Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease.

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

Land use classification of high-resolution remote sensing (HRRS) images is a challenging and prominent problem in which pretrained convolutional neural networks (CNNs) have made amazing achievements. However, single-structured pretrained CNNs have limitations to obtain high classification accuracy. Besides, each pretrained CNNs has different classification ability to classify land use. Therefore, taking advantages of different pretrained CNNs is essential for land use classification. In this study, we propose a novel classification approach based on multi-structure joint decision-making strategy and pretrained CNNs. The basic idea is to apply three CNNs to classify land use separately with the final classification results achieved by joint decision-making strategy. The proposed approach comprises of three steps. First, we create a new fully connected layer and Softmax classification layer. We combine them with the convolutional layers of AlexNet, Inception-v3, and ResNet18. AlexNet also includes the first two layers of fully connected layers. Secondly, we train these designed CNNs to converge by momentum-driven stochastic gradient descent. Thirdly, we utilize joint decision-making strategy to obtain the final prediction results by combining the prediction results of these designed CNNs. The performance of the proposed approach is evaluated on the UC Merced land use, AID, NWPU-45, OPTIMAL-31 datasets and further compared with the state-of-the-art methods. Results demonstrate that the proposed approach outperforms other methods. The benefits of the proposed approach are threefold. First, the multi-structure network maximizes different pretrained CNN structures to extract rich convolution features. Secondly, it could remarkably improve the classification accuracy of indistinguishable land use types of the HRRS images. Thirdly, it has great potential on small dataset with more land use types. The proposed CNN based on multistructure joint decision approach achieves accurate and reliable land use classification with these benefits.

Net primary productivity (NPP) can indirectly reflect vegetation’s capacity for CO${}_2$ fixation, but its spatiotemporal dynamics are subject to alterations to some extent due to the influences of climate change and human activities. In this study, NPP is used as an indicator to investigate vegetarian carbon ability changes in the vital ecosystems of the Yangtze River Basin (YRB) in China. We also explored the NPP responses to climate change and human activities. We conducted a comprehensive analysis of the temporal dynamics and spatial variations in NPP within the YRB ecosystems from 2003 to 2020. Furthermore, we employed residual analysis to quantitatively assess the contributions of climate factors and human activities to NPP changes. The research findings are as follows: (1) Over the 18-year period, the average NPP within the basin amounted to 543.95 gC/m${}^2$, displaying a noticeable fluctuating upward trend with a growth rate of approximately 3.1 gC/m${}^2$; (2) The areas exhibiting an increasing trend in NPP account for 82.55% of the total study area. Regions with relatively high stability in the basin covered 62.36% of the total area, while areas with low stability accounted for 2.22%, mainly situated in the Hengduan Mountains of the western Sichuan Plateau; (3) NPP improvement was jointly driven by human activities and climate change, with human activities contributing more significantly to NPP growth. Specifically, the contributions were 65.39% in total, with human activities contributing 59.28% and climate change contributing 40.01%. This study provides an objective assessment of the contributions of human activities and climate change to vegetation productivity, offering crucial insights for future ecosystem development and environmental planning.

Hyperspectral imaging technology are widely used in vegetation, agriculture, and other fields, especially in land cover classification of complex scenes. Higher spectral resolution has become the focus of the development of hyperspectral imaging technology for classification. The advent of airborne AISAIBIS sensor reaches 0.11nm ultra-hyperspectral resolution. The ultra-hyperspectral imagery shows great advantages in classification with its increasing spectral resolution. But its spatial resolution is limited because of the imaging mechanism, which brings great difficulties to the accurate extract of fine and regular objects. Therefore, we proposed an optimal fusion and classification strategy based on the complementary advantage information of ultra-hyperspectral and high spatial resolution image. The fusion feasibility and effectiveness were verified by various fusion methods. And a quality evaluation system was developed to assess the quality of fusion results. Besides, a multiresolution segmentation optimization and classification evaluation scheme was proposed to comparatively analyze the effect of optimal fusion result on improving classification accuracy. Results show that the classification accuracy of the optimal fused image reaches 88.10%, and 7.11%-19.03% higher than that of original images. It fully validates the effectiveness of the strategy proposed in this study.

Motion artifacts can cause baseline shifts and disrupt the waveform of the ECG signal. There will be a serious impact on the diagnosis when motion artifacts not removed. In this paper, a two-stage adaptive filter is proposed. In the first stage, the reference ECG signals are measured under static status, and the noise signals also be obtained. The obtained noise signals become reference signals for the second stage, and finally the desired ECG signal obtained. The experimental results show that the two-stage adaptive filter can filter out the noise of 0. $1\sim 10\mathrm{Hz}$ in the ECG signal which containing motion artifacts well.