Anomaly detection is playing an increasingly important role in hyperspectral image (HSI) processing. The traditional anomaly detection methods mainly extract knowledge from the background and use the ...difference between the anomalies and the background to distinguish them. Anomaly contamination and the inverse covariance matrix problem are the main difficulties with these methods. The low-rank and sparse matrix decomposition (LRaSMD) technique may have the potential to solve the aforementioned hyperspectral anomaly detection problem since it can extract knowledge from both the background and the anomalies. This paper proposes an LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection (LSMAD). This approach has the following capabilities: 1) takes full advantage of the LRaSMD technique to set the background apart from the anomalies; 2) explores the low-rank prior knowledge of the background to compute the background statistics; and 3) applies the Mahalanobis distance differences to detect the probable anomalies. Extensive experiments were carried out on four HSIs, and it was found that LSMAD shows a better detection performance than the current state-of-the-art hyperspectral anomaly detection methods.
Hyperspectral images provide great potential for target detection, however, new challenges are also introduced for hyperspectral target detection, resulting that hyperspectral target detection should ...be treated as a new problem and modeled differently. Many classical detectors are proposed based on the linear mixing model and the sparsity model. However, the former type of model cannot deal well with spectral variability in limited endmembers, and the latter type of model usually treats the target detection as a simple classification problem and pays less attention to the low target probability. In this case, can we find an efficient way to utilize both the high-dimension features behind hyperspectral images and the limited target information to extract small targets? This paper proposes a novel sparsity-based detector named the hybrid sparsity and statistics detector (HSSD) for target detection in hyperspectral imagery, which can effectively deal with the above two problems. The proposed algorithm designs a hypothesis-specific dictionary based on the prior hypotheses for the test pixel, which can avoid the imbalanced number of training samples for a class-specific dictionary. Then, a purification process is employed for the background training samples in order to construct an effective competition between the two hypotheses. Next, a sparse representation-based binary hypothesis model merged with additive Gaussian noise is proposed to represent the image. Finally, a generalized likelihood ratio test is performed to obtain a more robust detection decision than the reconstruction residual-based detection methods. Extensive experimental results with three hyperspectral data sets confirm that the proposed HSSD algorithm clearly outperforms the state-of-the-art target detectors.
In this paper, a new sparse representation-based binary hypothesis (SRBBH) model for hyperspectral target detection is proposed. The proposed approach relies on the binary hypothesis model of an ...unknown sample induced by sparse representation. The sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionary under the alternative hypothesis. The sparse vectors in the model can be recovered by a greedy algorithm, and the same sparsity levels are employed for both hypotheses. Thus, the recovery process leads to a competition between the background-only subspace and the target and background subspace, which are directly represented by the different hypotheses. The detection decision can be made by comparing the reconstruction residuals under the different hypotheses. Extensive experiments were carried out on hyperspectral images, which reveal that the SRBBH model shows an outstanding detection performance.
With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection ...methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods.
Abstract This study focuses on optimizing and designing the Delayed-Fix-Later Awaiting Transmission Encoding (DEFLATE) algorithm to enhance its compression performance and reduce the compression time ...for models, specifically in the context of compressing NX three-dimensional (3D) image models. The DEFLATE algorithm, a dual-compression technique combining the LZ77 algorithm and Huffman coding, is widely employed for compressing multimedia data and 3D models. Three 3D models of varying sizes are selected as subjects for experimentation. The Wavelet algorithm, C-Bone algorithm, and DEFLATE algorithm are utilized for compression, with subsequent analysis of the compression ratio and compression time. The experimental findings demonstrate the DEFLATE algorithm’s exceptional performance in compressing 3D image models. Notably, when compressing small and medium-sized 3D models, the DEFLATE algorithm exhibits significantly higher compression ratios compared to the Wavelet and C-Bone algorithms while also achieving shorter compression times. Compared to the Wavelet algorithm, the DEFLATE algorithm enhances the compression performance of 3D image models by 15% and boosts data throughput by 49%. While the compression ratio of the DEFLATE algorithm for large 3D models is comparable to that of the Wavelet and C-Bone algorithms, it notably reduces the actual compression time. Furthermore, the DEFLATE algorithm enhances data transmission reliability in NX 3D image model compression by 12.1% compared to the Wavelet algorithm. Therefore, the following conclusions are drawn: the DEFLATE algorithm serves as an excellent compression algorithm for 3D image models. It showcases significant advantages in compressing small and medium-sized models while remaining highly practical for compressing large 3D models. This study offers valuable insights for enhancing and optimizing the DEFLATE algorithm, and it serves as a valuable reference for future research on 3D image model compression.
