nils-herrmann/clean_scirepeval huggingface dataset:
Statistical Shape Modeling Using Morphological Representations

The aim of this paper is to propose tools for statistical analysis of shape families using morphological operators. Given a series of shape families (or shape categories), the approach consists in empirically computing shape statistics (i.e., mean shape and variance of shape) and then to use simple algorithms for random shape generation, for empirical shape confidence boundaries computation and for shape classification using Bayes rules. The main required ingredients for the present methods are well known in image processing, such as watershed on distance functions or log-polar transformation. Performance of classification is presented in a well-known shape database.
Spatially adaptive PDEs for robust image sharpening

We present here discrete and continuous partial differential equation (PDE) -based methods for image enhancement/ sharpening. Using more robust and spatially adaptive PDEs, multiscale morphological operators that account image features are introduced, and then, used to provide a discrete enhancement operator, thanks to the former Kramer and Bruckner filter. A novel PDE associated to the introduced enhancement operator is established in 2D. Both the discrete and PDE-based sharpening filters are illustrated on synthetic, binary and real images.
A deep spatial/spectral descriptor of hyperspectral texture using scattering transform

A technique to describe the spatial / spectral features of hyperspectral images is introduced. These descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations, so called spatial invariances. Moreover, we also consider spectral invariances which are related to the composition of the pixels. Our approach is based on the scattering transform, which provides an useful framework for deep learning classification. The goal through these descriptors is to improve pixel-wise classification of hyperspectral images.
Structurally adaptive mathematical morphology on nonlinear scale-space representations

Standard formulation of morphological operators is translation invariant in the space and in the intensity: the same processing is considered for each point of the image. A current challenging topic in mathematical morphology is the construction of adaptive operators. In previous works, the adaptive operators are based either on spatially variable neighbourhoods according to the local regularity, or on size variable neighbourhoods according to the local intensity. This paper introduces a new framework: the structurally adaptive mathematical morphology. More precisely, the rationale behind the present approach is to work on a nonlinear multi-scale image decomposition, and then to adapt intrinsically the size of the operator to the local scale of the structures. The properties of the derived operators are investigated and their practical performances are compared with respect to standard morphological operators using natural image examples.
Spatially-Variant Area Openings for Reference-Driven Adaptive Contour Preserving Filtering

Classical adaptive mathematical morphology is based on operators which locally adapt the structuring elements to the image properties. Connected morphological operators act on the level of the flat zones of an image, such that only flat zones are filtered out, and hence the object edges are preserved. Area opening (resp. area closing) is one of the most useful connected operators, which filters out the bright (resp. dark) regions. It intrinsically involves the adaptation of the shape of the structuring element parameterized by its area. In this paper, we introduce the notion of reference-driven adaptive area opening according to two spatially-variant paradigms. First, the parameter of area is locally adapted by the reference image. This approach is applied to processing intensity depth images where the depth image is used to adapt the scale-size processing. Second, a self-dual area opening, where the reference image determines if the area filter is an opening or a closing with respect to the relationship between the image and the reference. Its natural application domain are the video sequences.
Doppler spectrum segmentation of radar sea clutter by mean-shift and information geometry metric

Radar sea clutter inhomogeneity in range is characterized by Doppler mean and spectrum width variations. We propose a new approach for robust statistical density estimation and segmentation of sea clutter Doppler spectrum. In each range cell, Doppler is characterized by a Toeplitz Hermitian Positive Definite covariance matrix that is coded in Poincaré's unit poly-disk and we use adaptation of standard kernel methods to density estimation on this specific Riemannian manifold. Based on this non-parametric approach to estimate statistical density of Doppler Spectrum, we address the problem of sea clutter data mapping and segmentation by extending "Mean-Shift" tool for these densities on Poincaré's unit poly-disk. This statistical segmentation is requested for robust detection of targets in sea clutter, especially in case of high sea state.
Morphological operators for images valued on the sphere

The lack of a natural ordering on the sphere presents an inherent problem when defining morphological operators extended to unit sphere. We analyze here the notion of averaging over the unit sphere to obtain a local origin which can used to formulate ordering based operators. The notion of local supremum and infimum is introduced, which allows to define the dilation and erosion for images valued on the sphere. The algorithms are illustrated using polarimetric images.
Non-linearization of free schrodinger equation and pseudo-morphological complex diffusion operators

