Exploring geometrical structures in high-dimensional computer vision data

In computer vision, objects such as local features, images and video sequences are often represented as high dimensional data points, although it is commonly believed that there are low dimensional geometrical structures that underline the data set. The low dimensional geometric information enables us to have a better understanding of the high dimensional data sets and is useful in solving computer vision problems. In this thesis, the geometrical structures are investigated from different perspectives according to different computer vision applications. For spectral clustering, the distribution of data points in the local region is summarised by a covariance matrix which is viewed as the Mahalanobis distance. For the action recognition problem, we extract subspace information for each action class. The query video sequence is labeled by information regarding its distance to the subspaces of the corresponding video classes. Three new algorithms are introduced for hashing-based approaches for approximate nearest neighbour (ANN) search problems, NOKMeans relaxes the orthogonal condition of the encoding functions in previous quantisation error based methods by representing data points in a new feature space; Auto-JacoBin uses a robust auto-encoder model to preserve the geometric information from the original space into the binary codes; and AGreedy assigns a score, which reflects the ability to preserve the order information in the local regions, for any set of encoding functions and an alternating greedy method is used to find a local optimal solution. The geometric information has the potential to bring better solutions for computer vision problems. As shown in our experiments, the benefits include increasing clustering accuracy, reducing the computation for recognising actions in videos and increasing retrieval performance for ANN problems

New progress in the role of microRNAs in the diagnosis and prognosis of triple negative breast cancer

Triple negative breast cancer is distinguished by its high malignancy, aggressive invasion, rapid progression, easy recurrence, and distant metastases. Additionally, it has a poor prognosis, a high mortality, and is unresponsive to conventional endocrine and targeted therapy, making it a challenging problem for breast cancer treatment and a hotspot for scientific research. Recent research has revealed that certain miRNA can directly or indirectly affect the occurrence, progress and recurrence of TNBC. Their expression levels have a significant impact on TNBC diagnosis, treatment and prognosis. Some miRNAs can serve as biomarkers for TNBC diagnosis and prognosis. This article summarizes the progress of miRNA research in TNBC, discusses their roles in the occurrence, invasion, metastasis, prognosis, and chemotherapy of TNBC, and proposes a treatment strategy for TNBC by interfering with miRNA expression levels

Exploring geometrical structures in high-dimensional computer vision data

In computer vision, objects such as local features, images and video sequences are often represented as high dimensional data points, although it is commonly believed that there are low dimensional geometrical structures that underline the data set. The low dimensional geometric information enables us to have a better understanding of the high dimensional data sets and is useful in solving computer vision problems. In this thesis, the geometrical structures are investigated from different perspectives according to different computer vision applications. For spectral clustering, the distribution of data points in the local region is summarised by a covariance matrix which is viewed as the Mahalanobis distance. For the action recognition problem, we extract subspace information for each action class. The query video sequence is labeled by information regarding its distance to the subspaces of the corresponding video classes. Three new algorithms are introduced for hashing-based approaches for approximate nearest neighbour (ANN) search problems, NOKMeans relaxes the orthogonal condition of the encoding functions in previous quantisation error based methods by representing data points in a new feature space; Auto-JacoBin uses a robust auto-encoder model to preserve the geometric information from the original space into the binary codes; and AGreedy assigns a score, which reflects the ability to preserve the order information in the local regions, for any set of encoding functions and an alternating greedy method is used to find a local optimal solution. The geometric information has the potential to bring better solutions for computer vision problems. As shown in our experiments, the benefits include increasing clustering accuracy, reducing the computation for recognising actions in videos and increasing retrieval performance for ANN problems

Tomographic Image Reconstruction Using an Interpolation Method for Tree Decay Detection

Stress wave velocity has been traditionally regarded as an indicator of the extent of damage inside wood. This paper aimed to detect internal decay of urban trees through reconstructing tomographic image of the cross section of a tree trunk. A grid model covering the cross section area of a tree trunk was defined with some assumptions. Stress wave data were processed beforehand to obtain the propagation velocity and the coordinate values. An image reconstruction algorithm for detecting internal decay was proposed based on an interpolation method, which estimated the velocity values of unknown grid points by utilizing the values of the surrounding points. To test the effectiveness of this method, Cinnamomum camphora tree samples were selected and tested using a stress wave tool. The area, positions, and extent of decay in the representative samples were displayed in tomographic images constructed by the interpolation method, and the results demonstrate the performance of the method

Determination of Soil Salt Content Using a Probability Neural Network Model Based on Particle Swarm Optimization in Areas Affected and Non-Affected by Human Activities

