nils-herrmann/clean_scirepeval huggingface dataset:
Graph based Cross-shape Recognition for Palm Diagnosis

According to the palm medicine (PM), the palmprint is one of the biometric features which can be used to diagnose diseases. Some special shapes formed by the palm lines in some areas of the palm indicate some special diseases. Cross-shape ("+") is very important in PM. This paper proposes a novel approach to identify the cross-shape for palm diagnosis. In this approach, the palm lines are extracted firstly, and then an undirected graph (called palm line undirected graph, PLUG) is constructed to represent the structure of the palm lines. Finally, by using the theory of fuzzy set, the cross-shapes are recognized in the PLUG. The experimental results demonstrate the effective of the proposed approach
An Optimized Wavelength Band Selection for Heavily Pigmented Iris Recognition

Commercial iris recognition systems usually acquire images of the eye in 850-nm band of the electromagnetic spectrum. In this work, the heavily pigmented iris images are captured at 12 wavelengths, from 420 to 940 nm. The purpose is to find the most suitable wavelength band for the heavily pigmented iris recognition. A multispectral acquisition system is first designed for imaging the iris at narrow spectral bands in the range of 420-940 nm. Next, a set of 200 human black irises which correspond to the right and left eyes of 100 different subjects are acquired for an analysis. Finally, the most suitable wavelength for heavily pigmented iris recognition is found based on two approaches: 1) the quality assurance of texture; 2) matching performance-equal error rate (EER) and false rejection rate (FRR). This result is supported by visual observations of magnified detailed local iris texture information. The experimental results suggest that there exists a most suitable wavelength band for heavily pigmented iris recognition when using a single band of wavelength as illumination.
An improved LDA approach

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vectors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synthesizes these improvements. Experimental results on different image databases demonstrate that our improvements on LDA are efficient, and that ILDA outperforms other state-of-the-art linear discrimination methods.
Providing communication capabilities during disaster response: Airborne remote communication (ARC) platform

Disaster response efforts during natural or man-made disasters are often hampered by compromised communications (e,g. lack or outage of cellular coverage). This can create a dangerous lack of communication or reliance on ad hoc networks, stifling information sharing. Existing systems that target this compromised communication gap are often difficult to rapidly deploy, proprietary and not interoperable, or designed for military use that are expensive and not always interoperable with civilian systems. Additionally, for many first responders (law enforcement, fire, EMS, etc.), current airborne sensor and communication assets are expensive or unavailable. In response to this capability gap, The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) has sponsored MIT Lincoln Laboratory and Pennsylvania State University to design and fabricate a low power, low weight, reliable communication solution to provide essential information. The airborne remote communication (ARC) system trades bandwidth for mobility and reliability. The ARC system is partly based on CubeSat technology. CubeSats are miniaturized satellites favored by academia and amateur radio satellite builders. The ARC system consists of the CubeSat communication technology, ground-based hardware and software components, and a platform on which the communication technology is deployed. It is data agnostic and can support a variety of data types, including GPS coordinates, SMS texts, or Emergency Data Exchange Language (EDXL) data. This paper describes the ARC system and a demonstration highlighting the capabilities of an essential information system.
Metaface learning for sparse representation based face recognition

Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.
Correcting Instrumental Variation and Time-Varying Drift: A Transfer Learning Approach With Autoencoders

Electronic noses (e-noses) are instruments that can be used to measure gas samples conveniently. Based on the measured signal, the type and concentration of the gas can be predicted by pattern recognition algorithms. However, e-noses are often affected by influential factors, such as instrumental variation and time-varying drift. From the viewpoint of pattern recognition, the factors make the posterior distribution of the test data drift from that of the training data, thus will degrade the accuracy of the prediction models. In this paper, we propose drift correction autoencoder (DCAE) to address this problem. DCAE learns to model and correct the influential factors explicitly with the help of transfer samples. It generates drift-corrected and discriminative representation of the original data, which can then be applied to various prediction algorithms. We evaluate DCAE on data sets with instrumental variation and complex time-varying drift. Prediction models are trained on samples collected with one device or in the initial time period, then tested on other devices or time periods. Experimental results show that the DCAE outperforms typical drift correction algorithms and autoencoder-based transfer learning methods. It can improve the robustness of e-nose systems and greatly enhance their performance in real-world applications.
A Multiple Maximum Scatter Difference Discriminant Criterion for Facial Feature Extraction

