nils-herrmann/clean_scirepeval huggingface dataset:
Spatial Purity Based Endmember Extraction for Spectral Mixture Analysis

Spectral mixture analysis (SMA) has been widely utilized to address the mixed-pixel problem in the quantitative analysis of hyperspectral remote sensing images, in which endmember extraction (EE) plays an extremely important role. In this paper, a novel algorithm is proposed to integrate both spectral similarity and spatial context for EE. The spatial context is exploited from two aspects. At first, initial endmember candidates are identified by determining the spatial purity (SP) of pixels in their spatial neighborhoods (SNs). Several SP measurements are investigated at both intensity level and feature level. In order to alleviate local spectra variability, the average of the pixels in pure SNs are voted as endmember candidates. Then, the spatial connectivity is utilized to merge spatially related endmember candidates by finding connection paths in a graph so that the number of endmember candidates is further reduced, which results in computational efficiency and better performance in SMA by alleviating global spectral variability. Experimental results on both synthetic and real hyperspectral images demonstrate that the proposed SP based EE (SPEE) algorithm outperforms the other popular EE algorithms. It is also observed that feature-level SP measurements are more distinguishable than intensity-level SP measurements to discriminate pure SNs from mixed SNs.
Mutual Information and Non-fixed ANNs for Daily Peak Load Forecasting

In this paper, a new method for the daily peak load forecasting which uses mutual information (MI) and non-fixed artificial neural networks (ANNs) is presented. Although ANNs based predictors are more widely used for short-term load forecasting in recent years, there still exist some difficulties in choosing the proper input variables and selecting an appropriate architecture of the networks. Since there are lots of factors that may affect the load series, and the influencing factors are varied in different seasons, a varied structure ANN model including four ANN modules is proposed. The mutual information theory is first briefly introduced and employed to perform input selection and determine the initial weights of ANNs. Then each ANN module is trained using historical daily load and weather data selected to perform the final forecast. To demonstrate the effectiveness of the approach, daily peak load forecasting was performed on the Hang Zhou Electric Power Company in China, and the testing results show that the proposed model is feasible and promising for load forecasting
Nonlinear kernel sparse dictionary selection for video summarization

Sparse dictionary selection (SDS) has demonstrated to be an effective solution for keyframe based video summarization (VS), which generally assumes a linear relation among similar video frames. However, such a linear assumption is not always true for videos. In this paper, the nonlinearity among frames is taken into consideration and a nonlinear SDS model is formulated for VS, in which the nonlinearity is transformed to linearity by projecting a video to a high dimensional feature space induced by a kernel function. Moreover, a kernel simultaneous orthogonal matching pursuit (KSOMP) is proposed to solve the problem. In order to achieve an intuitive and flexible configuration of the VS process, an adaptive criterion is devised to produce video summaries with different lengths for different video content. Experimental results on benchmark video datasets demonstrate that the proposed algorithm outperforms several state-of-the-art VS algorithms.
Two-step similarity matching for Content-Based Video Retrieval in P2P, networks

Multimedia data, particularly, video data, has dominated peer-to-peer (P2P) networks. Therefore, it is demanding to provide content based retrieval in P2P networks. Similarity matching is one of the challenging issues. In this paper, we present a novel two-step method to reduce computational complexity of similarity matching in P2P networks. In the first step, an efficient maximum matching (MM) technique is employed to obtain an initial set of similar video candidates. In the second step, these candidates are further selected with a more accurate, but more computationally expensive optimal matching (OM) technique. In order to further improve the computational efficiency of the proposed method, four other matching techniques are proposed to replace MM technique. Various experimental results indicate that the proposed approach is more effective for CBVR while achieving significantly computational saving.
All-Fiber Tunable Supercontinuum Laser Source

We have demonstrated an all-fiber supercontinuum (SC) laser source with tunable average power with nearly constant spectra. The laser source consists of a repetition rate tunable master oscillator power amplifier (MOPA), seeding by an actively harmonic mode-locked Yb-doped fiber laser, and a SC generation system. By changing the repetition rate from 29.83 MHz to 1.104 GHz while maintaining the central wavelength as 1060 nm, the SC pump power can be varied from 4.45 W to 69.8 W with a same peak power about 8 kW. By launching the ultra-short pulses into a 5 m long photonic crystal fiber (PCF), we obtain tunable SC with similar spectra flatness with average power from 2.54 W to 35.56 W, and the wavelength range of the spectra extends from 540 nm to 1700 nm.
STRank: A SiteRank algorithm using semantic relevance and time frequency

