nils-herrmann/clean_scirepeval huggingface dataset:
Multiple-Rendezvous Multichannel MAC Protocol Design for Underwater Sensor Networks

Compared with traditional terrestrial radio transmissions in wireless sensor networks, the challenges of transmissions in underwater sensor networks (UWSNs) include lower transmission rate, longer delay time, and higher power consumption. In such a circumstance, the negative effects of transmission collisions deteriorate. Most of the existing UWSN medium access control (MAC) protocols handle the collision problem in a single-hop or light-loaded environment. They fail to function effectively in a multihop network consisting of more sensor nodes with heavier traffic loads. Using the concept of cyclic quorum systems, we propose a distributed multiple-rendezvous multichannel MAC protocol, MM-MAC, in this paper to reduce collision probability. The advantages of the proposed protocol are threefold: 1) Only one modem is needed for each node to solve the missing receiver problem which is often encountered in multichannel protocols; 2) multiple sensor node pairs can complete their channel negotiations on different channels simultaneously; and 3) data packets will not be collided by control packets and vice versa. Simulation results verify that our protocol can reduce collision probability significantly which enhances the network performance in a multihop UWSN.
Gradient descent cyclic algorithm — An algorithm to design the filter set in TDCFS method

Transmit diversity code filters set (TDCFSs) adopted in ATSC3.0 is a method using all-pass linear filters to minimize the possibility of cross-interference among the transmitted signals. This paper presents a new cyclic algorithm for the filter set design in TDCFS. In the case of designing the filter set to have almost identical correlation property, the proposed algorithm requires less iteration times than the algorithm from the literature because of its simplicity in calculation. Thus, it can be used to design the filter set consisting of a huge number of filters and long sequences filters with relatively lower complexity compared with the algorithms in literature. A numerical example and system simulation results are also provided in this paper to demonstrate the better performance than that of the state of the art.
Detecting Moving Objects by Ant Colony System in a MAP-MRF Framework

Background subtraction is a traditional method for detecting objects in stationary background. However, this traditional method is difficult to detect objects accurately in the real world, because the background is usually cluttered and not completely static. In this paper, we propose an object detection approach using Ant Colony System (ACS) in a MAP-MRF framework. For object segmentation, a MAP-MRF framework is constructed based on the background model which is represented by the adaptive kernel density estimation method. Then, the MAP-MRF based object detection is implemented by Ant Colony System (ACS). The pheromone trail in ACS is defined reliably via the local energy of MAP-MRF. Extensive experiments show the utility and performance of the proposed approach.
Finger vein image deblurring reconstruction based on Polak-Ribière conjugate Gradient Projection

In this paper, we presents a compressive sensing (CS) based application --Polak-Ribière conjugate gradient projection (PR-CGP) for solving bound constrained quadratic program to reduce noise in synthetic vein images and real finger vein images respectively which blurred with various noise. Then compares the result with the reconstruction by Gradient Projection for Sparse Restruction(GPSR) algorithm. The results show that the method has better performance than the GPSR method in reducing noise, and thus provides more accurate information for vein recognition and extraction.
Design, Fabrication, and Calibration of a Piezoresistive Stress Sensor on SOI Wafers for Electronic Packaging Applications

This paper presents the development of a piezoresistive stress sensor fabricated on silicon-on-insulator (SOI) wafers for measurement of electronic packaging stress at high temperature. The sensor consists of a series of sensor elements and calibration elements. The sensor elements comprise a 0deg-90deg p-type piezoresistor pair and a plusmn45deg n-type piezoresistor pair for stress measurement, and the calibration elements comprise two polar three-piezoresistor rosettes with specific angels to calibrate the piezoresistive coefficients. The sensor and the calibration piezoresistors are etched from the SOI layer as separate ldquosilicon islandsrdquo on the dielectric buried oxide (BOX) layer. This configuration exploits the excellent electrical insulation of the BOX layer, and enables high-temperature operation of the stress sensor by eliminating the leakage current. Design, fabrication, and the calibration of the piezoresistors at high temperatures show the feasibility of the SOI high-temperature stress sensor. The piezoresistive coefficients are calibrated versus stress and temperature, and the nonlinearity of the resistance versus temperature and the calibration errors are discussed in detail.
Interference analysis and simulation for GPS/Galileo signals

