bombs, etc.). The present work refines and tests the scientific algorithms at the heart of this problem. In particular, we combine the Ensemble Kalman Filter (EnKF), which provides a computationally feasible low-rank approximation of the un certainty of the estimate, with our recently developed Dynamic Adaptive Observation (DAO) algorithm for optimizing feasible sensor vehicle trajectories that minimize forecast uncertainty. A numerical experiment is performed which applies this combined EnKF/DAO algorithm to determine waypoints along optimized feasible sensor vehicle trajectories that improve the forecast of an environmental plume represented by a passive scalar convectively driven in a 2D fluid flow.
Visual Understanding via Multi-Feature Shared Learning With Global Consistency

Image/video data is usually represented with multiple visual features. Fusion of multi-source information for establishing attributes has been widely recognized. Multi- feature visual recognition has recently received much attention in multimedia applications. This paper studies visual understanding via a newly proposed l2-norm-based multi-feature shared learning framework, which can simultaneously learn a global label matrix and multiple sub-classifiers with the labeled multi-feature data. Additionally, a group graph manifold regularizer composed of the Laplacian and Hessian graph is proposed. It can better preserve the manifold structure of each feature, such that the label prediction power is much improved through semi-supervised learning with global label consistency. For convenience, we call the proposed approach global-label- consistent classifier (GLCC). The merits of the proposed method include the following: 1) the manifold structure information of each feature is exploited in learning, resulting in a more faithful classification owing to the global label consistency; 2) a group graph manifold regularizer based on the Laplacian and Hessian regularization is constructed ; and 3) an efficient alternative optimization method is introduced as a fast solver owing its speed to convex sub-problems. Experiments on several benchmark visual datasets-the 17-category Oxford Flower dataset, the challenging 101- category Caltech dataset, the YouTube and Consumer Videos dataset, and the large-scale NUS-WIDE dataset-have been used for multimedia understanding . The results demonstrate that the proposed approach compares favorably with state-of-the-art algorithms. An extensive experiment using the deep convolutional activation features also shows the effectiveness of the proposed approach. The code will be available on http://www.escience.cn/people/lei/index.html.
Modern researches on pulse waveform of TCPD

This article presents modern research on traditional Chinese pulse diagnosis (TCPD) by means of some modern signal processing methods. In order to demystify TCPD and prove its efficiency, its significance is first briefly described. A survey of recent developments in TCPD is provided. Some modern advanced signal processing methods are applied to the analysis of the pulse waveform and some experimental results are also illustrated. Furthermore, our pulse acquisition diagnosis system is introduced. Finally, the emphases and future works are also pointed out.
Finger-knuckle-print: A new biometric identifier

This paper presents a new biometric identifier, namely finger-knuckle-print (FKP), for personal identity authentication. First a specific data acquisition device is constructed to capture the FKP images, and then an efficient FKP recognition algorithm is presented to process the acquired data. The local convex direction map of the FKP image is extracted, based on which a coordinate system is defined to align the images and a region of interest (ROI) is cropped for feature extraction. A competitive coding scheme, which uses 2D Gabor filters to extract the image local orientation information, is employed to extract and represent the FKP features. When matching, the angular distance is used to measure the similarity between two competitive code maps. An FKP database was established to examine the performance of the proposed system, and the experimental results demonstrated the efficiency and effectiveness of this new biometric characteristic.
Tongue coating image retrieval

Along with the rapid growth of medical data, image retrieval, a kind of technology for browsing, searching and retrieving similar images of the given image, has become increasingly important from a large database of digital images. Tongue coating is the most important characteristic to reveal the pathological changes of the tongues for identifying diseases. In this paper, an efficient and effective technique is proposed to retrieve coating images. We obtain the pixel template value of pixels by applying thresholding segmentation based relative entropy. Then we use a Reduced K Nearest Neighbor algorithm to extract 20-dimension feature vector based on a prior layout distribution. Finally, a distance based the cumulative ratio is proposed for tongue coating image matching. The experimental results indicate that the proposed scheme eliminates the imprecision and uncertainty associated with medical tongue coating analysis.
Palmprint verification using consistent orientation coding