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart-multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD outperforms state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigen face, Fisher face, and complete LDA.
A novel recessed-channel tu nneling FET design with boosted drive current and suppressed leakage current

In this paper, we propose a recessed-channel (U-shape-channel) tunneling field-effect-transistor (TFET) for 16-nm technology applications. This new device structure has the advantages of boosted Ion and suppressed Ioff compared to the planar bulk-Si TFETs. Using Sentaurus TCAD simulations, it is found that the recessed channel will enlarge the tunneling area which will greatly enhance the drive current. Meanwhile, a steeper subthreshold swing (SS) will be realized in the U-shape TFET.
Finite horizon H/sub /spl infin// fixed-lag smoothing for time-varying continuous systems

In this paper, we aim to solve the long-standing H/sub /spl infin// fixed-lag smoothing problem for time-varying continuous systems. By applying a novel innovation analysis approach in an indefinite linear space, a sufficient and necessary condition for the existence of an H/sub /spl infin// fixed-lag smoother is derived. The H/sub /spl infin// smoother is calculated by performing the linear matrix differential equation and the integral equation.
Detecting Diabetes Mellitus and Nonproliferative Diabetic Retinopathy Using Tongue Color, Texture, and Geometry Features

Diabetes mellitus (DM) and its complications leading to diabetic retinopathy (DR) are soon to become one of the 21st century's major health problems. This represents a huge financial burden to healthcare officials and governments. To combat this approaching epidemic, this paper proposes a noninvasive method to detect DM and nonproliferative diabetic retinopathy (NPDR), the initial stage of DR based on three groups of features extracted from tongue images. They include color, texture, and geometry. A noninvasive capture device with image correction first captures the tongue images. A tongue color gamut is established with 12 colors representing the tongue color features. The texture values of eight blocks strategically located on the tongue surface, with the additional mean of all eight blocks are used to characterize the nine tongue texture features. Finally, 13 features extracted from tongue images based on measurements, distances, areas, and their ratios represent the geometry features. Applying a combination of the 34 features, the proposed method can separate Healthy/DM tongues as well as NPDR/DM-sans NPDR (DM samples without NPDR) tongues using features from each of the three groups with average accuracies of 80.52% and 80.33%, respectively. This is on a database consisting of 130 Healthy and 296 DM samples, where 29 of those in DM are NPDR.
The evolution of the HfSiOx/Si interface along with the increase of forming gas annealing temperature

Hafnium silicate-based Metal Oxide Semiconductor (MOS) capacitors were fabricated by atomic layer deposition. The interface evolution of the films with forming gas annealing was investigated. It is found that most of the slow interface states were passivated through Forming Gas Annealing (FGA), and the D<sub>it</sub> in the center of the band gap was estimated to be 2.3×10<sup>11</sup> eV<sup>-1</sup>cm<sup>-2</sup>. It is also found that interface layer can react with HfO<sub>2</sub> to form HfSiO<sub>x</sub> in the process of FGA at elevated temperature, and the interface thickness was reduced to 0.4 nm after FGA at 600°C.
A comparison of three types of pulse signals: Physical meaning and diagnosis performance

Pulse diagnosis has been extensively applied in China and Ayuredic for thousands of years. Recently more and more research interests have been given on computerized pulse diagnosis where sensor techniques are used to acquire the pulse signal and machine learning techniques are adopted to analyze the health condition based on the acquired pulse signals. By far, a number of sensors had been employed for pulse signal acquisition, which can be grouped into three categories, i.e., the pressure sensor, the photoelectric sensor, and the ultrasound sensor. To guide the sensor selection for computational pulse diagnosis, in this paper we analyze the physical meanings and sensitivities of signals sampled by these three types of sensors. The complementary information of different sensors is discussed from both cardiovascular fluid dynamics and comparative experiments by evaluating the disease classification performance. Signals acquired using different sensors are sensitive to different physiological and pathological factors. By combining signals from different sensor, improved diagnosis performance can be obtained.
A Survey of Dictionary Learning Algorithms for Face Recognition

