nils-herrmann/clean_scirepeval huggingface dataset:
Electrochemical assembled p-type Bi/sub 2/Te/sub 3/ nanowire arrays

Bi/sub 2/Te/sub 3/ nanowires have been assembled into the nanopores of alumina films by electrodeposition in the solution containing Bi/sup 3+/ and HTeO/sub 2//sup +/ with porous alumina template as cathode. The results show that p-type Bi/sub 2/Te/sub 3/ nanowires can be easily electrochemical organized into the nanopores of the template by controlling the deposition potential. The filling ratio of the nanowires in the nanopores of alumina template highly correlates with the structural uniformity of alumina template, the ionic concentration of Bi/sup 3+/ and HTeO/sub 2//sup +/ in the solution and the depositing rate of the nanowires. ESEM and TEM analyses indicate that the p-type Bi/sub 2/Te/sub 3/ nanowires were uniformly electrodeposited into the alumina template. The performance measurement verified that the Seebeck coefficient of p-type Bi/sub 2/Te/sub 3/ nanowire arrays fabricated in our lab was 270 /spl mu/V/K at 306 K, which is a little larger than that of bulk Bi/sub 2/Te/sub 3/ thermoelectric materials.
Remuneration system reform of China's state-owned enterprises after the financial crisis

In the fourth quarter of 2008, US subprime crisis swept through all over the world, whipping up an international financial storm. China was also involved in this around of crisis, with weak and fatigue market performance, lead to the depression of the macro-economy and the operation difficulties of domestic enterprises. It became a challenge facing the management layer to adjust human resource strategies and reform the remuneration management system in due time, so as to convert the crisis to opportunities. The current remuneration system of China's state-owned enterprises does not work with employees' requirements on living and working qualities, and has even become a shackle hindering the development of enterprises. In order to survive in the increasingly fierce competition, the remuneration system of state-owned enterprises must be reformed to meet the challenges. This paper will first briefly review theories and principles of remuneration design, and then analyze the problems in the current remuneration system of China's state-owned enterprises, followed with policy suggestions on solving these problems.
Research on the financing mode of small and micro enterprises basing on the peer-to-peer lending

As a new financial format, peer-to-peer lending(P2P lending) solve the financing problems of small and micro enterprises to a certain extent. This paper analyses the pure online financing mode and the combination of online and offline financing mode of small and micro enterprises under the P2P lending. The paper discusses the advantages and disadvantages of various financing models. Basing on a case, the article further analysis the advantages and disadvantages of the small and micro enterprise financing mode under the P2P lending, puts forward the countermeasures that can promote the healthy development of small and micro enterprises financing mode.
A new bag of words model based on fuzzy membership for image description

Bag of Words (BoW) as an efficient approach to describing the images has been attracting more and more attention. However, in traditional BoW, the maps between words in codebook and features extracted from images are ambiguous. We propose a new type of BoW based on Gaussian membership function (Gaussian-BoW) to describe images. In Gaussian-BoW, the codebook is obtained by using k-means like the traditional BoW. Then, words are assigned to the feature with Gaussian membership values. At last, histogram is generated by adding up the fuzzy membership values of each word to describe the images. The experimental results show that the proposed Gaussian-BoW outperforms traditional BoW for image description.
Correspondence - 3-D ultrasonic strain imaging based on a linear scanning system

This paper introduces a 3-D strain imaging method based on a freehand linear scanning mode. We designed a linear sliding track with a position sensor and a height-adjustable holder to constrain the movement of an ultrasound probe in a freehand manner. When moving the probe along the sliding track, the corresponding positional measures for the probe are transmitted via a wireless communication module based on Bluetooth in real time. In a single examination, the probe is scanned in two sweeps in which the height of the probe is adjusted by the holder to collect the pre- and postcompression radio-frequency echoes, respectively. To generate a 3-D strain image, a volume cubic in which the voxels denote relative strains for tissues is defined according to the range of the two sweeps. With respect to the post-compression frames, several slices in the volume are determined and the pre-compression frames are re-sampled to precisely correspond to the post-compression frames. Thereby, a strain estimation method based on minimizing a cost function using dynamic programming is used to obtain the 2-D strain image for each pair of frames from the re-sampled pre-compression sweep and the post-compression sweep, respectively. A software system is developed for volume reconstruction, visualization, and measurement of the 3-D strain images. The experimental results show that high-quality 3-D strain images of phantom and human tissues can be generated by the proposed method, indicating that the proposed system can be applied for real clinical applications (e.g., musculoskeletal assessments).
A new framework of target detection in hyperspectral images