With the building and modernization of satellite navigation systems, compatibility analysis between them had become a more and more important issue. According to the crude compatibility methodology proposed by Unite States and European Union joint work group, a more accurate methodology was proposed in this paper by deriving more precise signal models, which considered real PRN codes and data rate. The effects of different signal parameters onto Spectral Separation Coefficient (SSC) were not the same. By the way of simulation, it was concluded that the effect of signal with short code and low data rate onto SSC was obvious, and the effects of signals from different satellites with same modulation onto SSC were almost identical. Finally, the intrasystem and intersystem interference between GPS and Galileo in L1 was simulated. It was resulted that the major interference was GPS intrasystem interference in the case of receiving GPS C/A signals, maximum was 2.86dB; the major interference was the one from GPS C/A signals in the case of receiving Galileo E1B signals, maximum was 0.364dB. Moreover, the change of using MBOC as Galileo E1 Open Service signal instead of BOC(1,1) made the interference onto GPS L1 C/A signal reduced by 0.207dB.
On the application of data mining technique and genetic algorithm to an automatic course scheduling system

In this study, a data mining technique and a genetic algorithm are applied to an automatic course scheduling system to produce course timetables that best suit studentspsila and teacherspsila needs. Course scheduling in colleges and universities is a highly complicated task for satisfying multiple constraints. Conventional course scheduling is done mainly from the schoolpsilas point of view, such that courses and timetables are planned according to the characteristics of individual departments and institutes, with little attention to studentspsila interests and their needs in career development. This study develops a practical automatic course scheduling system based on studentspsila needs, wherein the course scheduling process is divided into two stages. In the first stage, studentspsila needs in course selection are considered and an association among courses selected by students is mined using the association mining technique; while in the second stage, the genetic algorithm is used to arrange the course timetable. More particularly, this study is based on studentspsila willingness in course selection, analyzes the effects of course arrangement in different class periods on studentspsila learning performance, takes into account teacherspsila preferred schedules, determines the cost function value of each class period, and then applies the genetic algorithm for class period exchange, so as to produce an optimal course timetable. It is shown in the experiment results that the automatic course scheduling system proposed in this study not only can efficiently replace the onerous operation of conventional manual course scheduling, but also produce course timetables that truly fulfill userspsila needs and increase studentspsila and teacherspsila satisfaction, thereby providing a win-win-win solution for students, teachers and the school.
Photovoltaic characteristics of MOS structure with photo enhanced trap assist tunneling current by oxide etching

In this work, MOS photovoltaic mechanism with side wall trap-assist tunneling path by oxide etching was demonstrated. It was clearly observed that the photo response of device with oxide etching is tremendously enhanced with respect to that without. The efficiency of MOS photovoltaic with side wall trap-assist tunneling is strongly dependent on the etched potion of oxide. The perimeter of electrode is critical for MOS photovoltaic application.
Is Physical Layer Error Correction Sufficient for Video Multicast over IEEE 802.11g Networks?

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. A promising solution to this problem is the use of packet level Forward Error Correction (FEC) mechanisms. However, the adjustment of the FEC rate is not a trivial issue due to the dynamic wireless environment. This decision becomes more complicated if we consider the multi-rate capability of the existing wireless LAN technology that adjusts the transmission rates based on the channel conditions and the coverage range. In this paper, we explore the dynamics of Forward Error Correction (FEC) schemes in multi-rate wireless local area networks. We study the fundamental behavior of a 802.11g network which already has embedded error correction in physical layer, under unicast and broadcast modes in a real outdoor environment. We then explore the effectiveness of packet level FEC over wireless networks with multi-rate capability. In order to evaluate the system quantitatively, we implemented a prototype using open source drivers, and ran experiments. Based on the experimental results, we provide guidelines on how to efficiently use FEC for wireless multicast services in order to improve the overall system performance. We argue that even there is a physical layer error correction, using a higher transmission rate together with stronger FEC is more efficient than using a lower transmission rate with weaker FEC for multicast.
Automatic Bibliometric Analysis of Research Literature in Adult Education

Due to the vast volume of publications in nowadays research environment, methods for analyzing, organizing, and accessing information from large databases are in great need. Through appropriate analytic tools, competitive intelligence can be revealed to enhance the organization or individual performance. This article will show how a free software package called CATAR carry on the analysis about the literature of the Adult Education science. Adult Education Quarterly (AEQ) is a scholarly forum of research and theory in adult education. The field of adult education is broad-based and inter disciplinary, during 1990 to 2010 having 672 papers from the ISI Web of Knowledge database was collected. The data analyzed include the distribution of authors' countries, topical clusters, the most cited references, the productivity rankings, and others to provide information for further study. Our analysis scenario also suggests a feasible way of analyzing other similar data for readers interesting in literature mining applications.
Modeling rate and perceptual quality of scalable video as functions of quantization and frame rate and its application in scalable video adaptation