The paper deals with a generalization of the complex diffusion in order to introduce pseudo-morphological complex filters which mimic dilation/erosion operators. The non-linearization paradigm is based on the counter-harmonic mean. The physical model underlying complex diffusion is the free Schro¨dinger equation and consequently the proposed operators can be interpreted as the asymptotic “pseudo-morphological” solution of this fundamental equation. Theoretical results are illustrated with some image filtering examples.
Supervised Ordering in  ${\rm I}\!{\rm R}^p$: Application to Morphological Processing of Hyperspectral Images

A novel approach for vector ordering is introduced in this paper. The generic framework is based on a supervised learning formulation which leads to reduced orderings. A training set for the background and another training set for the foreground are needed as well as a supervised method to construct the ordering mapping. Two particular cases of learning techniques are considered in detail: 1) kriging-based vector ordering and 2) support vector machines-based vector ordering. These supervised orderings may then be used for the extension of mathematical morphology to vector images. In particular, in this paper, we focus on the application of morphological processing to hyperspectral images, illustrating the performance with practical examples.
Notch-band UWB bandpass filter using slot-embedded short-circuited stub

A notch band ultra-wideband bandpass filter with good out-of-band rejection, controllable and multiple transmission zeros in notch band and good inband performance is presented in this paper. A shunt short-circuited stub is used to build up the initial ultra-wideband skirt. A slot is then embedded into the short-circuited stub to achieve cross-coupled effects. This cross-coupled circuit can effectively reject the harmonic in the upper-stopband. Based on this circuit, the 1st-order, 3rd-order and 5th-order bandpass filter is discussed to study the improvement of the selectivity and harmonic suppression. In addition, a notch-band is implemented by adding an open-circuited stub, which is with via to form a shunt connected LC resonator. By implementing multiple shunt LC resonator, multiple transmission zeros can be achieved in the notch-band. Finally, a notch-band filter prototype is fabricated to experimentally verify the predicted results.
500 kV substation robot patrol system

500kV substation on duty gradually tends to be unmanned with the development of smart grids, the mobile robot that used in substation equipment inspection emerge as the times require. Substation inspection robot system, which is described in this paper, can replace a fixed video monitoring, complete video remote transmission of information, equipment control, accident treatment and coordinated transport station monitoring system, what's more, it has the equipment on-site and remote inspection and remote video work guidance function. System has the function of infrared diagnosis equipment defects, using historical data to form analysis curve, operators can analyze the same equipment under different load, the temperature of the equipment, the change of temperature range. At the same time, the system can run on the current equipment condition diagnosis, diagnostic data can be stored in the infrared expert database, expert panel will automatically complete the diagnosis, and provide the result to operating personnel when similar equipment again.
Tuning maturity model of ecogeography-based optimization on CEC 2015 single-objective optimization test problems

Ecogeography-based optimization (EBO) is an extension of biogeography-based optimization (BBO) that evolves a population of solutions by continually migrating features among them, mimicking the principle of immigration and emigration of species from one habitat to another in biogeographical distribution. The original EBO uses a linear maturity model for balancing exploration and exploitation. In this paper we generalize the maturity model by introducing a single control parameter, and then tune the parameter for each test problem of the CEC 2015 learning-based benchmark problem suite in order to find the most effective model for solving the problem. We design a binary search method for conveniently and effectively tuning the model. The computational experiments show that the tuned algorithm can improve the solution quality on different problems of the benchmark suite significantly.
A Design of Wireless Monitoring System for Circular Knitting Machines Based on ZigBee

This monitoring program transmits data and realizes the real-time monitoring of each circular knitting machine by wireless network instead of the traditional wired network. Based on the ZigBee technology, the wireless sensor network in the program is constituted by ordinary sensor node, the sink node and network coordinator, and responsible for acquiring and transmitting condition information of circular knitting machines. This paper has described the network structure and nodes' hardware structure of the program and discussed the communication processes of the ZigBee modules.
Design of FIR narrow-band frequency selective filters using sliding DFT array processors

Introduces a kind of DFT array processors. Its weighting factors of processing element's (PE's) are time-invariable. A FIR narrow-band frequency selective filter using DFT array processors has been designed. DFT array processors can easily calculate the frequency spectrum of corresponding sequence of N length by sliding technique. Frequency sampling designs are used for narrow-band frequency selective filters where only a few of the samples of the frequency response are zero (or nonzero). So this filter has exactly linear phase. Then we get the last point output in time-domain, depending on DFT array processors, which have parallel input and single output. In general, by making more PE's for a DFT array processor, the filter yields more excellent results. Furthermore, the signal is not converged until the N-length sequence is put in. N is constrained to be integer multiple of k, which is stopband cutoff frequency. We also present some results of simulations for removal of powerline interference and baseline wander. It is believed that DFT array processors will have wide applications.<<ETX>>
An Adaptive Cross-Layer Scheduling Algorithm for Multimedia Networks

