The application of complex measurement matrices in image-compressed sensing (CS) has attracted significant attention; however, effectively combining their structural, random, and orthogonal characteristics remains a substantial challenge. We propose a complex measurement matrix that integrates randomness and orthogonality into a structured discrete Fourier transform (DFT) matrix, termed ROS-DFT. We first introduce randomness to the DFT matrix through random phase encoding (RPE). Subsequently, sequences generated by the Logistic map are employed to shuffle index arrays of the matrix, which further augments matrix randomness. Finally, the Gram–Schmidt orthogonalization is applied to the columns of the measurement matrix, recovering column orthogonality, which is lost during the randomization operation. In addition, sparsification of the measurement matrix is achieved by randomly preserving a fixed number of non-zero elements in each column to reduce the storage space and minimize the computational complexity. Extensive qualitative and quantitative experiments demonstrate the effectiveness of the proposed method in significantly enhancing the quality of reconstructed images across various CS ratios compared with state-of-the-art methods.
In the past few years, the development of natural language processing has been able to deal with many issues such as emotional analysis, semantic analysis, and so on. This review first introduces the development of natural language processing, and then summarizes their applications in financial technology, which mainly focuses on public opinion analysis, financial prediction and analysis, risk assessment, intelligent question answering, and automatic document generation. The analysis shows that natural language processing can give full play to its advantages in the financial field. Moreover, this paper also discusses the problems and challenges for financial technology that are developed based on natural language processing. Finally, this paper presents two developing trends of natural language processing in financial technology: deep learning and knowledge graph.
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