nils-herrmann/clean_scirepeval huggingface dataset:
Artificial neural networks model design of Lorenz chaotic system for EEG pattern recognition and prediction

This paper presents the preliminary work of a multidisciplinary brain research program. The goal of this research program is to generate accurate and effective signals for non-invasive brain stimulation, and deliver a hardware prototype to monitor and treat motion related mental disease such as Parkinson's and Epilepsy. It was shown in previous research that Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features. Artificial neural network (ANN) resembles brain biological neural network and can be used to simulate chaotic system. The trained ANN model can in turn be used to analyze and control brain activities. In order to investigate the chaotic phenomenons of EEG signals and develop function for automatic pattern recognition, large amount of EEG signals are required. However, EEG signals are prone to noise and the available data is very limited. It is possible to control and predict the time series outputs of chaotic systems with known equations. Therefore, in order to study the dynamic control of the brain neural networks, an ANN architecture is designed and optimized for implementing Lorenz attractor to simulate the chaotic states of EEG signals. The research includes chaotic system, ANN design and the optimization of ANN architecture, which is based on the consideration of hardware implementation. The designed ANN model is trained with Lorenz attractor outputs with a fixed set of system parameters and the optimized architecture is selected based on the training results of three training algorithms and 16 ANN architectures with different number of hidden neurons.
A fully integrated 60GHz four channel CMOS receiver with 7GHz ultra-wide bandwidth for IEEE 802.11ad standard

In this paper, a fully integrated CMOS receiver frontend for high-speed short range wireless applications centering at 60GHz millimeter wave (mmW) band is designed and implemented in 90nm CMOS technology. The 60GHz receiver is designed based on the super-heterodyne architecture consisting of a low noise amplifier (LNA) with inter-stage peaking technique, a single-balanced RF mixer, an IF amplifier, and a double-balanced I/Q down-conversion IF mixer. The proposed 60GHz receiver frontend derives from the sliding-IF structure and is designed with 7GHz ultra-wide bandwidth around 60GHz, supporting four 2.16GHz receiving channels from IEEE 802.11ad standard for next generation high speed Wi-Fi applications. Measured results show that the entire receiver achieves a peak gain of 12dB and an input 1-dB compression point of -14.5dBm, with a noise figure of lower than 7dB, while consumes a total DC current of only 60mA from a 1.2V voltage supply.
Adaptive robust slide mode trajectory tracking controller for lower extremity rehabilitation exoskeleton

An adaptive robust slide mode controller (ARSMC) was designed for lower extremity rehabilitation exoskeleton whose joints are driven by DC motors works in torque mode. The innovation point is that an adaptive robust controller was used in human gait tracking control for lower extremity rehabilitation exoskeleton. To complete the designed of the controller, the dynamic model of the robot was proposed in first; Then an adaptive robust controller was designed based on the dynamic model which was proposed forward. To satisfy the requirement of the robust performance a sliding surface which in Proportional-differential-integral form was proposed. Besides, a reaching law that has high rate of convergence and trifling buffeting with adaptive parameters was proposed in the permission of Lyapunov stability in order to resist external disturbance and make the system more adaptive to satisfy the veracity with different patient. Then the controller was simulated in MATLAB and was compared with PID controller to prove the superiority of this controller.
A broadband polarization-insensitive metamaterial absorber and its application in reducing mutual coupling between dual-polarized antennas

This paper proposes a broadband polarization insensitive meta-material absorber (MA). The unit cell of the MA is composed of the dielectric substrate sandwiched with metal open-loop resonator which is welded with lumped elements and continuous metal film. Due to its rotational symmetry, the absorber is polarization insensitive. The simulation result shows that in a lower frequency range from 1.65 GHz to 3.3GHz, the absorptivity of the meta-material absorber can achieve 80%. When this absorber is used to reduce the mutual coupling between two dual-polarized antennas, 10dB port isolation are improved.
An Oscillator-Based CMOS Magnetosensitive Microarray Biochip With On-Chip Inductor Optimization Methodology

