nils-herrmann/clean_scirepeval huggingface dataset:
A Class of Constrained Adaptive Beamforming Algorithms Based on Uniform Linear Arrays

A new class of adaptive beamforming algorithms is proposed based on a uniformly spaced linear array by constraining its weight vector to a specific conjugate symmetric form. The method is applied to the well-known reference signal based (RSB) beamformer and the linearly constrained minimum variance (LCMV) beamformer as two implementation examples. The effect of the additional constraint is equivalent to adding a second step in the derived adaptive algorithm. However, a difference arises for the RSB case since no direction-of-arrival (DOA) information of the desired signal is available, which leads to a two-stage structure for incorporating the imposed constraint. Compared to the traditional algorithms, the proposed ones can achieve a faster convergence speed and a higher steady state output signal-to-interference-plus-noise ratio, given the same stepsize.
A Survey on Visual Place Recognition for Mobile Robots Localization

Visual place recognition is an active research field in the robotic navigation and localization, which means the ability to recognize a known place in the environment using vision as the main sensor modality. Despite significant progress in computer vision and machine learning techniques, challenges remain especially in dynamic environments such as illumination change, viewpoint change and so on. In this paper, a survey and comparative study on existing approaches of visual place recognition is presented, including place feature extraction methods, image similarity metrics and searching algorithms, as well as some benchmark datasets and evaluation metrics. Experimental results show that the methods combining feature extraction using convolutional neural networks and sequential image searching achieve higher precision in large scale dynamic environment.
An approach to the chaotic synchronized secure communication based on CNNs

Because of the individual chaotic behavior of cellular neural networks (CNNs), the application of active-passive decomposition (APD) synchronization technique on 4 cells CNNs was investigated. Its synchronization stability and properties were analyzed. A 4-cell CNNs chaotic synchronized secure communication scheme using APD was proposed. By computer simulation using MATLAB, the synchronization setting time is 22.4 s under different initial state between the transmitter CNNs and the receiver CNNs, the message signal is recovered accurately at the receiver CNNs in the case of no noise on signal transmitter line. The CNNs chaotic synchronized secure communication scheme using APD is applicable to digital implementation.
BER Criterion for Transmit Beamforming in MISO-OFDM with Limited Feedback

In this paper, we propose a new beamforming vector selection criterion for transmit beamforming in multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) systems with codebook-based limited feedback. By dividing the total subcarriers into a series of clusters, from the codebook we choose the "proper" one, which directly minimizes the average bit error rate (BER) of the considered cluster, as the beamforming vector for this cluster. The simulation results show that the proposed beamforming vector selection criterion surpasses other approaches based on indirect performance indicators in [5-7] in terms of the uncoded BER performance.
A Visual Tele-operation System for the Humanoid Robot BHR-02

This paper presents a method to reconstruct the virtual scene of the humanoid robot teleoperation to overcome the problem of the poor vision images feedback. A virtual robot interface is built to render the data of the real robot. It has the same DOF set and the same size scale as the real robot. The interface can render the multiple real-time feedback data from the robot. In the data-fusion module an algorithm is adopted to determine the position and attitude of the robot body. Some experiments are done to confirm the effectiveness of the virtual scene
Analysis of frame synchronization algorithm and performance based on GPS L1C navigation signal

GPS L1C navigation signal is a representative of new system signals, whose navigation message in the IS-GPS-800 protocol is not fixed frame header structure. Because of the data has no constant frame header, traditional algorithms of frame synchronization unable to be used. Based on the characteristics of L1C signal, a common algorithm is correlation function algorithm of the overlay code. The following aspects are studied in this paper: Performance of this algorithm under low SNR; How to determine the detection threshold. In this paper the complete overlay code is used to analysis the relationship between SNR and success rate of frame synchronization.
Certificateless Blind Signcryption Scheme with Message Recovery Design

To the question that the transmission bandwidth is limited but requires to sign efficiently and safely, a new certificate less blind sign cryption scheme with partial message recovery is proposed in this article based on the exiting certificate less based blind sign cryption scheme and identify-based signature scheme with partial message recovery. This scheme can track KGC dishonest behavior. Adopted to shorten the total length of the message and its signature, and accomplish signature's authentication and confidentiality within a reasonable step. As a result, in bandwidth constrained environments, it can complete the signature safely, efficiently and accurately.
Application of adaptive support vector machines method in credit scoring

Credit scoring has attracted lots of research interests in the literature. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This article introduces support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the Australian and German credit datasets from UCI.
A pivot-based filtering algorithm for enhancing query performance of LSH

