Reconstruction of degraded image transmitting through ocean turbulence via deep learning.

When a laser carrying image information is transmitted in seawater, the presence of ocean turbulence leads to significant degradation of the received information due to the effect of interference. To address this issue, we propose a deep-learning-based method to retrieve the original information from a degraded pattern. To simulate the propagation of laser beams in ocean turbulence, a model of an ocean turbulence phase screen based on the power spectrum inversion method is used. The degraded images with different turbulence conditions are produced based on the model. A Pix2Pix network architecture is built to acquire the original image information. The results indicate that the network can realize high-fidelity image recovery under various turbulence conditions based on the degraded patterns. However, as turbulence strength and transmission distance increase, the reconstruction accuracy of the Pix2Pix network decreases. To further improve the image reconstruction ability of neural network architectures, we established three networks (U-Net, Pix2Pix, and Deep-Pix2Pix) and compared their performance in retrieving the degraded patterns. Overall, the Pix2Pix network showed the best performance for image reconstruction.

Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm.

Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.

Hybrid structured CoNi2S4/Ni3S2 nanowires with multifunctional performance for hybrid capacitor electrodes and overall water splitting.

Rational design of electrode materials plays a significant role in potential applications such as energy storage and conversion. In this work, CoNi2S4/Ni3S2 nanowires grown on Ni foam were synthesized through a facile hydrothermal approach, revealing a large capacitance of 997.2 F g-1 and cycling stability with 80.3% capacitance retention after 5000 cycles. The device was prepared using CoNi2S4/Ni3S2//AC as the positive electrode and active carbon as the negative electrode, and delivered an energy density of 0.4 mW h cm-3 at a power density of 3.99 mW cm-3 and an excellent cycle life with 79.2% capacitance retention after 10 000 cycles. In addition, the hybrid CoNi2S4/Ni3S2 nanowires demonstrate excellent OER performance with low overpotential of 360 mV at 30 mA cm-2 and overpotential of 173.8 mV at -10 mA cm-2 for the HER, a cell voltage of 1.43 V, and excellent cycle stability.

Constructing high-performance electrode materials using core-shell ZnCo2O4@PPy nanowires for hybrid batteries and water splitting.

Heterogeneity can be used as a promising method to improve the electrochemical performance of electrode materials; thus, ZnCo2O4@PPy samples were prepared using a facile hydrothermal route and an electrochemical deposition process. The as-prepared products possess a specific capacitance of 605 C g-1 at a current density of 1 A g-1. The asymmetric supercapacitor (ASC) possesses an energy density of 141.3 W h kg-1 at a power density of 2700.5 W kg-1 and capacity retention of 88.1% after 10 000 cycles, indicating its promising potential for energy devices. ZnCo2O4@PPy-50 exhibited an excellent OER performance and outstanding HER performance in alkaline media. As an advanced bifunctional electrocatalyst for overall water splitting, a voltage of 1.61 V at a current density of 50 mA cm-2 outperforms the majority of noble-metal-free electrocatalysts.

Adaboost-SVM-based probability algorithm for the prediction of all mature miRNA sites based on structured-sequence features.

The significant role of microRNAs (miRNAs) in various biological processes and diseases has been widely studied and reported in recent years. Several computational methods associated with mature miRNA identification suffer various limitations involving canonical biological features extraction, class imbalance, and classifier performance. The proposed classifier, miRFinder, is an accurate alternative for the identification of mature miRNAs. The structured-sequence features were proposed to precisely extract miRNA biological features, and three algorithms were selected to obtain the canonical features based on the classifier performance. Moreover, the center of mass near distance training based on K-means was provided to improve the class imbalance problem. In particular, the AdaBoost-SVM algorithm was used to construct the classifier. The classifier training process focuses on incorrectly classified samples, and the integrated results use the common decision strategies of the weak classifier with different weights. In addition, the all mature miRNA sites were predicted by different classifiers based on the features of different sites. Compared with other methods, the performance of the classifiers has a high degree of efficacy for the identification of mature miRNAs. MiRFinder is freely available at https://github.com/wangying0128/miRFinder .