Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement

Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration. Specifically, LCDBNet is composed of two branches, namely luminance adjustment network (LAN) and chrominance restoration network (CRN). LAN takes responsibility for learning brightness-aware features leveraging long-range dependency and local attention correlation. While CRN concentrates on learning detail-sensitive features via multi-level wavelet decomposition. Finally, a fusion network is designed to blend their learned features to produce visually impressive images. Extensive experiments conducted on seven benchmark datasets validate the effectiveness of our proposed LCDBNet, and the results manifest that LCDBNet achieves superior performance in terms of multiple reference/non-reference quality evaluators compared to other state-of-the-art competitors. Our code and pretrained model will be available.Comment: 14 pages, 16 figure

Direction-of-Arrival Estimation for Circulating Space-Time Coding Arrays: From Beamspace MUSIC to Spatial Smoothing in the Transform Domain

As a special type of coherent collocated Multiple-Input Multiple-Output (MIMO) radar, a circulating space-time coding array (CSTCA) transmits an identical waveform with a tiny time shift. It provides a simple way to achieve a full angular coverage with a stable gain and a low sidelobe level (SLL) in the range domain. In this paper, we address the problem of direction-of-arrival (DOA) estimation in CSTCA. Firstly, we design a novel two-dimensional space-time matched filter on receiver. It jointly performs equivalent transmit beamforming in the angle domain and waveform matching in the fast time domain. Multi-beams can be formed to acquire controllable transmit freedoms. Then, we propose a beamspace multiple signal classification (MUSIC) algorithm applicable in case of small training samples. Next, since targets at the same range cell show characteristics of coherence, we devise a transformation matrix to restore the rotational invariance property (RIP) of the receive array. Afterwards, we perform spatial smoothing in element domain based on the transformation. In addition, the closed-form expression of Cramer-Rao lower bound (CRLB) for angle estimation is derived. Theoretical performance analysis and numerical simulations are presented to demonstrate the effectiveness of proposed approaches

Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing

The learned denoising-based approximate message passing (LDAMP) algorithm has attracted great attention for image compressed sensing (CS) tasks. However, it has two issues: first, its global measurement model severely restricts its applicability to high-dimensional images, and its block-based measurement method exhibits obvious block artifacts; second, the denoiser in the LDAMP is too simple, and existing denoisers have limited ability in detail recovery. In this paper, to overcome the issues and develop a high-performance LDAMP method for image block compressed sensing (BCS), we propose a novel sparsity and coefficient permutation-based AMP (SCP-AMP) method consisting of the block-based sampling and the two-domain reconstruction modules. In the sampling module, SCP-AMP adopts a discrete cosine transform (DCT) based sparsity strategy to reduce the impact of the high-frequency coefficient on the reconstruction, followed by a coefficient permutation strategy to avoid block artifacts. In the reconstruction module, a two-domain AMP method with DCT domain noise correction and pixel domain denoising is proposed for iterative reconstruction. Regarding the denoiser, we proposed a multi-level deep attention network (MDANet) to enhance the texture details by employing multi-level features and multiple attention mechanisms. Extensive experiments demonstrated that the proposed SCP-AMP method achieved better reconstruction accuracy than other state-of-the-art BCS algorithms in terms of both visual perception and objective metrics.Comment: The content modification has been upgraded and corrected on a large scale, and request to withdraw this versio

Deformable channel non‐local network for crowd counting

Abstract Both global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel non‐local network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two well‐crafted designed modules: deformable channel non‐local block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes long‐range dependencies between pixels and the adaptive local correlation with channel non‐local and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the cross‐level information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models

Direction-of-Arrival Estimation for Circulating Space-Time Coding Arrays: From Beamspace MUSIC to Spatial Smoothing in the Transform Domain

As a special type of coherent collocated Multiple-Input Multiple-Output (MIMO) radar, a circulating space-time coding array (CSTCA) transmits an identical waveform with a tiny time shift. It provides a simple way to achieve a full angular coverage with a stable gain and a low sidelobe level (SLL) in the range domain. In this paper, we address the problem of direction-of-arrival (DOA) estimation in CSTCA. Firstly, we design a novel two-dimensional space-time matched filter on receiver. It jointly performs equivalent transmit beamforming in the angle domain and waveform matching in the fast time domain. Multi-beams can be formed to acquire controllable transmit freedoms. Then, we propose a beamspace multiple signal classification (MUSIC) algorithm applicable in case of small training samples. Next, since targets at the same range cell show characteristics of coherence, we devise a transformation matrix to restore the rotational invariance property (RIP) of the receive array. Afterwards, we perform spatial smoothing in element domain based on the transformation. In addition, the closed-form expression of Cramer-Rao lower bound (CRLB) for angle estimation is derived. Theoretical performance analysis and numerical simulations are presented to demonstrate the effectiveness of proposed approaches