In recent years, Locality Sensitive Hashing (LSH) (and its variant Euclidean LSH) has become a popular index structure for large-scale and high-dimensional similarity search problem. In this paper, we analyze a phenomenon we called “Non-Uniform” that degrades the query performance of LSH and propose a pivot-based algorithm to improve the query performance. We also provide a method to get optimal pivot for even larger improvement. Experiments show that our algorithm significantly improves the query performance of LSH.
Computation of radiated electric fields from cables using the 1D time-domain and frequency-domain TLM methods

An intermediate level modeling method aimed at providing a faster solution with less computing resources to allow designers to obtain rapid approximations is valuable. This paper presents a modeling technique to speed up the evaluation of radiated fields from the interconnect cables. Based on the Hertzian dipole radiation theory and the 1D time domain and frequency domain transmission line modeling (TLM) solutions, the radiating cables are modeled by the sum of a large number of short dipoles. The proposed prediction technique of radiation is verified by a bare wire cable radiation measurement.
Discriminative kernel transfer learning via l2,1-norm minimization

In this paper, we propose a l2,1-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l21-norm minimization. The DCC metric includes two parts: domain-consistency used to measure the between-domain distribution discrepancy and class-consistency used to measure the within-domain class separability. The objective of transfer learning is to maximize the proposed metric, while for easily formulating this metric in model, we propose to minimize the domain-class-inconsistency, such that both domain distribution mismatch and class inseparability are well addressed. Two advantages of the proposed method are that on one hand the robust sparse coding selects a few valuable source data with noises (outliers) removed during knowledge transfer, and the proposed DCC metric can help to pursue discriminative subspaces of different domains for classification based transfer learning tasks. Extensive experiments demonstrate the superiority of the proposed method over other state-of-the-art domain adaptation methods.
Control strategy for grid-connection converter under the distorted grid condition

With the development of wind industry, more power electronic converter devices are incorporated into the grid, which leads to the increase of harmonic content in the grid. So the harmonic suppression of grid current in double-fed induction generator (DFIG) converter has been a hot topic recently. The mathematical model of DFIG converter, in view of the 5- and 7+ order components of grid voltage harmonics, is proposed and analyzed in detail. Based on the analytical model, more studies are conducted on the current harmonic and power ripple problems under distorted grid. The harmonic suppression of the system with the current regulators based on proportional-integral and resonant (PIR) is compared with proportional-integral (PI). Based on the above analysis, two different control strategies were proposed to reduce the grid current harmonic components and power ripple. Simulation results verify the correctness of the control strategy.
A Variable-Decoupling- and MSR-Based Imaging Algorithm for a SAR of Curvilinear Orbit

For a synthetic aperture radar (SAR) onboard a platform with a rectilinear track, the range history of a point target can be accurately expressed hyperbolically. The track can be curvilinear for a maneuverable SAR platform. The hyperbolic equation becomes inadequate, and an expression with high-order terms is needed. Using the method of series reversion, we derived the 2-D spectrum for the return signal of the curvilinear SAR. There were five independent variables in the spectrum, but available imaging algorithms could only handle three in the focusing using the spectrum. Thus, a variable-decoupling method was developed to reparameterize the initial spectrum so that only three variables remained. After the incorporation of the decoupling method into the chirp-scaling algorithm, simulations of the SAR with a curvilinear track were studied. Promising results were obtained.
Convenient fabrication of composites with aqueous synthesized quantum dots for LED encapsulation

Organic approach is commercialized for fabrication of quantum dots (QDs) with the defects of high reagents expense, severe reaction conditions, toxic reaction residue etc. Although aqueous synthesis QDs has bad compatibility with common LEDs encapsulation material, like silicone and epoxy, it is relatively low cost, environmental friendly and simple. In this article, undoped and doped CdS were prepared through aqueous solution synthesis with 3-mercaptopropionic acid used as stabilizing agent. Convenient silica coating method was developed and mixed with silicone before curing since termination would occur when QDs were directly mixed with commercial silicone due to catalyst poisoning. Besides, quaternary ammonium salt was applied for fabricating QD-epoxy composite, which did not observe phase separation. The method is simple for acquiring the QDs without emission wavelength shift or quenching comparing with classical methods. The two methods can be guidances for assistance the development of LED with aqueous synthesized QDs.
Development of a Robotic 3D Scanning System for Reverse Engineering of Freeform Part

Nowadays, reverse engineering has been used more often for inspection and manufacturing in industry, such as the freeform part widely used in the engineering areas of automotives, aerospace and shipbuilding. However, due to the entire process is complicated and largely human intervened, the efficiency is low and the cost is very high. In this research, we propose a robotic 3D scanning system for parts having freeform surfaces that significantly reduce the time. Two scanning modes are designed for measuring different kinds of part automatically in this system. The scan plan and the robot scan program are also described. The system can be integrated in mechanical machining. An industrial application is presented.
Development of new pipeline maintenance system for repairing early-built offshore oil pipelines

