The electroencephalogram (EEG) rhythm and functional near-infrared spectroscopy (fNIRS) activation levels have not been compared between a healthy control group (HCG) and methamphetamine user group (MUG) with different addiction histories. This study used 64-electrode EEG and fNIRS to conduct an experiment that analyzed the resting and craving states. The EEG and fNIRS data of 56 participants were collected, including 14 healthy participants, 14 methamphetamine users with an addiction history of 0.5–5 years, 14 users with an addiction history of 5–10 years, and 14 users with an addiction history of 10–15 years. Isolated effective coherence (iCoh) within the brain network was used to process the EEG data. Statistical analysis was performed to compare differences in iCoh among the delta, theta, alpha, beta, and gamma bands and explore oxyhemoglobin activation levels in the ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, orbitofrontal cortex, and frontopolar prefrontal cortex (FPC) of the control group. Finally, the Kmeans, Gaussian mixed model (GMM), linear discriminant analysis (LDA), support vector machine (SVM), Bayes, and convolutional neural networks (CNN) algorithms were used to classify methamphetamine users based on drug and neutral images. A 3-class accuracy was achieved. Changes in EEG and fNIRS activation levels of HCG and MUG with varied addiction histories were demonstrated.

针对全波形激光雷达和多光谱数据下土地覆盖误分类问题，提出了融合陆地卫星（Landsat）多光谱遥感影像数据和星载全波形激光雷达全球生态系统动态调查（GEDI）数据进行土地覆盖分类的方法。首先，根据实地调查数据建立数据集；然后，采用支持向量机（SVM）方法来实现激光雷达足迹的土地覆盖分类；最后，对土地覆盖的分类结果进行评价。结果表明，在SVM方法下联合使用光谱特征和波形特征的总体准确率可以达到90.68%，相比仅使用光谱特征或波形特征时总体准确率可以提升8个百分点以上。融合光谱特征和波形特征的方法可以提高土地覆盖分类的准确性。

激光点云技术可用于苗圃树木生长状态监测与管理，为农业植保机器人提供有效的靶标信息。为了进一步提高树种分类和树冠、树干内部分割的精准性，提出一种基于改进PointNet++的激光点云苗圃树木分类与分割方法。首先，调整PointNet++深度网络邻居点云的相对特征值，同时融合三维点云的低维和高维特征，充分利用各层级点云的特征。然后，将坐标注意力模块与注意力池化融合，进一步增强局部特征提取的能力，提高分类和分割的准确性。最后，针对苗圃常见树木自制了包含7类苗圃景观树木点云的数据集并用于实验。实验结果表明，提出的树种识别方法总体精度可达92.50%，平均类别精度为94.22%；提出的树冠、树干分割方法的平均交并比为89.09%。所提方法在分类和分割性能方面均明显优于经典的PointNet和PointNet++，能够为苗圃树木检测识别和农业机器人作业提供更精确的信息。

目前主流的神经网络在面对复杂多样的地物目标时难以精确区分，同时样本数量少、弱监督条件也容易为神经网络带来大量噪声与错误。为此，在分析遥感影像的地物特点后，提出一种基于权重动态变形的双重网络遥感影像分类方法，通过构架灵活、简易却有效的权重动态变形结构，构建经过改进的分类网络与目标识别网络，形成双网络对照的自我验证，从而提高学习性能、修复误差、增补遗漏、提高分类精度。实验结果表明，所提方法在容易实施的基础上，表现出更强的地物认知能力和更强的噪声抵抗能力，即其能够适应各种遥感影像的分类任务，具有较为广阔的应用潜力。