This paper presents a high sensitivity oscillator-based CMOS magnetosensitive microarray biochip to detect clinical biomarkers in human samples and provide accurate information to diagnose human diseases. To increase the detection sensitivity, dynamic detecting range, and target number, a general methodology of designing an on-chip inductor with high quality factor, moderate size, and uniform surface magnetic field distribution is proposed, which meets all the electrical, magnetic and actual application requirements simultaneously, and also provides useful guidance for other oscillator-based magnetosensitive biochip design. As an implementation example, a microarray biochip with 14 sensor units in an area of  $2.3 \times 1.9\,\,\text {mm}^{2}$  is implemented in a 180-nm RF CMOS process to verify the biological sensing scheme and the correctness of the proposed theory and simulation. Finally, experiments using magnetic nanoparticles and cTnI were performed on our designed immunoassay detection system, which integrates the oscillator-based biosensor, microfluidic chip and immunoassay reagent into one platform for the first time, and the experimental results show a limit of detection of 50 pg/mL corresponding to 2.17 pM with a biological dynamic detecting range of 60 dB, proving the suitability of the biochip for quantitative analysis in practical medical diagnostics.
A framework for land-surface remote sensing data sharing and collaboration

In order to establish a collaborative decision making environment for scientists, we proposed an original approach to integrate remote sensing data and remote sensing analysis tools based on Web Service and GRID. The proposed framework consists of four tiers and provides three level services. Firstly, the analysis of RS information sharing is made with data resource sharing, computing resource sharing and knowledge resource sharing. Then, the framework of RS data sharing and collaboration is designed with the request of RS information sharing. The proposed framework consists of four tiers and can provide three level services. Finally, the implement of the framework is described. The three key systems, RS data retrieval and publishing system, RS processing service and Workflows system and Geo-collaboration and decision-making system are introduced in detail.
Analysis and Simulation of the GPS-Pseudolite Signals

Global positioning system has been extremely matured in the navigation and positioning field. However, there are still some limits in some aspects and some special application areas, the performance should be improved by other techniques. The pseudolite as a GPS-like ground transmitter provides a new research direction to get a high positioning accuracy and reliability. In this paper, we have described the core technologies of design and simulation realization about the C/A codes of the pseudolite system construction. It is shown that the implementation in Matlab contributes to analyzing of the signal structure and designing the C/A codes and both the simulation results and VHDL realization results are consistent.
A Majorization-Minimization Approach to Lq Norm Multiple Kernel Learning

Multiple kernel learning (MKL) usually searches for linear (nonlinear) combinations of predefined kernels by optimizing some performance measures. However, previous MKL algorithms cannot deal with Lq norm MKL if q<;1 due to the non-convexity of Lq (q<;1) norm. In order to address this problem, we apply a majorization-minimization approach to solve Lq norm MKL in this paper. It is noted that the proposed method only involves solving a series of support vector machine problems, which makes the proposed method simple and effective. We also theoretically demonstrate that the limit points of the sequence generated from our iterative scheme are stationary points of the optimization problem under proper conditions. Experiments on synthetic data and some benchmark data sets, and gene data sets are carried out to show the effectiveness of the proposed method.
Design and fabrication of a biomedical Lab-on-Chip system for SNP detection in DNA

A Lab-on-Chip system is proposed, capable of SNP (Single Nucleotide Polymorphism) detection in DNA. One of the core components is an advanced filter consisting of an ordered array of Si micro-pillars enabling fast and effective separation of 5 DNA segments with different length using chromatographic techniques. Also a dedicated micro-pump is fabricated based on conductive polymer actuation, generating the required high pressure to sustain the fluid flow through the total system. For the detector, a known detector principle is applied, but pronounced miniaturization is carried out in order to make a small and portable system.
A model-based fuzzing approach for DBMS

As one of critical components of information infra-structure, database management system (DBMS) faces various security challenges. Although fuzz testing has been used in the security evaluation of DBMS, most of current fuzzers focus on SQL syntax more than multi-phase interaction between the client and server of DBMS. This paper presents a model-based fuzzing approach to discover vulnerabilities of DBMSs, which supports state-aware and multi-phase fuzz testing. Based on the model-based fuzzing framework, a finite state machine model EXT-DBFSM is proposed to manipulate the fuzzing process and guarantee the validation of test cases. The approach is implemented and experimented on several DBMSs. The result has proved effectiveness of this approach, 14 vulnerabilities are discovered, including 10 unreleased ones.
A Tweet-Centric Algorithm for News Ranking

Ranking news is helpful because of the information explosion which overloads readers. It is also a challenging task since news is now published by both news portals and microblogging platforms in real time. Most traditional news ranking algorithms consider two factors: media focus and user attention independently. While in the paper, we propose a news ranking framework to combine the two factors together. A better ranking algorithm is obtained via the following two parts: (1) the Influence Method (IM) to rank news and (2) the News Flow Graph Method (NFGM) to locate the most influential source for duplicated news from multiple news sources. We present four strategies to evaluate user attention. Experiments show that decay strategy based on Ebbinghaus forgetting curve is the best one. To the best of our knowledge, our paper is the first attempt to utilize microblogging data for news ranking.
Class-Specific Reconstruction Transfer Learning via Sparse Low-Rank Constraint

