nils-herrmann/clean_scirepeval huggingface dataset:
A Wireless Mobile Monitoring System for Home Healthcare and Community Medical Services

This paper presents the design and implementation of a wireless mobile monitoring system, including the multiple physiological parameters close-fitting detector based on Bluetooth technology, the wireless monitoring terminal and the community health care monitoring center. The close-fitting detector with wireless communication is designed and developed with the plug-in module include several monitoring units. The design of the plug-in module has the advantages of good adaptability, easy to use, small size, low-cost and ease in wear, etc. Detectors and other monitoring terminals of digital family constitute a complete health care information detection system. To achieve the realization of wearable monitor, intensive short distance monitor and remote medical monitor, multiple physiological parameters can be real-time monitored at any time via the system for family and individual users. Users of the system can connect into the community medical service center through digital family equipments such as digital televisions, cell phones, etc. The system proposed in this paper is a kind of new wireless mobile monitoring system that is applicable to the family and the community medical health care services.
A Multi-Agent Based Information Integration Platform for Computer Supported Collaborative Design

This paper puts forward a multi-agent integrated platform. By studying the internal structures, ways of action and communication, functions, process algorithm, implementation patterns of different types of agents, and the specific method to realize autonomous and cooperative agent, it puts forward a mode of establishing enterprise's collaborative design environment. This mode takes the agents as distributed group decision making units; offers the enterprise information integration platform with clear structure, and makes it more open, integrated, and adaptive
Dual cycle shift data-weighted averaging technique for multi-bit Sigma-Delta Modulators

Data weighted averaging (DWA) is the most popular Dynamic Element Matching (DEM) technique to reduce the effects of DAC nonlinearity in multi-bit Sigma-Delta Modulators (MBSDM). However, due to the non-symmetry of the unit-elements in multi-bit DAC, in-band signal-dependent tones always appear when a low oversampling ratio (OSR) is employed in traditional DWA. Therefore, a novel DEM technique, dual cycle shift DWA (DCSDWA), is proposed to deal with the tone problem in this paper. Compared with other modified DWA techniques, DCSDWA is a more effective one with simple implementation. Using the proposed technique in a 4th-order MBSDM with an OSR of 32, the maximum in-band tone is reduced to 6 dB when a random DAC-element mismatching error of 0.5% is assumed. In addition, it results in a better linearity of signal-to-noise distortion ratio (SNDR) versus different input signal and a wider dynamic range (DR). The simulation results prove that the proposed DCSDWA is suitable for low OSR applications (OSR⩾8) with a good performance.
DVL aided fine alignment for marine SINS

A fine alignment method suitable for marine strapdown inertial navigation system is proposed. In this method, Doppler velocity log is applied to give velocity reference because of its accuracy and independence. System error equation is deduced to obtain fine alignment model. And the measurement equation corresponded to Doppler velocity log is given in this paper. In order to validate this method, we carry it out under several marine conditions, including movement with constant velocity and direction, three-axis sway and acceleration. The simulation results show that the proposed method has appropriate duration and great accuracy.
Deep into web general vs vertical search engine design based on secure and QoS

Vertical search engines are targeted to specific areas of the network information of the coverage is relatively high, with a reliable technical and information resources and support, with clear targeting search effectively compensate for a comprehensive search engine on a specific topic areas of expertise and information coverage too low. Mainly by the vertical search engine focused crawler module, the index module, search module, user interface components such as 4, it is the first to use the module from the specified URL reptiles seed starts to crawl, to crawl down the web page content analysis, determine the required after the extraction of information for the structured data, and then the data on the structure of Chinese words segmentation and indexing, and generate an index database, and finally create web pages for users to query the module to search. Database storage is a prerequisite for building the search. Foreground is the search engine system with the user interface.
NRProF: Neural response based protein function prediction algorithm

A large amount of proteomic data is being generated due to the advancements in high-throughput genome sequencing. But the rate of functional annotation of these sequences falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOfigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. The lack of annotation coverage of the existing methods advocates novel methods to improve protein function prediction. Here we present a automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. The main idea of this algorithm is to define a distance metric that corresponds to the similarity of the subsequences and reflects how the human brain can distinguish different sequences. Given query protein, we predict the most similar target protein using a two layered neural response algorithm and thereby assigned the GO term of the target protein to the query. Our method predicted and ranked the actual leaf GO term among the top 5 probable GO terms with 87.66% accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The NRProF program, the dataset, and help files are available at
Rational Analysis of Brass Paradox Based on Network Reliability

