nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Design on Broadband Circularly Polarized Microstrip Antenna

A square array of air columns is embedded in a dielectric substrate to form the PBG structure and the band-gap of such PBG structure is analyzed at first. Then a broadband circularly polarized antenna based on the PBG structure is introduced. Through selecting appropriate lattice period and air column radius, the surface wave can be suppress effectively and the circularly polarized characteristic can be improved when the resonant frequency (f=12.5 GHz) of antenna fell in the band-gap of PBG structure. After calculating and testing, the 20.6% (VSWR<2) bandwidth and 27.4% 3-dB axial ratio bandwidth are achieved respectively. Such circularly polarized antenna can be used extensively in communications field.
Nonparametric kernel estimation for error density

Consider a linear model, y/sub i/=x'/sub i//spl beta/+e/sub i/, i=1,2,..., x'/sub i/s are p(/spl ges/1) dimension known vectors and /spl beta/(/spl isin/R/spl deg/) is an unknown parametric vector and e/sub i/ are assumed i.i.d.r.v.'s from a common unknown density function f(x) with med (e/sub . Based on LAD (least absolute deviations) estimator /spl beta//spl tilde/ of /spl beta/, we propose a nonparametric method to estimate unknown f(x). A kernel estimator f/spl tilde//sub n/(x) is obtained. Large sample properties of f/spl tilde//sub n/(x) are studied. Some computational examples are also given.
Neighborhood Based SVM Multi-classification Method for Condition Assessment of Insulator

Support vector machines (SVMs) are a class of popular classification algorithms for high generalization ability. However they mainly solve with two-classification problem, while, in practice, there are lots of multi-classification problem still. Based on advantages and shortcomings of existing multi-classification, a kind of neighborhood based SVM multi-classification method is proposed in this paper. To classify Samples in K classes just need to construct K-1 SVM classifiers with this method. And classifiers at prediction stage are chosen according to neighborhood of test samples. By using neighborhood based SVM and pair wise SVM to train these five stages and forecast respectively, the usefulness of this multi-classification method and more efficient is proved. In addition, an insulator condition valuation model based on neighborhood is obtained.
Optimization of Response Performance in Graphics Monitoring System

With the development of network technology, image-based real-time monitoring system has become a hot topic in the area of industrial automation, but in the current real-time system, the large volume of data transmission and the frequent interaction between client and server become the bottlenecks of real-time system. For these problems, the paper analyzed the concept of SVG which is XML-based, and using advance compression technology to compress SVG files, which reduced the volume of data transmission. Besides, the paper present a new data acquisition mode which combines push and Ajax, experiment results show that proposed mode reduced the frequent interaction between client and server, enhanced the efficiency of transmission of real-time information.
Intelligent Video Monitor System Based on Neural Networks Analysis

Neural network analysis, an important branch in data mining, has been widely used in statistical analysis, pattern recognition, image processing, biological species division and customer division. Based on division method, the paper rationally selected initial class center, dynamically regulated the number of classification during image classification, and proposed an image recognition method. In the new method, multi-scale wavelet decomposition was firstly conducted for the image to be recognized. Then fisher transformation was performed on decomposition results of different scales which were defined as decomposition vectors. Finally, image recognition was realized in the fisher transformation domain according to the minimum absolute distance or comparative distance. The new method was proved to have high correct recognition rate and excellent recognition effect.
A monitoring method based on combination of EPCA and RPCA

Process monitoring is a very important measure which ensures process safety and stable operation. Considering the insufficient process information at the very beginning of new process and the necessity that process model needs to be updated constantly in order to adapt to process changes, a method based on a combination of extended principal component analysis (EPCA) and recursive principal component analysis (RPCA) is proposed in this article. Afterwards, the method proposed is applied in grinding processes and simulated, which shows that the method can not only effectively solve the problem of critical shortage of process information when a new process is run, but also improve the utilization rate of the normal followed-up samples, making process model more adaptable to process variability. Finally, the simulations results illustrate effectiveness and practicality of the investigated method.
Integrated Service Outsourcing: A Conceptual Model

The growing pressure to reduce costs and improve efficiency induces many organizations to undertake service outsourcing initiatives. This paper presents a conceptual model that help shed light on the complexities of outsourcing service and knowledge work. The models offer a simple yet powerful way to conceptualize outsourcing of knowledge and service work and to identify the inherent challenges that such strategies entail. The implications of the conceptual models have been discussed in a range of propositions that can be viewed as foundations for new inquiry in this research stream. A case study of the steel producer Baosteel of China highlights that the adoption of integrated service outsourcing is an appropriate strategy to pursue. Keywords-outsourcing; service;
A New Random Number Generate Algorithm for Cognitive Radio Networks

