The use of Transformer-based networks has been proposed for the processing of general point clouds. However, there has been little research related to multispectral LiDAR point clouds that contain both spatial coordinate information and multi-wavelength intensity information. In this paper, we propose networks for multispectral LiDAR point cloud point-by-point classification based on an improved Transformer. Specifically, considering the sparseness of different regions of multispectral LiDAR point clouds, we add a bias to the Transformer to improve its ability to capture local information and construct an easy-to-implement multispectral LiDAR point cloud Transformer (MPT) classification network. The MPT network achieves 78.49% $mIoU$, 94.55% $OA$, 84.46% $F_1$, and 0.92 $Kappa$ on the multispectral LiDAR point cloud testing dataset. To further extract the topological relationships between points, we present a standardization set abstraction (SSA) module, which includes the global point information while considering the relationships among the local points. Based on the SSA module, we propose an advanced version called MPT+ for the point-by-point classification of multispectral LiDAR point clouds. The MPT+ network achieves 82.94% $mIoU$, 95.62% $OA$, 88.42% $F_1$, and 0.94 $Kappa$ on the same testing dataset. Compared with seven point-based deep learning algorithms, our proposed MPT+ achieves state-of-the-art results for several evaluation metrics.

Convolutional neural networks (CNNs) have been prominent in most hyperspectral image (HSI) processing applications due to their advantages in extracting local information. Despite their success, the locality of the convolutional layers within CNNs results in heavyweight models and time-consuming defects. In this study, inspired by the excellent performance of transformers that are used for long-range representation learning in computer vision tasks, we built a lightweight vision transformer for HSI classification that can extract local and global information simultaneously, thereby facilitating accurate classification. Moreover, as traditional dimensionality reduction methods are limited in their linear representation ability, a three-dimensional convolutional autoencoder was adopted to capture the nonlinear characteristics between spectral bands. Based on the aforementioned three-dimensional convolutional autoencoder and lightweight vision transformer, we designed an HSI classification network, namely the “convolutional autoencoder meets lightweight vision transformer” (CAEVT). Finally, we validated the performance of the proposed CAEVT network using four widely used hyperspectral datasets. Our approach showed superiority, especially in the absence of sufficient labeled samples, which demonstrates the effectiveness and efficiency of the CAEVT network.

Hyperspectral images, characterized by rich spectral information, enable precise pixel-level classification and are thus widely employed in remote sensing applications. Although convolutional neural networks (CNNs) have demonstrated effectiveness in hyperspectral image processing, their limited receptive fields constrain their capacity to capture long-range dependencies. Transformers excel at modeling long-range features for hyperspectral image classification (HSIC). Yet, they often overlook effective representation of local spectral–spatial characteristics while incurring computational redundancy from numerous classification-irrelevant tokens. To address these challenges, we propose EDTST, a state-of-the-art Vision Transformer architecture specifically designed for efficient hyperspectral image classification. The model utilizes a large-kernel 3D convolution block to extract deep spectral–spatial features. A 2D convolution block further refines these features, followed by a novel attention mechanism with dynamic token pruning that substantially reduces the computational load by focusing on the most pertinent features. The process concludes with an adaptive average pooling layer and a fully connected layer for classification. Extensive experiments on four standard hyperspectral datasets demonstrate that EDTST achieves the highest classification accuracy, with a notable 3% improvement in overall accuracy on the WHU-Hi-HanChuan dataset, while requiring the shortest training and inference time among all compared state-of-the-art models from the past three years. These results validate the efficacy of our approach in achieving superior performance with markedly improved computational efficiency.