Brass paradox reveals that the total cost of travel of the residents will increase rather than reduce and the level of service drop anomaly after the addition of new sections. In this paper, we analyze the reliability of road network in-depth based on Brass paradox. At first, we calculate the travel time reliability, unblocked reliability and invulnerability while increasing one section. Then, we compare it with the previous results not increasing the path. This indicates that the reliability would not be reduced, but increased. So the efficiency of its services will be greatly enhanced with the increasing of a section of the road. The calculated results have practical value.
Open access and evaluation online infrastructure maintenance support service

Maintenance service is the basic function in higher education institutes support services to ensure normal teaching teachers and students in school and life is the ultimate goal. To do this service at least two things: rapid detection of failures; faster and better repair faults. Developing an online repair system, teachers and students found that the failure of infrastructure, you can too quickly through the system fault information will be reported to the maintenance department. Repair through the online system, students only need to fill out the information online, through the identification of key data can be easily and timely pass through the campus network maintenance center.
Collaborative Representation-Based Robust Face Recognition by Discriminative Low-Rank Representation

We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional subspace learning methods and recently proposed sparse representation based classification (SRC) might be degraded when corrupted training samples are provided. In addition, sparsity based approaches are time-consuming due to the sparsity constraint. To alleviate the aforementioned problems to some extent, in this paper, we propose a discriminative low-rank representation method for collaborative representation-based (DLRR-CR) robust face recognition. DLRR-CR not only obtains a clean dictionary, it further forces the sub-dictionaries for distinct classes to be as independent as possible by introducing a structural incoherence regularization term. Simultaneously, a low-rank projection matrix can be learned to remove the possible corruptions in the testing samples. Collaborative representation based classification (CRC) method is exploited in our proposed method which has a closed-form solution. Experimental results obtained on public face databases verify the effectiveness and robustness of our method.
Gradient Learning Approach for Variable Selection in Credit Scoring

The number of variables used for credit scoring can be quite large, and selecting the most relevant variables becomes an important topic. In this paper, we use gradient learning method for variable selection in credit scoring. The original method in the literature does not work on credit datasets because of the large sample size. To conquer this, we modify the algorithm by resampling data and voting effective variables. Compared with traditional variable selection methods, our approach can handle nonlinear models.
Model predictive control for the high-power grid-connecting PWM converters

Large-scale network of the renewable energy imposes increasing stringent requirements, especially with regard to harmonic distortion limits imposed by the grid code and the switching losses of the high-power PWM converter itself. This paper presents a new control strategy adopting the receding horizon policy, i. e., model predictive control (MPC), for a diode clamped three-level topology converter. With a simplification of the three-level SVPWM into a two-level SVPWM and taking the grid current tracking error, harmonic distortion limits, switching numbers per cycle and the deviation of the neutral point potential as components of the cost function, a relative good performance could be obtained with a high degree of robustness because of the utility of receding horizon policy.
Multi-Branch Fractional Multi-Bit Differential Detection of Continuous Phase Modulation with Decision Feedback

Differential detection of continuous phase modulation suffers from significant intersymbol interference. To improve the bit error rate performance, multi-branch fractional multi-bit differential detection with decision feedback is proposed. By introducing decision feedback in multi-bit differential detected signals, severe intersymbol interference can be removed. Simulation results show that the proposed structure can provide great improvement compared with MFMDD without decision feedback, and the best performance is achieved which is very near to the performance of the coherent detection.
Evaluation of system-of-systems using two layer fuzzy analytic network process model

The evaluation of system-of-systems is a very important content in the research of system-of-systems (SoS). An evaluation approach of system-of-systems using a two layer model based on fuzzy analytic network process is proposed in the paper. A two layer network, mission network and capabilities and systems network, is introduced to represent the complex interconnections and interactions in SoS under uncertain context. Experts' knowledge on SoS evaluation is extracted by a series of questions. Algorithm for deriving priorities from the model is proposed. Finally, the numerical example of air defense system-of-systems evaluation demonstrates the application of the proposed approach.
A 4.95mW 114dB SNDR delta-sigma modulator based on SC circuits for audio chip

A low-power high-resolution delta-sigma modulator for consumer audio chip is described. The modulator uses a 4th-order CRFF topology based on switched-capacitor (SC) circuits with a 3-bit quantizer and pseudo data-weighted-averaging (PDWA) technique. To reduce power consumption, several techniques including a novel structure OTA, two-step summation, OTA-shared and an improved algorithm are proposed. The simulation results show that the proposed modulator achieves 114dB signal noise distortion ratio (SNDR) and costs 4.95mW with a 3.3V supply and 6.25MHz sampling frequency by Global-Foundries 0.18μm BCD process, which could be used for lowpower audio applications.
Power optimization of high performance ΔΣ modulators for portable measurement applications

