nils-herrmann/clean_scirepeval huggingface dataset:
Robust filtering for uncertain discrete-time systems: an improved LMI approach

This paper discusses the robust filtering problems for linear discrete-time systems with polytopic parameter uncertainty under the H/sub 2/ and H/sub /spl infin// performance. We aim to derive a less conservative design than existing sufficient linear matrix inequality (LMI) based conditions. It is shown that a more efficient evaluation of robust H/sub 2/ or H/sub /spl infin// performance can be obtained by a matrix inequality condition which contains additional free parameters as compared to existing characterizations. When applying this new matrix inequality condition to the robust filter design, these parameters give additional freedoms in optimizing the guaranteed H/sub 2/ or H/sub /spl infin// performance. The optimization will then lead to a less conservative design. The results will recover the existing robust H/sub 2/ and H/sub /spl infin// filtering ones when the additional free parameters are set to be zero. We also propose an iterative algorithm to further refine the suboptimal filter. Examples are given to demonstrate the less conservatism of the proposed approaches.
An adaptive model of person identification combining speech and image information

The paper introduces a combination of adaptive neural network systems and statistical method for integrating speech and face image information for person identification. The method allows for the development of models of persons and their on-going adjustment based on new speech and face images. The method is illustrated with a modeling and classification of different persons, when speech and face images are presented in an incremental way. In this model, there are two subnetworks, one for face image and one for speaker recognition. A higher-level layer is applied to make a final decision. In the speaker recognition subnetwork, a text-dependant model is built using evolving connectionist systems (ECOS) [N. Kasabov, 2002]. In the face image recognition subnetwork, composite profile technique is applied for face image feature extraction and zero instruction set computing (ZISC) [ZISC Manual, 2000] technology is used to build the neural network. In the higher-level conceptual subsystem, final recognition decision is made using statistical method. The experiments show that ECOS and ZISC are appropriate techniques for the creation of evolving models for the task of speaker and face recognition individually. It is also shown that the integration of the speech and image information using statistical method improves the person identification rate.
Palm line extraction and matching for personal authentication

The palm print is a new and emerging biometric feature for personal recognition. The stable line features or "palm lines", which are comprised of principal lines and wrinkles, can be used to clearly describe a palm print and can be extracted in low-resolution images. This paper presents a novel approach to palm line extraction and matching for use in personal authentication. To extract palm lines, a set of directional line detectors is devised, and then these detectors are used to extract these lines in different directions. To avoid losing the details of the palm line structure, these irregular lines are represented using their chain code. To match palm lines, a matching score is defined between two palm prints according to the points of their palm lines. The experimental results show that the proposed approach can effectively discriminate between palm prints even when the palm prints are dirty. The storage and speed of the proposed approach can satisfy the requirements of a real-time biometric system
Face attractiveness improvement using beauty prototypes and decision

To improve the attractiveness of a face, intuitively, one can drive the face approaching some beautiful faces. There are two major problem to solve for implementing the intuitive solution. One problem is that how to define and discover suitable beauty prototypes. Another is that how to determine the balance between the original face and the beauty prototype to produce the desired face. In this paper, we proposed a quantitive method to solve these two problems. First, a set of beautiful face prototypes are identified as cluster centers of beautiful faces, which avoid involving specific personal facial characteristic. Second, a beauty decision function is learned as a classifier that can tell whether a face is beautiful or not. Then, the facial attractiveness improvement procedure finds the nearest beauty prototype for the original face, and then approaches the prototype from the original face until the beauty decision function tells the approaching face is beautiful. With this method, the face is beautified and the difference between the beautified face and the original face is minimized. The experimental results verify the validity of the proposed methods.
Parallel VLSI neural system design for time-delay speech recognition computing

Neural system, as processors of time-sequence patterns, have been successfully applied to several speaker-dependent speech recognition computing. They can be efficiently implemented by a pipelined architecture. In this paper, parallel time-delay speech recognition computing for VLSI neural systems is presented. The system design methodology is to emphasize coordination between computational model, architectural description, and VLSI systolic implementation. Examples of time-delay speech recognition applications to VLSI neural system design and performance analysis are given to illustrate effectiveness of the parallel computation.
A face and palmprint recognition approach based on discriminant DCT feature extraction

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.
A sub-10nm U-shape FinFET design with suppressed leakage current and DIBL effect

In this paper, a U-shape fin field-effect transistor (U-FinFET) is proposed for sub-10nm technology and low power applications. Compared with the conventional tri-gate fin field-effect transistor (FinFET), this structure has the advantages of relatively low off-state channel leakage current (Ioff) and sub-threshold swing (SS). By using Sentaurus TCAD simulations, it is found that the U-shape channel can also suppress the drain induced barrier lowering (DIBL) effect.
The Failure Mechanism of Gate Resistance Testing for Power MOSFET

