nils-herrmann/clean_scirepeval huggingface dataset:
Experimental investigation on natural convection heat loss characteristics of photovoltaic panel

In this study, natural convection heat loss of PV panel with non-uniform heat flux was investigated experimentally. The flat plate having the similar physical characteristics is adopted to simulate PV panel. Electrical heating is employed to simulate the solar power. Main concerns are directed to the effects of tilt angles thetas, k (gradient of the heat flux) and heat flux qw on the heat transfer from the flat plate. Regarding of this, the flat plates having different tilt angles and thermal boundary conditions were tested. The results reveal that the surface temperature of plate with non-uniform heat flux always rises with the heat flux rising, while the effects of tilt angle are very small. Compared with the uniform heat flux, the average natural convection heat transfer of the nonuniform heat flux is stronger when the tilt angle exceeds about 40°. Besides, with the heat flux increasing, the convective heat transfer also grows. However, the average natural convection heat loss for non-uniform heat flux decreases firstly and then increases with the tilt angle increasing, and it reaches the lowest value at about 45°.
Determination of effective parameters in negative-index optical metamaterials

We present an enhanced algorithm based on the Kramers-Kronig (K-K) relations, which extracts the effective parameters such as the electric permittivity ε and magnetic permeability μ of a composite electromagnetic structure from calculated or measured transmission and reflection data. For the general structure, the retrieved parameters are broadly similar whether obtained by the traditional retrieval algorithm or the improved one. For the multilayer negative-index optical metamaterials, the improved algorithm is competent instead of the traditional one due to the calculation involves a branching problem, and the K-K relations can be applied to remove the problem.
A fully integrated voltage controlled phase shifter with lossless active inductors

This paper presents a method to design a voltage controlled phase shifter in the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18-μm 1P6M CMOS process. The proposed phase shifter includes series hybrid structure and terminated ports2 and port3 of the hybrid with MOS varactor, however, the active inductors was used instead. The S21 and phase controlled performance get greatly improved. The chip size of the proposed phase shifter could also be reduced. The simulation results showed that a small variation in S21 (0.64±0.19dB) with the control voltage changing from −0.5V to 1V.
Human hand modeling, analysis and animation in the context of HCI

The use of human hand as a natural interface device serves as a motivating force for research in visual analysis of highly articulated hand movement. Since hand motion covers a huge domain, the scope of this paper is limited to the developments of 3D model-based approaches. Numerous 3D models that have been used to analyze hand motion are studied. Various approaches to articulated motion analysis are discussed. Some realistic synthesis methods are also included in this paper. We conclude with some thoughts about future research directions.
A novel metamorphic high electron mobility transistors with (InxGa1-xAs)m/(InAs)n superlattice channel layer for millimeter-wave applications

High performance MHEMTs using (In<inf>x</inf>Ga<inf>1-x</inf>As)<inf>m</inf>/(InAs)<inf>n</inf> superlattice structure as a channel layer have been fabricated successfully. These HEMTs with 80 nm gate length exhibit high drain current density of 392 mA/mm at drain bias 1.0 V and transconductance of 991 mS/mm at drain bias 1.2 V. Comparison with regular In<inf>x</inf>Ga<inf>1-x</inf>As channel, the superlattice channel HEMTs show an outstanding performance because of high electron mobility, and better carrier confinement in the (In<inf>x</inf>Ga<inf>1-x</inf>As)<inf>m</inf>/(InAs)<inf>n</inf> channel layer. The current gain cutoff frequency (f<inf>T</inf>) and maximum oscillation frequency (f<inf>max</inf>) were extracted to be 304 GHz and 162 GHz, respectively. The device demonstrated a 0.75 dB noise figure with an associated gain 9.6 dB at 16 GHz. The excellent device performance shows that the superlattice channel can be practically used for high-frequency and millimeter-wave application.
Job-shop scheduling using genetic algorithm

Job-shop scheduling is an important step in planning and manufacturing control in the CIMS environment. Research on job-shop scheduling focuses on knowledge-based approaches and heuristic searches which are useful except for the difficulty of obtaining the knowledge. Genetic algorithms are an optimization method which use the ideas of evolution of nature. Genetic algorithms are also efficient. A novel genetic algorithm model is presented to design a job-shop schedule algorithm. Since a valid solution of scheduling is hard to search for, we introduce a punishment factor to distinguish the valid solution and invalid solution in the solution space. The simulation result shows the efficiency of this approach.
SoftCuts: A Soft Edge Smoothness Prior for Color Image Super-Resolution

Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for evaluating their smoothness. This paper characterizes soft edge smoothness based on a novel SoftCuts metric by generalizing the Geocuts method . The proposed soft edge smoothness measure can approximate the average length of all level lines in an intensity image. Thus, the total length of all level lines can be minimized effectively by integrating this new form of prior. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image SR, by adaptively normalizing image edges according to their alpha-channel description. This leads to the adaptive SoftCuts algorithm, which represents a unified treatment of edges with different contrasts and scales. Experimental results are presented which demonstrate the effectiveness of the proposed method.
An efficient TDOA and FDOA based source localization algorithm via importance sampling

The location and velocity information gives crucial impacts and supports in many applications. In this paper, we address the the problem of source localization based on time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. The nonlinear and nonconvex maximum likelihood estimation can be approximated by Monte Carlo importance sampling technique. We derive the importance function analogous to Gaussian distribution based on Fisher information matrix (FIM) where the global convergence can be guaranteed with sufficient samples. Then we consider the source localization under sensor location uncertainties. The localization information of both sensors and source is jointly estimated by importance sampling. The simulation results illustrate that the proposed method is superior than other existing methods and it can attain the Cramér-Rao lower bound at moderate noise levels.
Deformable Template As Active Basis

This article proposes an active basis model and a shared pursuit algorithm for learning deformable templates from image patches of various object categories. In our generative model, a deformable template is in the form of an active basis, which consists of a small number of Gabor wavelet elements at different locations and orientations. These elements are allowed to slightly perturb their locations and orientations before they are linearly combined to generate each individual training or testing example. The active basis model can be learned from training image patches by the shared pursuit algorithm. The algorithm selects the elements of the active basis sequentially from a dictionary of Gabor wavelets. When an element is selected at each step, the element is shared by all the training examples, in the sense that a perturbed version of this element is added to improve the encoding of each example. Our model and algorithm are developed within a probabilistic framework that naturally embraces wavelet sparse coding and random field.
Vital sign estimation from passive thermal video

Conventional wired detection of vital signs limits the use of these important physiological parameters by many applications, such as airport health screening, elder care, and workplace preventive care. In this paper, we explore contact-free heart rate and respiratory rate detection through measuring infrared light modulation emitted near superficial blood vessels or a nasal area respectively. To deal with complications caused by subjectspsila movements, facial expressions, and partial occlusions of the skin, we propose a novel algorithm based on contour segmentation and tracking, clustering of informative pixels, and dominant frequency component estimation. The proposed method achieves robust subject regions-of-interest alignment and motion compensation in infrared video with low SNR. It relaxes some strong assumptions used in previous work and substantially improves on previously reported performance. Preliminary experiments on heart rate estimation for 20 subjects and respiratory rate estimation for 8 subjects exhibit promising results.
Mining discriminative co-occurrence patterns for visual recognition

The co-occurrence pattern, a combination of binary or local features, is more discriminative than individual features and has shown its advantages in object, scene, and action recognition. We discuss two types of co-occurrence patterns that are complementary to each other, the conjunction (AND) and disjunction (OR) of binary features. The necessary condition of identifying discriminative co-occurrence patterns is firstly provided. Then we propose a novel data mining method to efficiently discover the optimal co-occurrence pattern with minimum empirical error, despite the noisy training dataset. This mining procedure of AND and OR patterns is readily integrated to boosting, which improves the generalization ability over the conventional boosting decision trees and boosting decision stumps. Our versatile experiments on object, scene, and action categorization validate the advantages of the discovered discriminative co-occurrence patterns.
Context-aware discovery of visual co-occurrence patterns.

Once an image is decomposed into a number of visual primitives, e.g., local interest points or regions, it is of great interests to discover meaningful visual patterns from them. Conventional clustering of visual primitives, however, usually ignores the spatial and feature structure among them, thus cannot discover high-level visual patterns of complex structure. To overcome this problem, we propose to consider spatial and feature contexts among visual primitives for pattern discovery. By discovering spatial co-occurrence patterns among visual primitives and feature co-occurrence patterns among different types of features, our method can better address the ambiguities of clustering visual primitives. We formulate the pattern discovery problem as a regularized k-means clustering where spatial and feature contexts are served as constraints to improve the pattern discovery results. A novel self-learning procedure is proposed to utilize the discovered spatial or feature patterns to gradually refine the clustering result. Our self-learning procedure is guaranteed to converge and experiments on real images validate the effectiveness of our method.
Improved Vese-Chan Model for Fast Image Segmentation Based on Split Bregman Method

This paper presents an improved active contour model for fast multiphase image segmentation based on the piecewise constant Vese-Chan model and the split Bregman method. We first define a new energy functional by applying the globally convex image segmentation technique to the Vese-Chan energy functional and incorporating the edge information with a non-negative edge detector function. Then we apply the split Bregman method to fast minimize the new energy functional. The efficiency of the improved model compared with the Vese-Chan model is demonstrated by experimental results. This is the main advantage of our improved model over the Vese-Chan model.
Deep Networks for Degraded Document Image Binarization through Pyramid Reconstruction