This paper, power optimization of two high performance ΔΣ modulators for portable measurement applications is presented. One modulator is a single-loop single-bit topology which achieves an 89.8dB peak SNDR and consumes 20μW with a 1.5V supply. Here, a new power efficient current mirror Class-AB OTA is introduced to reduce the power. The other modulator adopts both multi-bit technique and switched-opamp (SO) technique to realize the ultra-low power target. Its total power consumption is only 9μW at a 1.8V supply, and the peak SNDR reaches 80.5dB. Especially, a new fully-clocked SO is proposed in this modulator to achieve a 50% power saving and double Figure-of-Merit (FOM) over the traditional type. Besides, to realize a zero-optimization coefficient of 1/100 and improve the performance, a novel resonator idea applicable to SO technique is adopted with 75% power and 70% area reduction. Both modulators are fabricated in a low cost 0.35μm CMOS process with a bandwidth of 1 kHz. The measured results show high FOM of the designed modulators.
FDI and Chinese Firms' Secondary Innovation

As one of the most important drives, FDI has been paid more and more attention for its effects on constructing the technological innovation capability and sustainable strategic competitiveness in the less developed countries. Innovation is the primary driving force of economic growth and globalization. Under the circumstance of economic and technological globalization, innovation process presents new features. How to gain, maintain, consolidate and develop competitive advantages is an exigent question that attracts the attention from both scholars and entrepreneurs. This paper tries to have a look into the characteristics of Chinese firms' secondary innovations in global competition. Some countermeasures for Chinese firms to survive and grow in global manufacturing are proposed. And implications on competitive strategies to gain late comer's advantage are discussed.
Subthreshold voltage startup module for stepup DC-DC converter

The design of a subthreshold voltage startup module for low input voltage applications, down to 0.55 V, is introduced. The proposed startup module can generate a relatively high voltage from input to assist a stepup DC-DC converter to start. It employs a dynamic substrate biasing technique to realise subthreshold operation. The design was verified by simulation and measurement in a commercial 0.35 μm CMOS process.
Research on the method of characteristic extraction and classification of Phonocardiogram

Phonocardiogram (PCG) is able to reflect the activities of the heart valve. The analysis of PCG has clinical importance in the diagnosis of heart disease. In this paper, the Wavelet Transform is used to extract the envelope of PCG involving normal and abnormal ones; the envelope is used to achieve the accurate position of S1 and S2. Support Vector Machines (SVM) is also used to calculate two eigen parameters, the area of PCG envelope and the wavelet energy in order to determine the condition of heart sounds. Experiment results show that this algorithm has about 95% accuracy and has strong practicality. On the other hand, SVM and Neural Network train the Power Spectral Entropy from the signals of mitral stenosis and mitral insufficiency respectively. In this method, the classification capacity reaches a high level. This indicates that the Information Entropy Power Spectrum is a valid indicator to analyze the abnormal PCG.
Experimental analysis on radio transmission and localization of a Zigbee-based wireless healthcare monitoring platform

Zigbee-compliant wireless sensor network (WSN) platforms using TI CC2430 or CC2431 chipsets with Z-stack were widely used in hospitals, sanatoriums and other public applications. In this paper, we created a wireless healthcare monitoring platform, and then evaluated two key performance-related issues in regard to this platform, i.e. the radio transmission issue and the radio localization issue. Experimental results suggested that radio interferences could be minimized by choosing suitable Zigbee communication channel and frame length, and the localization service provided by the CC2431 modules was capable of tracking the geographical location with averaging localization errors being less than 2 m. We also presented some tentative suggestions for future development.
Orthogonal Least Squares in Partition of Unity Surface Reconstruction with Radial Basis Function

In this paper, a least squares formulation with radial basis function for surface reconstruction is presented and OLS (orthogonal least squares) algorithm is proposed to select centers and eliminate numerical ill-conditioning. The two objectives are fused into a single iterative process in OLS algorithm, which makes the reconstruction fast and robust. In the end, in order to deal with large point sets, we organize a point set with an octree; reconstruct surfaces in octree cells and blend them into a global surface by partition of unity (POU) method. To sum up, the first method is dedicated to reconstructing a surface with a smaller number of RBFs, and the last one is a local method to bypass impractical global reconstructions. Effectiveness of our proposed methods is demonstrated with results of real world point sets
Location for audio signals based on empirical mode decomposition

