nils-herrmann/clean_scirepeval huggingface dataset:
Merging SoC and LOC together

A promising combination of the semiconductor integrated system-on-a-chip (SoC) with the lab-on-a-chip (LOC) is brought in this paper. In this paper, we propose a new technology which enables monolithic integration of the self-assembled biological system, the MEMS structure, the microfluidic system, and CMOS electronic circuits together. Utilizing this approach, more functionality is introduced into the SoC, while the LOC becomes more intelligent and controllable. A post-CMOS fabrication approach that meets the requirements is demonstrated to validate the idea.
A New Algorithm for Detecting Singular Points in Fingerprint Images

The singular points of a fingerprint image contain important information, so it is very significant to detect the position, number and type of singular points. The Poincare Index method can’t accurately detect the singular points in arch fingerprints and some tented arch fingerprints. To resolve the problem, we propose an improvement of the Poincare Index method. The experimental results show that our method is better than the Poincare Index method.
Lifetime Maximization via a New Cooperative MAC Protocol in Wireless Sensor Networks

Lifetime extension is a key design issue for wireless sensor networks (WSN) with battery-operated nodes. Compared with direct transmission, transmission power can be significantly reduced by cooperative communication, because it can effectively mitigate multi-path fading by introducing space diversity. In this paper, BER analysis for M-PSK and M-QAM modulation in non-cooperative and cooperative situations is firstly presented. Then power is optimally allocated to source and relay nodes with the objective of minimizing the total transmission power under the average BER constraint. Based on these works, a new distributed cooperative MAC protocol is proposed to improve the lifetime of WSN. In this protocol, both channel state information (CSI) and residual energy information (REI) of sensor nodes are considered to choose the cooperative nodes. Simulation results show that when the access point (AP) is above a certain height, the proposed cooperative protocol can significantly prolong network lifetime compared with non-cooperative MAC protocols.
An improved method of state estimation for Markov jump linear system with quantized measurements

This paper is concerned with the problem of state estimation for Markov jump linear system (MJLS) subject to the quantization effects due to the network communication channel constraints. After taking into account the statistical knowledge of the quantized measurements, an exact minimum mean square error (MMSE) estimate algorithm is derived, referred to as IMM Kalman-like particle (IMMKLP) algorithm. The proposed IMMKLP algorithm redesigns the kernel filter under the interacting multiple model (IMM) framework, based on the exact state posterior probability density conditioned on quantized measurements. A simulation example is provided demonstrating that IMMKLP achieves considerably high estimate accuracy and is computationally appealing by reducing the particle number dramatically.
A GA-based approach to optimize single-product flow-line configurations of RMS

Generating economical single-product flow-line configurations as candidates for a given demand period is a key optimization problem for reconfigurable manufacturing systems (RMS) at both initial design and reconfiguration stages. The optimization problem addresses the questions of selecting number of workstations, number and type of machines as well as assigned operations for each workstation. Given an operation precedence graph for a product and machine options for each operation, the objective is to minimize the capital costs of the configurations subject to constraints on space, initial investment, production functionality and capacity. A genetic algorithm (GA) based approach is presented to identify a set of economical configurations for the complicated constrained optimization problem. To overcome the complexity of search space, a novel procedure is introduced to guide GA to search within a refined feasible solution space which only includes the optimal configurations associated with feasible operation sequences. A case study illustrates the effectiveness and efficiency of the GA based approach.
Design of proportional-integral observers for discrete-time descriptor linear systems

A type of proportional-integral observers for discrete-time linear systems is proposed. Based on a complete parametric solution to the generalised Sylvester matrix equation, a parametric design approach for such a type of observers is proposed. The proposed approach provides all the gain matrices of the observer, gives the parametric expression for the corresponding finite left eigenvector matrix of the observer system matrix and guarantees the regularity of the observer system. A numerical example is provided to show the design procedure and illustrate the effectiveness of the proposed approach.
Application of foundation settlement prediction based on improved particle swarm algorithm and wavelet de-noising ground settlement prediction

In dealing with the problem of premature, the swarm was divided into different types and different update strategy was carried on each swarm. In order to improve the algorithm's convergence precise we also introduced the chaos mutation operations to increase particles' diversity. Meanwhile in order to remove the noise in the raw foundation settlement data, we introduced the wavelet algorithm. And we also made a compare with the standard particle swarm optimization to forecast the foundation settlement. The experiment indicated that this method had a better global and local searching ability and a high forecast precision.
A Novel Classification Scheme for Breast Masses Based on Multi-View Information Fusion

The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, one novel scheme based on multi-view information fusion is proposed, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates. Five contour and shape features of the masses are chosen, and two contour features are introduced. In this paper, 304 ROIs (regions of interest) that compose 152 multi-view pairs according to the corresponding breasts are chosen from DDSM dataset and divided into three groups, according to the characteristics of the masses in each pair. Different groups of ROIs are respectively used to check the effectiveness of the classification scheme and the experimental results of three ROIs groups demonstrate that the proposed classification scheme achieves a higher accuracy than those schemes using the individual classifiers in single-view, and the two introduced features could improve the classification performance significantly.
Building the Financing Platform for the Small and Medium Enterprises in China's High-Tech Zones under the Financial Crisis