Most of the researches on web information processing are concentrated on the web pages and the hyperlinks among them. One of the important facts that a web page is just one building block of the whole website had been ignored. But the situation is gradually changed in recent years for the needs of website reputation calculation, the high level website structure mining etc. It causes the website ranking become one of the hot research topics and various site ranking algorithms, such as SiteRank, AggregateRank etc., had been proposed. But most of existing website ranking algorithm just take use of website link graphs and the content of websites are usually not put into consideration. It is obviously not enough for a reliable ranking of websites. To address this issue, this paper introduces two content based features, i.e., semantic relevance and time frequency and proposes a new STRank algorithm based on these two features. We firstly conduct a series of experiments to verify the feasibility of these two factors in site ranking task. Then the semantic relevance is applied in the calculation of transition probability, and the updating frequency of sites is combined into the ranking task. Since traditional Kendall's τ distance and Spearman's Footrule distance is not appropriate for the evaluation of site ranking, we make some modifications accordingly to evaluate website ranking algorithms. Finally, our experiments show that the STRank algorithm outperforms existing approaches on both effectiveness and efficiency.
The design of duplexer with TE01δ mode dielectric resonator

Compared to the traditional metal cavity device, duplexer with TE01δ mode dielectric resonator (DR) has more advantages such as smaller size, lower insertion loss (IL), better temperature stability of resonant frequency and so on. Based on microwave theory, this paper proposes a new type of duplexer which partly consists of TE01δ mode DR. It takes full advantage of the performance of different materials to make it conform as closely as possible to the signal transmission characteristics of the different frequency bands and to optimize the quality of the transmission channel. The design of TE01δ mode DR is presented at first of this paper. Then it gets the equivalent circuit of duplexer by formula deduction. The duplexer is simulated by using HFSS and CoupleFil software and it comes to conclusion that the test and the simulation results are consistent at last. In the fields of engineering of application, this design program can improve the performance and reduce the size of duplexer.
New thoughts for onboard compression of satellite images

Compression has become a must for efficient storing and transmission of data acquired by satellites with increasing resolution and swath. However, for compression of raw data satellite images, impact of striping noise is inevitable. Variances of Digital Number (DN) values introduced by striping noise will surely impair continuity and smoothness of satellite image and reduce the efficiency of onboard compression. In this paper, using Beijing-1 small satellite images, origin and characteristics of striping noise caused by double channel linear CCD and its impacts on the compression process are analyzed. Then, based on properties of striping noise, an improved method for compression of raw data satellite images is proposed. The new compression method is applied to Beijing-1 small satellite raw data images and yields significant boost in compression performance. Ideas of the proposed algorithm can be easily realized with circuit modification and no adaptation is needed for post processing of the compressed images.
Path Planning for First Responders in the Presence of Moving Obstacles With Uncertain Boundaries

In this paper, we study path planning for first responders in the presence of uncertain moving obstacles. To support the path planning, in our research we use hazard simulation to provide the predicted information of moving obstacles. A major problem in using hazard simulation is that the simulation results may involve uncertainty due to model errors or noise in the real measurements. To address this problem, we provide an approach to handle the uncertainty in the information of moving obstacles, and apply it to the case of toxic plumes. Our contribution consists of two parts: 1) a spatial data model that supports the representation of uncertain obstacles from hazard simulations and their influence on the road network and 2) a modified A* algorithm that can deal with the uncertainty and generate fast and safe routes passing though the obstacles. The experimental results show the routing capability of our approach and its potential for the application to real disasters.
Dictionary learning-based image compression

Dictionary learning based image compression has attracted a lot of research efforts due to the inherent sparsity of image contents. Most algorithms in the literature, however, suffer from two drawbacks. First, the atoms selected for image patch reconstruction scatter over the entire dictionary, which leads to a high coding cost. Second, the sparse representation of image patches is performed independently from the quantization of sparse coefficients, which may result in a sub-optimal solution. In this paper, we propose the entropy based orthogonal matching pursuit (EOMP) algorithm and quantization KSVD (QKSVD) algorithm for dictionary learning-based image compression. An entropy regularization term is utilized in EOMP to restrict atom selection, and hence reduces the coding cost, and an adaptive quantization method is incorporated into the dictionary learning procedure in QKSVD to minimize the reconstruction error and quantization error simultaneously. Experimental results on 10 standard benchmark images demonstrate that our proposed approach achieves better performance than several state-of-the-art ones at low bit rate, such as KSVD based compression approach, JPEG, and JPEG-2000.
Unsupervised Spectral Mixture Analysis with Hopfield Neural Network for hyperspectral images

