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

1. Implementation Details 1.1. Network Architecture We implement our models in PyTorch [18]. Our architectures for the networks are illustrated in Fig. 1. Note that we choose a relatively small network size for the skinning weight network and warping network, as the skinning weight field and warping field are typically smooth in 3D space and thus do not require high-capacity networks to be modeled well. We use a positional encoding [16] with 4 frequency components. The shape codes and details codes are 64-dimensional each.

Supplementary for gDNA: Towards Generative Detailed Neural Avatars

Objective: To address cross-user variability problem in the myoelectric pattern recognition, a novel method for domain generalization and adaptation using both mix-up and adversarial training strategies, termed MAT-DGA, is proposed in this paper. Methods: This method enables integration of domain generalization (DG) with unsupervised domain adaptation (UDA) into a unified framework. The DG process highlights user-generic information in the source domain for training a model expected to be suitable for a new user in a target domain, where the UDA process further improves the model performance with a few unlabeled testing data from the new user. In this framework, both mix-up and adversarial training strategies were also applied to each of both the DG and UDA processes by exploiting their complementary advantages towards enhanced integration of both processes. The performance of the proposed method was evaluated via experiments of classifying seven hand gestures using high-density myoelectric data recorded from extensor digitorum muscles of eight intact-limbed subjects. Results: It yielded a high accuracy of 95.71±4.17% and outperformed other UDA methods significantly (p<0.05) under cross-user testing scenarios. Moreover, it reduced the number of calibration samples required in the UDA process (p<0.05) after its initial performance had already been lifted by the DG process. Conclusion: The proposed method provides an effective and promising way of establishing cross-user myoelectric pattern recognition control systems. Significance: Our work helps to promote development of user-generic myoelectric interfaces, with wide applications in motor control and health.

A Unified User-Generic Framework for Myoelectric Pattern Recognition: Mix-Up and Adversarial Training for Domain Generalization and Adaptation

COPYRIGHT © 2023 Liu, Ma, Zhang, Wei, Chen and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Multimodal brain image fusion: Methods, evaluations, and applications

Editorial: Multimodal brain image fusion: Methods, evaluations, and applications

<inline-formula> <tex-math notation="LaTeX">$\textit {Objective:}$ </tex-math></inline-formula> This study aims to develop a flexible myoelectric pattern recognition (MPR) method based on one-shot learning, which enables convenient switching across different usage scenarios, thereby reducing the re-training burden. <inline-formula> <tex-math notation="LaTeX">$\textit {Methods}$ </tex-math></inline-formula>: First, a one-shot learning model based on a Siamese neural network was constructed to assess the similarity for any given sample pair. In a new scenario involving a new set of gestural categories and/or a new user, just one sample of each category was required to constitute a support set. This enabled the quick deployment of the classifier suitable for the new scenario, which decided for any unknown query sample by selecting the category whose sample in the support set was quantified to be the most like the query sample. The effectiveness of the proposed method was evaluated by experiments conducting MPR across diverse scenarios. Results: The proposed method achieved high recognition accuracy of over 89% under the cross-scenario conditions, and it significantly outperformed other common one-shot learning methods and conventional MPR methods (<inline-formula> <tex-math notation="LaTeX">${p} < 0.01$ </tex-math></inline-formula>). <inline-formula> <tex-math notation="LaTeX">$\textit {Conclusion}$ </tex-math></inline-formula>: This study demonstrates the feasibility of applying one-shot learning to rapidly deploy myoelectric pattern classifiers in response to scenario change. It provides a valuable way of improving the flexibility of myoelectric interfaces toward intelligent gestural control with extensive applications in medical, industrial, and consumer electronics.

https://ieeexplore.ieee.org/ielx7/7333/4359219/10061471.pdf

Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces

Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram (sEMG). This paper aims at developing a novel syllable-level decoding method for continuous silent speech recognition (SSR) using spatio-temporal end-to-end neural network. In the proposed method, the high-density sEMG (HD-sEMG) was first converted into a series of feature images, and then a spatio-temporal end-to-end neural network was applied to extract discriminative feature representations and to achieve syllable-level decoding. The effectiveness of the proposed method was verified with HD-sEMG data recorded by four pieces of 64-channel electrode arrays placed over facial and laryngeal muscles of fifteen subjects subvocalizing 33 Chinese phrases consisting of 82 syllables. The proposed method outperformed the benchmark methods by achieving the highest phrase classification accuracy (97.17 ± 1.53%, <inline-formula> <tex-math notation="LaTeX">${p} < 0.05$ </tex-math></inline-formula>), and lower character error rate (3.11 ± 1.46%, <inline-formula> <tex-math notation="LaTeX">${p} < 0.05$ </tex-math></inline-formula>). This study provides a promising way of decoding sEMG towards SSR, which has great potential applications in instant communication and remote control.

Xun Chen

Papers

Supplementary for gDNA: Towards Generative Detailed Neural Avatars

A Unified User-Generic Framework for Myoelectric Pattern Recognition: Mix-Up and Adversarial Training for Domain Generalization and Adaptation

Editorial: Multimodal brain image fusion: Methods, evaluations, and applications

Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces

Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network