nils-herrmann/clean_scirepeval huggingface dataset:
Components in the Pipeline

State-of-the-art scientific instruments and simulations routinely produce massive datasets requiring intensive processing to disclose key features of the artifact or model under study. Scientists commonly call these data-processing pipelines, which are structured according to the pipe and-filter architecture  Different stages typically communicate using files; each stage is an executable program that performs the processing needed at that point in the pipeline.The MeDICi (Middleware for Data-Intensive Computing) Integration Framework supports constructing complex software pipelines from distributed heterogeneous components and controlling qualities of service to meet performance, reliability and communication requirements.
Analysis on Spatial Heterogeneity of H1N1 Flu based on GIS Spatial Analysis Technology

The spatial distribution of disease epidemiology has always been the hotspot and difficulty, information technology and GIS technology have been widely applied in epidemiological studies, along with the continuous deepening of the disease spatial analysis development. In this paper taking H1N1 flu as the research object, given that China and Shandong Province, the number of infections and deaths' data was used in geographic information system software and the statistical software for the spatial analysis. The results showed that: the number of infections and deaths in coastal areas is larger than that in inland multi-region, geographic distribution showed a change distribution trend from south to north and from east to west.
A new digital control algorithm to improve the dynamic response of dual-transistors forward converter

In this paper, a new digital control algorithm is presented to improve the dynamic performance of the dual-transistors forward converter (DTFC) during a transient event. The algorithm uses a linear control scheme under the stable-state condition and the charge balance control scheme under the transient condition. Based on the principle of capacitor charge balance, the proposed algorithm predicts the switch over time to achieve optimal transient response for a DTFC, thus the minimal voltage derivation and recovery time are achieved when the load current has a step change. Compared with a conventional voltage mode controller, the proposed algorithm provides a much better dynamic performance. Furthermore, simulation and experiment results demonstrate the effectiveness of the algorithm.
A Robust Channel-Calibration Algorithm for Multi-Channel in Azimuth HRWS SAR Imaging Based on Local Maximum-Likelihood Weighted Minimum Entropy

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is an essential tool for modern remote sensing. To effectively deal with the contradiction problem between high-resolution and low pulse repetition frequency and obtain an HRWS SAR image, a multi-channel in azimuth SAR system has been adopted in the literature. However, the performance of the Doppler ambiguity suppression via digital beam forming processing suffers the losses from the channel mismatch. In this paper, a robust channel-calibration algorithm based on weighted minimum entropy is proposed for the multi-channel in azimuth HRWS SAR imaging. The proposed algorithm is implemented by a two-step process. 1) The timing uncertainty in each channel and most of the range-invariant channel mismatches in amplitude and phase have been corrected in the pre-processing of the coarse-compensation. 2) After the pre-processing, there is only residual range-dependent channel mismatch in phase. Then, the retrieval of the range-dependent channel mismatch in phase is achieved by a local maximum-likelihood weighted minimum entropy algorithm. The simulated multi-channel in azimuth HRWS SAR data experiment is adopted to evaluate the performance of the proposed algorithm. Then, some real measured airborne multi-channel in azimuth HRWS Scan-SAR data is used to demonstrate the effectiveness of the proposed approach.
Adaptive Local Gamma Correction Based on Mean Value Adjustment

In complicated high dynamic scene, current gamma correction methods sometimes suffer from a loss of local details in certain regions of different luminance. In this paper, an adaptive local gamma correction method based on mean value adjustment is proposed, which is aimed at improve local details in both dark and bright regions in image. As the preprocessing, a global enhancement method is applied to improve the global contrast and then the proposed local gamma correction is undertook. Based on the Shih-Chia Huang's method, the proposed local gamma correction method has made improvement in two aspects and applied it to the local regions. For one thing, the average gray is regarded as one of the critical factors to determine the gamma value in the corresponding local region, which could adaptively improve the contrast both in dark and bright areas while preserve the original luminance of the regions that already own good contrast. For the other, followed by color correction, a novel histogram modification method aimed at avoiding the over-enhancement in smooth regions is used, which manager to maintain the histogram's original shape and suppress the large components in it at the same time. Experiments prove that the proposed method could get much more visual-pleasing and natural results.
An Improved Algorithm for Image Decomposing Based on Separse Representation

A novel image decomposing algorithm is proposed in this paper, it can separate images into texture and cartoon parts under the complicated background. The main idea is the use of two appropriate dictionaries, one for the representation of textures, and the other for the cartoon parts assumed to be piecewise-smooth. The cartoon parts in the image can be sparsely represented by the local Ridgelet and the local Discrete Cosine transform is appropriate representation for texture parts. Furthermore, the use of Basic-Pursuit and Total-Variation regularization methods leads to the desired separation, along with noise removal as a by-product. The experiment results show that the image decomposition by the method is according with the human visual system.
A similarity measure between unordered vector sets with application to image categorization