Cognitive radio networks (CRN) brings rapid development of communication technologies and provides a good solution for heterogeneous networks merging. However, security of CRN becomes the key concern issue. Security of the encryption key is one of the most important factors in communication. Through analysis, we found that: key generation of cognitive radio networks is more dependent on the network environment and terminal users' location information. Therefore, a new random number generate algorithm (ULSI) which is based on users' location information and spectrum information is proposed. This paper mainly analyzes parameter test results and compares ULSI algorithm with PMMLCG, GFSRG, and Super-prime method. Simulation results show that ULSI algorithm has better performance in statistical distribution.
Cooperative and cognitive wireless networks for communication-based train control (CBTC) systems

In this paper, with recent advances in cooperative and cognitive wireless networks, we propose a CBTC system to enable train-train direct communications. In addition, the proposed system is optimized with the cognitive control method. Unlike the exiting works on cooperative wireless networks, in this paper, train control performance in CBTC systems is explicitly used as the performance measure in the design. Reinforcement learning is applied to obtain the optimal handover decision and adaption policy of communication parameters. Simulation result shows that the performance of train control can be improved significantly in our proposed CBTC system.
DASH sub-representation with temporal QoE driven layering

For realizing finer granularity rate adaptation with tradeoff between visual quality and network bandwidth, DASH provides sub-representation structure that can further refine QoE options with multiple temporal quality operating point at a fixed spatial quality, i.e., PSNR. In order to facilitate streaming QoE driven decisions with sub-representations, in this paper, we shall develop a new structure for constructing DASH sub-representations. Besides, we utilize the fame significance metric to characterize the temporal quality and introduce a gradient driven temporal quality layer packing algorithm based on the frames loss induced distortion, which can be used as one theoretical foundation of constructing DASH sub-representations for matching the available throughput with the improved spatio-temporal distortion tradeoff.
Flexible Dual-Gate Oxide TFTs Gated by Chitosan Film on Paper Substrates

Low-voltage flexible dual-gate indium-tin-oxide-based thin-film transistors (TFTs) are self-assembled on SiO<sub>2</sub>-covered paper substrates by one shadow mask diffraction method. Solution-processed chitosan gate dielectric films have a large gate specific capacitance (5.8 μF/cm<sup>2</sup>) due to the electric-double-layer effect. The subthreshold swing, drain-current on/off ratio, and field-effect mobility are estimated to be 80 mV/dec, 4 ×10<sup>6</sup>, and 9.3 cm<sup>2</sup>/V·s, respectively. Low-voltage operation mechanism and threshold voltage modulation of such dual-gate paper TFTs are investigated.
Optimal measurement point planning for workpiece localization

This paper addresses the problem of measurement point planning for 3-D workpiece localization in the presence of part surface errors and measurement errors. A number of frame-invariant norms of the infinitesimal rigid body displacement are defined to quantify the localization accuracy required by manufacturing processes. Then, two kinds of frame-invariant indices are derived to characterize the sensitivities of the accuracy measures to the sampling errors at the measurement points. With a dense set of discrete points on the workpiece datum surfaces pre-defined as candidates for measurement, planning of probing points for accurate recovery of part location is modeled as a combinatorial problem focusing on minimizing the accuracy sensitivity index. A heuristic floating forward search algorithm is presented to efficiently find a near-optimal solution. An example confirms the validity of the presented criteria and algorithm.
Improved Quantum Efficiency in Semipolar  $(1\bar{1}01)$ InGaN/GaN Quantum Wells Grown on GaN Prepared by Lateral Epitaxial Overgrowth

We investigated the comparative structural and optical properties of semipolar InGaN/GaN multiple quantum wells (MQWs) grown on the (1̅101) facet GaN/sapphire substrate by metal-organic chemical vapor deposition using lateral epitaxial overgrowth. The scanning electron microscopy (SEM), photoluminescence (PL), and temperature-varying time-resolved photoluminescence measurement were performed to investigate the structure and optical properties. The cross-sectional SEM image shows that the stripe triangular structure of the QW with semipolar (1̅101) planes is obtained as sidewall facets with the mask stripes aligned along the GaN α-axis. The structural and optical advantages of semipolar orientations were confirmed by a modurate shift of the PL peak energy, higher internal quantum efficiency, and lower radiative recombination lifetime than the MQWs on (0001) GaN grown by conventional methods. The results were obtained because of the reduced polarization fields in semipolar InGaN/GaN MQWs comparing with that in polar (0001) MQWs.
A novel satellite selecting algorithm for BeiDou navigation satellite system