Binarization of document images is an important processing step for document images analysis and recognition. However, this problem is quite challenging in some cases because of the quality degradation of document images, such as varying illumination, complicated backgrounds, image noises due to ink spots, water stains or document creases. In this paper, we propose a framework based on deep convolutional neural-network (DCNN) for adaptive binarization of degraded document images. The basic idea of our method is to decompose a degraded document image into a spatial pyramid structure by using DCNN, with each layer at different scale. Then the foreground image is sequentially reconstructed from these layers in a coarse-to-fine manner by using deconvolutional network. Such kind of decomposition is quite beneficial, since multi-resolution supervision information can be directly introduced into network learning. We also define several loss functions about label consistency and foregrounds smoothing to further regularize the training of the network. Experimental results demonstrate the effectiveness of the proposed method.
Motion divergence fields for dynamic hand gesture recognition

Although it is in general difficult to track articulated hand motion, exemplar-based approaches provide a robust solution for hand gesture recognition. Presumably, a rich set of dynamic hand gestures are needed for a meaningful recognition system. How to build the visual representation for the motion patterns is the key for scalable recognition. We propose a novel representation based on the divergence map of the gestural motion field, which transforms motion patterns into spatial patterns. Given the motion divergence maps, we leverage modern image feature detectors to extract salient spatial patterns, such as Maximum Stable Extremal Regions (MSER). A local descriptor is extracted from each region to capture the local motion pattern. The descriptors from gesture exemplars are subsequently indexed using a pre-trained vocabulary tree. New gestures are then matched efficiently with the database gestures with a TF-IDF scheme. Our extensive experiments on a large hand gesture database with 10 categories and 1050 video samples validate the efficacy of the extracted motion patterns for gesture recognition. The proposed approach achieves an overall recognition rate of 97.62%, while the average recognition time is only 34.53 ms.
Record low specific contact resistivity (1.2×10−9 Ω-cm2) for P-type semiconductors: Incorporation of Sn into Ge and in-Situ Ga doping

Record-low specific contact resistivity ρ<inf>c</inf> down to 1.2×10<sup>−9</sup> Ω-cm<sup>2</sup> is achieved for Ti/p<sup>+</sup>-Ge<inf>0.95</inf>Sn<inf>0.05</inf> contact by incorporating Sn into Ge and insitu Ga doping with active doping concentration of 1.6×10<sup>20</sup> cm<sup>−3</sup>. As compared with Ni(GeSn)/p<sup>+</sup>-Ge<inf>0.95</inf>Sn<inf>0.05</inf> contact, Ti/p<sup>+</sup>-Ge<inf>0.95</inf>Sn<inf>0.05</inf> contact exhibits lower ρ<inf>c</inf> and is more thermally stable. In addition, theoretical calculation shows that for a given doping concentration, incorporating Sn into Ge lowers the p<inf>c</inf> as compared with metal/p-Ge contacts.
A New Economics Architecture for Manufacturing Grid

This paper has a discussion and research on building a new economics model for manufacturing grid (MG) based on beneficial drive. It firstly introduces the economics roles into manufacturing gird to express the economic attributes of the resources of MG. It also analyses the relationship between different roles. And paper focuses on building a new economics architecture supporting multi economics models. It adopts the MGeQoS to indicate how to deal with the selection of economics modes, and does the WSDI expression to describe MGeQoS attributes. And paper puts forward a matching service negotiation mechanism of economics model to realize the selection of economics model and do the management and scheduling of resource via the user MGeQoS. Based on these techniques, we present the conception model of MG economics architecture, and finally we build an effective and complete MG economics architecture which can support multi economics models.
Application of Blind Source Separation in Ultrasonic NDE

Blind source separation (BSS) allows the recovery of unknown signals from observed signals mixed by an unknown propagation medium, and is a promising technique for signal processing and data analysis. In ultrasonic nondestructive evaluation (NDE), BSS can serve at least three purposes: defect classification, system modeling and noise reduction. This paper discusses the application of BSS in ultrasonic NDE, and explores the implementation of independent component analysis (ICA) in separation of ultrasonic signals. Simulation results were given, which showed that different source signals can be separated using ICA, and the conclusion is that BSS can play a very important role in ultrasonic NDE.
Performance analysis of cooperative spectrum sensing in rayleigh channel

In cognitive radio networks, cooperative spectrum sensing is employed to detect the presence of primary user quickly. In this paper, we study the sensing performance of a cognitive user U1 by allowing another cognitive radio user U2 to act as its relay (cooperation). We take the average power rather than the instantaneous power as the decision statistic, so, we can use multiple signal samples in each observation to make the decision on if the primary user is present. We derive formulas of false alarm and detection probability in Rayleigh channel for the case when U2 acts as U1's relay and for the case when each cognitive user detects the presence of primary user independently. We then analyze the improvement of sensing performance by using cooperation, and discuss the impact of number of sampling points.
Knowledge discovery of customer satisfaction and dissatisfaction using ontology-based text analysis of critical incident dialogues