The vertical DMOS (double diffused MOSFET) is widely used in power microelectronics, its switching performance is determined mainly by the gate resistance and the input capacitance. Thus a gate resistance testing technique is developed in order to determine its device functionality. In this paper the authors discuss various processes induced device failures, such as the poor interconnect of the poly gate and the metal, the bonding wire, and the etch process, and their impact to the performance and reliability of the devices as well as the in-line testing method used for the performance validation
Application of a New Power Distribution Scheme for Complex Printed Circuit Boards for High-Speed Signaling

This paper presents a new power distribution scheme using power transmission lines (PTLs) applied to complex printed circuit boards. The structure consists of three daughter cards stacked on a motherboard where the board-board connectors used for connection are highly inductive. We show that in spite of the inductive connections, good signal integrity (SI) and power integrity (PI) are possible without relying too much on decoupling capacitors. This is achieved using a combination of PTL that replaces the voltage planes and current steering techniques. The PTL scheme described in this paper can be used for high-speed digital signaling. The SI and PI are demonstrated for up to 3-Gbit/s transmission between the boards.
Temperature Modulated Gas Sensing E-Nose System for Low-Cost and Fast Detection

Constant sensor-heating voltage is commonly used in electronic nose, such that multiple sensors should be integrated as a sensor array in order to differentiate multiple odor analytes. However, supplying a constant heating voltage for each sensor cannot provide rich pattern information, resulting in high cost and weak capability of an E-nose in detection. To address this issue, this paper aims at introducing an optimal temperature modulation technique of gas sensors for achieving low-cost, fast, and accurate detection. The contributions of this paper include: 1) the temperature modulated gas sensing system proposed in this paper operates in a linearly dynamical region by generating a linear control signal waveform of sensors' heating voltage; 2) with a highly efficient extreme learning machine that follows a random projection-based learning mechanism, the proposed system is developed for simultaneous gas classification and gas concentration prediction; and 3) the optimal heating voltage analysis of gas sensors is explored by using machine learning methods for providing some perspective and insight for optimal heating voltage selection. The experimental results and comparisons in terms of gas classification accuracy, concentration prediction error, system cost, and power consumption significantly demonstrate the high precision and efficacy of our proposed E-nose system. The data are available in http://www.escience.cn/people/lei/index.html.
A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.
Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition

The paper presents an integrated approach to incremental learning in autonomous systems, that includes both pattern recognition and feature selection. The approach utilizes evolving connectionist systems (ECoS) and is applied on on-line image and speech pattern learning and recognition tasks. The experiments show that ECoS are a suitable paradigm for building autonomous systems for learning and navigation in a new environment using both image and speech modalities.
Feature Extraction of Radial Arterial Pulse

Radial arterial pulse is an important physiological signal that has been applied in Traditional Chinese Medicine (TCM) for thousands of years. From ancient times, pulse has been recognized as an empirical science and plays a decisive influence on the TCM diagnosis. However it's objective and lack visible database, which blocks the development of TCM. In Recent years, many pulse systems based on various kinds of sensors have been introduced to collect the computerized pulse waveforms. Meanwhile, pulse diagnosis using statistical learning theory is attracting more and more attention. This paper mainly presents the pulse feature extraction algorithm for removing the redundant and irrelevant information. Though many researches on pulse feature have been published, most of them emphasize on a certain aspect and hardly utilize the experience in TCM. We propose an integrated framework of pulse features and introduce the corresponding extraction algorithms. The experiments show that the features are extracted accurately and they performance well in disease diagnosis.
Correcting Instrumental Variation and Time-Varying Drift Using Parallel and Serial Multitask Learning

When instruments and sensor systems are used to measure signals, the posterior distribution of test samples often drifts from that of the training ones, which invalidates the initially trained classification or regression models. This may be caused by instrumental variation, sensor aging, and environmental change. We introduce transfer-sample-based multitask learning (TMTL) to address this problem, with a special focus on applications in machine olfaction. Data collected with each device or in each time period define a domain. Transfer samples are the same group of samples measured in every domain. They are used by our method to share knowledge across domains. Two paradigms, parallel and serial transfer, are designed to deal with different types of drift. A dynamic model strategy is proposed to predict samples with known acquisition time. Experiments on three real-world data sets confirm the efficacy of the proposed methods. They achieve good accuracy compared with traditional feature-level drift correction algorithms and typical labeled-sample-based MTL methods, with few transfer samples needed. TMTL is a practical algorithm framework which can greatly enhance the robustness of sensor systems with complex drift.
Blood glucose prediction by breath analysis system with feature selection and model fusion