We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. The main contribution of this paper is to model each vector set with a GMM adapted from a common ldquouniversalrdquo GMM using the maximum a posteriori (MAP) criterion. The advantages of this approach are twofold. MAP provides a more accurate estimate of the GMM parameters compared to standard maximum likelihood estimation (MLE) in the challenging case where the cardinality of the vector set is small. Moreover, there is a correspondence between the Gaussians of two GMMs adapted from a common distribution and one can take advantage of this fact to compute efficiently the probabilistic similarity. This work is applied to the image categorization problem: images are modeled as bags of low-level features and classification is performed using a kernel classifier based on the proposed similarity measure. Experimental results on the PASCAL VOC 2006 and VOC 2007 databases show the excellent performance of our approach.
Design and Description of a 600 bps Speech Coder Based on Melpe

This paper describes a 600 bps speech coder based on the enhanced mixed excitation linear prediction model (MELPe). The algorithm of this speech coder includes features of MELPe, which can obtain high quality synthesized speech and is robust in difficult background noise environments. To reduce the bit rate, we have developed a modified multi-frame joint vector quantization that takes advantage of inherent inter-frame redundancy. The predicted multi-stage vector quantizer (PMSVQ) is designed to quantize the line spectrum frequency (LSF) parameters. Simulation results have proven that an efficient and high quality coding has been achieved at bit rate 600 bps, and the proposed coder is better than the existing 2400 bps LPC10e standard
An Optimal Control Method for Photovoltaic Grid-Tied-Interleaved Flyback Microinverters to Achieve High Efficiency in Wide Load Range

Boundary conduction mode (BCM) and discontinuous conduction mode (DCM) control strategies are widely used for the flyback microinverter. The BCM and DCM control strategies are investigated for the interleaved flyback microinverter concentrating on the loss analysis under different load conditions. These two control strategies have different impact on the loss distribution and thus the efficiency of the flyback microinverter. For the interleaved flyback microinverter, the dominant losses with heavy load include the conduction loss of the power MOSFETs and diodes, and the loss of the transformer; while the dominant losses with light load include the gate driving loss, the turn-off loss of the power MOSFETs and the transformer core loss. Based on the loss analysis, a new hybrid control strategy combing the two-phase DCM and one-phase DCM control is proposed to improve the efficiency in wide load range by reducing the dominant losses depending on the load current. The optimal design method based on the boundary condition of the hybrid control is also presented. The experimental results verify the benefits of the proposed control.
An improved driving method for synchronous rectifier using drain-source voltage sensing

Traditional voltage sensing method has already been widely used for the synchronous rectifier. However, the parasitic parameters will cause the false turn-on and early turn-off problem. In this paper, the conception of zero-crossing noise filter is presented. By applying this filter to the synchronous rectifier, the false turn-on and early turnoff problem can be resolved. Only three passive components are needed in the proposed filter which makes it reliable and easy to implement. Simulation and experimental results show that the zero-crossing noise filter can significantly improve the reliability as well as the efficiency of the power circuit. A 400V to 12V 100W Flyback converter and a 600W half bridge LLC resonant converter prototype are built to verify the advantages of the new zero-crossing noise filter.
A Data Layout Method in Heterogeneous RAID

The data layout of traditional RAID can’t adapt to the heterogeneous RAID that consists of disks with different capacity and/or performance, because it will cause waste of disk storage space and decrease of the integrated disks’ bandwidth. Based on the concept of “stripe pattern”, we bring out the concept of “pseudo stripe pattern” and propose to place data in heterogeneous RAID with repetitions of pseudo stripe pattern. It takes into account of storage space and array I/O bandwidth, while promising acceptable data management overhead. The results of simulations validate that heterogeneous RAID applying our data layout policy exhibits better and steadier performance compared with that applying traditional homogeneous RAID data placement.
A Survey of RST Invariant Image Watermarking Algorithms

In this paper, we review the algorithms for rotation, scaling and translation (RST) invariant image watermarking. There are mainly two categories of RST invariant image watermarking algorithms. One is to resynchronize the RST transformed image before conducting watermark extraction. Another is to embed and extract watermark in an RST invariant or semi-invariant domain. We discuss in detail the work principles of the typical RST invariant image watermarking algorithms, analyze the performance of these typical algorithms through implementation and point out their advantages and disadvantages.
Cyberspace Situation Prediction Based on Gene Expression Programming

