nils-herrmann/clean_scirepeval huggingface dataset:
A gradient histogram preservation based texture enhanced model for image deblurring

Image blurring attenuate crucial textures and thus always result in a pretty dismal visual experience. Unfortunately, image blurring is difficult to avoid during the image acquisition, hence, lots of recent research focus on how to preserve subtle textures while suppress visual artifacts during image deblurring. Among all of the existing image deblurring methods, image priors, such as non-local priors of image gradient, play an important role. Although using prior knowledge improves noise and ringing artifact removal, fine textures always are attenuated as noise or ringing artifact. In order to solve this problem, we introduce a gradient histogram preservation (GHP) based deblurring model. Combining the GHP model with the non-local sparse prior, we impose both a global constraint and a non-local sparse constraint, and are capable of synthesizing rich textures. We introduce an alternative image deblurring scheme, the problem can be separated into several sub-problems and alternatively solved. Our deblurring method constraints the gradient distribution of restored images to approaching the gradient distribution of the latent sharp image. Furthermore, the experimental results on both uniform and non-uniform blurry images substantiate that the proposed deblurring method performs superior to most of the state-of-the-arts.
Wrist Pulse Diagnosis Using Complex Network

Pulse signal contains important information about health status and pulse diagnosis has been extensively applied in oriental medicine. In recent years more and more research interests have been given on computerized pulse diagnosis. Pulse feature extraction plays an important role in computerized pulse diagnosis. The most popular pulse feature extraction methods can be grouped into two categories, i.e. time domain feature extraction method and frequency domain feature extraction method. The pulse signal is a pseudo periodic signal while the common feature extraction methods usually assume it is a periodic signal and only a typical period or an averaged period was used in the feature extraction, while the difference between periods was less emphasized. In this paper we use complex network to transform the pulse signal from time domain to network domain and use the statistics parameters which describe the organization of the complex network as the features to characterize the difference between pulse periods. The experiment shows that the complex network features are useful in characterizing the relationship between different pulse periods the diagnosis performance on diabetes are similar with the multi scale sample entropy. By combining complex network features with sample entropy features, higher diagnosis performance can be further obtained.
From Point to Set: Extend the Learning of Distance Metrics

Most of the current metric learning methods are proposed for point-to-point distance (PPD) based classification. In many computer vision tasks, however, we need to measure the point-to-set distance (PSD) and even set-to-set distance (SSD) for classification. In this paper, we extend the PPD based Mahalanobis distance metric learning to PSD and SSD based ones, namely point-to-set distance metric learning (PSDML) and set-to-set distance metric learning (SSDML), and solve them under a unified optimization framework. First, we generate positive and negative sample pairs by computing the PSD and SSD between training samples. Then, we characterize each sample pair by its covariance matrix, and propose a covariance kernel based discriminative function. Finally, we tackle the PSDML and SSDML problems by using standard support vector machine solvers, making the metric learning very efficient for multiclass visual classification tasks. Experiments on gender classification, digit recognition, object categorization and face recognition show that the proposed metric learning methods can effectively enhance the performance of PSD and SSD based classification.
Online Palmprint Identification

Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.
Palmprint verification: an implementation of biometric technology

As the important implementation of biometric technology, palmprint verification is one of the most reliable personal identification methods. In this paper, a new approach to palmprint verification based on line feature matching and datum point invariance is presented. The datum points of palmprint which act as the important registrations, are defined owing to their remarkable advantage of invariable location. Then, the simple and efficient methods of line feature extraction and matching are proposed. Several palmprint images are adopted to test the approach and the experimental results show the effectiveness of palmprint verification.
Discriminant subclass-center manifold preserving projection for face feature extraction

Manifold learning is an effective feature extraction technique, which seeks a low-dimensional space where the manifold structure, in terms of local neighborhood, of the data set can be well preserved. A typical manifold learning method constructs a local neighborhood centered at individual samples. In this paper, we propose to construct local neighborhoods that centered at subclass centers, and seek an embedded space where such neighborhood is well preserved. We show from a probability perspective that, neighbors of a subclass center would contain more intra-class data than inter-class data, which may be desirable for discrimination. Meanwhile, we simultaneously enhance the discriminative power of extracted features by maximizing the Fisher ratio of embedded data based on subclass centers. Experimental results on CAS-PEAL and FERET face databases demonstrate that our proposed approach is more effective than most typical manifold learning methods and their supervised extensions in classification performance.
Atomic-Layer-Deposited HfLaO-Based Resistive Switching Memories With Superior Performance

