nils-herrmann/clean_scirepeval huggingface dataset:
Computer engineering education reform based on CDIO

Through detailed analysis of Conceive, Design, Implement, and Operate (CDIO) engineering education mode, the education of computer engineering is innovated in combining basis theory, personal capability and collaboration of team and environment of society. In view of Capability Maturity Model (CMM), a model of computer engineering education reform Based on CDIO is developed, and the program optimizing method called Prepare, Assess, Summary, and Adjust (PASA) based on CDIO is put forward, furthermore, the basic tasks and demands of the method are also discussed in detail. When the CDIO engineering education model with the project as its carrier is build, the Flexible, Module, and Season (FMS) organization mode of teaching is actively promoted, and the requirements of the project implementation arrangements are met. From the method had been implemented since 2010, the system has got the approval of the teachers and students, which stimulates students' interest, and has a better effect in practice.
Recognition of Fanout-free Functions

Factoring is a logic minimization technique to represent a Boolean function in an equivalent function with minimum literals. When realizing the circuit, a function represented in a more compact form has smaller area. Some Boolean functions even have equivalent forms where each variable appears exactly once, which are known as fanout-free functions. John P. Hayes (Hayes, 1975) had devised an algorithm to determine if a function can be fanout-free and construct the circuit if fanout-free realization exists. In this paper, we propose a property and an efficient technique to accelerate this algorithm. With our improvements, execution time of this algorithm is more competitive with the state-of-the-art method (Golumbic, 2001).
A Combination Scheme for Fuzzy Partitions Based on Fuzzy Weighted Majority Voting Rule

This paper devotes to the combination of fuzzy partitions with the same number of clusters by means of generalizing the weighted majority voting rule to fuzzy weighted majority voting rule. The difficulties of this generalization are to establish the correspondences among the classes and determine the weight coefficients of component fuzzy partitions. We propose a class-matching algorithm based on Hungarian method and generalize pattern recognition rate to fuzzy pattern recognition rate to overcome the difficulties. Employing the proposed class-matching algorithm and the fuzzy weighted majority voting rule, a combining scheme for fuzzy partitions is developed.Experimental results on real datasets show that the proposed ensemble of fuzzy partitions outperforms or is comparable to other two existed ensembles of fuzzy partitions in terms of most evaluation indexes for fuzzy partition.
A Modulation Technique for Neutral Point Voltage Control of the Three-Level Neutral-Point-Clamped Converter

The neutral-point voltage-balancing control is a critical issue of a neutral-point-clamped converter. The conventional control strategy is to inject the specific zero-sequence voltage to regulate the redundant vector, but it is unacceptable to the three-phase four-wire system. This paper provides a neutral-point voltage-balancing control by the dipolar modulation, and the control freedom of the zero-sequence voltage is flexible. Therefore, the proposed method is suitable for both three-phase three-wire and three-phase four-wire systems. Besides, the proposed method improves the control dynamic in a reactive power system. The proposed neutral-point voltage-balancing control is verified by the mathematical analysis and experimental results. The control dynamic, power loss estimation, and the output current performance are analyzed in this paper.
Shrinkage Degree in $L_{2}$ -Rescale Boosting for Regression

<inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-rescale boosting (<inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-RBoosting) is a variant of <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-Boosting, which can essentially improve the generalization performance of <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-Boosting. The key feature of <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-RBoosting lies in introducing a shrinkage degree to rescale the ensemble estimate in each iteration. Thus, the shrinkage degree determines the performance of <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-RBoosting. The aim of this paper is to develop a concrete analysis concerning how to determine the shrinkage degree in <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-RBoosting. We propose two feasible ways to select the shrinkage degree. The first one is to parameterize the shrinkage degree and the other one is to develop a data-driven approach. After rigorously analyzing the importance of the shrinkage degree in <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-RBoosting, we compare the pros and cons of the proposed methods. We find that although these approaches can reach the same learning rates, the structure of the final estimator of the parameterized approach is better, which sometimes yields a better generalization capability when the number of sample is finite. With this, we recommend to parameterize the shrinkage degree of <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-RBoosting. We also present an adaptive parameter-selection strategy for shrinkage degree and verify its feasibility through both theoretical analysis and numerical verification. The obtained results enhance the understanding of <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-RBoosting and give guidance on how to use it for regression tasks.
On automatic-verification pattern generation for SoC withport-order fault model

