nils-herrmann/clean_scirepeval huggingface dataset:
A Spherical Rectification for Dual-PTZ-Camera System

In this paper we proposed a novel stereo rectification method for dual-PTZ-camera system, which is called spherical rectification. This method can be divided into two parts, offline pre-settings and online rectification. The offline pre-settings include camera calibration and building the spherical coordinate system. The online rectification only requires the pan, tilt and zoom values and does not need any image information such as feature points. So, compared with traditional rectification approaches, our method is more convenient and time-saving.
The study on metal-insulator-metal capacitor performance improvement

Metal-insulator-metal capacitor (MIMc) is one of key components in mixed signal (MS) and radio frequency (RF) circuit due to its high performance. In this work, we studied MIMc dielectric, bottom/top plate materials, thickness influence on mismatch, voltage coefficient and temperature coefficient; then a near zero voltage and temperature coefficient stacked MIMc structure was proposed and demonstrated for the first time.
Unsupervised Discriminant Projection Analysis for Feature Extr

This paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method - locality preserving projection (LPP, which considers the local information only) for classification tasks. The proposed method is applied to face biometrics and examined using the ORL and FERET face image databases. Our experimental results show that UDP consistently outperforms LPP, PCA, and LDA
Principal line based ICP alignment for palmprint verification

Image alignment is a crucial step in palmprint verification. However, most of the existing palmprint alignment methods use only some key points between fingers or in palm boundary to extract the region of interest (ROI), which is consequently used for feature extraction and matching. Such alignment methods can only give a coarse alignment of the palmprint images. This paper presents a new effective refinement method for palmprint alignment by adapting the iterative closest point (ICP) method to the palmprint principal lines. The proposed method offers a more accurate alignment of palmprints by correcting efficiently the shifting, rotation and scaling variations introduced in data acquisition. The experimental results show that the proposed method can greatly improve the palmprint verification accuracy in real time.
A novel 25V DDD NMOS design in 500–700V ultra high voltage BCD process

This article reports a high voltage DDD NMOS with 25V operating voltage and 40V breakdown voltage. Usually the DDD MOS in the logic process has a typical breakdown voltage lower than 18V. The further application requirement in higher breakdown voltage adopts LDMOS structures. In order to achieve high enough on-state breakdown voltage, the DDD NMOS presented in this paper has relatively high (about 1e13/cm2) doping level in the diffused drain region. In the case of the diffusion drain region with high level doping, the off-state breakdown voltage is affected, but the DDD NMOS in this paper adopts a novel design. Thus, the on/off-state breakdown voltage can reach 40V. The optimized device has excellent input/output characteristics and excellent on-resistance characteristics. The Ioff of the device is slightly larger and will be further optimized.
Reinitialization-Free Level Set Evolution via Reaction Diffusion

This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is completely free of the costly reinitialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then solve the diffusion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly reinitialization procedure is completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variational level set method and partial differential equation-based level set method. The RD-LSE method shows very good performance on boundary antileakage. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach.
Semi-supervised classification on “warm or cool” color in tongue images

Examination of the tongue condition is a standard diagnostic method in Traditional Chinese Medicine (TCM) and takes account of a wide variety of features including shape, texture, and color. The terms “warm”, “neutral”, and “cool” are used to refer to a kind of chromatics characteristic of the tongue color and are associated with various health states. In this paper, we propose a semi-supervised (cluster and label) scheme for tongue color analysis on “warm or cool”. In the training part, the proposed scheme makes use of a classical clustering algorithm, Expectation Maximization, to divide all pixels in tongue gamut into 150 clusters. Then we construct two auxiliary images for each cluster and manual labeling endows these clusters with category labels of “warm or cool”. Finally, each trained category on “warm or cool” is set up by sum some clusters approximately. In the testing part, we use a lookup table to divide all pixels in an input image into three distinct categories of “warm or cool”. In experiments conducted on a total of 392 tongue samples, our system achieved an accuracy of 91.1%.
On hierarchical palmprint coding with multiple features for personal identification in large databases