The excellent resistive switching characteristics of atomic-layer-deposited HfLaO-based devices are investigated for nonvolatile memory applications. With the help of nonlattice oxygen ions which is designed to incorporate into the film by decomposition of H2O2 during process, highly uniform and reproducible resistance switching cycles could be observed with the resistive ratio as high as 106 for more than 10 000 cycles. In addition, the fast operation speed (10 ns) has been demonstrated. Conduction of the off state is dominated by the space-charge-limited hopping, while the ohmic behavior dictates the on state, which suggests a filamentary conduction mechanism. Moreover, the estimated readout characteristics under different read voltages from 0.3 to 1 V were sufficiently stable to fulfill the requirement for memory application. Considering the excellent memory switching behavior, a resistance switch device composed of a promising high-k HfLaO dielectric film is a possible candidate to be integrated into future memory processes.
A fuzzy clustering neural networks (FCNs) system design methodology

A system design methodology for fuzzy clustering neural networks (FCNs) is presented. This methodology emphasizes coordination between FCN model definition, architectural description, and systolic implementation. Two mapping strategies both from FCN model to system architecture and from the given architecture to systolic arrays are described. The effectiveness of the methodology is illustrated by: 1) applying the design to an effective FCN model; 2) developing the corresponding parallel architecture with special feedforward and feedback paths; and 3) building the systolic array (SA) suitable for very large scale integration (VLSI) implementation.
A Novel Line-Scan Palmprint Acquisition System

Biometric recognition systems have been widely used globally. However, one effective and highly accurate biometric authentication method, palmprint recognition, has not been popularly applied as it should have been, which could be due to the lack of small, flexible and user-friendly acquisition systems. To expand the use of palmprint biometrics, we propose a novel palmprint acquisition system based on the line-scan image sensor. The proposed system consists of a customized and highly integrated line-scan sensor, a self-adaptive synchronizing unit, and a field-programmable gate array controller with a cross-platform interface. The volume of the proposed system is over 94% smaller than the volume of existing palmprint systems, without compromising its verification performance. The verification performance of the proposed system was tested on a database of 8000 samples collected from 250 people, and the equal error rate is 0.048%, which is comparable to the best area camera-based systems.
Real-time palmprint acquisition system design

Capturing high quality palmprint images in a very short time period is a fundamental step in palmprint research. While a palmprint scanner capable of this function is especially important for developing a real-time online palmprint identification system, limited research effort has been put into designing and implementing palmprint acquisition systems. A novel palmprint acquisition system is presented, which is designed for various civilian applications such as access control, ATM, etc. The novel contactless design on the user interface prevents the distortion of the palm skin introduced by the traditional glass plate of a palm scanner. The system is designed to have the best performance at reasonable price. It has a resolution of 150 dpi that is able to obtain palmprint features, including the principal lines, wrinkles and ridge texture. The proposed system provides an important step for implementing a personal identification system using palmprint. Over 9,400 palmprint images were collected by the proposed system. An online palmprint identification system was then developed based on the proposed acquisition system.
Robust Kernel Representation With Statistical Local Features for Face Recognition

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems

This paper addresses an important issue known as sensor drift, which exhibits a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly owing to the frequent acquisition and labeling process for gas samples' recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semisupervised, and unsupervised learning problems in single domain (i.e., source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework called domain adaptation extreme learning machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gas recognition in E-nose systems, without losing the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called source DAELM (DAELM-S) and target DAELM (DAELM-T) are proposed in this paper. In order to perceive the differences among ELM, DAELM-S, and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.
Ultra-low frequency P(VDF-TrFE) piezoelectric energy harvester on flexible substrate