Nowadays wireless networks standards, e.g. IEEE 802.16, aim to accommodate diverse QoS requirements for multimedia users. The standard provided four kinds of multimedia data services, associated with QoS parameters and left the QoS scheduling algorithm undefined. In this paper, we propose an adaptive cross physical (PHY) layer and MAC layer scheduling algorithm for IEEE 802.16 BWA system. The proposed scheduler employs adaptive modulation and coding (AMC) scheme at the PHY layer according to the signal to noise ratio (SNR) on wireless fading channels. And we also define each kind of multimedia connections the cost function based on its service status, throughout or deadline in MAC layer. The simulation results show that our proposed scheduler can provide diverse QoS guarantees, maximum throughput and achieve an optimum tradeoff between throughput and fairness.
Formation Problem of Multi-Agent Systems with Switching Topologies and Time Delays in Flocking

Formation in flocking is universal. In this paper, we discuss formation in flocking without leaders. Both constant time delays and variable time delays are considered. Switching topologies are considered for multi-agent system. Our aim in the paper is to solve formation under time delays in inputs and agents in the double integrators. First, Liapunov-Razumikhin theorem is applied in formation when inputs are affected by constant time delay. Second, Linear Matrix inequality method is used to formation when inputs are affected by variable time-delay.
Hybrid Recommendation Algorithm for E-Commerce Website

Traditional recommendation algorithms face some serious problems, including data sparsity, cold start and inefficiency. To better address the problems above, the paper proposes a hybrid recommendation algorithm based on improved collaborative filtering of user context fuzzy clustering and content-based. For collaborative filtering, firstly, user classification is based on fuzzy clustering according to user context, and then collaborative filtering is used to recommend products for similar users. And the improved content-based algorithm sets up feature vectors for users and items dynamically. Experiments show that the hybrid algorithm can avoid defects of single algorithm and improve the performance in both recommendation quality and efficiency, which opens up exciting avenues for future research.
An Improved Wiener Filtering Algorithm Based on Dynamic Noise Power Spectrum Estimation

The problem of enhancing speech degraded by uncorrelated additive noise, when only the noisy speech is available, has been widely studied in the past and it is still an active field of research. Wiener filter, which is the most fundamental approach, has been delineated in different forms and adopted in diversified applications. An improved wiener filtering algorithm is proposed in this study, which utilizes band-partitioning spectral entropy to achieve accurate and robust speech endpoint detection and a dynamic noise power spectrum is estimated for updating a priori SNR. Experimental results reveal that the proposed algorithm can extract the embedded speech segments from utterances containing a variety of background noise successfully.
A phase correcting filter algorithm based on cross power spectrum estimation

A phase correcting filtering algorithm is proposed. It utilizes cross power spectrum estimation to get the information about the signal's frequency value and phase shift and designs a phase corrector to complete the compensation of the filtered signal's phase and zero-phase filtering. This method has the advantages of easy implementation, high precision, good real-time performance, etc.. The results of MATLAB simulation show that the method has a reliable phase compensation performance.
On Metrics-Driven Software Process

Metrics can drive software processing, because they found the base of its quantizing management. There are two fundamental requirements in engineering: formal modeling and quantitative modeling. We must emphasize that metrics for software engineering is insufficient in quantification now. In order to improve software and software processing, the factors that affect schedule, cost and quality of software development should be measured. Metrics produces adjustable and iterative motions in software processing. Based on Jaynes' maximum entropy principle, this paper establishes a model to quantify the factors and introduces distance to compare the metric indicators. The authors propose that the metric estimation tree can be used and the nodes that stand for the software attributes in the tree can be marked with their corresponding evaluation values. Dynamic feedback in the software processing will be combined with AHP (analytic hierarchy process), and the project and process of development will be learned and analyzed entirely, concentratedly and dynamically.
Improved SPICE3 implementation algorithms of compound element pseudo-transient analysis for solving nonlinear dc circuits

This paper presents three implementation algorithms of compound elements pseudo-transient analysis to find DC solutions for nonlinear LSI circuits. In former researches, CEPTA was implemented in SPICE-like simulator with the merits that the size of Jacobian matrix is not expanded during the calculation. While the inserted pseudo parts are converted to some certain equivalent circuits, the conventional implementation does not consider time step size limitation. They can not ensure convergence (DC solution can be found) and for large scale circuits, the CPU time needed to converge is not satisfactory. The proposed implementation algorithms can significantly improve the simulation efficiency for some test cases. The improvement of simulation efficiency and convergence performance is demonstrated by test circuits.
Secure Key Management of Mobile Agent System Using TPM-Based Technology on Trusted Computing Platform