Automatic personal identification is a significant component of security systems with many challenges and practical applications. The advances in biometric technology have led to the very rapid growth in identity authentication. This paper presents a new approach to personal identification using palmprints. To tackle the key issues such as feature extraction, representation, indexing, similarity measurement, and fast search for the best match, we propose a hierarchical multifeature coding scheme to facilitate coarse-to-fine matching for efficient and effective palmprint verification and identification in a large database. In our approach, four-level features are defined: global geometry-based key point distance (Level-1 feature), global texture energy (Level-2 feature), fuzzy "interest" line (Level-3 feature), and local directional texture energy (Level-4 feature). In contrast to the existing systems that employ a fixed mechanism for feature extraction and similarity measurement, we extract multiple features and adopt different matching criteria at different levels to achieve high performance by a coarse-to-fine guided search. The proposed method has been tested in a database with 7752 palmprint images from 386 different palms. The use of Level-1, Level-2, and Level-3 features can remove candidates from the database by 9.6%, 7.8%, and 60.6%, respectively. For a system embedded with an Intel Pentium III processor (500 MHz), the execution time of the simulation of our hierarchical coding scheme for a large database with 10/sup 6/ palmprint samples is 2.8 s while the traditional sequential approach requires 6.7 s with 4.5% verification equal error rate. Our experimental results demonstrate the feasibility and effectiveness of the proposed method.
Plasma enhanced atomic layer deposited platinum thin film on Si substrate with TMA pretreatment for platinum silicide application

Benefit from the good etching and patterning possibilities and its electrical properties, platinum silicide attracts renewed attention as a suitable candidate for next generation CMOS technologies recently. This work focuses on the effects of the trimethylaluminum (TMA) pretreatment on the PEALD growth of Pt film on Si substrate by using MeCpPtMe3 and ammonia plasma as precursors. The incubation period of ALD Pt was shortened by TMA pretreatment, and the saturation growth rate reached 0.2iA/cycle. X-ray diffraction showed that as-deposited films are strongly preferred orientation of the (iii) plane. The findings are important for obtaining well defined silicide films which would be used in the advanced CMOS source and drain technology.
Partial Deconvolution With Inaccurate Blur Kernel

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: 1) a partial map in the Fourier domain for modeling kernel estimation error, and 2) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.
Improved Bounds on the  $L(2,1)$-Number of Direct and Strong Products of Graphs

The frequency assignment problem is to assign a frequency which is a nonnegative integer to each radio transmitter so that interfering transmitters are assigned frequencies whose separation is not in a set of disallowed separations. This frequency assignment problem can be modelled with vertex labelings of graphs. An <i>L</i>(2,1)-labeling of a graph <i>G</i> is a function <i>f</i> from the vertex set <i>V</i>(<i>G</i>) to the set of all nonnegative integers such that |<i>f</i>(<i>x</i>)-<i>f</i>(<i>y</i>)| ges 2 if <i>d</i>(<i>x</i>,<i>y</i>)=1 and |<i>f</i>(<i>x</i>)-<i>f</i>(<i>y</i>)| ges 1 if <i>d</i>(<i>x</i>,<i>y</i>)=2 , where <i>d</i>(<i>x</i>,<i>y</i>) denotes the distance between <i>x</i> and <i>y</i> in <i>G</i>. The <i>L</i>(2,1) -labeling number lambda(<i>G</i>) of <i>G</i> is the smallest number <i>k</i> such that <i>G</i> has an <i>L</i>(2,1)-labeling with max{<i>f</i>(<i>v</i>):<i>v</i> isin <i>V</i>(<i>G</i>)}=<i>k</i>. This paper considers the graph formed by the direct product and the strong product of two graphs and gets better bounds than those of Klavzar and Spacapan with refined approaches.
Efficient Solutions for Discreteness, Drift, and Disturbance (3D) in Electronic Olfaction

In this paper, we aim at presenting the new challenges of electronic noses (E-noses) and proposing effective methods for handling the new challenging scientific issues to be solved, such as signal discreteness (reproducibility), systematical drift and nontarget disturbances. We first review the progress of E-noses in applications, systems, and algorithms during the past two decades. Recall a number of significant achievements and motivated by the current issues that hinder large-scale application pace of E-nose technology, we propose to address three key issues: 1) discreteness; 2) drift; and 3) disturbance (simplified as 3D issues), which are sensor induced and sensor specific. For each issue, a highly effective and efficient method is proposed. Specifically, for discreteness issue, a global affine transformation method is introduced for E-nose instruments batch calibration; for drift issue, an unsupervised feature adaptation model is proposed to achieve effective drift adaptation; additionally, for disturbance issue, we proposed a simple targets-to-targets self-representation classifier method for fast nontargets detection, without knowing any prior knowledge of thousands of nontarget disturbances in real world. For each method, a closed form solution can be analytically determined and the simplicity is guaranteed. Experiments demonstrate the effectiveness and efficiency of the proposed methods for addressing the proposed 3D issues in real applications of E-noses.
Relaxed collaborative representation for pattern classification