This paper proposes a flexible piezoelectric energy harvester using the piezoelectric copolymer P(VDF-TrFE) films as active elements to convert mechanical inputs to electrical energy. The repeatedly spin-coated P(VDF-TrFE) films were thermally poled and then characterized by SEM, ferroelectric hysteresis and FTIR. The piezoelectric performance of the fabricated harvester was measured under periodical mechanical inputs at ultra-low frequencies. The electrical output of average Vpp was as high as 0.96 V at 0.65 Hz.
Selecting a Reference High Resolution for Fingerprint Recognition Using Minutiae and Pores

High-resolution automated fingerprint recognition systems (AFRSs) offer higher security because they are able to make use of level-3 features, such as pores, that are not available in lower resolution ( <; 500-dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and performance of an AFRS is the choice of image resolution. In this paper, we identify the optimal resolution for an AFRS using the two most representative fingerprint features: minutiae and pores. We first designed a multiresolution fingerprint acquisition device to collect fingerprint images at multiple resolutions and captured fingerprints at various resolutions but at a fixed image size. We then carried out a theoretical analysis to identify the minimum required resolution for fingerprint recognition using minutiae and pores. After experiments on our collected fingerprint images and applying three requirements for the proportions of minutiae and pores that must be retained in a fingerprint image, we recommend a reference resolution of 800 dpi. Subsequent tests have further confirmed the proposed reference resolution.
Human Gait Recognition via Sparse Discriminant Projection Learning

As an important biometric feature, human gait has great potential in video-surveillance-based applications. In this paper, we focus on the matrix representation-based human gait recognition and propose a novel discriminant subspace learning method called sparse bilinear discriminant analysis (SBDA). SBDA extends the recently proposed matrix-representation-based discriminant analysis methods to sparse cases. By introducing the L1 and L2 norms into the objective function of SBDA, two interrelated sparse discriminant subspaces can be obtained for gait feature extraction. Since the optimization problem has no closed-form solutions, an iterative method is designed to compute the optimal sparse subspace using the L1 and L2 norms sparse regression. Theoretical analyses reveal the close relationship between SBDA and previous matrix-representation-based discriminant analysis methods. Since each nonzero element in each subspace is selected from the most important variables/factors, SBDA is potential to perform equivalent to or even better than the state-of-the-art subspace learning methods in gait recognition. Moreover, using the strategy of SBDA plus linear discriminant analysis (LDA), we can further improve the performance. A set of experiments on the standard USF HumanID and CASIA gait databases demonstrate that the proposed SBDA and SBDA + LDA can obtain competitive performance.
Joint Registration and Active Contour Segmentation for Object Tracking

This paper presents a novel object tracking framework by joint registration and active contour segmentation (JRACS), which can robustly deal with the non-rigid shape changes of the target. The target region, which includes both foreground and background pixels, is implicitly represented by a level set. A Bhattacharyya similarity based metric is proposed to locate the region whose foreground and background distributions best match those of the tracked target. Based on this metric, a tracking framework that consists of a registration stage and a segmentation stage is then established. The registration step roughly locates the target object by modeling its motion as an affine transformation, and the segmentation step refines the registration result and computes the true contour of the target. The robust tracking performance of the proposed JRACS method is demonstrated by real video sequences where the objects have clear non-rigid shape changes.
Development of a Resistor-Loaded Ultrawideband Absorber With Antenna Reciprocity

A novel method of developing an electromagnetic absorber is proposed through the design of an ultrawideband resistor-loaded planar dipole absorber. With the method, an antenna array structure is first designed for its best transmission performance and configured as an absorber with the application of the reciprocal principle. The designed planar dipole antenna has achieved a fractional bandwidth of 105% for an absorbing rate of more than 90% under the normal incident. In addition, the thickness of the proposed structure is 0.128λ0 with λ0 being the wavelength at the center operating frequency. Good agreement between the simulated and measured results demonstrates the validity of the proposed method.
Rotation-Invariant Nonrigid Point Set Matching in Cluttered Scenes