This paper introduces a set of new pipeline maintenance system for early-built long-distance offshore oil pipelines. An on-line intelligent inspection pig is developed using ultrasonic tool to detect defects in oil pipelines and the acquired data is stored for off-line analysis. Through analysing the data, defects can be found, sized, classed, and besides, the plan to locating them is made. An autonomous pipeline robot is developed to carry out the locating defects tusk, which adopts the way of detecting girth welds, combined with odometer wheels to locate the defects' position, so the problem of locating defects in early-built offshore pipelines unequipped with magnetic markers is solved. The paper introduces major components of the intelligent inspection pig and the autonomous pipeline robot and describes the process of maintaining offshore oil pipelines with the developed system.
Development of the traveling crane main girder CAE platform based on ANSYS

The finite element modeling and the numerical simulation of general main girder demand profound mechanical knowledge and engineering experience, so it is very difficult and needs more time. For this situation, this paper puts forward a secondary development based on ANSYS and sets up a virtual simulation platform used in general main girder's design. The complexity of verification and checking on the design plan of crane main girder is reduced and a quick FEA modeling and simulation is made possible. It also offered an effective help on manufacture of crane products.
A Selective Co-Channel Interference Mitigation Method for Alamouti Code

Alamouti Code makes use of a rich algebraic structure to provide diversity gains with small decoding complexity. In this work, we investigate the problem of co-channel interference (CCI) mitigation for two users applying AC in flat fading and quasistatic wireless channels. By exploring the algebraic properties of Alamouti matrix, we develop a selective CCI mitigation method at the receiver. Assuming that zero-forcing (ZF) or minimum mean-squared error (MMSE) interference suppression techniques are used to separate two users and according to post-filtering instantaneous signal-to-noise-ratio (ISNR) or instantaneous signal-to-interference-plus-noise ratio (ISINR), we derive a very simple unified selection formula, avoiding computing the inverse (pseudoinverse) of channel matrix. Through the selection, we detect the user with higher post-filtering ISNR or ISINR, and then subtract its contribution from the received signals and detect another user. The complexity analysis and simulation results show that our method demonstrates better performance versus complexity tradeoff compared to the existing methods.
Analysis on Bump-Jump threshold in BOC(1,1) signal tracking

Compared with the traditional navigation modulation, binary offset carrier (BOC) has advantages in resistance to interference, multipath fading and positioning accuracy. But its multi-peak autocorrelation function will lead possible biased tracking. Bump-Jump (BJ) technique can effectively solve the problem. This paper derives the false alarm probability, detection probability and the average detection time of BJ technique focusing on BOC(1,1), at the same time, give the method to choose threshold, and proposes the modified BJ technique. Analysis shows the threshold and average detection time changing rules with the variation of Carrier-to-Noise Ratio (CNR) and coherent accumulated time. Modified Bump-Jump technique filters the detection samples and constraints the implemented time which more fits for the project implement.
A New Static Detection Method of Malicious Document Based on Wavelet Package Analysis

More and more advanced persistent threat attacks has happened since 2009. This kind of attacks usually use more than one zero-day exploit to achieve its goal. Most of the times, the target computer will execute malicious program after the user open an infected compound document. The original detection method becomes inefficient as the attackers using a zero-day exploit to structure these compound documents. Inspired by the detection method based on structural entropy, we apply wavelet analysis to malicious document detection system. In our research, we use wavelet analysis to extract features from the raw data. These features will be used todetect whether the compound document was embed malicious code.
Analysis of factors in phase array antenna and RF units on system performance of the OFDM PHY of IEEE 802.11ad standard

This paper presents a system-level analysis based on the OFDM PHY of IEEE 802.11ad standard and phase array antenna application. The quantitative relationships between system non-ideal factors and system level parameter error vector magnitude (EVM) are given. The focused factors in phase shift antenna are mainly phase shifter resolution, antenna configuration, and in RF impairments are phase noise, PA nonlinearity and IQ mismatch. Besides, phase array antenna's gain superposition is specially studied and illustrated. This work provides RF designers with comprehensive system-level overviews of 802.11ad RF transceivers and phase array antennas for multi-giga bits/s wireless communications on a pre-silicon base.
An MCD-based local ACE algorithm for hyperspectral imagery target detection