Hyperspectral Image (HSI) is used widely in many areas, especially in the remote sensing field. Compared with the traditional remote sensing HSI, the large-scale and high-resolution HSI (LHHSI) which has big data and large size is high-resolution both in spatial domain and spectral domain. However, traditional methods of automatic target detection do not apply to LHHSI. Therefore, this paper proposes a novel framework of automatic target detection for LHHSI based on spatial-spectral interest point (SSIP). It contains five key steps. Firstly, bands selection of LHHSI is used to reduce spectral dimension of LHHSIs. Second, we extract candidate SSIPs from the LHHSIs. Third, we need to determine whether there exist potential target regions by using spectral curves of many selected key SSIPs. And next, the image which contains the potential target regions is divided into image blocks by using quad-tree segmentation, and then every image block is represented by a vector with BoW model based on the selected SSIPs. Finally, these image blocks are classified with SVM. During the classification, if the result is what we need, the quad-tree segmentation of the current block will be ended. The experimental results show that the proposed algorithm has a better performance than traditional algorithms.
Anomaly detection of Logo images in the mobile phone using convolutional autoencoder

There are Logo images printed in the mobile phone. Anomaly detection of Logo image is an important quality control task in the intelligent manufacture. In real applications, there are not enough negative samples in the production line for us to study their difference from normal samples. In this paper, we propose an unsupervised learning method based on convolutional autoencoder (CAE) to generate the template of sample and detect the abnormal information through comparing test images with the adaptive template. Firstly, several methods of data augmentation are introduced to expand the scale of positive samples, aiming to improve the performance of CAE. Secondly, the topology of proposed CAE model is introduced. Thirdly, we introduce the image processing methods to detect and locate the abnormal information in the Logo image. A series of experiments on three group of different Logo image have shown that the method we proposed can effectively detect most of the anomalies in the image and achieve the average accuracy of 98.9%.
Motion States Recognition System Based on Ultrasound for Automatic Door Management

The paper describes a novel motion states recognition system based on ultrasound to make up for the shortcomings of existing motion states recognition systems. Characteristics of this system are shown as follows: The motion states of people beside the door can be identified quickly and exactly by motion states recognition algorithm based on Doppler effect, an innovative divisional detection algorithm has been brought forward to estimate the number of people who want to go across the automatic door, the ultrasonic sensor gives a good performance on anti-interference in addition to its low energy consumption and price. Therefore, using ultrasonic probes can improve the performance of the system while reducing the cost.
Parallelism of Extended-Field-of-View Sonography based on Scale Invariant Feature Transform

EFOV sonography can acquire a full field of vision, which can help doctors to make more objective and accurate diagnosis. This paper explores parallel implementation techniques for Extended-Field-of-View (EFOV) Sonography based on Scale Invariant Feature Transform (SIFT). In our method, the SIFT algorithm is used to more accurately register the images. Bidirectional matching algorithm is chosen to improve the accuracy of image registration. For real-time applications, we propose a parallel computing approach to speed up the generation of panorama. Based on the cluster of PCs, we erect a parallel computing platform and perform preliminary experiments. The results demonstrate the effectiveness of our approach by comparing to traditional methods.
Conditional and unconditional CRB of DOA estimation in spherical harmonics domain

The Cramer-Rao bound (CRB) is widely used in signal processing to characterize the estimation performance. In this paper, the conditional and unconditional CRBs are developed in spherical harmonics domain for direction-of-arrival (DOA) estimation of far-field sources using spherical arrays. The CRBs of azimuth and elevation decrease with the increasing of signal-to-noise ratios (SNRs) and snapshots. Moreover, the CRB of elevation is lower than that of azimuth.
The research of data representation and processing method in logic chip equivalent function description

In this paper, we studied the characteristic of the data acquired by both the combinatory logic electric circuit and the timing circuit. And with the emphasis laid on the consideration of representation requirements in the situations with and without feedback, the paper established the format suitable for both. Through the analysis of the requirements of truth table data characteristics during the logical synthesis and multiple sampling process, a method that makes the data pre-processed was proposed: the data should be sorted before sent to the logic synthesis, which could raise the logic synthesis processing speed greatly verified by the test.
Identifying nuclear protein subcellular localization using feature dimension reduction method

