The impact of a sense of belonging on lethargy and career maturity

In this study, the authors treated a combination of psychological apathy and decreased motivation as a tendency to lethargy, and implemented a survey into the tendency to lethargy demonstrated by students, in order to study the impact of a sense of belonging in the four relationships between the student and the people considered most likely to be interacted with during university life—those with family, friends at university, friends outside university and boyfriend/girlfriend. In addition, the authors implemented a survey and study that included additional categories relating to career maturity. The study was performed on 250 university students, using an anonymous questionnaire that graded responses using criteria to measure a sense of belonging, psychological apathy characteristics, areas of decreased motivation, and career maturity. The subjects were classified by the school year to which they belonged, their gender, and whether or not they had a boyfriend/girlfriend, and consideration was given to the relationship between psychological apathy, decreased motivation, career maturity and a sense of belonging. In terms of gender difference in regard to each of the criteria, partially, the study indicated that male students score significantly higher than female students in terms of a sense of belonging, females score significantly higher than males for decreased motivation in regard to classes, and males score significantly higher than females in relation to career maturity. No significant difference in scores was noted between males and females in relation to psychological apathy. The impact of a sense of belonging on psychological apathy, decreased motivation and career motivation was seen in the fact that across all categories, those students with a good relationship with friends at university had a suppressed level of decreased motivation in regard to university by the portions given in this document.

Construction of the Sense of Belonging of Place Spirit in the Renovation of Historical Blocks: A Case Study of Ancient Town Street of Ming and Qing Dynasties in Hekou, Qianshan

The renovation of historical blocks often stays at the material level of buildings, ignoring the deeper spiritual level, which can not arouse people’s sense of belonging to the city memory. Based on the protection of local Qianshan area culture, taking Hekou ancient town street of Ming and Qing Dynasty as an example, with the construction of place spirit and sense of belonging as the primary goal, and combining the history and culture of the city, the current situation of the streets and the characteristics of the buildings, this paper put forward the relevant renovation measures to provide a kind of thinking for the refined and rational renovation of the blocks in the future.

Place and Belonging: How Rural Primary School Principals can Promote Boarders’ Development by Building their Sense of Belonging to School?

Rural boarding schools in compulsory education in China have proliferated with school merger program.This paper analyzes the relationship between school belonging and student development and the factors that influence students'sense of belonging in rural boarding schools.The paper examines how principals in rural boarding schools in China can promote student development by building a sense of belonging.The paper argues that building this sense of belonging can serve as a solution to the current problems affecting rural boarding schools,improve the quality of rural primary education,and promote student development.

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.

Improving wavelet reconstruction algorithm to achieve comprehensive application of thermal infrared remote sensing data from TM and MODIS

According to the data characteristics of Landsat thematic mapper （TM） and MODIS, a new fu sion algorithm about thermal infrared data has been proposed in the article based on improving wave let reconstruction. Under the domain of neighborhood wavelet reconstruction, data of TM and MO DIS are divided into three layers using wavelet decomposition. The texture information of TM data is retained by fusing highfrequency information. The neighborhood correction coefficient method （NC CM） is set up based on the search neighborhood of a certain size to fuse lowfrequency information. Thermal infrared value of MODIS data is reduced to the space value of TM data by applying NCCM. The data with high spectrum, high spatial and high temporal resolution, are obtained through the al gorithm in the paper. Verification results show that the texture information of TM data and high spec tral information of MODIS data could be preserved well by the fusion algorithm. This article could provide technical support for high precision and fast extraction of the surface environment parame ters.

Declined trends of chlorophyll a in the South China Sea over 2005–2019 from remote sensing reconstruction

Chlorophyll a concentration(CHL)is an important proxy of the marine ecological environment and phytoplankton production.Long-term trends in CHL of the South China Sea(SCS)reflect the changes in the ecosystem’s productivity and functionality in the regional carbon cycle.In this study,we applied a previously reconstructed 15-a(2005–2019)CHL product,which has a complete coverage at 4 km and daily resolutions,to analyze the long-term trends of CHL in the SCS.Quantile regression was used to elaborate on the long-term trends of high,median,and low CHL values,as an extended method of conventional linear regression.The results showed downward trends of the SCS CHL for the 75th,50th,and 25th quantile in the past 15 a,which were−0.0040 mg/(m^(3)·a)(−1.62%per year),−0.0023 mg/(m^(3)·a)(−1.10%per year),and−0.0019 mg/(m^(3)·a)(−1.01%per year).The negative trends in winter(November to March)were more prominent than those in summer(May to September).In terms of spatial distribution,the downward trend was more significant in regions with higher CHL.These led to a reduced standard deviation of CHL over time and space.We further explored the influence of various dynamic factors on CHL trends for the entire SCS and two typical systems(winter Luzon Strait(LZ)and summer Vietnam Upwelling System(SV))with single-variate linear regression and multivariate Random Forest analysis.The multivariate analysis suggested the CHL trend pattern can be best explained by the trends of wind speed and mixed-layer depth.The divergent importance of controlling factors for LZ and SV can explain the different CHL trends for the two systems.This study expanded our understanding of the long-term changes of CHL in the SCS and provided a reference for investigating changes in the marine ecosystem.