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Feature distortions of data are a typical problem in remote sensing image classification, especially in the area of transfer learning. In addition, many transfer learning-based methods only focus on ...spectral information and fail to utilize spatial information of remote sensing images. To tackle these problems, we propose spectral-spatial weighted kernel manifold embedded distribution alignment (SSWK-MEDA) for remote sensing image classification. The proposed method applies a novel spatial information filter to effectively use similarity between nearby sample pixels and avoid the influence of nonsample pixels. Then, a complex kernel combining spatial kernel and spectral kernel with different weights is constructed to adaptively balance the relative importance of spectral and spatial information of the remote sensing image. Finally, we utilize the geometric structure of features in manifold space to solve the problem of feature distortions of remote sensing data in transfer learning scenarios. SSWK-MEDA provides a novel approach for the combination of transfer learning and remote sensing image characteristics. Extensive experiments have demonstrated that the proposed method is more effective than several state-of-the-art methods.
Owing to the increasingly complex economic environment and difficult employment situation, a large number of new occupations have emerged in China, leading to job diversification. Currently, the ...overall development status of new occupations in China and the structural characteristics of new occupation practitioners in different cities are still unclear. This study first constructed a development index system for new occupation practitioners from five dimensions (group size, cultural appreciation, salary level, occupation perception, and environmental perception). Relevant data to compare and analyze the development status of new occupation practitioners were derived from the big data mining of China's mainstream recruitment platforms and the questionnaire survey of new professional practitioners which from four first-tier cities and 15 new first-tier cities in China. The results show that the development level of new occupation practitioners in the four first-tier cities is the highest, and the two new first-tier cities, Chengdu and Hangzhou, have outstanding performance. The cities with the best development level of new occupation practitioners in Eastern, Central, and Western China are Shanghai, Wuhan, and Chengdu, respectively. Most new occupation practitioners in China are confident about the future of their careers. However, more than half of the 19 cities are uncoordinated in the five dimensions of the development of new occupation practitioners, especially those cities with middle development levels. A good policy environment and social environment have not yet been formulated to ensure the sustainable development of new occupation practitioners. Finally, we proposed the following countermeasures and suggestions: (1) Establish a classified database of new occupation talents. (2) Implement a talent industry agglomeration strategy. (3) Pay attention to the coordinated development of new occupation practitioners in cities.
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By taking advantage of the elevation domain, three-dimensional (3-D) multiple input and multiple output (MIMO) with massive antenna elements is considered as a promising and practical technique for ...the fifth Generation mobile communication system. So far, 3-D MIMO is mostly studied by simulation and a few field trials have been launched recently. It still remains unknown how much does the 3-D MIMO meet our expectations in versatile scenarios. In this paper, we answer this based on measurements with 56 × 32 antenna elements at 3.5 GHz with 100-MHz bandwidth in three typical deployment scenarios, including outdoor to indoor (O2I), urban microcell (UMi), and urban macrocell (UMa). Each scenario contains two different site locations and 2-5 test routes under the same configuration. Based on the measured data, both elevation and azimuth angles are extracted and their stochastic behaviors are investigated. Then, we reconstruct two dimensional and 3-D MIMO channels based on the measured data, and compare the capacity and eigenvalues distribution. It is observed that 3-D MIMO channel which fully utilizes the elevation domain does improve capacity and also enhance the contributing eigenvalue number. However, this gain varies from scenario to scenario in reality, O2I is the most beneficial scenario, then followed by UMi and UMa scenarios. More results of multiuser capacity varying with the scenario, antenna number and user number can provide the experimental insights for the efficient utilization of 3-D MIMO in future.
N6-methyladenosine (m6A) methylation is the most prevalent mRNA modification in eukaryotes, and it is defined as the methylation of nitrogen atoms on the six adenine (A) bases of RNA in the presence ...of methyltransferases. Methyltransferase-like 3 (Mettl3), one of the components of m6A methyltransferase, plays a decisive catalytic role in m6A methylation. Recent studies have confirmed that m6A is associated with a wide spectrum of biological processes and it significantly affects disease progression and prognosis of patients with gynecologic tumors, in which the role of Mettl3 cannot be ignored. Mettl3 is involved in numerous pathophysiological functions, such as embryonic development, fat accumulation, and tumor progression. Moreover, Mettl3 may serve as a potential target for treating gynecologic malignancies, thus, it may benefit the patients and prolong survival. However, there is a need to further study the role and mechanism of Mettl3 in gynecologic malignancies. This paper reviews the recent progression on Mettl3 in gynecologic malignancies, hoping to provide a reference for further research.
The mammalian molecular clock comprises a complex network of transcriptional programs that integrates environmental signals with physiological pathways in a tissue-specific manner. Emerging ...technologies are extending knowledge of basic clock features by uncovering their underlying molecular mechanisms, thus setting the stage for a 'systems' view of the molecular clock. Here we discuss how recent data from genome-wide genetic and epigenetic studies have informed the understanding of clock function. In addition to its importance in human physiology and disease, the clock mechanism provides an ideal model to assess general principles of dynamic transcription regulation in vivo.
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