This paper addresses the problem of rotation-invariant nonrigid point set matching. The shape context (SC) feature descriptor is used because of its strong discriminative nature, whereas edges in the graphs constructed by point sets are used to determine the orientations of SCs. Similar to lengths or directions, oriented SCs constructed this way can be regarded as attributes of edges. By matching edges between two point sets, rotation invariance is achieved. Two novel ways of constructing graphs on a model point set are proposed, aiming at making the orientations of SCs as robust to disturbances as possible. The structures of these graphs facilitate the use of dynamic programming (DP) for optimization. The strong discriminative nature of SC, the special structure of the model graphs, and the global optimality of DP make our methods robust to various types of disturbances, particularly clutters. The extensive experiments on both synthetic and real data validated the robustness of the proposed methods to various types of disturbances. They can robustly detect the desired shapes in complex and highly cluttered scenes.
An automatic recoverable organic fuse based on Au/PEDOT:PSS/Au structure

In this paper an automatic recoverable organic fuse composed of Au/Poly (3,4-ethylene-dioxy-thio-phene):poly(styrenesulfonate)(PEDOT:PSS)/Au structure is demonstrated. An off/on fuse resistance ratio of ~103 was observed. It is revealed that the switching off process is determined by the critical electric field intensity. But once the fuse switches to the OFF state it will remain as long as the applied field is not removed, even if the field intensity is much smaller than the critical one. The OFF state can automatically switch to the ON state if the applied field is completely removed. The fuse working principle is attributed to the transition between the double electrical states of PEDOT, which is explained by the molecular orbit and energy band theory.
Statistical Analysis of Tongue Images for Feature Extraction and Diagnostics

In this paper, an in-depth analysis on the statistical distribution characteristics of human tongue color that aims to propose a mathematically described tongue color space for diagnostic feature extraction is presented. Three characteristics of tongue color space, i.e., tongue color gamut that defines the range of colors, color centers of 12 tongue color categories, and color distribution of typical image features in the tongue color gamut, are elaborately investigated in this paper. Based on a large database, which contains over 9000 tongue images collected by a specially designed noncontact colorimetric imaging system using a digital camera, the tongue color gamut is established in the CIE chromaticity diagram by an innovatively proposed color gamut boundary descriptor using one-class SVM algorithm. Thereafter, centers of 12 tongue color categories are defined accordingly. Furthermore, color distributions of several typical tongue features, such as red points and petechial points, are obtained to build a relationship between the tongue color space and color distributions of various tongue features. With the obtained tongue color space, a new color feature extraction method is proposed for diagnostic classification purposes, with experimental results validating its effectiveness.
Resistive Switching and Synaptic Behaviors of TaN/Al2O3/ZnO/ITO Flexible Devices With Embedded Ag Nanoparticles

This letter investigates the switching behavior of TaN/Al2O3:Ag:ZnO/ITO memristors fabricated on flexible substrates, for flexible nonvolatile memory and neuromorphic computing applications. The embedded Ag nanoparticles provide for improved device yield and reduced variability in resistance, from more than 160% to 30%. They also provide for reduced variability in set and reset voltages, from 35% to 18% and 40% to 11%, respectively. A synaptic behavior of this flexible device is demonstrated, making it a potential candidate for neuromorphic circuit applications. Furthermore, its low-power requirements make it more competitive for emulating biological systems.
Effects of Al 2 O 3 Capping and Post-Annealing on the Conduction Behavior in Few-Layer Black Phosphorus Field-Effect Transistors

Ambient instability has been proven challenging in black phosphorus field-effect transistors (BP FETs) and a capping layer is thus needed for their practical applications. In this paper, we have examined the effects of Al2O3 capping and O2 post-annealing on the conduction characteristic of BP FETs. With the Al2O3 capping, it forms ${p}$ -type into ambipolar transport and the electron mobility dramatically increases to 20–110 cm2/V·s in our case. Interestingly, with the O2 post-annealing, the transport can be tuned from ambipolar back to ${p}$ -type as the annealing time extends. It is attributed that the Al2O3 capping introduces an ${n}$ -type doping in BP channel while the O2 post-annealing dopes BP back into ${p}$ -type. Moreover, after the O2 post-annealing the interfacial POx might be formed, resulting in the degradation of subthreshold swing and on/off current ratio.