Subspace learning and reconstruction have been widely explored in recent transfer learning work and generally a specially designed projection and reconstruction transfer matrix are wanted. However, existing subspace reconstruction based algorithms neglect the class prior such that the learned transfer function is biased, especially when data scarcity of some class is encountered. Different from those previous methods, in this paper, we propose a novel reconstruction-based transfer learning method called Class-specific Reconstruction Transfer Learning (CRTL), which optimizes a well-designed transfer loss function without class bias. Using a class-specific reconstruction matrix to align the source domain with the target domain which provides help for classification with class prior modeling. Furthermore, to keep the intrinsic relationship between data and labels after feature augmentation, a projected Hilbert-Schmidt Independence Criterion (pHSIC), that measures the dependency between two sets, is first proposed by mapping the data from original space to RKHS in transfer learning. In addition, combining low-rank and sparse constraints on the class-specific reconstruction coefficient matrix, the global and local data structures can be effectively preserved. Extensive experiments demonstrate that the proposed method outperforms conventional representation-based domain adaptation methods.
Automatic Measurement System for Shock Wave Parameters Based on Image Analysis

The traditional manual method for measuring shock wave parameters is inefficient and poor robustness, and neglects the dynamic character of the flow field. In this paper, an automatic measurement system measuring shock wave parameters is designed for SB301 wind tunnel platform according to image analysis, and the techniques and measuring procedures of the method are described. In this measuring system, an image edge detection method combining Sobel operator and flexible morphology is proposed, accordingly overcome the shortcoming of flexible morphology method losing partial edge, and the anti-noise ability is better than conventional gradient detection operator; the fast moment algorithm based on Delta is improved, consequently eliminates the limitation of Delta method only adapting to horizontal continuous image. The results for measuring shock wave parameters in plane cascade flow field show that the method is highly active and actuate one and can satisfy the precision and robustness requirement in blade profile design
A Kernel Classification Framework for Metric Learning

Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions is proposed for pairs of doublets or triplets. Then, a kernel classification framework is established to generalize many popular metric learning methods such as LMNN and ITML. The proposed framework can also suggest new metric learning methods, which can be efficiently implemented, interestingly, using the standard support vector machine (SVM) solvers. Two novel metric learning methods, namely, doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods but with significantly less training time.
A Prior-Free Weighting Scheme for Binary Code Ranking

Fast similarity search has been a research focus in recent years. Binary hashing, which embeds high-dimensional data points into Hamming space, is a promising way to accelerate similarity search, since its search process can be performed in real-time by using Hamming distance as similarity metric. However, as Hamming distance is discrete and bounded by code length, its resolution is limited. In practice, there are often many results sharing the same Hamming distance to a query, which poses a critical issue for problems where ranking is important. This paper proposes a weighted Hamming distance ranking algorithm (WhRank) to give a better ranking of results with equal Hamming distances to a query. By assigning different bit-level weights to different bits, WhRank is able to distinguish between the relative importance of different bits, and to rank the results at a finer-grained hash code level rather than the original integer Hamming distance level. We show that an effective weight is not only data-adaptive but also query-sensitive, and give a simple yet effective prior-free weight learning algorithm. Evaluations on three large-scale image datasets containing up to one million points demonstrate the efficacy of the proposed algorithm.
Decoupling of sea surface and rain backscatter for spaceborne scatterometers

For the operational spaceborne scatterometers, there is no effective way to eliminate the influence of the rain. Echoes contaminated by the rain are discarded directly. In this paper, a special designed spaceborne scatterometer and its decoupling methods of echoes from sea surface and rain are introduced. The main steps of decoupling methods are as follows. First, the scatterometer system parameters are slightly adjusted to acquire both the rain and sea surface echoes simultaneously. Next, the data of the scatterometer are copied and processed separately, one copy for rain retrieving and the other copy for sea surface backscatter retrieving. Finally, the rain attenuations are compensate to modify the bottom rain rate and the sea surface backscatter energy.
A High Performance Strategy for Constructing Dynamic MIB

SNMP protocol is a simple network management protocol. As an important part of most network management software, its resource wastage and efficiency have important influence on the performance of the whole system. According to study, the main bottleneck of SNMP software is MIB objects' storage and search. This paper proposes to use multi-path tree to keep the entire structure of MIB to eliminate the repeated storage of mutual identifiers by analyzing the structure of MIB tree. Compared to widely used Hash table, multi-path tree promotes the efficiency of resource usage. On the basis of that, this paper also proposes a high speed algorithm to search MIB objects by the combination of multi-path tree and AVL tree. The method above has been successfully applied to the Bidirectional HDTV Signal Transmitting System and it is proved to be efficient and reliable in actual network environment.
A new evolutionary algorithm for the shortest path planning on curved surface