During the past several years, as one of the most successful applications of sparse coding and dictionary learning, dictionary-based face recognition has received significant attention. Although some surveys of sparse coding and dictionary learning have been reported, there is no specialized survey concerning dictionary learning algorithms for face recognition. This paper provides a survey of dictionary learning algorithms for face recognition. To provide a comprehensive overview, we not only categorize existing dictionary learning algorithms for face recognition but also present details of each category. Since the number of atoms has an important impact on classification performance, we also review the algorithms for selecting the number of atoms. Specifically, we select six typical dictionary learning algorithms with different numbers of atoms to perform experiments on face databases. In summary, this paper provides a broad view of dictionary learning algorithms for face recognition and advances study in this field. It is very useful for readers to understand the profiles of this subject and to grasp the theoretical rationales and potentials as well as their applicability to different cases of face recognition.
Region-mapping neural network model for pattern recognition

In general, the process for multilayer feedforward neural network in pattern recognition is composed of two phases: training and classifying. The aim of the training phase is to make it possible for the network output to meet the desired output given by the training patterns. It demands a map of point to point, which is so strict that it often causes the criterion inconsistence between training and classifying. Consequently, the recognition rate would be decreased. The region-mapping model has changed the output space from one point to a certain supervisor region so that it has overcome the shortcoming of the inconsistent problem between training and testing as a common multilayer perceptron (MLP) does. Furthermore, it can save much of the computing time by mapping the input data to an output area rather than an output point. This paper presents a region-mapping model with quarter hyper globe as a supervisor region. The gradient decent algorithm is applied to this model. In order to illustrate the effect of our propounded model, a handwritten letter recognition problem was carried out in an experiment. Moment invariant features are used as input parameters. The simulation results show that the region-mapping model has much better characteristics than those common multiplayer perceptrons. Also, the quarter hyper globe rule is more reasonable than the hypercube one.
Wavelet based palm print recognition

Palm print is a new biometric method to recognize a person. Features in a palm print include principal lines, wrinkles and ridges, etc. The fact that the principal lines, wrinkles and ridges have different resolutions motivates us to analyze the palm print using a multiresolution analysis method. A novel palm print feature, named wavelet energy features (WEF), is defined employing wavelet which is a powerful tool of multiresolution analysis. In this paper. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in several directions at different wavelet decomposition levels (scales), so its ability to discriminate palms is very strong. To compute with ease is another virtue of WEF. The very high recognition rates obtained in experiments shows the effectiveness of the proposed method.
Multilinear Sparse Principal Component Analysis

In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data. MSPCA can be viewed as a further extension of the classical principal component analysis (PCA), sparse PCA (SPCA) and the recently proposed multilinear PCA (MPCA). The key operation of MSPCA is to rewrite the MPCA into multilinear regression forms and relax it for sparse regression. Differing from the recently proposed MPCA, MSPCA inherits the sparsity from the SPCA and iteratively learns a series of sparse projections that capture most of the variation of the tensor data. Each nonzero element in the sparse projections is selected from the most important variables/factors using the elastic net. Extensive experiments on Yale, Face Recognition Technology face databases, and COIL-20 object database encoded the object images as second-order tensors, and Weizmann action database as third-order tensors demonstrate that the proposed MSPCA algorithm has the potential to outperform the existing PCA-based subspace learning algorithms.
FinFET doping with PSG/BSG glass mimic doping by ultra low energy ion implantation

A FinFET doping method with PSG/BSG glass mimic doping was presented and a simplified process flow was introduced. Numerical simulation and experiment results of sheet resistance and SIMS profiles indicated a uniform doping of the 3D FinFET structure with the presented method, by using a proper dielectric layer and conducting an optimized subsequent annealing process.
Palmprint recognition using Fisher's linear discriminant