This paper investigates the impact of frame rate and quantization on the bit rate and perceptual quality of a scalable video with temporal and quality scalability. We propose a rate model and a quality model, both in terms of the quantization stepsize and frame rate. The quality model is derived from our earlier quality model in terms of the PSNR of decoded frames and frame rate. Both models are developed based on the key observation from experimental data that the relative reduction of either rate and quality when the frame rate decreases is quite independent of the quantization stepsize. This observation enables us to express both rate and quality as the product of separate functions of quantization stepsize and frame rate, respectively. The proposed rate and quality models are analytically tractable, each requiring only two content-dependent parameters. Both models fit the measured data very accurately, with high Pearson correlation. We further apply these models for rate-constrained bitstream adaptation, where the problem is to determine the optimal combination of quality and temporal layers that provides the highest perceptual quality for a given bandwidth constraint.
Multi-object visual tracking based on reversible jump Markov chain Monte Carlo

Markov chain Monte Carlo-based multi-object visual tracking has been investigated here. To improve the confidence of sampling and perform the iteration effectively, a new approach to multi-object visual tracking is proposed based on reversible jump Markov chain Monte Carlo sampling. The tracking problem is formulated as computing the maximum a posteriori estimation given image observations. Four types of reversible and jump moves are designed for Markov chains dynamics, and prior proposal distributions of objects are developed with the aid of association match matrix. The joint likelihood distribution measurement is presented at two levels of clustered blocks subsets and pixels. Experimental results and quantitative evaluation demonstrate that the proposed approach is effective for challenge situations.
Position-Sensorless Hybrid Sliding-Mode Control of Electric Vehicles With Brushless DC Motor

This paper proposes an efficient and robust control scheme for the position-sensorless electric vehicle (EV) with a brushless dc motor. The back electromotive force detection method is first adopted and improved to implement sensorless control of the motor. The equivalent circuits of the control system are depicted, and the mathematical models under normal driving and energy regeneration are then derived, respectively. By combining the advantages of nonsingular terminal sliding mode with the high-order sliding-mode method, a hybrid terminal sliding-mode (HTSM) control scheme for EV is put forward to guarantee both system performance and robust stability. Experimental results show that the scheme can perfectly implement position-sensorless control without Hall sensors and that the HTSM exhibits better performance and higher efficiency than the proportional-integral-differential (PID) controller. Furthermore, more energy is recovered, and by the proposed scheme, the driving range is improved 5.7% more than using the traditional controller.
The compensation control of reactive power and three phase unbalance load based on the method of sequence component

A static var compensator (Static Synchronous Compensator, STATCOM) is compensated reactive power and three phase unbalance load for the distribution grid. According to analyzing the positive sequence equivalent circuit and negative sequence equivalent circuit under the three phase unbalance load of distribution grid, using the symmetrical component method and superposition principle, a new cascade loop control strategy of positive sequence current and negative sequence current is proposed. Using δ-θ control, the positive sequence loop is compensating reactive power of distribution grid, and maintains stability of STATCOM DC voltage. Using ε-θ control, the negative sequence loop is used to compensating three phase unbalance load of distribution grid. Simulation results are presented to verify the validity of the proposed control strategy.
Joint scene classification and segmentation based on hidden Markov model

Scene classification and segmentation are fundamental steps for efficient accessing, retrieving and browsing large amount of video data. We have developed a scene classification scheme using a Hidden Markov Model (HMM)-based classifier. By utilizing the temporal behaviors of different scene classes, HMM classifier can effectively classify presegmented clips into one of the predefined scene classes. In this paper, we describe three approaches for joint classification and segmentation based on HMM, which search for the most likely class transition path by using the dynamic programming technique. All these approaches utilize audio and visual information simultaneously. The first two approaches search optimal scene class transition based on the likelihood values computed for short video segment belonging to a particular class but with different search constrains. The third approach searches the optimal path in a super HMM by concatenating HMM's for different scene classes.
Robust Tensor Factorization with Unknown Noise

Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of tensor factorization has been applied for the recovery of a low dimensional subspace from high dimensional visual data. Generally, the recovery is achieved by minimizing the loss function between the observed data and the factorization representation. Under different assumptions of the noise distribution, the loss functions are in various forms, like L1 and L2 norms. However, real data are often corrupted by noise with an unknown distribution. Then any specific form of loss function for one specific kind of noise often fails to tackle such real data with unknown noise. In this paper, we propose a tensor factorization algorithm to model the noise as a Mixture of Gaussians (MoG). As MoG has the ability of universally approximating any hybrids of continuous distributions, our algorithm can effectively recover the low dimensional subspace from various forms of noisy observations. The parameters of MoG are estimated under the EM framework and through a new developed algorithm of weighted low-rank tensor factorization (WLRTF). The effectiveness of our algorithm are substantiated by extensive experiments on both of synthetic data and real image data.
A perceptual video quality model for mobile platform considering impact of spatial, temporal, and amplitude resolutions

In this paper, we investigate the impact of spatial, temporal and amplitude resolution (STAR) on the perceptual quality of a compressed video. Subjective quality tests were carried out on the TI Zoom2 mobile development platform (MDP). Seven source sequences are included in the tests and for each source sequence we have 32 test configurations generated by JSVM encoder (4 QP levels, 5 spatial resolutions, and 3 temporal resolutions) and a total of 224 processed video sequences (PVSs). Videos coded at different spatial resolutions are displayed at the full screen size of the mobile platform. We report the impact of the spatial resolution (SR), temporal resolution (TR) and quantization stepsize (QS) on the perceptual quality, individually as well as jointly. We found that the impact of SR, TR and QS can each be captured by a function with a single content-dependent parameter. The joint impact of SR, TR and QS can be modeled by the product of these three functions. The complete model correlates well with the subjective ratings with a Pearson Correlation Coefficient (PCC) of 0.99.
Application of an artificial neural network on railway passenger flow prediction

This paper applies the theory of data warehouse and data mining and its derived technology to deal with the huge amount of data of the Ticket Selling and Reserving System of Chinese Railway (TRS). We use effective data mining in passenger flow analysis to establish a logical forecasting and analysis model. Our model can provide accurate decision-making information and an advanced way of forecast for the railway transit authority to make a optimal planning for passenger transport, scientific organization of the railway system and high efficiency of transport energy.
Survivable traffic grooming in WDM mesh networks under SRLG constraints

Survivable traffic grooming (STG) is a promising approach to provide reliable and resource-efficient multi-granularity connection services in optical networks. In this paper, we study the static STG problem in WDM mesh networks employing path protection at the lightpath level. To make connections survivable under various failures such as fiber cut and duct cut, we consider the general shared risk link group (SRLG) diverse routing constraints. In addition to providing the results from the integer linear programming (ILP) approach, we propose three efficient heuristics, namely separated grooming algorithm (SGA), integrated grooming algorithm (IGA) and tabu search grooming algorithm (TSGA). While SGA and IGA correspond to an overlay model and a peer model respectively, TSGA further improves SGA and IGA by incorporating the tabu search method. Numerical results show that the heuristics use much shorter running times to generate network throughputs close to those of the ILP formulations.
GPF-based method for evaluating EVs' free charging impacts in distribution system

Conventional electric vehicles' (EVs) free charging impacts evaluation in a separate distribution system may result in an “optimistic” result. Therefore, a new method based on global power flow (GPF) with transmission and distribution grids included, defined as GPF-based method, was proposed in this paper. With EVs' free charging loads model, the power injected into the distribution system was obtained. Then, supposing global system is a master-slave-typed system, GPF-based evaluation was performed with master-slave-solver where transmission and distribution power flow were iteratively calculated. When global power flow came to convergence, the iterative relations of transmission and distribution systems were fully reflected and a more realistic evaluation result was achieved. A global power system was constructed and the simulation results of different evaluation methods were compared. GPF-based method obtained a more reliable evaluation result. Therefore, it is necessary to adopt GPF-based method to evaluate EVs' charging impacts, especially when they are largely integrated in smart grid.
Tensor RPCA by Bayesian CP Factorization with Complex Noise

The RPCA model has achieved good performances in various applications. However, two defects limit its effectiveness. Firstly, it is designed for dealing with data in matrix form, which fails to exploit the structure information of higher order tensor data in some pratical situations. Secondly, it adopts L1-norm to tackle noise part which makes it only valid for sparse noise. In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG). The use of tensor structure to raw data allows us to make full use of the inherent structure priors, and MoG is a general approximator to any blends of consecutive distributions, which makes our approach capable of regaining the low dimensional linear subspace from a wide range of noises or their mixture. The model is solved by a new proposed algorithm inferred under a variational Bayesian framework. The superiority of our approach over the existing state-of-the-art approaches is demonstrated by extensive experiments on both of synthetic and real data.