Developing accurate and robust palmprint verification algorithms is one of the key issues in automatic palmprint recognition systems. Recently, orientation based coding algorithms, such as Competitive Code (CompCode) and Orthogonal Line Ordinal Features (OLOF), have been proposed and have been attracting much research attention. Such algorithms could achieve high accuracy with high feature matching speed for real time implementation. By investigating the relationship between these two different coding schemes, we propose in this paper a feature-level fusion scheme for palmprint verification. Only the stable features which are consistent between the two codes are extracted for matching. The experimental results on the public palmprint database show that the proposed fusion code could achieve at least 14% EER (Equal Error Rate) reduction compared with either of the original codes.
Modified Principal Component Analysis: An Integration of Multiple Similarity Subspace Models

We modify the conventional principal component analysis (PCA) and propose a novel subspace learning framework, modified PCA (MPCA), using multiple similarity measurements. MPCA computes three similarity matrices exploiting the similarity measurements: 1) mutual information; 2) angle information; and 3) Gaussian kernel similarity. We employ the eigenvectors of similarity matrices to produce new subspaces, referred to as similarity subspaces. A new integrated similarity subspace is then generated using a novel feature selection approach. This approach needs to construct a kind of vector set, termed weak machine cell (WMC), which contains an appropriate number of the eigenvectors spanning the similarity subspaces. Combining the wrapper method and the forward selection scheme, MPCA selects a WMC at a time that has a powerful discriminative capability to classify samples. MPCA is very suitable for the application scenarios in which the number of the training samples is less than the data dimensionality. MPCA outperforms the other state-of-the-art PCA-based methods in terms of both classification accuracy and clustering result. In addition, MPCA can be applied to face image reconstruction. MPCA can use other types of similarity measurements. Extensive experiments on many popular real-world data sets, such as face databases, show that MPCA achieves desirable classification results, as well as has a powerful capability to represent data.
On automated tongue image segmentation in Chinese medicine

Automated tongue image segmentation in Chinese medicine is difficult due to two special factors: (1) There are a lot of pathological details on the surface of the tongue, which have a large influence on edge extraction; (2) the shapes of tongue bodies captured from various diseases or persons are quite different, so they are impossible to be properly described by a predefined deformable template. To address these problems, We propose a technique based on the combination of a bi-elliptical deformable template and an active contour model, namely the bi-elliptical deformable contour. Applying our approach to clinical tongue images, the experimental results indicate that it is superior over both traditional deformable templates and active contour model or snakes with respect to stability and veracity.
Supervised and Unsupervised Parallel Subspace Learning for Large-Scale Image Recognition

Subspace learning is an effective and widely used image feature extraction and classification technique. However, for the large-scale image recognition issue in real-world applications, many subspace learning methods often suffer from large computational burden. In order to reduce the computational time and improve the recognition performance of subspace learning technique under this situation, we introduce the idea of parallel computing which can reduce the time complexity by splitting the original task into several subtasks. We develop a parallel subspace learning framework. In this framework, we first divide the sample set into several subsets by designing two random data division strategies that are equal data division and unequal data division. These two strategies correspond to equal and unequal computational abilities of nodes under parallel computing environment. Next, we calculate projection vectors from each subset in parallel. The graph embedding technique is employed to provide a general formulation for parallel feature extraction. After combining the extracted features from all nodes, we present a unified criterion to select most distinctive features for classification. Under the developed framework, we separately propose supervised and unsupervised parallel subspace learning approaches, which are called parallel linear discriminant analysis (PLDA) and parallel locality preserving projection (PLPP). PLDA selects features with the largest Fisher scores by estimating the weighted and unweighted sample scatter, while PLPP selects features with the smallest Laplacian scores by constructing a whole affinity matrix. Theoretically, we analyze the time complexities of proposed approaches and provide the fundamental supports for applying random division strategies. In the experiments, we establish two real parallel computing environments and employ four public image and video databases as the test data. Experimental results demonstrate that the proposed approaches outperform several related supervised and unsupervised subspace learning methods, and significantly reduce the computational time.
A Novel 1T-1D DRAM Cell for Embedded Application

A novel one transistor and one diode (1T-1D) dynamic random access memory cell for embedded applications is proposed. This memory cell consists of a floating-gate (FG) MOSFET and a gated diode. The anode of the diode is connected to the FG, so that the threshold voltage of the FG MOSFET can be modulated by the current through the diode. In this paper, basic device operations, speed, retention, and disturb performance are investigated using Silvaco technology computer-aided design simulation tools. The process compatibility is also investigated by integrating the memory array and specific sense amplifier.
A study of aggregated 2D Gabor features on appearance-based face recognition