In this paper, a novel method for palmprint recognition is proposed. In this method, each pixel of a palmprint image is considered as a coordinate in a high-dimensional image space. A linear projection based on Fisher's linear discriminant (FLD) is used to project palmprints from this high-dimensional space to a significantly lower dimensional feature space, in which the ratio of the determinant of the between-class scatter to that of the within-class scatter is maximized. High accuracies of 99% and 99.2% have been obtained in one-to-one matching and one-to-300 matching test respectively and the speed of this method is rapid enough for real-time palmprint recognition.
Accurate iris segmentation based on novel reflection and eyelash detection model

The authors propose a novel noise detection model for accurate segmentation of an iris. Eyelash, eyelid and reflection are three main noises. Eyelid has already been solved by a traditional eye model; however, eyelash and reflection have not been tackled. To determinate a pixel in an eyelash, the model presented follows the three criteria: 1) separable eyelash condition; 2) non-informative condition; and 3) connective criterion. The first and second conditions handle separable and multiple eyelashes respectively. The last criterion avoids misclassification of strong iris texture as a single and separable eyelash. For reflection, strong reflection points are detected by a threshold and the weak reflection points around the strong points are determined by connective criterion and statistical test. A number of images are selected to evaluate the accuracy and necessity of our noise detection model and the results are encouraging.
Fuzzy directional element energy feature (FDEEF) based palmprint identification

Palmprint is a novel biometric method to identify a person. Generally, there are two types of features in palmprint: structural features and statistical features. Structural features, such as lines, can characterize a palm exactly, but are difficult to be extracted and represented; while statistical features can be extracted and represented easily, but are unable to reflect the structural information of a palmprint. Since the principal features of both Chinese characters and palmprint are lines we attempt to use methods of Chinese character recognition to identify palmprint. In this paper, we use the idea of an efficient Chinese character recognition method, the directional element feature, to define a novel palmprint feature, named the fuzzy directional element energy feature (FDEEF) which is a statistical feature containing some line structural information about palmprints. It can be extracted and represented easily and, at the same time, has a strong ability to distinguish palms. Two other low-dimensional features: global fuzzy directional element energy feature and block edge energy feature are also derived from FDEEF in this paper. The experimental results demonstrate the effectiveness of this method.
A first-principle analysis of resistive switching enhancement of HfO2 thin film induced by zinc doping method

Significant performance enhancement of HfO2 based resistive switching random access memory (RRAM) devices were achieved by Zn doping method. The non-volatile memory properties including operation voltage and uniformity were fully characterized. What's more, from first-principle analysis results, The Zn-3d states are shifted upward when the oxygen vacancy locates at the nearest neighboring site of Zn atom. Dopant-assisted formation and migration of oxygen vacancies would induce the suppression of randomicity of conductive filaments' behavior, which is possible lead to such RRAM characteristics improvement.
Process match between DSA and LSA for ultra-shallow junction formation

Laser anneal (LA) is one of major millisecond anneal techniques (MSA) for forming ultra-shallow and highly activated junctions. There are two major commercially available laser anneal systems which are called laser spike annealing (LSA) and dynamic surface annealing (DSA) respectively. LSA and DSA are quite different in terms of laser source, wavelength, scanning mode and so on, their hardware and process specifications are definitely distinct from each other. In this work, the process match between DSA and LSA for ultra-shallow junction formation was studied using implanted blanket wafers as well as 45nm logic device wafers. It was found that through some process tuning knobs adjustments, such as peak temperature, dwell time, DSA can match LSA thermal budget well.
Wrist-Pulse Signal Diagnosis Using ICPulse

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 for objectifying and standardizing TCPD. This paper presents an automated wrist-pulse signal diagnosis approach by using the independent component analysis (ICA) to extract pulse signal features. A wrist-pulse signal dataset is established, which includes both the samples from healthy people and patients with certain diseases. With the extracted features by ICA, a nearest neighborhood classifier is used to distinguish the unhealthy samples from healthy samples. The experimental results on the established dataset demonstrate the efficiency of the proposed ICA based pulse signal analysis method.