Embedded cores are being increasingly used in the design of large system-on-a-chip (SoC). Because of the high complexity of SoC, the design verification is a challenge for system integrators. To reduce the verification complexity, the port-order fault (POF) model has been used for verifying core-based designs (Tang and Jou, 1998). In this paper, we present an automatic-verification pattern generation (AVPG) for SoC design verification based on the POF model and perform experiments on combinational and sequential benchmarks. Experimental results show that our AVPG can efficiently generate verification patterns with high POF coverage.
A Method to Calibrate Vehicle-Mounted Cameras Under Urban Traffic Scenes

We address the problem of vehicle-mounted camera calibration under urban traffic scenes regarding the fact that the traditional calibration methods are practically restricted, since the internal parameters should be calibrated in the laboratory and it is impossible for recalibration that resulted from the parameters drifting or re-focusing when driving on roads. In this paper, we propose to utilize the manual lines lying in Manhattan directions in the scenes to compute their corresponding vanishing points for camera calibration, as the urban traffic scenes are usually man-made and the important lines and signs for driving are typically lying in the Manhattan directions. For “Manhattan world” scenes, where there are plenty of lines lying in Manhattan directions, the lines in the scene are detected automatically, and the clusters corresponding to Manhattan directions are obtained using RANSAC-like methods. For the more general “quasi-Manhattan world” scenes, where only the lines in two directions can be found naturally, while the lines in the other direction are usually detected trivially or even can be hardly detected, we propose a method to estimate the lines in the third direction to improve the vanishing point estimation accuracy. The method proposed is tested on both two types of scenes, and the accuracy and practicability of this method are demonstrated. Furthermore, calibration experiments on both one image and multiple images are conducted, which show that the results can be more accurate when more images are used.
A Novel Video Quality Metric for Low Bit-Rate Video Considering Both Coding and Packet-Loss Artifacts

This paper investigates the perceptual quality of video affected by packet losses. We focus on low-resolution and low bit-rate video coded by the H.264/AVC encoder and the packet loss patterns likely in low bit-rate wireless networks. We examine the impact of several factors on the perceptual quality, including the error length (the error propagation duration after a loss), the loss severity (measured by the pixel difference between reference and distorted video in the area affected by a loss), loss location, the number of losses, and loss patterns. Based on our findings, we propose an objective metric for the quality degradation due to packet losses that considers all these factors. We also validate a prior metric relating the quality degradation due to compression artifacts and the peak-signal-to-noise ratio (PSNR). We finally propose a full-reference metric that measures the overall quality degradation due to both packet losses and lossy compression. The proposed metric correlates very well with subjective ratings, for a large set of video clips with different loss patterns, coding artifacts, and scene contents.
A Study of SNR and BER in Heat-Assisted Magnetic Recording

Heat-assisted magnetic recording (HAMR) is a promising approach to increase the areal density of hard disk drives. The purpose of this paper is to provide a computational framework for optimizing the HAMR system. In order to do that, an integrated HAMR simulation system was built. We then studied several factors that could affect the playback signal-to-noise ratio (SNR), such as the switching time of the writing field, the peak temperature of the heat spot, the standard deviation of anisotropy field and exchange parameters, and the size of the reader element. We found that bit length (BL) becomes the key factor for the value of SNR for small, e.g., 10 nm, BLs. We established a relationship between the SNR and the bit error rate so that it will be easy to predict the storage capacity based on the playback SNR. This curve is used to predict the possible storage capacity for different BLs.
Applying Q-Learning Algorithm to Study Line-Grasping Control Policy for Transmission Line Deicing Robot

Ice coating in power networks could result in power-tower collapse and power interruption. This paper introduces a preliminary design of deicing robot, which travels on transmission lines and automatically remove ices. Inevitably, the deicing robot will encounter some obstacles. To cross an obstacle, the deicing robot needs to control its arms to grasp transmission line over the obstacle. In this paper, Q-learning, one of reinforcement learning algorithms is applied to study the line-grasping control strategies for deicing robot. We implement a graphical simulation environment and use it to evaluate the Q-learning based line-grasping control policy study algorithm. Simulation results show that the proposed algorithm is promising for the line-grasping control of deicing robot.
Auto Mutual Information Analysis with Order Patterns for Epileptic EEG