Ontology based systems have long been recognized by researchers as the starting point for automated text analysis (or text mining) of consumer dialogues. Therefore, this research creates an ontology schema for consumer complaint dialogues related to mass rapid transportation systems. Based on the complaint ontology, the critical incident technique is used to construct an open-ended customer questionnaire to collect the positive and negative text dialogues of passengers describing their transportation experiences. Several valid and reliable methods have been developed to cluster significant text using the frequency of key words. An example would be the use of keyword frequency (KF) analysis and the formation of clusters based on KF to study patents and technology trends. The intention of this research is to use these methods to automatically text mine consumer dialogues, create significant dialogue clusters, and, from these clusters, derive meaningful trends, baselines, and interpretations of consumer satisfaction and dissatisfaction with a mass transit system in a major metropolitan city.
Soft sensing of the burning through point in iron-making process

In the iron-making process, the state of burning through point (BTP) is the closure of sintering which is one of the most important parameters in judging the state of sintering. Based on the PSO (Particle Swarm Optimization)-inversion soft-sensing method, the BTP which can not be directly measured in the iron-making process is soft-sensed in this paper. Firstly, the principle of sintering is studied. Four parameters are employed to forecast the BTP, including the suction pressure of main chimney flue, air input, velocity of sintering machine and ignition temperature. And then, a prediction model using PSO is established. At last, the model is applied to production process. It is proved to be effective.
Coding for asynchronous-CDMA systems

A coding scheme for asynchronous code-division multiple-access (CDMA) systems with finite delays is presented in this paper. All transmitted codewords are assumed to reach the receiver within finite delay of D digits. This coding scheme combines source encoding, synchronous-CDMA (S-CDMA) spreading sequences, and interleaving to support error-free asynchronous multiple-access communications. It can support at least m(log/sub 2/m-2)/(2log/sub 2/3)+1/log/sub 2/3 users and the lower bound of its maximum code rate approaches asymptotically to (1-D/T)(log/sub 2/m)/(2log/sub 2/3) where m is the length of S-CDMA spreading sequence, and T>D+log(D+2).
Exploring Success Factors for Web-based E-Government Services: Behavioral Perspective from End Users

This study aims to exploring the key success factors affecting the service provided on the web portal of e-government. Based on the Theory of Planned Behavior(TPB) Technology Acceptance Model (TAM) and Selfservice Technology(SST), the study proposes an intergraded model for the empirical examination of the users' intention and behavior for using e-government's services. An electronic questionnaire is used to collect sample data. Respondents are under-graduate students from six universities who have exposed to the e-government. Then this model is tested by means of the statistical analysis method of Structure Equation Model (SEM). The results show that users' overall satisfaction, specifically explained by Perceived Usefulness and Perceived service quality of the web-based service, most significantly affect their intention to use e-government. In addition, increasing the relevant knowledge and skill of the users effectively enhance their intention and the actual usage as well. Discussion and implication are presented in the end.
A merged CMOS digital near-end crosstalk canceller and analog equalizer for multi-lane serial-link receivers

A digital near-end crosstalk (NEXT) canceller merged with an analog equalizer for multi-lane serial-link receivers has been realized in 0.13mum CMOS technology. With the proposed sign-sign block least-mean-square (SSB-LMS) circuit, a 5 Gb/s PRBS of 231mnplus1 suffered from both the channel loss and NEXT for 10-inch FR4 traces is successfully equalized. The measured BER is 10-12 and the measured maximum peak-to-peak jitter is 49.7 ps. This chip occupies 0.56times0.76 mm2 and consumes 177 mW including buffers from a 1.2 V supply.
Decomposing and regularizing sparse/non-sparse components for motion field estimation

Regularizing motion field is critical to achieve accurate estimation of the motion field. As the motion field may include discontinuity (e.g., at the motion boundaries), traditional smoothness regularization may not work well. Among many approaches to handling motion discontinuity, recent attempts pursued a sparse representation of the motion field for regularization, and achieved quite encouraging results. However, statistics show that these methods tend to over-sparsify the motion field, and thus confronted by the non-sparse noise in practice. In this paper, we propose to decompose the motion field into sparse and non-sparse components for the motion boundaries and small universal noises, respectively. This separation approach regularizes these two sources differently. We propose a novel and efficient optimization algorithm to solve this problem. In addition, our study reveals the in-depth connection between this noise separation approach and the influence function approach in robust statistics. We validate and evaluate our new approach on the Middlebury benchmark, and have achieved outstanding testing performance.