Spectral Mixture Analysis (SMA) has been widely utilized to address the mixed-pixel problem in the quantitative analysis of hyperspectral remote sensing images. Recently Nonnegative Matrix Factorization (NMF) has been successfully utilized to simultaneously perform endmember extraction (EE) and abundance estimation (AE). In this paper, we formulate the solution of NMF by performing EE and AE iteratively. Based on our previous Hopfield Neural Network (HNN) based AE algorithm, an HNN is also constructed for EE to solve the multiplicative updating problem of NMF for SMA. As a result, SMA is conducted in an unsupervised manner and our algorithm is able to extract virtual endmembers without assuming the presence of spectrally pure constituents in hyperspectral scenes. We further extend such strategy to solve the constrained NMF (cNMF) models for SMA, where extra constraints are imposed to better model the mixed-pixel problem. Experimental results on both synthetic and real hyperspectral images demonstrate the effectiveness of our proposed HNN based unsupervised SMA algorithms.
Windowing technique for the DCT based retinex algorithm to handle videos with brightness variations coded using the H.264

Conventional block based motion estimators assume constant inter-frame object brightness. Pixel discrepancy is resulted primarily from motion factor without considering the influence of brightness changes. In this paper, we propose a simple and efficient windowing technique using the Hamming window and integrate it to our previously proposed algorithm. The algorithm is based on retinex approach using the DCT technique and designed to handle brightness variations. The new technique manages to greatly reduce the influence of ripple effect and further increase the compression efficiency without adding any extra overhead bits to the bit-stream. We applied the scheme to H.264 for testing. Experimental results show that the retinex based approach is an effective technique to handle inter-frame brightness variations and outperforms the H.264 system with weighted prediction enabled for sequence involving brightness variations. With our proposed windowing technique using the Hamming window, the coding efficiency can be further improved by a maximum of 0.17 dB.
Cultural Promote Economic Development under the Factor Analysis - in Japan, for Example

Based on analysis of the key factors of Japanese culture to the country's economic development has made outstanding contributions, particularly in the Japanese anime culture, sports and culture is characterized by the formation of a strong cultural industry system. Further, as a manifestation of national consciousness and the national spirit, culture affects the formation of individual corporate culture, and gradually promote the rapid increase in Japan's national economy and abundant reserves, the paper extract promote cultural factors in economic terms, namely: enterprise culture, national consciousness and consumer culture, education and innovation culture, and to assess the factors influence these factors.
Research on Time Domain Characteristics and Mathematical Model of Electromagnetic Radiation Noise Produced by Single Arc

Current-carrying sliding friction pair is the key part of many systems such as high-speed railway’s pantograph-catenary system, electromagnetic gun’s launch system, and space satellite’s solar panel system. Arc discharge phenomenon will occur due to sliding electrical contact fault. It will radiate high-intensity electromagnetic noise. The electromagnetic radiation noise (EMRN) will disturb communication system, even cause failure of the whole system. EMRN experiments under single arc conditions were carried out with the self-developed experimental system in anechoic chamber environment. The time domain characteristics of EMRN were analyzed under different measure distance and contact current conditions. The time domain waveform of EMRN is symmetrical and convergent. Its continuous time is shorter than 0.8  $\mu \text{s}$ . A mathematical model of the time domain waveform was established by using the least-square method. The performance of the model was evaluated by using determination coefficient, residual standard deviation, and correlation coefficient. The time domain waveform obtained from the model is almost the same as the waveform measured from experiments.
Research on feature of series arc fault based on improved SVD

In order to study the feature and extraction methods of series arc fault, the series arc fault experiments under different current conditions were carried out with the motor load and inverter respectively. A method of feature extraction based on improved singular value decomposition was proposed, and arc faults were distinguished by support vector machine (SVM). SVM was optimized by genetic algorithm (GA). Current signals were used to structure the attractor track matrix, and the time- delay step of the matrix was reconstructed by autocorrelation analysis. By means of singular value decomposition of the trace matrix, singular values of the matrix were obtained, the feature of arc fault were obtained by screening these values. Finally, GA- SVM was used to test the feature of the arc fault. The results showed that the method could effectively extract the series arc fault feature in the motor and inverter load circuit.
CCD Noise Effect on Data Transmission Efficiency of Onboard Lossless-Compressed Remote Sensing Images

It has to be aware that remote sensing images need to be encoded onboard using lossless compression method if all information of the image is required to be fully conserved for ground use. Also, to acquire as much onboard data as possible during one available pass of the satellite, it is necessary to consider efficiency of onboard data compression and transmission between satellite and ground segment. However, noise inevitably induced from CCD when the image is captured will affect efficiency of data compression and transmission. Therefore, main CCD noises and their effect on transmission of lossless-compressed Beijing-1 panchromatic images were investigated in this paper. It is proved that CCD noise should be considered adequately if requiring increasing lossless compression ratio and data transmission efficiency. It is further indicated that shot and readout noise modeled as Poisson noise and, thermal and dark-current noise modeled as Gaussian noise are principle factors for data transmission efficiency; impulse noise induced by CCD detector malfunctions has the least effect on data transmission efficiency. Finally, a FPGA-based model of noise suppression is proposed for future development of onboard data processing system. Keywords-charge-coupled device (CCD); Beijing-1; noise; lossless compression; compression ratio (CR); data transmission efficiency (DTE)
Structural Image Classification with Graph Neural Networks