In this study, we investigated auto mutual information (AMI), based on order patterns analysis, as a tool to evaluate the dynamical characteristics of electroencephalogram (EEG) during interictal, preictal and ictal phase, respectively. Permutation entropy quantifies regularity in time series, while AMI detects the mutual information (MI) between a time series and a delayed version of itself. The results show that AMI method was able to reveal that the highest entropy values were assigned to interictal EEG recordings and the lowest entropy values were assigned to ictal EEG recordings. The classification ability of the AMI measures is tested using ANFIS classifier. Test results confirm that AMI method has potential in classifying the epileptic EEG signals.
Consensus algorithm for multiple quadrotor systems under fixed and switching topologies

The distributed leadless consensus problem for multiple quadrotor systems under fixed and switching topologies is investigated. The objective is to design protocols achieving consensus for networked quadrotors' positions and attitudes. Because the model of a quadrotor is a strong high-order nonlinear coupling system, the approach of feedback linearization is employed to transform the model into a group of four linear subsystems among which there is no coupling. Then, a consensus algorithm is proposed which consists of a local feedback controller and interactions from the finite neighbors under fixed undirected topologies. Especially, the problem of choosing the parameters in the consensus algorithm is also addressed, enlightened by the results of the robust control theory. Furthermore, it is proved that the proposed algorithm also guarantees the consensus under undirected switching topologies. Simulation results show the effectiveness of the proposed algorithm.
Rate-distortion modeling for multiscale binary shape coding based on Markov random fields

The purpose of this paper it to explore the relationship between the rate-distortion characteristics of multiscale binary shape and Markov random field (MRF) parameters. For coding, it is important that the input parameters that will be used to define this relationship be able to distinguish between the same shape at different scales, as well as different shapes at the same scale. We consider an MRF model, referred to as the Chien model, which accounts for high-order spatial interactions among pixels. We propose to use the statistical moments of the Chien model as input to a neural network to accurately predict the rate and distortion of the binary shape when coded at various scales.
Design and Evaluation of Reliability Mechanisms in P2PSIP-Based Conference System

This paper presents a suite of reliability mechanisms for P2PSIP-based conference services. These mechanisms can be divided into two categories. One is backup mechanism, which is designed to elect backup conference provider in a distributed and dynamic manner among all the participants of conference. The other category contains handover and failover mechanisms, which are designed to maintain continuous service for other nodes after the master conference provider leaves. Besides, the performance of proposed mechanisms is evaluated in a mixture network environment containing both wireless and wired links. The results have proved that the proposed reliability mechanisms are feasible in network environment where P2PSIP-based conference system is deployed.
Rough Neural Network Modeling Through Supervised G-K Fuzzy Clustering

On the basis of fuzzy rough data model (FRDM), a method to construct rough neural network is proposed. By adaptive Gaustafason-Kessel (G-K) clustering algorithm, fuzzy partition can be accomplished in input data space. Then based on the search of cluster number, optimal FRDM will be found, and by integrating it with neural network technique, corresponding rough neural network is constructed. The experiment results indicate that rough neural network is superior to traditional Bayesian and learning vector quantization (LVQ) methods, moreover, rough neural network has more powerful synthetic decision-making ability than single FRDM model.
Image Representation Using Block Pattern Models and Its Image Processing Applications

An image representation scheme using a set of block pattern models (BPMs) consisting of three categories (constant, oriented, and irregular) is introduced. Algorithms for model classification, model parameter estimation, and image reconstruction from model parameters are presented, and these provide the necessary vehicles for applying the proposed representation scheme to various image processing tasks. The applications of the proposed models in image coding, image zooming, and image smoothing are described. Satisfactory coded images have been obtained at bit rates between 0.5 approximately 0.6 b.p.p. (bits per pixel) with a high-rate realization and between 0.3 approximately 0.5 b.p.p. with a low-rate realization. The high-rate realization has a simple structure suitable for real-time implementation. The methods for image zooming and smoothing are similar, where both adapt the processing for each pixel according to the model of its neighborhood. By using directional filters in oriented regions, edges and lines are rendered sharper in a smoother manner than with conventional linear filtering approaches, which leads to significant improvement in perceived image quality.
Layered Wireless Video Multicast Using Relays