In the future, BeiDou Navigation Satellite System (BDS) will has a more widely application in various fields, and high accuracy and fast positioning will be increasingly demanded. Satellite selecting algorithm has a vital influence on positioning accuracy and speed. Therefore, optimal algorithm to select satellites is one of important steps; it will be benefit to improve positioning precision and real-time of navigation. The paper proposes a new satellite selecting algorithm for BDS that bottom satellites are selected in turn after the top and the first bottom satellite are selected. The algorithm selects the optimal satellites for navigation by making full use of available information and the simulated and validated by some simulation results. The results show that the algorithm can improve real-time performance and choose better satellite combination. In addition, it has the advantages of simple, practical, and easy to implement.
Debris Growth And Migration In The Slider-Disk Interface

A debris causing serious wear and spacing deviation is often attached to, or slipping slowly on the slider. Its motion is dominated by contact forces, especially by the angle of the contact force with respect to the slider surface. The contact forces can be high if the debris is compressed as it slips. This occurs most often when the pitch (or wall-angle) is about 10/spl deg/. The debris may be dislodged when the angles of contact forces change, especially due to skew. A simple model is developed to predict the tendency of debris motion, given the coefficient of friction, the skew, and the pitch angle. The model is programmed in Excel spreadsheet. The results are verified by experiments.
Robust fuzzy control for Uncertain Nonlinear System based on multi-objective optimization

This paper deals with the problem of robust control for Uncertain Nonlinear System via fuzzy control approach. First, a fuzzy controller with robust control capability is proposed. For avoiding complex controller parameters adjustment, Multi-objective particle swarm Optimization (PSO) algorithm is adopted during the controller design. Secondly, the multi-model is employed to represent the nonlinear system with norm-bounded parameter uncertainties. Finally, a simulation example illustrates the effectiveness and the feasibility of the proposed approach.
Scalable Support for 3D Graphics Applications in Cloud

Recent advances in virtualization technology and wide acceptance of the cloud computing model are having significant impact on the software service industry. Though cloud computing and virtualization technology has been widely applied in supporting the information processing needs of conventional enterprise and business applications, there has been little success to-date in enabling realtime 3D virtual appliances in the cloud. This paper aims to address this deficiency by presenting SHARC, a solution for enabling scalable support of realtime 3D applications in a cloud computing environment. The solution uses a scalable pipelined processing infrastructure which consists of three processing networks according to the principle of division-of-labor, a virtualization server network for running 3D virtual appliances, a graphics rendering network for processing graphics rendering workload with load balancing, and a media streaming network for transcoding rendered frames into H.264/MPEG-4 media streams and streaming the media streams to a cloud user. The paper describes a prototype implementation of SHARC and reports test results that demonstrate the viability of this approach.
Soil concentration reverse method based on special spectral position

Soil is an important parameter on water quality, and plays a key role in water quality evaluation, especially for inland water. Many remote-sensing methods have now been developed to reverse soil concentration. However, in natural water, the existence of chlorophyll-a affects the soil concentration reverse precision. In order to remove the effect of chlorophyll-a, an experiment is designed in this work to study the water spectra with different soil concentrations and one chlorophyll-a concentration. A reverse method is developed through analyzing shifts and changes of characteristic spectral positions. It is based on special spectral positions where the influence of algae is the least.
A new AL-FEC coding scheme with limited feedback

For the next generation mobile video broadcasting, especially in-band solutions that serves the mobile devices, a limited feedback scheme via cellular channel polling is feasible to give accurate real-time information on the broadcast receivers' channel erasure rate, and decoding buffer status. In this work, we propose an AL-FEC coding degree scheme based on this feedback, to achieve a better decode efficiency and save the code redundancy. Simulation results demonstrate the effectiveness of this solution, and open up new opportunities in the next generation broadcasting system design.
Improving scanning speed of the AFMs with inversion-based feedforward control

This paper presents the design and experimental implementation of an inversion-based feedforward controller to achieve accurate tracking and fast scanning for an atomic force microscopy (AFM). The proposed controller reduces the tracking error by inverting the vibration dynamics and the hysteresis of the piezoelectric tube scanner (PTS). The hysteresis is compensated by directly constructing an inverse Prandtl-Ishlinskii model, while the vibration dynamics is suppressed by a zero magnitude error tracking controller. A comparison of the experimental images using the proposed controller and a dc-gain open-loop controller is given. The experimental results demonstrate the effectiveness of the proposed controller.
Phase regression approach for estimating the parameters of a noisy multifrequency signal