Existing approaches to holistic appearance based face recognition require a high dimensional feature space to attain fruitful performance. We have proposed a relatively low feature dimensional scheme to deal with the face recognition problem. We use the aggregated responses of 2D Gabor filters to represent face images. We have investigated the effect of "duplicate" images and the effect of facial expressions. Our results show that the proposed method is more robust than the PCA-based method under varying facial expressions, especially in recognizing "duplicate" images.
Wavelet based independent component analysis for palmprint identification

This work presents a multi-resolution analysis based independent component analysis (ICA) method for automatic palmprint identification. The ICA is well known by its feature representation ability recently, in which the desired representation is the one that minimizes the statistical independence of the components of the representation. Such a representation can capture the essential feature and the structure of the palmprint images. At the same time, the palmprints have a great deal of different features, such as principal lines, wrinkles, ridges, minutiae points and texture, which can be regarded as multi-scale features. Then, it is reasonable for us to integrate the multi-resolution analysis method and ICA to represent the palmprint features. The experiment results show that the integrated method is more efficient than ICA algorithm.
Enhancing the Bandwidth of Low-Dropout Regulators Using Power Transmission Lines for High-Speed I/Os

Power supply rejection peaking in low-dropout (LDO) voltage regulators can lead to reduced bandwidth and efficiency. This paper presents a new design method by combining power transmission lines (PTLs) with LDOs for enhancing its bandwidth and efficiency. This approach is applicable for LDOs regulating high-speed I/O drivers. The PTL combined with decoupling capacitors on the package or board is used to mitigate the power supply noise (PSN). This methodology is demonstrated using printed circuit board test vehicles with off-the-shelf components. Compared to conventional approaches, the PTL solution showed an ~80% lower PSN.
PEALD Ru/RuOx films for ULSI applications and its transition control between metal and metal oxide

Ruthenium (Ru) is a promising material for the future backend of line of semiconductor technologies beyond 20 nm. In this work, high quality Ru and ruthenium oxide (RuO<sub>x</sub>) thin films were grown on SiO<sub>2</sub> substrates by plasma-enhanced atomic layer deposition (PEALD) technique using bis-(ethylcyclopentadienyl)-ruthenium [Ru(EtCp)<sub>2</sub>] as the precursor and oxygen (O<sub>2</sub>) plasma as the reducing agent. The impacts of O<sub>2</sub> plasma and H<sub>2</sub> annealing on the film properties will be discussed. Finally, we found a feasible method to control the transition between Ru and RuO<sub>x</sub> films.
A novel approach of palm-line extraction

Palm-lines, including the principal lines and wrinkles, can describe a palmprint clearly. This paper presents a novel approach of palm-line extraction for the online palmprints. This approach is composed of two stages: coarse-level extraction stage and fine-level extraction stage. In the first stage, morphological operations are used to extract palm-lines in different directions. In the second stage, for each extracted line, a recursive process is devised to further extract and trace the palm-line using the local information of the extracted part. Experimental results show that the proposed approach is suitable for palm-line extraction.
Global and local weight sharing

We provide a unified view and comparison of different methods in the invariance problem. We show that the local symmetry network can reduce the number of weights, that the hardware implementation can be localized and that the number of multiplications is far less than in the global symmetry network. The geometric interpretations of global and local networks are also given. Finally, we examine the performance differences of the two network configurations and discuss the improvement on generalization for the local symmetry method.
Mobility Enhancement and OFF Current Suppression in Atomic-Layer-Deposited ZnO Thin-Film Transistors by Post Annealing in O2

The impacts of postdeposition annealing in O<sub>2</sub> on the electrical performance of atomic-layer-deposited ZnO thin-film transistors (TFTs) have been investigated. The TFTs are fabricated on transparent quartz substrates with Al<sub>2</sub>O<sub>3</sub> dielectrics, ZnO active layers, and Cr/Au electrodes. The best field-effect mobility of 21.3 cm<sup>2</sup>/Vs and a large ON/OFF current ratio of 10<sup>7</sup> are obtained under 400°C annealing treatment in O<sub>2</sub>. Furthermore, analysis indicates that reduction of OH bonds in both ZnO and Al<sub>2</sub>O<sub>3</sub> layers is the key issue to achieve the excellent properties.
FCM-based orientation selection for competitive coding-based palmprint recognition