The subcellular location of a protein is closely correlated to its function. Facing the deluge of protein sequences generated in the post-genomic age, it is necessary to develop useful machine learning tools to identify the protein subcellular localization. DR (Dimensional Reduction) method is one of most famous machine learning tools. Some researchers have begun to explore DR method for computer vision problems such as face recognition, few such attempts have been made for classification of high-dimensional protein data sets. In this paper, DR method is employed to reduce the size of the features space. Comparison between linear DR methods (PCA and LDA) and nonlinear DR methods (KPCA and KLDA) is performed to predict subcellular localization of nuclear proteins. Experimental results thus obtained are quite encouraging, which indicate that the DR method is used effectively to deal with this complicated problem of viral proteins subcellular localization prediction. The overall jackknife success rate with KLDA is the highest relative to the other DR methods.
DOA estimation of mixed near-field and far-field sources using spherical array

In practical applications, the observations collected by a spherical array may be mixed far-field and near-field signals. A new approach which exploits the recursive relationship of spherical harmonics is proposed to estimate the direcitons of arrival (DOAs) of multiple mixed sources. The algorithm is independent of the far-field or near-field assumption in the spherical harminics domain. It avoids high dimensional search and provides accurate estimation. Simulation results demonstrate better estimation performance.
A Kinect-based automatic ultrasound scanning system

Now in hospitals, most of the sonography examinations are performed by doctors manually. Numerous patients are to be examined, doctors suffer from hard work. In this paper, a Kinect-based automatic ultrasound scanning system is presented. Depth data of the tissue surface is acquired by Kinect and transformed to 3D translating device coordinate system, scan path is automatically planned, then 3D translating device controls an ultrasound probe to scan the tissue automatically, probe's positions and corresponding B-scans are recorded for volume reconstruction. Force sensor is adopted for force measurement and stop the scanning process in case of emergency. Phantom and in vivo experiments are conducted, and the experimental results show that this system is available for automatic ultrasound scanning and 3D reconstruction.
Distributed state estimation with PMU using grid computing

State estimation is an essential tool in monitoring the power system. As the size of the electric power system continues to grow, a state estimator has to be more computationally efficient and robust. This can be achieved by technical advancement in computational science and improving the state estimation algorithm. Distributed state estimation (DSE) decomposes the power system into smaller subsystems. This provides a way to simultaneously poll the measurements and execute the state estimation for a smaller size power system. PMUs can synchronize the measurements between different areas which are perfectly suited for distributed state estimation. The results obtained by local state estimation will be sent to the coordinator for further processing with consideration of the reference angle and boundary estimates. The phase angles and voltages estimated in each sub-area are usually based on their own area reference bus. Considering there are PMUs in the power system, this paper proposes a new method of calculating the reference angle difference between different areas. Grid computing has been used to help in DSE computation as a novel approach. The preliminary test results have presented for a small scale test case.
A fast method for panoramic ultrasound imaging

Current Panoramic ultrasound suffers from the low computation speed due to a very large amount of ultrasound data to be processed. This paper describes a technique to register 2D multiframe ultrasound images and produce panoramic images in a rapid manner. For registration of any two adjacent images, we propose to select less image blocks which are regarded as the most valid blocks based on the importance of image content. In registration of a sequence of images, with an assumption that the moving direction and speed of the probe are nearly identical during the data collection, we estimate the moving speed of the probe at the beginning of data collection and ignore redundant image data by processing a smaller number of frames according to a frame interval. The experimental results show that our method can accurately produce EFOV images in real-time.
Fully distributed source localization via sparse reconstruction in wireless sensor network

For energy-based multi-source localization in wireless sensor networks (WSNs), multi-resolution search and expectation maximization algorithms are of either high energy consumption or low localization accuracy. In this paper, we propose a multi-source localization algorithm via sparse reconstruction based on fully distributed structure (MLSR-FD) in WSNs. The fully distributed structure is used to reduce the energy consumption of WSNs. It requires the local data transmission and processing. Each sensor node transmits energy observations to its nearest neighbors (nodes within one-hop communication range) and also receives energy observations from its nearest neighbors. In order to reduce the computational complexity, multi-resolution redundant dictionary is used to locate sources using sparse reconstruction. Finally, the robust consensus algorithm fuses the local source position estimates of the nearest nodes and converges to the global estimates. Simulation results show that the proposed MLSR-FD algorithm provides a good tradeoff between localization accuracy and energy consumption.
Small-sized loudspeaker equalization based SVD-Krylov model reduction and virtual bass enhancement