It has been shown that the concentration of acetone in breath is correlated with the subject's blood glucose level (BGL). Therefore, noninvasive BGL monitoring of diabetics can be achieved by the analysis of components in breath. In this paper, a breath analysis device with 10 gas sensors is designed to measure breath samples. Transient features are extracted from the signals of the sensors. Sequential forward selection is applied on the features to find the most informative ones. In order to reduce the interference brought by the inter-subject variance of breath acetone, global and local BGL prediction models are built and fused. The two models are based on different training strategies and have different advantages. Experiments were conducted using 203 breath samples from 36 diabetic subjects. Results show that the accuracy of the proposed feature is better than other similar features and the model fusion strategy is effective. The mean absolute error and mean relative absolute error of the system are 2.07 mmol/L and 20.69%, respectively.
Adaptive baseline wander removal in the pulse waveform

The pulse waveform plays an important role in pulse diagnosis, which is the key technique in traditional Chinese medicine. However, its baseline wander introduced in the acquisition process will result in misdiagnosis. Therefore a wavelet based cascade adaptive filter to remove this wander is presented. This cascade adaptive filter works in two stages. The first stage is a discrete Meyer wavelet filter and the second stage is the cubic spline estimation. Compared with some traditional methods, such as cubic spline estimation and linear-phase FIR least-squares error minimization digital filter, the proposed approach has better performance for removing the baseline wander of the pulse waveform.
Robust Visual Knowledge Transfer via Extreme Learning Machine-Based Domain Adaptation

We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine (ELM)-based cross-domain network learning framework, that is called ELM-based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the ℓ2,1-norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross-domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as the base classifiers. The network output weights cannot only be analytically determined, but also transferrable. In addition, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semisupervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition demonstrate that our EDA methods outperform the existing cross-domain learning methods.
FSIM: A Feature Similarity Index for Image Quality Assessment

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.
Monogenic Binary Pattern (MBP): A Novel Feature Extraction and Representation Model for Face Recognition

A novel feature extraction method, namely monogenic binary pattern (MBP), is proposed in this paper based on the theory of monogenic signal analysis, and the histogram of MBP (HMBP) is subsequently presented for robust face representation and recognition. MBP consists of two parts: one is monogenic magnitude encoded via uniform LBP, and the other is monogenic orientation encoded as quadrant-bit codes. The HMBP is established by concatenating the histograms of MBP of all sub-regions. Compared with the well-known and powerful Gabor filtering based LBP schemes, one clear advantage of HMBP is its lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. The experimental results on the AR and FERET face databases validate that the proposed MBP algorithm has better performance than or comparable performance with state-of-the-art local feature based methods but with significantly lower time and space complexity.
Detecting Iris Lacunae Based on Gaussian Filter

Lacunae is a typical sign of iris, and the detection of lacunae is of great importance in automatic iris diagnosis. In this paper, we first introduce the lacunae detection in iris images. Although current literature has a variety of edge detection algorithms, they do not always lead to acceptable results in extracting various features in an image. Lacunae usually have poor local contrast and the application of existing edge detection algorithms yield results which are not satisfactory. Thus, we proposed a lacunae detecting approach based on Gaussian filters, which is robust to noise. When the Gaussian filter in the vertical orientation is implemented to normalized iris image, the time complexity of this approach is reduced considerably. Experimental results show the validity of this approach.
Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization

Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space. We propose maximum independence domain adaptation (MIDA) and semi-supervised MIDA to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time. Then, MIDA learns a subspace which has maximum independence with the domain features, so as to reduce the interdomain discrepancy in distributions. A feature augmentation strategy is also designed to project samples according to their backgrounds so as to improve the adaptation. The proposed algorithms are flexible and fast. Their effectiveness is verified by experiments on synthetic datasets and four real-world ones on sensors, measurement, and computer vision. They can greatly enhance the practicability of sensor systems, as well as extend the application scope of existing domain adaptation algorithms by uniformly handling different kinds of distributional change.
A Novel 3-D Palmprint Acquisition System

Palmprints have been widely studied for personal authentication because they are highly accurate and incur low costs. Most of the previous work has focused on two-dimensional (2-D) palmprint identification. However, the inner surfaces of palms contain not only texture information but also shape information. Unfortunately, 2-D palmprint systems lose the shape information when capturing palmprint images. Hence, three-dimensional (3-D) information is important for palmprint systems. In this paper, we have designed and developed a novel 3-D palmprint acquisition system based on structured-light imaging technology. The acquisition system can obtain 3-D palmprint information and, at the same time, the corresponding 2-D texture, which are used for personal authentication. A 3-D palmprint database that contains 8000 samples has been established by using the developed acquisition system, and the test results illustrate the effectiveness of our system.
Fast 3D point cloud ear identification by slice curve matching

Current 3D ear identification methods require capturing ear range image data to work. In this work, based on slice curve matching, a new 3D point cloud ear identification approach is presented. Aiming to acquire 3D point cloud ear data in a low cost way, line structure light data sampling system is also introduced in this work. Experimental results and some comparisons show the comparable advantages in recognition speed and performance of the proposed method over existing range image data ways.