As an important new paradigm for distributed computing, the mobile agent (MA) technology has a lot of potential applications in practical fields. However, the success of MA depends on its security. The common important problem of all existing security solutions in MA system is secure key management. This paper presented the key management tree architecture provided by core hardware module of trusted computing platform. So the keypsilas secure storage feature was discussed. Then generation and using of secure session key were proposed on the application to secure communication between host and agent.
Mechanical Model and Finite Element Analysis of the Loader Drive Axle Housing in Typical Operating Conditions

The loader drive bridge is the main bearing components of the loader and is also the axle housing of transmission system. This paper detailed deduces the force status for the drive axle housing in four kinds of typical operating conditions. Then, the finite element analysis was executed for the axle housing structure. The force state of weld part of axle tube was focused in analysis where it is easy broken during operation.
Impact of M-Commerce on Business Models

With the popularization of information technology, the development trend of internet is transferring from PC- Internet to Mobile-Internet. More and more enterprises adopt M-commerce business model to achieve competitive advantage. Based on the review of present business model theory, this article clarifies the concept of M-commerce as a kind of emerging business model, and analyses the advantage of M-commerce in business model elements in comparison to traditional commerce and E-commerce from the perspective of business model. Moreover, this article explores the effect of M-commerce business model on enhancing the competitive advantage of enterprises.
Motion Compensation Based Detecting and Tracking Targets in Dynamic Scene

In most circumstances of target tracking in video sequences, the scene is dynamic due to the mobile cameras. So developing an approach to handle this problem is of great importance. Aiming at this problem, we propose a motion compensation based target detection and tracking algorithm, in which, motion compensation is generated by pyramidal optical flow. In target detection phase, motion compensation and background update algorithms are developed, while in the tracking phase, motion compensation and RJ-MCMC particle filtering algorithms are used. Experiment shows that the proposed approach can handle detection and tracking problems well in dynamic scene
No-Reference quality assessment for multiply distorted images based on deep learning

Most of the existing algorithm of objective image quality evaluation are often for a single type of distortion, and the effect of multi-distortion image quality evaluation is poor, In this paper presents a no reference image quality assessment method based on phase congruency and convolution neural network, to evaluate the mixed distorted image, Firstly, the input image is divided into blocks and phase congruency, and then the quality score of the image is trained and predicted by using the convolution network, The convolutional network structure consists of 4 layers of convolutions, 3 layers of maximum pooling, and 2 fully connected layers, The experimental results on the LIVE mulitipy distortion quality evaluation database show that the proposed method has a good consistency between the image quality and the subjective quality score
The Development of Online Innovative Education Program in Our University

Grants are offered for faculty to develop technology courseware projects in universities including Hangzhou Dianzi University (HDU). One of the important factors in courseware development is innovation. Approaches to strengthen innovation are used in the courseware project in Computer School of HDU. The project is aimed at reaching the most students per Chinese Yuan spent. Seminar courses are directed toward large interactive lecture courses through Internet and information technology.
Optical stimulation of primary motor cortex with 980nm infrared neural stimulation

To explore the penetration depth with short-wavelength infrared light, 980nm pulse infrared light was used to stimulate the primary motor cortex of rat. The heating model was created to simulate the temperature distribution for 1875nm and 980nm infrared neural stimulation. Post-stimulus time histogram was used to observe the neural response induced by Infrared neural stimulation on primary motor cortex. The model predicted the penetration depth of 980nm was deep into 1.2mm. Cortical neural located between 500μm to 1000μm were successfully activated by 980nm INS. The preliminary results suggested that, 980nm pulse INS could serve as a candidate for deep tissue stimulation.
A channel-estimation-based equalization algorithm for fast frequency hopping radio

This paper presents a channel estimation based equalization algorithm for high speed fast hopping radio. The equalizer coefficients are directly calculated from the impulse response of the channel which is obtained by rapid channel estimation in time domain employing cyclically extended binary sequence. Simulation is done under general two-path channel model and rapidly fading channels. Theoretical analysis and computer simulation results show that compared with traditional adaptive coefficients adjusting equalizer or MLSE receiver, this algorithm is more robust and has less computational overhead, which is desirable under limited time resources.