Coding based methods are among the most promising palmprint recognition methods. As one representative coding method, the competitive code first convolves the palmprint image with a bank of Gabor filters with different orientations and then encodes the dominant orientation into its bitwise representation. Despite its effectiveness, few investigations have been given to study the influence of the number of filters and the orientation of each filter. In this paper, based on the statistical orientation distribution and the orientation separation principle, we propose a modified fuzzy C-means cluster algorithm to determine the orientations of filters. Experimental results indicate that, the proposed method achieves higher verification accuracy while compared with that of the original competitive code and several state-of-the-art methods. Considering both the computational complexity and the verification accuracy, six filters would be the optimal choice for proposed method.
Rotational Invariant Dimensionality Reduction Algorithms

A common intrinsic limitation of the traditional subspace learning methods is the sensitivity to the outliers and the image variations of the object since they use the <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2}} $ </tex-math></inline-formula> norm as the metric. In this paper, a series of methods based on the <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2,1}} $ </tex-math></inline-formula>-norm are proposed for linear dimensionality reduction. Since the <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2,1}} $ </tex-math></inline-formula>-norm based objective function is robust to the image variations, the proposed algorithms can perform robust image feature extraction for classification. We use different ideas to design different algorithms and obtain a unified rotational invariant (RI) dimensionality reduction framework, which extends the well-known graph embedding algorithm framework to a more generalized form. We provide the comprehensive analyses to show the essential properties of the proposed algorithm framework. This paper indicates that the optimization problems have global optimal solutions when all the orthogonal projections of the data space are computed and used. Experimental results on popular image datasets indicate that the proposed RI dimensionality reduction algorithms can obtain competitive performance compared with the previous <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2}}$ </tex-math></inline-formula> norm based subspace learning algorithms.
Novel Multi-Level Cell TFT Memory With an In–Ga–Zn-O Charge Storage Layer and Channel

Amorphous indium-gallium-zinc oxide (a-IGZO) thin-film transistor nonvolatile memory devices with an IGZO charge storage layer were evaluated for the first time for multi-level cell memory applications. The pristine device was defined as the original state (OS), which can be switched to the programmed state (PS) after a positive gate voltage pulse (for example, 12 V for 10 ms), and to the erased state (ES) after a negative gate voltage pulse (for example, -15 V for 10 ms). The writing mechanism was attributed to Fowler-Nordheim tunneling of electrons from the channel to the charge storage layer under a positive gate bias and inverse tunneling under a negative gate bias. The devices demonstrated superior electrical programmable and erasable characteristics. A memory window of 2.4 V between OS and PS was maintained after 100 programming/erasing cycles, and a memory window of 2.66 V between OS and ES as well. The memory windows relative to OS are equal to 1.91 and 1.30 V for PS and ES, respectively, for a retention time of 105 s.
Complementary Resistive Switching in Flexible RRAM Devices

Complementary resistive switching (CRS) behavior in a flexible plastic substrate was studied for the first time in this letter. CRS behaviors in Al/Ni/NiAlOx/Al<sub>2</sub>O<sub>3-x</sub>/ITO device were demonstrated with excellent performance. A high-resistance ON/OFF ratio (~10<sup>3</sup>), 50-ms switch speed was successfully obtained. In addition, the mechanism of CRS behavior was interpreted by the redistribution of oxygen vacancies in NiAlO<sub>x</sub>/Al<sub>2</sub>O<sub>3-x</sub> stack layers. Such a CRS style flexible RRAM device is a possible solution for future integrated circuits application.
Investigation of spin-on-dopant for fabricating high on-current tunneling field effect transistor

Spin-on-dopant and rapid thermal diffusion are used as the doping method for fabricating planar tunneling field effect transistor (TFET) in this paper to acquire abrupt doping profile and high surface doping concentration. Due to the heavy surface doping the on-current of TFET is enhanced. The ambipolar characteristics of TFET are also inhibited by reducing the drain doping concentration.
Coarse iris classification based on box-counting method

This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79% and 34.17%. The iris images are classified with the double threshold algorithm, which classifies iris images with an accuracy of 94.61%. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.