In this paper, empirical mode decomposition (EMD) denoising is applied in audio signals location when speech signals are received at several spatially separated sensors in the presence of noise. Firstly, Prior to cross correlation, each of the sensor outputs is separated into several intrinsic mode functions (IMFs) using EMD. Then, we compute normal energy of each IMFs and denoise IMFs according to a thresholding rule. The signals is restructured only using main IMFs in order to increase the input signal-to-noise ratio. Lastly, time delay is estimated by generalized cross correlation — phase transform (GCC-PHAT) between signals and location is completed by solving the geometry equation. The results show that the proposed method may provide not only an increase in the location but also reliability in the noise entironment.
Space-time processing for increased capacity of wireless CDMA

Spatial and temporal processing are combined to increase the capacity of CDMA-based wireless communications systems. Degrees of freedom provided by space-time processing are exploited to combat both fading and co-channel interference (the near-far effect). The following methods are formulated and studied: (1) space-time diversity, (2) cascade optimum spatial/diversity (RAKE) temporal, (3) cascade optimum spatial/optimum temporal, and (4) joint domain optimum processing. It is shown that, due to its interference cancellation capability, optimum combining provides significantly better performance than diversity processing. In particular, in a typical CDMA scenario with two antennas, the joint domain optimum combining system provides at least a 30% increase in capacity over diversity processing. Optimum combining may also be applied to compensate for imperfect power control of the signals received at the base station.
Multi-focus image fusion using quaternion wavelet transform

To avoid the introduction of false information during the fusion progress, a novel multi-focus image fusion method is proposed in quaternion wavelet transform domain. To obtain the dependency in different high frequency subbands, a quaternion wavelet contextual hidden Markov model (Q-CHMM) is established for modeling quaternion wavelet coefficients. And for better image representations, several features are proposed by analyzing the transform coefficients, phases of coefficients and the statistical attribution of coefficients. Different from the traditional fusion methods basing on a single feature, a comprehensive feature is constructed by using quaternion matrix to fuse the high frequency subbands. Experimental results demonstrate that the proposed method possess good fusion performance.
An Improved GAPSO Hybrid Programming Algorithm

GAPSO hybrid programming algorithm, which is a concise, effective and stable algorithm to solve the hierarchical problem based on GP algorithm. In terms of the specific characteristics of discrete magnitude and continuous magnitude, as well as the superiority of PSO in continuous quantity optimization, in this paper we propose an improved algorithm, which optimizes continuous magnitude by PSO while using GP for discrete magnitude optimization. Then through mass contrast experiments with GAPSO hybrid programming algorithm, we could see that Improved GAPSO hybrid programming algorithm is more stable and effective in function modeling. GAPSO;function modeling; GP
A novel comparator-assisted switching strategy used in Successive Approximation Register ADC

Recently Successive Approximation Register (SAR) ADC is more and more important in low power and small layout area application field such as implantable medical system. SAR ADC has moderate speed, moderate resolution and very low power characteristics. Some researchers have focused on improving the efficiency and layout area of SAR ADC. This work proposes a novel comparator-assisted switching strategy. By this strategy, the capacitor array can be just one in eight of the counterpart one which is used in conventional switching strategy. And the power consumption is substantially reduced. This switching strategy can be used in implantable medical system.
Diagnosis of breast cancer tumor based on manifold learning and Support Vector Machine

This paper proposes an efficient algorithm based on manifold learning and support vector machine (SVM) for the diagnosis of breast cancer tumor. First, Isomap algorithm is implemented to project high-dimensional breast tumor data to much lower dimensional space, then the processed data are classified by the SVM. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method can greatly speed up the training and testing of the classifier and get high testing correct rate, superior to the classical principal component analysis (PCA) algorithm.
Analysis of Bulletin Board System Hot Topics Based on Multiple Keywords Combination

Collection and analysis of information about network public opinion has currently become an effective means to get people thinking and recommendations by the government departments. In this paper, we presents a method of BBS(Bulletin Board System) hot topic analysis based on multiple keywords combination, this method, which use Chinese automatic word segmentation technology, firstly extracts keywords by counting the word frequency and then removes useless words from word frequency table and obtains the hot topic through the keywords positive connection. We described the method in details with a experiment which can prove the validity and rationality of the analytical method at the end of the paper.