Regularized linear representation learning has led to interesting results in image classification, while how the object should be represented is a critical issue to be investigated. Considering the fact that the different features in a sample should contribute differently to the pattern representation and classification, in this paper we present a novel relaxed collaborative representation (RCR) model to effectively exploit the similarity and distinctiveness of features. In RCR, each feature vector is coded on its associated dictionary to allow flexibility of feature coding, while the variance of coding vectors is minimized to address the similarity among features. In addition, the distinctiveness of different features is exploited by weighting its distance to other features in the coding domain. The proposed RCR is simple, while our extensive experimental results on benchmark image databases (e.g., various face and flower databases) show that it is very competitive with state-of-the-art image classification methods.
Enhancement of Resistive Switching Characteristics in $ \hbox{Al}_{2}\hbox{O}_{3}$-Based RRAM With Embedded Ruthenium Nanocrystals

Resistive switching behaviors of Al2O3-based memory devices with and without ruthenium nanocrystals (RuNCs) fabricated by atomic layer deposition are investigated for nonvolatile-memory applications. Large resistance ratios (>; 105) of high- to low-resistance states were observed with nanocrystals contribution. Moreover, improvements of stability device yield and retention performance were also achieved by embedding RuNCs. Therefore, the Al2O3-based resistive memory device composed of embedded nanocrystals is a possible solution for future integrated standalone storage class memory processes.
Optimal integrated production and inventory cycles in a whole green manufacturing supply chain network with coordination

This paper proposes integrated production and inventory cycle in a whole green manufacturing supply chain network which consists of multiple-raw materials suppliers named as tier-2 suppliers, multiple-parts suppliers named as tier-1 suppliers, a manufacturer, multiple distributors, multiple retailers and a third party product collector. In a whole manufacturing supply chain network, tier-2 suppliers supply raw materials to tier-1 suppliers producing parts. Parts from tier-1 suppliers are then supplied to a manufacturer which manufactures and assembles parts into finished products. Then, the finished products will be delivered to distributors distributing them to retailers. Because each player wishes to minimize total cost for itself and can supply multiple raw materials and parts, there is a need for coordination among players of the supply chain. The coordination which is used in this paper is for both centralized and decentralized decision processes. Due to environmental and ecological consciousness and responsibility as a result of increasingly rigid environmental legislations and growing environmental concerns in recent years, this paper also considers recovery option of finished products where some of the parts will be reused in new finished products. A mathematical model with multiple decision variables is developed. A numerical example is presented and results discussed.
Combining Left and Right Palmprint Images for More Accurate Personal Identification

Multibiometrics can provide higher identification accuracy than single biometrics, so it is more suitable for some real-world personal identification applications that need high-standard security. Among various biometrics technologies, palmprint identification has received much attention because of its good performance. Combining the left and right palmprint images to perform multibiometrics is easy to implement and can obtain better results. However, previous studies did not explore this issue in depth. In this paper, we proposed a novel framework to perform multibiometrics by comprehensively combining the left and right palmprint images. This framework integrated three kinds of scores generated from the left and right palmprint images to perform matching score-level fusion. The first two kinds of scores were, respectively, generated from the left and right palmprint images and can be obtained by any palmprint identification method, whereas the third kind of score was obtained using a specialized algorithm proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right palmprint images into account, it can properly exploit the similarity of the left and right palmprints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palmprint identification methods.
The nano-scale resistive memory effect of graphene oxide

An attractive+ to graphene for a range of applications is graphene oxide (GO). GO is an insulator because of the hydroxyl, carboxyl, carbonyl and epoxide functional groups presenting on the basal surface or edge and becomes a semiconductor or semimetal as it is reduced back toward graphene. Here we demonstrate that graphene oxide can be reversibly reduced and oxidized in nanometer-scale by applying bias voltages by the nano-tip of conductive atomic force microscopy system. The low resistance state (LRS) when reduced and a high resistance state (HRS) when oxidized can be achieved under the opposite applied bias direction. The LRS (around 10 KΩ and HRS (around 40 M Ω) were stable for more than 103 s, and no obvious degradation was observed during the tests. Threshold voltages for reduction and oxidation, which can be considered as the set and reset voltages is around -6.5 V and +7 V, respectively. It is shown that the hydrogen (H+) ions and hydroxyl ions (OH-) dissociated from the water meniscus formed between the tip and GO in ambient condition at room temperature plays an essential role in the resistive memory switching. It is also found that the negative bias is responsible for the reduction, which is related the transition from HRS to LRS, and the positive bias is responsible for the oxidation, which is related the transition from LRS to HRS, respectively. Raman spectroscopy and X-ray photoelectron spectroscopy is performed to confirm this resistive memory switching behaviors.
Assembled matrix distance metric for 2DPCA-based face and palmprint recognition