Finding the shortest path between two given points over a curved surface is very important both in theoretically and practically. This article introduces the frame of genetic algorithm and with the help of the biological theory of DNA technology, it simulates double helix structure and utilizes molecular satisfying the Watson-Crick complementary condition to code and operate. DNA-GA inherited global search capability, enhanced the validity and the speed of convergence, and avoided the premature and lower convergent speed of the classic genetic algorithm, It fits for solving the problem of finding the shortest path on a curved surface. A numerical simulation example was presented at the end of the paper in order to testify the effectiveness and validity
A multi-tone interference equivalence method for analyzing the impact of LFM interference on the BER of communications data link system

The Direct Spread Communications Data Link System (CDLS) bears strong Linear Frequency Modulation (LFM) interference from collocated radars, which makes it necessary to analyze the influence of the LFM interference on the performance of CDLS. LFM interference has both the characteristics of an instantaneously single-tone and cumulatively multi-tone signals. For this reason, the derivation of an analytic Bit Error Rates (BER) formulation is difficult. This paper presents a BER model that can transform calculation of the BER under LFM interference into the BER under equivalent multi-tone interference, and then derive the equivalence factor between the two. We conduct simulations to analyze the impact of the single-tone and multi-tone as well as LFM interference on the BER of CDLS. Then, we verify the equivalence relationship between LFM interference and multi-tone interference with different center frequencies and FM modulation coefficients. The simulation results show that the equivalence error is less than 2.5% when the BER is higher than 10−3, which is the typical case of CDLS. The LFM interference equivalence BER calculation method proposed in this paper can quantify the equivalence coefficient, and accurately reflect the influence of LFM interference on the BER of CDLS, which contributes to the research of effective analysis of the interference mechanism of CDLS.
A Packet Scheduling Algorithm Using Utility Function for Mixed Services in the Downlink of OFDMA Systems

In this paper, we propose a downlink packet scheduling algorithm in OFDMA systems for the mixed services, such as real-time (RT) and non-real-time (NRT) services. The proposed scheduling algorithm is based on the channel state information and utility function of queuing delay. Simulation results show that this algorithm can not only guarantee the fairness and delay requirements of users with different types of services but also obtain good system throughput.
A Template-Based Orchestration Framework for Hybrid Services

The convergence of the Internet and telecom network changes the way we communicate in the daily life. Telecom operators and network vendors are trying to seek for a lower cost way to meet the constantly evolving market demands. In this paper, we propose a novel hybrid services orchestration framework based on semantic process template. Hybrid services are a set of services that combines the Internet services functionalities and telecommunication services functionalities. Users can be provided with customized services leverage on the orchestration of the hybrid services. Templates are used to abstract the reusable part of the process, and with the inferencing of the domain/service ontologies, semantic templates instantiation can be more intelligent and flexible. Because of the existence of the telecom services features such as real time, event-driven and underlying protocol heterogeneous, a service logic execution environment is also introduced to meet above requirements. At last, some real world scenarios are provided to demonstrate the feasibility of the framework, and its advantages and are also analyzed too.
An Advanced Study on Ion Impurities on Solder Resist and the Solutions for High-Temperature Reliability Performance

It is known that the high-temperature corrosion of Cu/Al inter-metallic compound (IMC) can be accelerated by ionic impurities of epoxy molding compounds (EMC). However, to our knowledge, there is seldom report in terms of the effect of ion impurities for solder resist. In the present work, an advanced ion extraction method by PCT was developed specifically to investigate the ions emitted from the surface of cured solder resist, and the influence of the ion impurities on the reliability of High Temperature Storage Life (HTSL) test and Highly Accelerated Stress Test (HAST) have been demonstrated. The results have suggested that even though the ion impurities in solder resist may not have dominant effect on the enhancement of crack failure under HTSL conditions, it could still be considered as a key factor for the reliability of package under elevated temperature, relative high humidity and voltage bias conditions. Since the presence of ion impurities in solder resist might have catalyzed the dry metal oxidation of Cu in HTSL test or act as a catalyst to accelerate Cu corrosion electrochemical reaction under the HAST condition. For this purpose, we achieved a new type of solder resist with extremely low ion impurities content to meet the needs of high temperature reliability in the future automotive application.