Small-sized loudspeakers due to high cutoff frequency, perform poor reproduction response, especially in low frequency. Traditional equalization methods for small-sized loudspeaker suffer from huge computational complexity when high accurate results are expected, which may lead to unacceptable time delay or pre-echo in reproduction sound. In this paper, instead of complicated low frequency equalization, virtual bass enhancement technology is introduced to restore low frequency auditory. In combination, SVD-Krylov algorithm is introduced to reduce loudspeaker model to promote the efficiency of high frequency equalization. Comparative experiments are presented and results show that the proposed approach can substantially reduce the time consumption without deterioration in accuracy of loudspeaker equalization.
Development of a Wireless and Near Real-Time 3D Ultrasound Strain Imaging System

Ultrasound elastography is an important medical imaging tool for characterization of lesions. In this paper, we present a wireless and near real-time 3D ultrasound strain imaging system. It uses a 3D translating device to control a commercial linear ultrasound transducer to collect pre-compression and post-compression radio-frequency (RF) echo signal frames. The RF frames are wirelessly transferred to a high-performance server via a local area network (LAN). A dynamic programming strain estimation algorithm is implemented with the compute unified device architecture (CUDA) on the graphic processing unit (GPU) in the server to calculate the strain image after receiving a pre-compression RF frame and a post-compression RF frame at the same position. Each strain image is inserted into a strain volume which can be rendered in near real-time. We take full advantage of the translating device to precisely control the probe movement and compression. The GPU-based parallel computing techniques are designed to reduce the computation time. Phantom and in vivo experimental results demonstrate that our system can generate strain volumes with good quality and display an incrementally reconstructed volume image in near real-time.
Source localization using constrained Kalman beamforming in spherical harmonics domain

A constrained Kalman beamforming (CKB) method in spherical harmonics domain is proposed to localize multiple sources captured by spherical arrays. It scans the Euclidean norm of the weights in three-dimensional space to estimate source directions. It can improve the accuracy of sound source localization when steering vector mismatch exits. Moreover, it has the advantage of rapid convergence and online implementation. Compared with the desired minimum variance distortionless response (MVDR) and diagonal loading (DL) methods, simulation results show that the proposed algorithm has robustness to arbitrary steering vector mismatch and low number of snapshots.
A Novel Method to Obtain Modulus Image of Soft Tissues Using Water Jet Compression

Tissue stiffness is generally known to be associated with pathologic changes. Ultrasound indentation is able to assess the mechanical properties of soft tissues. Conventional ultrasound indentation devices use rigid flat-ended transducers to directly contact with the tissues and typically operate in the frequency ranging between 2 M Hz and 10 MHz. This paper introduced a novel ultrasound indentation system using water jet compression. The water jet served as an indenter as well as a medium for the propagation of the ultrasound beam. High frequency focused transducer was used to measure the indentation deformation at a microscopic level. It has been demonstrated that the system could effectively assess the tissue-mimicking phantoms with different stiffness. Moreover, another advantage of this novel indentation system was to apply C-scan on soft tissues rapidly and conveniently. By applying different pressures on C-scan sequences, the modulus image of the tissue could be obtained. This paper presented the preliminary results on gel phantoms. The spatial resolution and the sensitivity of the measurements, the reproducibility of the results were also discussed
Mining functional biclusters of DNA microarray gene expression data

A subset of genes sharing compatible expression patterns under a subset of conditions can be found from DNA microarray data using biclustering algorithms. In this paper, we present a novel geometrical biclustering algorithm in combination with gene ontology annotations to identify the gene functional biclusters. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometrical interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our bottom-up biclustering algorithm, the well-known Hough transform is first employed in pair-column spaces to reduce the computation complexity and then the resulting patterns are merged step by step into large-size biclusters incorporated with gene functional modules. The algorithm integrates the numerical characteristics in a gene expression matrix and the gene functions in the biological activities. Our experiments on real data show that the new algorithm outperforms most existing methods for mining gene functional biclusters.
A Graph-Based Segmentation Method for Breast Tumors in Ultrasound Images

This paper introduces a graph-based image segmentation method for detecting breast tumors in ultrasound images. The proposed segmentation algorithm based on the minimum spanning trees in a graph generated from an image, can automatically detect tumor regions and segment lesions in ultrasound images. The algorithm for segmenting breast ultrasound images consists of 3 steps, i.e. the nonlinear coherent diffusion model for speckle reduction, the graph construction for mapping the image to a graph, and the mergence of smaller regions. A pairwise region comparison predicate comparing the inter-component differences with the within component differences, is used to determine whether or not two regions should be merged. Experimental results have shown that the proposed segmentation algorithm is simply structured, robust to noises, highly efficient and much flexible in comparison with Fuzzy C means clustering. It can successfully detect tumors and extract lesions in ultrasound images more accurately. We hope that our method could be useful in various medical practices, providing an alternative way for ultrasound image analysis.