In 2006 china made strategic decisions about building an innovation-oriented country. While building an innovative country is inseparable from the vigorous development of small and medium sized enterprises, high-tech zones are the collection areas of small and medium enterprises. This paper begins with the status quo of SME financing, combining with the financial characteristics of SME and the analysis of the shortcomings of the existing financing channels. Then, the paper uses Caohejing Development Zone in Shanghai as an example to analyze SME financial platform for the construction of the financial platform for small and medium sized enterprises. At last the paper suggests a simple, convenient and practical way to solve the financial problem in the context of the financial crisis.
In situ field emission measurement and field emission enhancement of few-layer graphene decorated with Au nanoparticles

We demonstrate the potential for integrating field emitters with two-dimensional (2D) graphene for directly transferred vacuum nanoelectronics. Improved field emission (FE) properties were revealed with few-layer grapheme (FLG) decorated with gold nanoparticles (Au NPs). In situ field emission measurement on local graphene film was applied, achieving a maximum current density of 37.8 mA/cm2. Grapehen-Au NPs nanocomposite retains the turn-on electric field of 3.84 V/μm which is much lower than the pristine graphene, and the emission has been proved to be very stable. With graphene directly transferred onto the pre-prepared structure, we demonstrate the possibility of improving the efficiency of manufacturing large scale nano-devices with high-precision integration.
A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.
An optimization of NURBS interpolation algorithm

I put forward a new interpolation called Adams-Bashforth interpolation of NURBS (Non-Uniform Rational B-Splines) curve in order to realize higher precision under the condition of satisfying the speed demand. IN this method, I calculated next parameter point by Adams-Bashforth equation and I used difference equation instead of derivation. I simplified and approximated the interpolation algorithm so that the speed and precision of interpolation could be ensured. Finally, we conducted simulations on this interpolation based on MATLAB and compared it with Taylor expansion algorithm so that we could prove its feasibility and correctness. We can conclude that it has a higher precision.
Dispersion Free 450-V p GaN-Gated CAVETs With Mg-ion Implanted Blocking Layer

In this letter, a GaN-based current aperture vertical electron transistor (CAVET) with a p-type gate layer and an implantation-based current blocking structure is presented. The devices measured showed a breakdown voltage of 450 V and no dispersion. The factors limiting higher breakdown voltages in these devices were carefully studied and discussed. The devices were grown on sapphire and relied on a box-shaped Mg implanted current blocking scheme. This is the first demonstration of an implantation-based CAVET, grown on sapphire blocking of 450 V with respectable on-state characteristics.
Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.
Intelligent technology for predicting water bloom engendering

Main factors which make water bloom engendering in river and lakes is analyzed, and the modeling method of short-time predicting for water bloom based on RBF neural network, including supervise learning method for the center, width and weight of base function in RBF neural network, error-correction algorithm based on gradient descent of RBF, is proposed. The effect which hidden layer of RBF brings to network performance is compared, and fitting capacity between RBFpsilas width and generalization capability of network is discussed. According to the results of network training and water bloom forecast, RBF neural network can be used to forecast the change of Chi_a in short term. Because of the strong generalization capability, high forecast precision and good fitting performance, the model has established a solid foundation for further research on short-term forecast methods of water bloom in river and lakes and the simulation result showed that the method is very practice and useful.
Hidden Markovian Chain based occurrence time prediction for GSM-R down fault

GSM for Railways (GSM-R) is a wireless mobile network used for train-ground communication. Down is one of the main faults in the routine of train running. It is a procedure inherently composed of several degradation levels, which was formulated as a Hidden Markovian Chain (HMC). In the framework of HMC, a scheme based on viterbi algorithm and absorption probability was proposed to predict occurrence time of GSM-R down fault. Distinguished to its static version, there was a correction mechanism based on current GSM-R quality, which modified the prediction dynamically. The effectiveness of the scheme was testified by simulations, revealing that it possesses more accuracy than its static version and the deflection is restrained in a rational level.
A DSP-based underwater communication modem using electric current method

Electric-current communication method has been considered to be a feasible solution to explore in case of short-range distance. Compared to underwater acoustic communication, electric-current communication method shows its advantages such as fast propagation, high band width and being not prone to noise due to mechanical movement. In this paper, a high-speed digital communication modem using electric current method is designed and evaluated. Long-time temperature monitoring and data retrieval are achieved with this platform in our experiments.
Location-Aided Transmit Strategy in Bidirectional Relay over MISO Rician Channels

In this paper, performance of location-aided transmit strategy in bidirectional relay networks over Rician fading channel is studied. This work is based on a three node relay system, where on the relay node, multiple antennas are deployed, and single antenna on other nodes. The idea of the location-aided transmit strategy is to acquire angle-of-arrival (AoA) of signal through relative location of devices, and to make use of the AoA in designing transmit beamformers. It is presented in this paper that this practical method for low-cost systems yields good system performance in terms of spectral efficiency when Line-of-Sight components exist in the propagation channel. In addition, the achievable rate performance of such relay network system in the broadcasting phase, which is impacted by Rician K-factor and the relative position between devices, are analyzed.
Fast image segmentation method based on threshold