Two-dimensional principal component analysis (2DPCA) is a novel image representation approach recently developed for image recognition. One advantage of 2DPCA is that it can extract feature matrix using a straightforward image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two image databases, the ORL face and the PolyU palmprint. The experimental results show that the assembled matrix distance metric is very effective in 2DPCA based image recognition.
A novel kernel discriminant feature extraction framework based on mapped virtual samples for face recognition

In this paper, we propose a novel kernel discriminant feature extraction framework based on the mapped virtual samples (MVS) for face recognition. We calculate a non-symmetric kernel matrix by constructing a few virtual samples (including eigen-samples and common vector samples) in the input space, and then express kernel projection vectors by using mapped virtual samples (MVS). Under this framework, we realize two MVS-based representative kernel methods including kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA). Experimental results on the AR and CAS-PEAL face databases demonstrate that the proposed framework can effectively improve the classification performance of kernel discriminant methods. In addition, the MVS-based kernel approaches have a lower computational cost in contrast with the related kernel methods.
On-line signature verification for e-finance and e-commerce security system

Online signature verification is a new and active topic in the research and application fields of biometrics. This paper proposes a low cost online signature verification method based on the matching of curves about x- and y-axis attaching some dynamic features. Just as human being's behavior, different local weight and unfixed threshold are introduced to improve the performance of signature verification system. Finally, this paper presents some applications of online signature verification on ATM, onsite credit card verification and Internet e-commerce.
Performance Improvement of Atomic Layer-Deposited ZnO/Al2O3 Thin-Film Transistors by Low-Temperature Annealing in Air

High-performance thin-film transistors (TFTs) with atomic layer-deposited (ALD) ZnO channel/Al<sub>2</sub>O<sub>3</sub> dielectric were fabricated under a maximum processing temperature of 200 °C. The effects of postannealing temperature and time on the performance of the TFT were investigated. Under annealing at 200 °C in air, the performance of the TFT was greatly improved by increasing the annealing time to 120 min, showing a very low OFF-current of 2.98 × 10<sup>-13</sup> A, a small subthreshold swing (SS) of 244 mV/decade, a quite large I<sub>ON</sub>/I<sub>OFF</sub> ratio of 4 × 10<sup>8</sup>, and a high field-effect electron mobility of 21.9 cm<sup>2</sup>/V·s. Furthermore, good electrical stabilities were also demonstrated under gate-bias stress, such as a threshold voltage shift (ΔV<sub>th</sub>) of -1.1 V and a Δ SS of 86 mV/decade under -20 V for 3000 s, a ΔV<sub>th</sub> of 0.29 V, and a Δ SS of -44 mV/decade under +20 V for 3000 s. The above results are attributed to the gradual passivation of oxygen vacancies in the ZnO channel and interface traps at the interface of ZnO/Al<sub>2</sub>O<sub>3</sub> with an increment of annealing time. Thus, the current ZnO TFT with a low thermal budget and high performance is very promising for flexible electronic applications.
Part-based convolutional neural network for visual recognition

Mid-level element based representations have been proven to be very effective for visual recognition. We present a method to discover discriminative elements based on deep Convolutional Neural Networks (CNNs), namely Part-based CNN (P-CNN), which acts as the role of encoding module in part-based representation. The P-CNN can be attached at arbitrary layer of a pre-trained CNN and be trained using image-level labels. The training of P-CNN essentially corresponds to the optimization and selection of discriminative mid-level visual elements. For an input image, the output of P-CNN is naturally the part-based coding and can be directly used for image recognition. By applying P-CNN to multiple layers of a pretrained CNN, more diverse visual elements can be obtained for visual recognitions. Experiments are conducted on two recognition tasks and their results demonstrate the effectiveness of the proposed method.