A novel approach for estimating the parameters of a multifrequency signal from discrete samples corrupted by additive noise is presented. An established mathematical model indicates that noise influence on the discrete phase and amplitude spectra is equivalent to additive phase and amplitude noise, respectively. On this basis, a simple algorithm is proposed to estimate the frequency and phase of each sinusoid component by linear regression on the phase spectra of segmented signal blocks, while an amplitude estimator is directly derived from the spectrum of the window function. The circular nature of the phase spectrum is thoroughly explored. Also, an algorithmic scheme is presented. The derived variances of the estimators show that for a noisy signal this approach provides superior accuracy over the traditional approaches. Simulations and engineering application confirm the validity of the presented method.
Application of backtracking algorithm in college dormitory assignment management

The multi-constraint conditions, including the entrance examination for college, the region and the dormitory classification and so on, were analyzed and studied enough by assigning management dormitory reasonably. A new backtracking algorithm based on matrix storage was proposed, and the realization detail of the algorithm was discussed. Further, the complexity of the algorithm was analyzed. The analysis result show the algorithm is more efficient comparing the similar method, and the application result show it is effective and reasonable in practice.
An efficient compression scheme for the multi-camera light field image

Current light field compression techniques lack robustness to handle both rate-distortion optimized motion compensation as well as latency during the encoding and decoding process. This paper focuses on a contribution approach that uses advanced frame prediction with affine and translational motion models and optimized view prediction structures. This method allows a significant compression performance gain over the current state of art of hierarchical temporal coding by 13.9%. The proposed method introduces an optimized encoding order that takes advantage of each group of pictures structure in order to leverage the dense perspective model of light field imagery. Both a global perspective model and a local affine model can be combined to show substantial distortion reduction at low processor costs. This contribution approach leads to an efficient and robust compression scheme for light field datasets.
Scene-aware image dehazing based on sky-segmented dark channel prior

An improved dehazing algorithm based on dark channel theory is proposed, in order to solve the problems of colour distortion and halo effect which still exists in dark channel prior algorithm. The dark channel prior theory may lead to colour distortion in sky region. Firstly, the guided filter is used to refine the segmentation of the sky region, and the atmospheric light is estimated accurately. Then, the median filter is used to obtain the detailed edge information. So a more clear transmission can be gotten which effectively suppress the halo problem. Finally, the gamma correction is applied to enhance image lightness with an empirically selected gamma parameter. The experimental results show that the proposed algorithm can effectively remove the haze. It can correct the colour distortion of the sky area and eliminate the halo effect at the edge of the scene.
Robust feature selection with self-matching score

With the increasing power of mobile headsets and mobile networks, mobile visual search applications have gained popularity and became tractable. One of the key technologies to enable visual search are the robust and compact features, which are extracted from an image and are invariant to recapturing variations. One of the key factors for compact visual descriptors is the selection of local features. The size of the compact visual descriptors and the computational complexities of a visual search system increase with the number of features selected. In this sense, ranking the descriptors extracted from a single image according to their importance in terms of recapturing is very necessary and important. In this paper, we attack this problem by proposing a novel self-matching selection. In this method, we randomly apply an out-of-plane rotation to the target image and match the original features to the features that are extracted from the out-of-plane rotated image. The importance of the features is ranked according to the self-matching score. This method is proven to be better than other peak strength and edge strength based methods by 30% from experiments on a large database.
A portable embedded automobile exhaust detection device based on ARM11

There is an obvious pressing need for the rapid, sensitive and highly portable identification of automobile exhaust. In our study, a battery-operated, low-cost and portable embedded gas detection device is fabricated with ARM11 S3C6410 core as the main application processor. Results indicated that gas detection device exhibited outstanding properties such as stable and durable intensity. The automobile exhaust sensor system could cover a linear detection range of 20 ppm–3000 ppm (R=0.9981, n=7 ) and the detection limit of about 8ppm (S/N=3). Furthermore, the exhaust sensor system showed outstanding selectivity to automobile exhaust compared with other seven sorts of common volatile organic compounds (VOCs). This device shows how the discriminatory capacity utilizing CTL intensity pattern recognition. The performance of gas detection device suggested the promising application for the qualitative and quantitative identification of automobile exhaust in private cars.
High-Bandwidth Control of Nanopositioning Stages via an Inner-Loop Delayed Position Feedback

This paper presents a novel high-bandwidth control approach for piezo-actuated nanopositioning stages. A delayed position feedback (DPF) controller is first developed in the inner loop to damp the resonant mode of piezo-actuated stages. A generalized Runge-Kutta method (GRKM) is proposed to determine the parameters of the DPF controller with pole placement. The benefit of the DPF for active damping is its simple structure and ease of implementation. Then, a high-gain proportional-integral (PI) controller is designed in the outer loop to deal with the hysteresis nonlinearity, disturbance and modeling errors. The stability of the control system is analyzed via a graphical method. Finally, experiments are conducted to demonstrate the effectiveness and superiority of the proposed approach in terms of tracking accuracy at high speed as compared to the PI controller.