Image segmentation is a key part in image processing field Though traditional threshold methods based on maximum entropy principle could sometimes divide the image into object and background, the size of search space increase very rapidly when the number of parameters needed to determine the membership function increase, so itpsilas time-consuming is often an obstacle. In this paper, based on the traditional threshold methods, the membership function is simplified and a new recursive scheme is introduced which decreases the computation complexity of the traditional method. The approach does not need the calculation of the membership function and increases the calculated speed. The experiments prove this novel approach is not only segment accuracy, but also decrease the computation time. Comparing with the traditional methods, the new method shows better real timing and noise restraining performance.
ARED: a novel adaptive congestion controller

Active queue management (AQM) is an effective method to provide an early notification of network congestion by proactively dropping or marking packets. In this paper, we propose a novel adaptive RED scheme in order to overcome the drawbacks of original RED gateway, which adjust the maximum drop ratio to keep the average queue length around the target value using gradient descent method based on discrete deterministic mathematical model of TCP/RED. Our adaptive RED scheme not only maintains its performance independent of traffic loads, but also converges to the target value faster than other enhanced RED algorithms. Moreover, simulation results show that our adaptive RED outperforms existing AQMs like PI and REM at least in the perspective of queue dynamics.
Controller design of T-S fuzzy systems with standard fuzzy partition inputs

A controller design approach of T-S fuzzy control systems is proposed in this paper. Firstly, a concept of the efficient maximal overlapped-rules group (EMORG) is presented, and a relaxed sufficient condition is derived to guarantee the quadratic stability of the closed-loop T-S fuzzy systems globally. This condition only requires finding a local common positive definite matrix in each EMORG. Then, by using the method of parallel distributed compensation (PDC), a fuzzy controller is designed. Finally, simulation studies for an illustrative example are conducted to show the effectiveness of the proposed method.
Dynamic modeling and model order reduction for vapor compression cycle

A dynamic simulation model of vapor compression cycles is built using the lumped parameter, moving boundary approach. A linearized version of the model suitable for dynamic analysis and controller design is proposed using a Taylor series expansion method, where 2nd order and higher order terms are omitted. Analysis indicates that the linearized model indicates that the system exhibits multiple time-scale behavior and that model reduction is appropriate. For the purpose of control, a model order reduction method, time-scale residualization (TSR), is presented based on the Schur decomposition and singular perturbation technique. Comparing with the other reduction models, the TSR model not only removes the multiple-time scale behavior of the system, but also preserves the DC-gain and the low-medium frequency properties of the system. This reduction model is appropriate for the design of the advance controller.
Acoustic wave propagation along cladded fibers of cubic crystal symmetry

An approximate method is proposed to analyze the acoustic wave propagation characteristics in nonpiezoelectric cladded fibers of cubic crystal symmetry. The fiber axis coincides with the crystalline Z axis. This approximate method utilizes coupled mode equations and the exact solutions for the corresponding cladded isotropic fiber are used as the approximation base. We concentrate our analysis on cubic fibers with weak anisotropy where the first order approximation obtained from the coupled mode equations can provide accurate results. Dispersion curves of a few lower order flexural, torsional and radial-axial modes in such a fiber with infinite cladding are presented for the first time. Higher order approximations are also discussed. We show the accuracy of the perturbation formula, reveal that the torsional and radial-axial modes are not pure in cubic fibers, and inspect the modal birefringence phenomenon.<<ETX>>
Coordinated nonlinear sliding mode control of STATCOM and generator excitation based on t-S Model

A coordinated nonlinear controller of static synchronous compensator (STATCOM) and generator excitation are designed according to the direct feedback linearization (DFL) theory and the T-S type fuzzy sliding mode variable-structure control (FSMC) theory. The proposed coordinated controller can improve both the power angle stability of generator and the voltage behavior at the STATCOM location. It is designed based on local measurements and possess good robustness. The simulation studies indicate the proposed coordinated controller can improve the accuracy of voltage control and strengthen the stability of power systems.
Epileptic Seizure Classification of EEGs Using Time–Frequency Analysis Based Multiscale Radial Basis Functions

The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time–frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time–frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
Mobile Robot Localization Based on Topological Model with Tiered Features

Perceiving the position of the mobile robot in an environment is an important element for an autonomous or semi-autonomous robot. In this paper we present a novel localization paradigm using topological map with tiered features, which is robust to rotation, illumination variations and scale changes. The topological model is extracted from natural landmarks and the normalized Zernike moments are calculated as main landmark features. By color matching and Zernike moments matching based on a probabilistic voting method between input scene and image models, natural landmarks can be recognized. The result is then fused with sonar data. The technique is especially adapted to indoor environment. Experimental results validate the favorable performance of this approach