Wireless video multicast enables delivery of popular events to many mobile users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. In this paper, an integration of layered video coding, packet level forward error correction, and two-hop relaying is proposed to enable efficient and robust video multicast in infrastructure-based wireless networks. First, transmission with conventional omni-directional antennas is considered where relays have to transmit in non-overlapping time slots in order to avoid collision. In order to improve system efficiency, we next investigate a system in which relays transmit simultaneously using directional antennas. In both systems, we consider a non-layered configuration, where the relays forward all received video packets and all users receive the same video quality, as well as a layered setup, where the relays forward only the base-layer video. For each system setup, we consider optimization of the relay placement, user partition, transmission rates of each hop, and time scheduling between source and relay transmissions. Our analysis shows that the non-layered system can provide better video quality to all users than the conventional direct transmission system, and the layered system enables some users to enjoy significantly better quality, while guaranteeing other users the same or better quality than direct transmission. The directional relay system can provide substantial improvements over the omni-directional relay system. To support our results, a prototype is implemented using open source drivers and socket programming, and the system performance is validated with real-world experiments.
A novel virtual age reliability model for Time-to-Failure prediction

Mean Time To Failure (MTTF) is widely accepted as the main reliability metric in industry. However, several works indicate that MTTF does not accurately capture the reliability characteristics of Integrated Circuits (ICs) and systems given their relatively short operating lifetime. To overcome the MTTF weakness, this paper proposes a novel virtual age based reliability model, which is able to predict the electronic systems Time-To-Failure (TTF). The aging and degrading factors that have an influence on the system's reliability are modeled as cumulative increments of the system's virtual age, and the system's failure point is defined as a proper cut-off cumulative failure rate during its operating lifetime. Thus, system's TTF can be easily estimated based on its virtual age, which reflects the current and historical reliability status of the system. The proposed model is computationally recursive and provides real-time reliability status and prediction, which are critical requirements for enabling reliability-aware resource management and computing.
Simulation of expected areal density gain for heat assisted magnetic recording

Areal density increases in conventional perpendicular magnetic recording are becoming increasingly difficult to achieve. Heat assisted magnetic recording is viewed as a potential technique to extend magnetic recording into the multiple-terabit range. Over the last 5 years, we have performed extensive simulations of Heat Assisted Magnetic Recording on both granular and bit-patterned media. For this purpose, we represent the behavior of granular media near the Curie temperature with renormalized blocks of spins of order 1 nm3. The change in magnetization of these blocks can then be evaluated using the Landau-Lifshitz-Gilbert equation. The behavior of bit patterned media is typically evaluated using an atomistic approach. Optical spots are calculated using a finite difference time domain technique and heat flow is evaluated using the usual Fourier differential equation. We have also evaluated the performance of competing technologies including both conventional and shingled recording of both granular and bit patterned media.
Near-field transport imaging application of photovoltaic materials

We applied a novel analytical technique-near-field transport imaging (TI)-to photovoltaic materials for charge-carrier transport mapping in nanometer-scale. We measured the diffusion length of a well-controlled gallium arsenide (GaAs) thin-film samples and it agrees well with the results calculated by time-resolved photoluminescence. We report for the first time on TI experiments on thin-film cadmium telluride, including the effective carrier diffusion length, as well as the first near-field imaging of the effect of a single small defect on carrier transport and recombination in a GaAs sample. Furthermore, by changing the scanning setup, we were able to do near-field cathodoluminescence (CL), and correlated the results with standard CL results. The TI technique shows great potential for high spatial resolution mapping transport properties in solar cell materials.
Teacher Teaching Effectiveness Test on Landscape CAD Course

Teacher teaching effectiveness test is a very important link in the teaching procession. A test model on the teacher teaching effectiveness of the landscape CAD course is established with Group Decision Analytical Hierarchy Process so as to provide a basis for improving the teaching quality. This test model is made up of 3 layers including the target layer, the element layer and the index layer. The element layer includes content of courses, computer practice and media application, the index layer is made of 11 factors, including specific aim, reasonable capacity, practical content, fresh knowledge, time distribution, acceptant teaching method, skilled operation, appropriate method, patience, advanced technology, abundant attraction. The model tree is established by the relationship of administrative subordination. The results show that the teacher teaching effectiveness of the landscape CAD course in Henan Institute of Science and Technology is excellent, and the test system based on Group Decision Analytical Hierarchy Process combining qualitative analysis and quantitative analysis is easy to use. This system could also be adapted to the teacher teaching effectiveness test of other courses.
A scheme of intelligent control applied to a servo system

This paper presents a novel intelligent control scheme. It is a fuzzy logic controller (FLC) in combination with a neural network controller (NNC) based on a microcomputer. The combined controller is named a neural fuzzy controller (NFC). With the help of FLC inference, NNC can accelerate its self-learning process. Therefore the NFC, with the proposed scheme gains an excellent control reasoning and adaption. This has been proved effective both by simulations and a practical application in a servo system.