Direction-of-Arrival Method Based on Randomize-Then-Optimize Approach

The direction-of-arrival(DOA)estimation problem can be solved by the methods based on sparse Bayesian learning(SBL).To assure the accuracy,SBL needs massive amounts of snapshots which may lead to a huge computational workload.In order to reduce the snapshot number and computational complexity,a randomize-then-optimize(RTO)algorithm based DOA estimation method is proposed.The“learning”process for updating hyperparameters in SBL can be avoided by using the optimization and Metropolis-Hastings process in the RTO algorithm.To apply the RTO algorithm for a Laplace prior,a prior transformation technique is induced.To demonstrate the effectiveness of the proposed method,several simulations are proceeded,which verifies that the proposed method has better accuracy with 1 snapshot and shorter processing time than conventional compressive sensing(CS)based DOA methods.

The Convergence of Two Algorithms for Compressed Sensing Based Tomography

The constrained total variation minimization has been developed successfully for image reconstruction in computed tomography. In this paper, the block component averaging and diagonally-relaxed orthogonal projection methods are proposed to incorporate with the total variation minimization in the compressed sensing framework. The convergence of the algorithms under a certain condition is derived. Examples are given to illustrate their convergence behavior and noise performance.

Coherence-based performance analysis of the generalized orthogonal matching pursuit algorithm

The performance guarantees of generalized orthogonal matching pursuit（ gOMP） are considered in the framework of mutual coherence. The gOMP algorithmis an extension of the well-known OMP greed algorithmfor compressed sensing. It identifies multiple N indices per iteration to reconstruct sparse signals.The gOMP with N≥2 can perfectly reconstruct any K-sparse signals frommeasurement y = Φx if K 〈1/N（1/μ-1） ＋1,where μ is coherence parameter of measurement matrix Φ. Furthermore,the performance of the gOMP in the case of y = Φx ＋ e with bounded noise ‖e‖2≤ε is analyzed and the sufficient condition ensuring identification of correct indices of sparse signals via the gOMP is derived,i. e.,K 〈1/N（1/μ-1）＋1-（2ε/Nμxmin） ,where x min denotes the minimummagnitude of the nonzero elements of x. Similarly,the sufficient condition in the case of G aussian noise is also given.

Sense of Belonging

Africans living in Beijing find spiritual upliftment in their own unique style of worship via church services ancl community outreach initiatives

Seismic data reconstruction based on CS and Fourier theory

Traditional seismic data sampling follows the Nyquist sampling theorem. In this paper, we introduce the theory of compressive sensing （CS）, breaking through the limitations of the traditional Nyquist sampling theorem, rendering the coherent aliases of regular undersampling into harmless incoherent random noise using random undersampling, and effectively turning the reconstruction problem into a much simpler denoising problem. We introduce the projections onto convex sets （POCS） algorithm in the data reconstruction process, apply the exponential decay threshold parameter in the iterations, and modify the traditional reconstruction process that performs forward and reverse transforms in the time and space domain. We propose a new method that uses forward and reverse transforms in the space domain. The proposed method uses less computer memory and improves computational speed. We also analyze the antinoise and anti-aliasing ability of the proposed method, and compare the 2D and 3D data reconstruction. Theoretical models and real data show that the proposed method is effective and of practical importance, as it can reconstruct missing traces and reduce the exploration cost of complex data acquisition.

Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction

To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.

Reconstruction of electrical capacitance tomography images based on fast linearized alternating direction method of multipliers for two-phase flow system

Electrical capacitance tomography(ECT)has been applied to two-phase flow measurement in recent years.Image reconstruction algorithms play an important role in the successful applications of ECT.To solve the ill-posed and nonlinear inverse problem of ECT image reconstruction,a new ECT image reconstruction method based on fast linearized alternating direction method of multipliers(FLADMM)is proposed in this paper.On the basis of theoretical analysis of compressed sensing(CS),the data acquisition of ECT is regarded as a linear measurement process of permittivity distribution signal of pipe section.A new measurement matrix is designed and L1 regularization method is used to convert ECT inverse problem to a convex relaxation problem which contains prior knowledge.A new fast alternating direction method of multipliers which contained linearized idea is employed to minimize the objective function.Simulation data and experimental results indicate that compared with other methods,the quality and speed of reconstructed images are markedly improved.Also,the dynamic experimental results indicate that the proposed algorithm can ful fill the real-time requirement of ECT systems in the application.

