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| 1 | In vivo fiber photometry of neural activity in response to optogenetically manipulated inputs in freely moving mice显示文摘In vito fber photometry is a powerful technique to analyze the dy namics of population neurons during fiunctional study of neuroscience.Here,we introduced a detailed protocol for fiber photometry-based calciun reording in freely moving mice,covering from virus injection,fiber stub insertion,optogenetical stimulation to data procurement and analysis.Furthemnore,we applied this protocol to explore neuronal activity of mice latenal-posterior(LP)thalaric nucleus in response to optogenetical stimulation of primary visual cortex(V1)neurons,and explore axon clusters activity of optogenetically evoked V1 neurons.Final confirmation of virus-based protein expression in V1 and precise fber insertion indicated that the surgery procedure of this protocol is reliable for functional calcium recording.The scripts for data analysis and some tips in our protocol are provided in details.Together,this protocol is simple,low-cost,and effective for neuronal activity detection by fiber photometry,which will hep neuroscience researchers to carry out fiunctional and behavioral study in vivo. | Liang Li Yajie Tang Leqiang Sun Khaista Rahman Kai Huang Weize Xu Jinsong Yu Jinxia Dai Gang Cao | 2017 | Journal of Innovative Optical Health Sciences2017，，5： | 1 |
| 2 | Subspace approach for fast and accurate single-tone frequency estimation显示文摘 | So H C Chan F K W Sun Weize | 2011 | IEEE Transactions on Signal Processing2011，59，2： | 1 |
| 3 | l_(1)-norm Based GWLP for Robust Frequency Estimation显示文摘In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency estimator is developed.Since the proposed method employs the weighted l_(1)-norm on the LP errors,it can be regarded as an extension of the l_(1)-generalized weighted linear predictor.Computer simulations are conducted in the environment of α-stable noise,indicating the superiority of the proposed algorithm,in terms of its robust to outliers and nearly optimal estimation performance. | Yuan Chen Liangtao Duan Weize Sun Jingxin Xu | 2019 | Journal on Big Data2019，1，3： | 0 |
| 4 | Two-Dimensional Interpolation Criterion Using DFT Coefficients显示文摘In this paper,we address the frequency estimator for 2-dimensional(2-D)complex sinusoids in the presence of white Gaussian noise.With the use of the sinc function model of the discrete Fourier transform(DFT)coefficients on the input data,a fast and accurate frequency estimator is devised,where only the DFT coefficient with the highest magnitude and its four neighbors are required.Variance analysis is also included to investigate the accuracy of the proposed algorithm.Simulation results are conducted to demonstrate the superiority of the developed scheme,in terms of the estimation performance and computational complexity. | Yuan Chen Liangtao Duan Weize Sun Jingxin Xu | 2020 | Computers, Materials & Continua2020，，2： | 0 |
| 5 | Control of the morphology of electrodeposited three-dimensional copper foam by tuning the pressure显示文摘Three-dimensional(3D) copper foams have been formed by electrodeposition at different nitrogen pressures and examined by scanning electron microscopy.The results indicate that an increase in system pressure leads to a decrease of the pore size of the copper foam due to the suppressed coalescence of hydrogen bubbles,while the thickness of the copper foam increases with decreasing pressure.Also,the walls around the pores on the copper foam consist of copper dendrites,and the copper dendrites are made up of copper grains with sizes less than 1 μm.The average sizes of the copper grains decrease with increasing system pressure.It has been demonstrated that copper foams with controllable 3D structure formed by electrodeposition at different pressures are comparable to those obtained by electrodeposition at normal pressure in the presence of specific additives. | PANG Kun HOU YuCui WU WeiZe TIAN ShiDong SUN NanNan | 2012 | Science China Chemistry2012，55，7： | 0 |
| 6 | Outlier Detection for Water Supply Data Based on Joint Auto-Encoder显示文摘With the development of science and technology,the status of the water environment has received more and more attention.In this paper,we propose a deep learning model,named a Joint Auto-Encoder network,to solve the problem of outlier detection in water supply data.The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data,and then reconstructs the input data effectively into an output.The outliers are detected based on the network’s reconstruction errors,with a larger reconstruction error indicating a higher rate to be an outlier.For water supply data,there are mainly two types of outliers:outliers with large values and those with values closed to zero.We set two separate thresholds,and,for the reconstruction errors to detect the two types of outliers respectively.The data samples with reconstruction errors exceeding the thresholds are voted to be outliers.The two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic(ROC)curve.We have also performed comparisons between the Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data set and the MNIST data set.As a result,our model has proved to outperform the vanilla Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94 percent in water supply data. | Shu Fang Lei Huang Yi Wan Weize Sun Jingxin Xu | 2020 | Computers, Materials & Continua2020，，7： | 0 |
| 7 | Accurate and Computational Efficient Joint Multiple Kronecker Pursuit for Tensor Data Recovery显示文摘This paper addresses the problem of tensor completion from limited samplings.Generally speaking,in order to achieve good recovery result,many tensor completion methods employ alternative optimization or minimization with SVD operations,leading to a high computational complexity.In this paper,we aim to propose algorithms with high recovery accuracy and moderate computational complexity.It is shown that the data to be recovered contains structure of Kronecker Tensor decomposition under multiple patterns,and therefore the tensor completion problem becomes a Kronecker rank optimization one,which can be further relaxed into tensor Frobenius-norm minimization with a constraint of a maximum number of rank-1 basis or tensors.Then the idea of orthogonal matching pursuit is employed to avoid the burdensome SVD operations.Based on these,two methods,namely iterative rank-1 tensor pursuit and joint rank-1 tensor pursuit are proposed.Their economic variants are also included to further reduce the computational and storage complexity,making them effective for large-scale data tensor recovery.To verify the proposed algorithms,both synthesis data and real world data,including SAR data and video data completion,are used.Comparing to the single pattern case,when multiple patterns are used,more stable performance can be achieved with higher complexity by the proposed methods.Furthermore,both results from synthesis and real world data shows the advantage of the proposed methods in term of recovery accuracy and/or computational complexity over the state-of-the-art methods.To conclude,the proposed tensor completion methods are suitable for large scale data completion with high recovery accuracy and moderate computational complexity. | Weize Sun Peng Zhang Jingxin Xu Huochao Tan | 2021 | Computers, Materials & Continua2021，，8： | 0 |
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