The accurate prediction of cyberspace situation is fundamental to intrusion prevention in large scale networks. After analyzing the cyberspace situation, a cyberspace situation prediction model based on gene expression programming (GEP-CSP) is proposed, to predict the time series of cyberspace situation. Besides, since its own intrinsic characteristics, GEP-CSP solves the problem that the traditional time series methods can’t make an accurate prediction without the pre-knowledge. By employing GEP-CSP, the experiments on Abilene network ¿ow data reached the expectation and made a precise prediction.
Collaborative Sensor Registration without a Priori Association

Sensor registration is an important prerequisite for successful multi-sensor data fusion. In this paper, we consider a cooperative sensor registration scenario that the Precise Location Messages (PLM) can be received by the tracker periodically from cooperative targets through wireless data link and utilized to estimate sensor systematic biases. A 2-D sensor registration algorithm is presented to jointly estimate the site location bias and the measurement bias without a priori knowledge of track-to-track association. At first, a point set is obtained by mapping all possible pairs of sensor and PLM measurements. Next, a credit function is defined as the arithmetic mean of the likelihood function of these points. Since the registration biases can be estimated by finding the maximum credit point, a two-step searching algorithm is proposed to jointly estimate location and measurement biases. Its statistical performance is compared to the hybrid Cramér-Rao lower bound (HCRLB).
A Communication Protocol With Data Compression for Isolated Digital Power Supplies

High-cost isolators and their speed limits are the shortcomings of digital control in isolated power supplies. Different feedback schemes result in different numbers and speed requirements of the isolators, which impact the cost and the decision making. This paper compares existing solutions, and proposes a communication solution featuring data compression and time-division multiplexing, which provides the highest output accuracy and many advanced features of digital control, whereas the cost can potentially be in the range of conventional analog solutions.
A new design method for PI-like fuzzy logic controllers for DC-to-DC converters

This paper proposes a novel design procedure of Pi-like fuzzy logic controller (FLC) for DC-DC converters that integrates linear control techniques with fuzzy logic. The design procedure allows the small signal model of the converter and linear control design techniques to be used in the initial stages of FLC design. This simplifies the small signal design and the stability assessment of the FLC. By exploiting the fuzzy logic structure of the controller, heuristic knowledge is incorporated in the design, resulting in a nonlinear controller with improved performance over linear PI controllers.
Analysis and implementation of a new PFC digital control method

A predictive algorithm for digital controlled PFC is introduced in this paper. Based on this algorithm, all of the duty cycles required to achieve unity power factor in a half line period can be calculated by the DSP in advance. The main advantage of this digital control PFC method is that the high switching frequency of the PFC can be achieved because it does not directly depend on the processing speed of DSP. The small signal model is set up for the purpose of designing the voltage loop and analyzing the dynamic performance of DSP controlled PFC system. Input voltage feed forward compensation makes the output voltage insensitive to the input voltage variation and guarantees the input current sinusoidal even if the input voltage is distorted. Simulation results show that the predictive algorithm for digital controlled PFC works well.
Component Architecture and Modeling for Microkernel-Based Embedded System Development

Microkernel-based approach provides operating system support for developing embedded systems with high performance and safety through memory protection. This allows us to introduce architectural mechanisms that enable good separation of concerns, while still satisfy the performance, security and reliability needs of embedded systems. Organizing embedded software as interacting components with well-defined interfaces is compatible with modeling methods. The challenge issue is to seamlessly transform models and integrate tools at different levels of design, implementation and deployment. In this paper we present our solution to this issue. Our contribution is twofold: first, we device a unified model driven and component based development approach. We adopt a layered architecture to construct a tool chain, which allows flexible extension at different layers. Second, we develop a software tool suite in order to support and demonstrate our solution, which includes a UML-based modeling environment and a set of component development tools on top of a microkernel operating system.
A new dual channel resonant gate drive circuit for synchronous rectifiers

At high frequency applications, the gate drive loss of the power MOSFET becomes quite significant. A new dual channel low side resonant gate drive circuit is proposed in this paper. The proposed drive circuit can provide two symmetrical drive signals for driving two MOSFETs. It can recover most of the driving energy and clamp the gate and source voltage of the driven MOSFETs to either drive voltage or ground via a low impedance path. The circuit can also alleviate the dv/dt issue. The proposed circuit consists of four switches and a single winding inductor. The proposed resonant gate drive circuit can be used to drive the synchronous MOSFETs in a current doubler or full-wave rectifier. It can also be used to drive the primary MOSFETs in push-pull converters
Deep Learning Solutions to Computational Phenotyping in Health Care