Unstructured detectors such as KGLRT, ACE and AMF are widely applied for target detection in hyperspectral imagery (HSI). However, conventional global and local approaches construct background model without considering the contamination caused by anomalies and suspected targets. This paper proposes a local ACE algorithm based on the minimum covariance determinant (MCD) estimator. In the proposed algorithm, a spectral angle based clustering method is applied to the whitened hyperspectral data to form several disjoint clusters over the whole image. Then for each cluster, the robust estimations of its background statistics are obtained using the MCD estimator. Finally, the ACE detector is applied to each pixel utilizing the robust background statistics of the cluster. With experimental results on two different real datasets, the superiority of the proposed algorithm is demonstrated.
Demonstration of a Directed Optical Comparator Based on Two Cascaded Microring Resonators

We propose a directed optical comparator, which is composed of two microring resonators (MRRs) and a 2 × 1 optical combiner. The two MRRs are modulated by two electrical signals, which represent the two binary numbers to be compared. Three optical signals are output by the device, which represent the comparison results. As a proof of concept, a directed optical comparator based on thermo-optic effect is fabricated and the device can perform the comparison function of two binary numbers at the speed of 10 kb/s.
Develop of the Blast Furnace Soft Water Temperature Measurement System

In order to ensure normal operation of the blast furnace (BF) closed loop soft water cooling system, cooling water temperature and the heat load must be controlled. It is the most important how the hundreds of large-scale blast furnace soft water temperature points for real-time detection. The DS18B20 digital thermometer was used as a temperature sensor. The DS18B20 communicates over a 1-Wire bus that by definition requires only one data line for communication, in addition to solving the problem of wiring the scene, after the temperature sensor calibration, also Spot temperature measurement accuracy requirements can be achieved. Signal acquisition, data processing and real-time communications problems were resolved by the digital temperature converters used two MCU shared memory. Soft water temperature can be collected by digital temperature converters, and transferred to the control system through the Ethernet. This soft water temperature measurement system has been used in two 3600 M3 blast furnace cooling system.
A DHT-based multicarrier modulation system with pairwise ML detection

This paper presents a complex-valued discrete multicarrier modulation (MCM) system based on the real-valued discrete Hartley transform (DHT) and its inverse (IDHT). Unlike the conventional discrete Fourier transform (DFT), the DHT cannot diagonalize multipath fading channels due to its inherent properties, and this results in mutual interference between subcarriers of the same mirror-symmetrical pair. We explore this interference pattern in order to seek an optimal solution to utilize channel diversity for enhancing the bit error rate (BER) performance of the system. It is shown that the optimal channel diversity gain can be achieved via pairwise maximum likelihood (ML) detection, taking into account not only the subcarrier's own channel quality but also the channel state information of its mirror-symmetrical peer. Performance analysis indicates that DHT-based MCM can mitigate fast fading effects by averaging channel power gains of each mirror-symmetrical pair of subcarriers. Simulation results show that the proposed scheme has a substantial improvement in BER over the conventional DFT-based MCM system.
Analysis on the transmission delay of priority-based secondary users in cognitive radio networks

In this paper, we employ the preemptive resume priority (PRP) M/M/1 queuing model to analyze the transmission delay of priority-based secondary users (SUs) in the opportunistic spectrum access (OSA)-based cognitive radio networks (CRNs). The SUs in the OSA-based CRNs are with different priority levels. The SU with the high priority level can access a channel in preference to the low priority level SU when the channel is available. Moreover, to reduce the transmission delay of the interrupted SUs, the interrupted SUs are assigned the higher priority than the newly arrived SUs with the same priority level. The expression of the transmission delay for the interrupted SUs is derived based on the PRP M/M/1 queuing model. Simulation results show that the queuing model with priority-based SUs can effectively guarantee the quality of service of the high priority level SUs and reduce the overall transmission delay of the interrupted SUs.
An Overview on Thermal Safety Issues of Lithium-ion Batteries for Electric Vehicle Application

Lithium-ion batteries (LIBs) are being intensively studied and universally used as power sources for electric vehicle applications. Despite the staggering growth in sales of LIBs worldwide, thermal safety issues still turn out to be the most intolerable pain point, and remain the focus of research for technological improvements. This paper presents a comprehensive overview on thermal safety issues of LIBs, in terms of thermal behavior and thermal runaway modeling and tests for battery cells, and safety management strategies for battery packs. Considering heat generation mechanism and thermal characteristics of LIBs, heat generation, dissipation and accumulation inside a cell are elaborated. The triggering factors leading to thermal runaway are also summarized. Finally, thermal runaway detection and prevention strategies for both cell- and pack-levels are introduced. Different engineering approaches from material refinement and additive adoption to thermal, electrical, and mechanical design are presented for thermal runaway prevention.