Traditional partial least squares regression (PLSR) and artificial neural networks (ANN) have been widely applied to estimate salt content from spectral reflectance in many different saline environments around the world. However, these methods entail a great amount of calculation, and their accuracy is low. To overcome these problems, a probability neural network (PNN) model based on particle swarm optimization was used in this study to build soil salt content models. Furthermore, there is a clear correlation between the level of human activities and the degree of salinization of an environment. This paper is the first to discuss this matter. Here, the performance of the PNN model to estimate soil salt content from reflectance data was investigated in areas non-affected (Area A) and affected (Area B) by human activities. The study area is located in Xingjinag, China. Different mathematical procedures, five wave band intervals, and two types of signal input sources were used for cross analysis. The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) index values were compared to verify the reliability of the model. Particle swarm optimization was used to adjust the optimal smoothing parameters of the PNN model and to avoid the long training processes required by the traditional ANN. The results show that the optimal wave band interval of the PNN is between 1000 nm and 1350 nm in Area A and between 400 nm and 700 nm in Area B. The reciprocal (1/R) transformation after Savitzky-Golay (SG) smoothing of the signal source is optimal for both areas. The RPD for both is greater than 30, which shows that the PNN model is applicable to areas with and without human activities and the prediction results are very good. The results indicated that the optimal wave band intervals for PNN modeling differed in areas affected and non-affected by human activities. The optimal interval of the artificial activities region falls in the visible light portion of the spectrum, and the optimized wave band region without human activities falls in the near-infrared short-wave portion of the spectrum

Design and optimization of reverse salient permanent magnet synchronous motor based on controllable leakage flux

In this paper, a controllable leakage flux reverse salient permanent magnet synchronous motor (CLF-RSPMSM) is designed, which has the advantages of wide speed range and low irreversible demagnetization risk. Firstly, the principle of controllable leakage flux and reverse saliency effect is introduced, and the design of the rotor flux barrier is emphatically discussed. Secondly, multiple design variables are stratified by the comprehensive sensitivity method, and the main variables are screened out. Then the relationship between the main variables and the optimization goal is discussed according to the response surface diagram. Thirdly, a sequential nonlinear programming algorithm (SNP) is used to optimize the three optimization objectives comprehensively. Finally, the electromagnetic performance of the proposed motor is compared with the initial IPM motor, the mechanical strength of the proposed rotor is analyzed, and the results verify the effectiveness of the design and optimization method of the proposed motor

Impact of Fractional Calculus on Correlation Coefficient between Available Potassium and Spectrum Data in Ground Hyperspectral and Landsat 8 Image

As the level of potassium can interfere with the normal circulation process of biosphere materials, the available potassium is an important index to measure the ability of soil to supply potassium to crops. There are rarely studies on the inversion of available potassium content using ground hyperspectral remote sensing and Landsat 8 multispectral satellite data. Pretreatment of saline soil field hyperspectral data based on fractional differential has rarely been reported, and the corresponding relationship between spectrum and available potassium content has not yet been reported. Because traditional integer-order differential preprocessing methods ignore important spectral information at fractional-order, it is easy to reduce the accuracy of inversion model. This paper explores spectral preprocessing effect based on Grünwald−Letnikov fractional differential (order interval is 0.2) between zero-order and second-order. Field spectra of saline soil were collected in Fukang City of Xinjiang. The maximum absolute of correlation coefficient between ground hyperspectral reflectance and available potassium content for five mathematical transformations appears in the fractional-order. We also studied the tendency of correlation coefficient under different fractional-order based on seven bands corresponding to the Landsat 8 image. We found that fractional derivative can significantly improve the correlation, and the maximum absolute of correlation coefficient under five spectral transformations is in Band 2, which is 0.715766 for the band at 467 nm. This study deeply mined the potential information of spectra and made up for the gap of fractional differential for field hyperspectral data, providing a new perspective for field hyperspectral technology to monitor the content of soil available potassium

Distribution of meiofaunal abundance in relation to environmental factors in Beibu Gulf, South China Sea

State Oceanic Administration of ChinaThis study aims to explore the distribution of meiofaunal abundance in relation to environmental factors in the Beibu Gulf, a natural semi-enclosed part of the South China Sea, surrounded by China and Vietnam. Meiofauna and ten benthic environmental factors were determined at 27 sampling stations in the Beibu Gulf in four surveys during 2006-2007. The results show a clear geographical trend in meiofaunal abundance, water depth, salinity and clay content. The meiofaunal abundance and the clay content decreased, whereas the water depth and the salinity increased from the north to the south of the Gulf. The percentage of meiofaunal abundance in the 0-2 cm layer increased, whereas in the 2-5 cm and 5-10 cm layers it decreased from the north to the south of the Gulf. Correlation analysis show significant negative correlations between meiofaunal abundance and water depth, benthic temperature, salinity and pH, but significant positive correlations between meiofaunal abundance and dissolved oxygen, chlorophyll a and clay content