Many approaches to image classification tend to transform an image into an unstructured set of numeric feature vectors obtained globally and/or locally, and as a result lose important relational information between regions. In order to encode the geometric relationships between image regions, we propose a variety of structural image representations that are not specialised for any particular image category. Besides the traditional grid-partitioning and global segmentation methods, we investigate the use of local scale-invariant region detectors. Regions are connected based not only upon nearest-neighbour heuristics, but also upon minimum spanning trees and Delaunay triangulation. In order to maintain the topological and spatial relationships between regions, and also to effectively process undirected connections represented as graphs, we utilise the recently-proposed graph neural network model. To the best of our knowledge, this is the first utilisation of the model to process graph structures based on local-sampling techniques, for the task of image classification. Our experimental results demonstrate great potential for further work in this domain.
A Heuristic Explanation for the Superluminal Behaviors of Evanescent Waves

Traditionally, the superluminal behaviors of evanescent waves have been studied by means of tunneling time theory. Seeing that evanescent waves are identical with near fields, to present a heuristic and straightforward explanation for the superluminal behaviors of evanescent waves, in this paper we study the superluminal behaviors of the near fields of an electric dipole antenna, and obtain the results in agreement with the phase time model of tunneling time theory.
Physical-layer network coding with partial CSI

In this paper, we investigate the physical-layer network coding (PNC) with partial channel state information (CSI) in the context of two way relay communication. We consider the typical scenario that one end node exchanges information with another via a relay without knowing the CSI knowledge between the relay and the other end node. A relay scheme called power weighted mapping (PWM) is proposed to implement the PNC with partial CSI, which is actually working by mapping the observations at the relay to a specific sending constellation. For the case of QPSK modulations at the end nodes, we develop another two relay schemes called 3QAM mapping (3QAMM) scheme and QPSK mapping (QPSKM) scheme to improve the performance, under the principle of increasing the minimum distance of the constellation at the relay. The simulation results demonstrate the effective of our proposed schemes, which greatly reduce the system load while still reserve high performance of about only 1dB performance loss compared with the schemes with full CSI.
Mechanical properties of a lead-free solder alloys

Lately, environmental problems have gained more and more attention from many countries; because of environmental pollution from lead-based solders and compounds, many countries are about to prohibit the using of lead-based solders in microelectronic packaging; lead-free solder alloys will replace tin-lead (Sn-Pb) eutectic solders. However, the mechanical properties of lead-free solders have not been clarified The tensile property of Sn96.5Ag3Cu0.5 was investigated. The tensile strength of this lead-free solder was obtained at 25/spl deg/C, 50/spl deg/C, 75/spl deg/C, 100/spl deg/C, 125/spl deg/C and 150/spl deg/C; the tensile strength of the lead-free solder decreases with increasing test temperature at a constant strain rate. The tensile strengths of a lead-free and a lead-based solder were compared, and the tensile strength of Sn96.5Ag3Cu0.5 was found to be close to that of Sn63Pb37; this lead-free solder has some good mechanical properties, and will be a promising candidate solder alloy in microelectronic packaging.
Exploration of CDIO engineering education mode for BEEE

According to China's current problems in higher engineering education, the concept of CDIO engineering education was introduced. On this basis, combined with the actual situation of building environment and equipment engineering (hereinafter short for BEEE), discussed the pattern of education reform of BEEE based on the CDIO educational idea, and proposed the concrete measures of teaching reform. The hierarchy of cultivation orientation, aim, cultivation plan, course system and practice system of electronic information engineering program were completely conceived and designed. According to training objectives, a set of effective teaching system and practice teaching system are set up.
Decision tree based sleep stage estimation from nocturnal audio signals

Noncontact sleep quality monitoring and early diagnosis of sleep disorders has attracted increasing attentions recently due to its advantages over traditional Polysomnography (PSG). In this study, we proposed a novel audio-based method to estimate four sleep stages. The method consists of three steps, breathe/snore detection based on Adaptive Vertical Box (AV-Box), nonlinear feature and sleep pattern feature extraction, and sleep stage classification utilizing a decision tree classifier. A seven-hour-long nocturnal audio signals is recorded for one healthy male subject with PSG in a sleep laboratory of Nanjing University of Science and Technology. Estimation performance of six types of decision tree classifiers is evaluated using k-fold cross-validation. The average prediction accuracy is 74.3%.