Leu-M

Fast Fourier Transform

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

Deep learning in remote sensing applications: A meta-analysis and review

FusionGAN: A generative adversarial network for infrared and visible image fusion

Infrared and visible image fusion methods and applications: A survey

In this letter, a convolutional sparsity regularization (CSR) is introduced into the framework of nonlinear iterative methods for solving inverse scattering problems (ISPs). The permittivity image of scatterers is sparsely represented in a convolutional form with pre-learned dictionary filters. The CSR is then incorporated with the subspace-based optimization method (SOM), termed as (SOM-CSR), to reconstruct the target image as a sparse coding by dictionary filters. The whole optimization function of SOM-CSR is solved using an alternative iteration method. Both synthetic and experimental data are employed to validate the effectiveness of the proposed SOM-CSR method. The results demonstrate that the CSR, as a structural constraint, is beneficial to nonlinear iterative reconstruction methods for solving ISP in contrast to pixel-based inversion.

A convolutional sparsity regularization for solving inverse scattering problems

Decoding silent speech from high-density surface electromyographic data using transformer

The interpatient variability still poses a great challenge for the real-world application of electroencephalogram (EEG)-based seizure prediction, where most previous methods could only work under the patient-specific fashion and fail to generalize across patients. To address this issue, some latest studies applied supervised domain adaptation (SDA), utilizing limited labeled data from the target patient to calibrate the model. However, as the epileptic EEG representation even varies within one single patient, limited target data could hardly cover the data distribution, thus the model still shows poor generalization on the target patient. To this end, we introduce a novel semi-supervised domain adaptive seizure prediction model (SSDA-SPM), using limited labeled target data and extra unlabeled target data for adaptation. SSDA-SPM mainly consists of two unsupervised modules, namely feature alignment (FA) module and consistency regularization (CR) module. The FA module aims to transfer knowledge from existing patients to the target patient coarsely by globally aligning the data distribution between them. Then the CR module further enhances the discriminability on the target patient by pushing the decision boundary into the low-density area. Our proposed method achieves 88.8% sensitivity (Sens), 0.182/h false prediction rate (FPR), and 0.849 area under the receiver operating characteristic curve (AUC) on the CHB-MIT database and 75.7% Sens, 0.165/h FPR, and 0.763 AUC on the Kaggle database. Experimental results demonstrate that our method has provided a promising solution to improve the cross-patient generalization for seizure prediction.

Semi-Supervised Domain-Adaptive Seizure Prediction via Feature Alignment and Consistency Regularization

Given specific scene configurations and target functions, automatic shader simplification searches for the best simplified shader variant from an optimization space with many candidates. Although various speedup methods have been proposed, there is still a costly render-and-evaluate process to obtain variant’s performance and quality, especially when the scene changes. In this paper, we present a deep learning-based framework for predicting a shader’s simplification space, where the shader’s variants can be embedded into a metric space all at once for efficient quality evaluation. The novel framework allows the one-shot embedding of a space rather than a single instance. In addition, the simplification errors can be interpreted by mutual attention between shader fragments, presenting an informative focus-aware simplification framework that can assist experts in optimizing the codes. The results show that the new framework achieves significant speedup compared with existing search approaches. The focus-aware simplification framework reveals a new possibility of interpreting shaders for various applications.

Xun Chen

Papers

A convolutional sparsity regularization for solving inverse scattering problems

Decoding silent speech from high-density surface electromyographic data using transformer

Semi-Supervised Domain-Adaptive Seizure Prediction via Feature Alignment and Consistency Regularization

ShaderTransformer: Predicting Shader Quality via One-shot Embedding for Fast Simplification

High-order implicit weighted compact nonlinear scheme for nonlinear coupled viscous Burgers' equations