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops two accelerators, as the core of the signal measurement system, for intelligent signal processing. Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. Subsequently, another accelerator is designed with the efficient hardware structure of the long short-term memory (LSTM) + DNN model, demonstrating outstanding performance with a classification accuracy of 98.82%, mean absolute error (MAE) of 0.27°, and root mean square errors (RMSE) of 0.392° after model compression. Moreover, parallel optimization strategies are exploited to further reduce latency and support simultaneous frequency and direction measurement tasks. Finally, we test the actual collected signal data on the XCVU13P field programmable gate array (FPGA). The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). In addition, the parallel strategies further decrease the delay by 23.9% and 37.5% when processing continuous data. The FPGA-based and deep learning-assisted hardware accelerators significantly improve real-time performance and provide a promising solution for signal measurement.

Background: Studies investigating debulking devices with drug-coated balloons (DCBs) in the treatment of femoropopliteal (FP) artery in-stent restenosis (ISR) are limited. We aimed to evaluate the safety and midterm outcome of percutaneous mechanical atherectomy plus thrombectomy (MATH) using the Rotarex®S (Straub Medical, Wangs, Switzerland) catheter followed by a DCB in the treatment of FP-ISR.Methods: This study was a single-center single-arm trial. Patients with symptomatic (Rutherford category 2–5) de novo restenosis lesions of FP-ISR were treated with MATH and subsequent DCB. From June 2016 to May 2018, 59 patients with FP-ISR were enrolled. The primary endpoint was target lesion revascularization (TLR) and changes in the Rutherford category of the target limb at 12 months. Secondary endpoints included primary and secondary patency at 12 months, technical success rate, major adverse events, and ankle-brachial index (ABI). Risk factors for TLR were analyzed using Cox proportional hazard model.Results: The average follow-up time was 33 ± 8 months. The rate of technical success was 88.1% (52/59). Nine patients received bailout stenting. The rate of freedom from TLR was 84.7% (50/59) at 1 year, the Rutherford category changed at 12 months were significantly improved from baseline (p < 0.01). The primary patency rates and the secondary patency at the 12-month follow-ups were 82.5 and 92.5%, respectively. The ABI changed at 12 months were significantly improved from baseline (p < 0.01). Global limb anatomic staging system (GLASS) classification III [hazard ratio (HR) 18.44, 95% CI (1.57–215.99), p = 0.020] and postoperative Rutherford classification ≥4 [HR 8.28, 95% CI (1.85–37.06), p = 0.006] were identified as independent predictors of TLR.Conclusion: Our preliminary data suggested that MATH using a Rotarex®S catheter combined with DCB angioplasty is a safe, minimally invasive, and effective treatment for FP-ISR with favorable, immediate, and midterm outcomes.Clinical Trial Registration:http://www.chictr.org.cn, identifier [ChiCTR2000041380].

Hyperspectral imagery (HSI), characterized by the integration of both spatial and spectral information, is widely employed in various fields, such as environmental monitoring, geological exploration, precision agriculture, and medical imaging. Hyperspectral image classification (HSIC), as a key research direction, aims to establish a mapping relationship between pixels and land-cover categories. Nevertheless, several challenges persist, including difficulties in feature extraction, the trade-off between effective integration of local and global features, and spectral redundancy. We propose FreqMamba, a novel model that efficiently combines CNN, a custom attention mechanism, and the Mamba architecture. The proposed framework comprises three key components: (1) A novel multi-scale deformable convolution feature extraction module equipped with spectral attention, which processes spectral and spatial information through a dual-branch structure to enhance feature representation for irregular terrain contours; (2) a novel group-separated attention module that integrates group convolution with group-separated self-attention, effectively balancing local feature extraction and global contextual modeling; (3) a newly introduced bidirectional scanning Mamba branch that efficiently captures long-range dependencies with linear computational complexity. The proposed method achieves optimal performance on multiple benchmark datasets, including QUH-Tangdaowan, QUH-Qingyun, and QUH-Pingan, with the highest overall accuracy reaching 97.47%, average accuracy reaching 93.52%, and a Kappa coefficient of 96.22%. It significantly outperforms existing CNN, Transformer, and SSM-based methods, demonstrating its effectiveness, robustness, and superior generalization capability.