NUMERICAL SIMULATION OF PRESSURE FLUCTUATION IN DRAFT TUBE OF LARGE FRANCIS TURBINE

ABSTRACT The pressure fluctuation caused by swirling flow in draft tube is one of the main reasons of vibration in hydraulic turbine. It directly affects the steady operation of hydraulic turbine unit. The pressure fluctuation in draft tube of a large Francis turbine can't be obtained accurately by similarity law from model test, and prototype test is difficult to carry out and costs too much. Therefore, it is necessary to predict pressure fluctuation in draft tube numerically and provide scientific reference for mitigating and suppressing pressure fluctuation. This paper describes a numerical study of unsteady flow in the draft tube of a large Francis turbine in a Hydropower Station of China by using the Reynolds averaged Navier-Stokes (RANS) approach with a Reynolds stress transport model (RSM), validating the numerical results against experimental data. The numerical results successfully represent the vortex rope. The pressure fluctuation patterns in different parts of the draft tube including the cone, elbow and diffuser are analyzed. The pressure fluctuation in the cone and elbow is relative steady, and it has an obvious dominant frequency which is approximately 0.28 and 0.3 times of the runner rotational frequency. These results show very good agreement with experiments. The largest pressure amplitude appears in the draft tube cone downstream side and the draft elbow inside. The pressure fluctuation in the diffuser is stochastic, and the amplitude is small. Additionally, the pressure distributions on the horizontal computational section of the draft tube are analyzed

Nuclear mitochondria-related genes-based molecular classification and prognostic signature reveal immune landscape, somatic mutation, and prognosis for glioma

Background: Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms. Methods: Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets. Results: We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified MGME1 as a vital gene associated with infiltrating immune cells in multiple types of tumors. Conclusion: In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. MGME1 was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management

Effects of chronic stress on smartphone addiction: A moderated mediation model

IntroductionBased on the compensatory Internet use theory and diathesis-stress model, the present study explores the effects of chronic stress on smartphone addiction (SPA). As intolerance of uncertainty and emotion-related variables are important factors that affect addictive behavior, we explore the mediating role of intolerance of uncertainty and the moderating role of emotion differentiation.MethodsWe conducted a questionnaire survey of 286 participants (13.64% female; Mage = 22.88; SD = 3.77; range = 17–39) on chronic stress, SPA, intolerance of uncertainty, and emotion differentiation. SPSS 28.0 was used to analyze the descriptive statistics and correlations and test the moderated mediation model.ResultsWe find that (1) intolerance of uncertainty, SPA, and chronic stress are positively correlated with each other. Positive emotion differentiation is positively correlated with intolerance of uncertainty and negative emotion differentiation. (2) Intolerance of uncertainty plays a mediating role in chronic stress and SPA. (3) Positive emotion differentiation significantly moderates the relationship between chronic stress and SPA. Under the condition of low positive emotion differentiation, chronic stress is more effective in predicting SPA.DiscussionThese findings may contribute to intervention and prevention programs for SPA. Thus, the intervention and prevention of SPA can start from two directions-reduce the intolerance of uncertainty and enhance the ability to experience positive emotion differentiation

Enhancing the Output Charge Density of TENG via Building Longitudinal Paths of Electrostatic Charges in the Contacting Layers

The surface charge density of the tribolayer is the most parameter for developing a high performance triboelectric nanogenerator (TENG). Most previous works focused on the surface structural/chemical modification. Nevertheless, the internal space of the tribolayer and its mechanism exploration were less investigated. Herein, in this work, internal-space-charge zones are built through imbedding ravines and gullies in criss-crossed gold layers in the near-surface of the tribolayer, which leads to the high output performance of TENG. As experimental results manifest, the transfer charge density of gold-PDMS TENG (G-TENG) reaches 168 μC m^–2. Through theoretical analyses, it is determined that gold layers act as the passageways and traps of the triboelectric charges when the charges drift to the internal space of the tribomaterial. Moreover, the transport and storage process of triboelectric charges in the frictional layer are investigated comprehensively by quantum mechanics for the first time. The calculation method of the output current of TENG is proposed, and the theoretical calculation results coincide with the test results well. The results verify the application of the theoretical model and help with the construction and development of the theoretical system of TENG. Meanwhile, the relative results can be directly attained by this new theoretical model, and it is possible to make full use of the theoretical analysis to achieve a better performance for TENG. This study paves an easy and novel way for enhancing the charge density of the tribolayer by internal space construction and a new underlying theoretical model

Boosting output performance of sliding mode triboelectric nanogenerator by charge space-accumulation effect

Improving the output performance of sliding mode triboelectric nanogenerators is a great challenge. Herein, a space charge accumulation effect, based on alternating shielding and blank-tribo areas, is demonstrated and effectively promotes charge density output