Seismic data reconstruction based on iterative linear expansion of thresholds

Based on the compressive sensing,a novel algorithm is proposed to solve reconstruction problem under sparsity assumptions.Instead of estimating the reconstructed data through minimizing the objective function,the authors parameterize the problem as a linear combination of few elementary thresholding functions,which can be solved by calculating the linear weighting coefficients.It is to update the thresholding functions during the process of iteration.The advantage of this method is that the optimization problem only needs to be solved by calculating linear coefficients for each time.With the elementary thresholding functions satisfying certain constraints,a global convergence of the iterative algorithm is guaranteed.The synthetic and the field data results prove the effectiveness of the proposed algorithm.

Distributed Adaptive Compressed Video Sensing Using Smoothed Projected Landweber Reconstruction

A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this paper.In this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS sampling.The key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber(SPL)algorithm using multi-hypothesis predictions.For the non-key frames,a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information(SI)generated by the conventional interpolation or extrapolation at the decoder;then,another group of CS measurements are sampled again upon the decoder’s request.To fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge features.Finally,the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the SI.Experimental results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.

Compression and reconstruction of speech signals based on compressed sensing

Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.

Video Compressed Sensing Reconstruction Based on Multi-Dimensional Reference Frame Multi Hypothesis Rediction

In this paper,a video compressed sensing reconstruction algorithm based on multidimensional reference frames is proposed using the sparse characteristics of video signals in different sparse representation domains.First,the overall structure of the proposed video compressed sensing algorithm is introduced in this paper.The paper adopts a multi-reference frame bidirectional prediction hypothesis optimization algorithm.Then,the paper proposes a reconstruction method for CS frames at the re-decoding end.In addition to using key frames of each GOP reconstructed in the time domain as reference frames for reconstructing CS frames,half-pixel reference frames and scaled reference frames in the pixel domain are also used as CS frames.Reference frames of CS frames are used to obtain higher quality assumptions.Themethod of obtaining reference frames in the pixel domain is also discussed in detail in this paper.Finally,the reconstruction algorithm proposed in this paper is compared with video compression algorithms in the literature that have better reconstruction results.Experiments show that the algorithm has better performance than the best multi-reference frame video compression sensing algorithm and can effectively improve the quality of slowmotion video reconstruction.

基于DINEOF方法重构台湾海峡叶绿素a遥感缺失数据的初步研究

卫星遥感观测是研究海洋环境变化的一种重要方法,但由于观测周期和天气影响等原因,观测数据经常存在一定的缺失,这使得遥感数据在海洋环境连续变化的应用研究中受到一定的限制.为解决此问题,本文采用了经验正交函数分解插值方法（DINEOF）重建缺失的遥感观测数据.首先,基于SeaWiFS（1998年1月至2010年12月）、MODIS-Aqua（2002年7月至2014年12月）和MODIS-Terra（2000年2月至2014年12月）三级叶绿素a月平均数据产品,按像素点平均的方法组合成原始资料集;其次,利用DINEOF方法重构该资料集的缺失部分,从而得到完整的1998-2014年台湾海峡及邻近海域叶绿素a浓度的月平均数据集;再通过分析重构影像与原始资料的时空误差和验证重构影像的时空变化特征等方法,评价了所用算法和重构数据集的合理性.结果表明：基于遥感组合数据,采用DINEOF方法重构的叶绿素a遥感影像,能够有效地反映研究海域叶绿素a浓度的时空变化规律.研究还表明,该方法操作简便,无需先验信息,且重构精度高,能有效重构大面积缺失的影像数据资料,为探索海洋环境和生态的长期变化规律提供了较好的基础.

自适应匹配追踪的OFDM系统窄带干扰检测

针对在OFDM通信系统中,基于压缩感知的窄带干扰检测时间过长的问题,提出了将自适应匹配追踪算法应用到压缩感知理论的信号重构过程中的算法。该算法可在未知干扰稀疏度的情况下,自适应地调整候选集原子的个数,在低于奈奎斯特采样频率前提下,快速实现单个及多个窄带干扰信号检测。仿真结果表明:所提算法能够有效地实现窄带干扰检测且运行速度快,改善了OFDM通信系统的性能。

基于自适应匹配追踪的MIMO-OFDM窄带干扰检测

针对基于压缩感知的MIMO-OFDM窄带干扰检测存在检测时间较长的问题,提出一种基于压缩感知的自适应匹配追踪窄带干扰信号检测算法。通过建立窄带干扰在频域的稀疏模型,提出将目前压缩感知中解决稀疏信号重构效果较好的自适应匹配追踪算法应用到窄带干扰信号检测中。理论分析与实验仿真结果表明,在干扰数目增加的情况下,本文算法不仅能够精准地重构窄带干扰信号,完成窄带干扰信号的精确检测,而且在检测时间方面具有更为明显的优势。