Exponential growth in electronic health record (EHR) data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases, i.e., computational phenotyping. Recent success and development of deep learning provides promising solutions to the problem of prediction and feature discovery tasks, while lots of challenges still remain and prevent people from applying standard deep learning models directly. In this paper, we discussed three key challenges in this field: how to deal with missing data, how to build scalable models, and how to get interpretations of features and models. We proposed novel and effective deep learning solutions to each of them respectively. All proposed solutions are evaluated on several real-world health care datasets and experimental results demonstrated their superiority over existing baselines.
A low-power CMOS ASK clock and data recovery circuit for cochlear implants

The ASK modulation is widely adopted in wireless communication and also in bio-implanted system. In this paper, a low-power CMOS ASK clock and data recovery (CDR) circuit is designed for cochlear implants based on the proposed monostable circuit under CMOS 0.18µm technology. This monostable circuit has a great robustness and the transient time only differs 5% under 36°C, and behaves just 10% variety in different PVTs. This monostable circuit uses no resistor and is easy for on-chip integration, it dissipates an average current 16.6µA and 10.1µA when recovers clock from “0” and “1” separately. And the whole CDR circuit could recovery correct clock and data within a broad duty cycle range and consumes only 29.52µW.
A method to analyze the anti-interference capability of digital communication system

In this paper, a behavior-lever model is introduced to analyze communication system. In the article, first establishing the transmitter and receiver model corresponding with QPSK modulation and demodulation, and running a simulation of ideal communication link; then based on the simulation of the model's performance on of Gaussian noise, The model's Correctness and effectiveness can be validated. At last a simulation method is proposed, which can get the anti-interference performance of the receiver, and has important guiding significance on communication system design and frequency allocation.
A novel signal-cancellation-based channel phase bias calibration algorithm for spaceborne multi-channel HR WS SAR in azimuth

Combined with digital beam forming (DBF), multichannel synthetic aperture radar (MC-SAR) in azimuth can achieve high-resolution and wide-swath (HRWS) SAR imaging. However, inevitable channel errors lead to a great degradation of DBF performance in practice. To address this problem, a novel channel phase error calibration algorithm is proposed in this letter. Theoretical analysis reveals that the signal of the reference channel can be reconstructed from those of other channels. Although signal cancellation can be roughly realized by using this reference signal and the original signal, there are still residues due to the existence of channel phase errors. Therefore by minimizing the difference between these two signals, the channel phase errors can be precisely derived. The effectiveness of the proposed algorithm is validated by experimental results.
An Accurate Design Algorithm for LLC Resonant Converters—Part I

This paper proposes a new approach to optimally design LLC converters. The core of the proposed approach is an accurate algorithm that can find all the possible designs satisfying the peak gain requirement. Designers can conveniently evaluate several design results and find the optimal one for their respective applications. Part I of this paper proposes the accurate design algorithm, which is developed based on time-domain resonant waveforms, law of charge conservation, and conservation of energy. Design procedures are also provided for computer automation.
Analysis of the Starting Mechanism Under Stress Reaction Calculate

Based on the stress reaction model, this paper analyzes the start mechanism. The methods include: 1) taking the inhibition chain in the model as the flexible resource. When intruded, it can be used to scan for the reply plan; 2) when finding the programming solution, and starting a reply plan, it can be used to inhibit the system's excessive reaction. Therefore, it extremely utilizes the limited resource, multi-utilizes the seizing resources under the limited resources, and thus improve the system's speed
Design of Near-Allpass Strictly Stable Minimal-Phase Real-Valued Rational IIR Filters

In this brief, a near-allpass strictly stable minimal-phase real-valued rational infinite-impulse response filter is designed so that the maximum absolute phase error is minimized subject to a specification on the maximum absolute allpass error. This problem is actually a minimax nonsmooth optimization problem subject to both linear and quadratic functional inequality constraints. To solve this problem, the nonsmooth cost function is first approximated by a smooth function, and then our previous proposed method is employed for solving the problem. Computer numerical simulation result shows that the designed filter satisfies all functional inequality constraints and achieves a small maximum absolute phase error.
A parallelized multi-objective particle swarm optimization model to design soil sampling network

Optimization of soil sampling network is a complex optimization problem, which must reconcile a series of conflicts such as survey budget, sampling efficiency and sampling barriers, etc.. High computational cost of this problem motivated the applications of parallel computation algorithms. Our study proposes a parallelized multi-objective particle swarm optimization model (PMOPSO), which combines minimum mean kriging variance and minimum survey budget as the objectives. The model was applied to optimize soil sampling network of Hengshan County in loess hilly area in China. The performance of the PMOPSO model was compared to that of sequential MOPSO. The results indicate that the PMOPSO model can improve the computational efficiency and fitness values of the objectives significantly at the expense of the convergence rate.