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

The invention discloses an intelligent text input method. The method comprises the following steps: acquiring an electromyographic signal and a motion signal when a finger knocks in a current input mode; Performing multi-dimensional feature extraction on the collected electromyographic signals and motion signals to obtain a feature matrix; According to the current input mode, constructing a corresponding recognition model by utilizing the feature matrix, calculating a knock letter probability matrix; And generating text characters according to the knocked letter probability matrix in combination with a text reasoning algorithm, performing context analysis on the text characters according to a grammar rule, and performing text restoration according to an analysis result, thereby obtaining corresponding text data. According to the method, free text input can be realized by capturing muscle electric signals and hand action information during finger knocking under the condition of any plane or complete separation from a plane carrier; and the limitation requirement of a traditional input keyboard on input equipment and an input plane is eliminated, the freestylization of keyboard inputis realized to a greater extent, and the convenience is greatly improved.

Intelligent text input method

Electroencephalography (EEG) signals are easily contaminated by various artifacts, making noise removal an essential step in EEG analysis. In recent years, deep-learning-based methods have provided a promising avenue for EEG denoising, with superior performance on several benchmarks. However, most existing deep models only rely on the temporal features of the signal and ignore the intrinsic characteristics in other domains, possibly limiting their ability to suppress artifacts. Therefore, we propose a cross-domain framework by integrating knowledge of both time and frequency domains into a deep-learning model for ocular artifact removal from EEG recordings. The proposed framework can be flexibly implemented on various deep denoising networks to improve their denoising performance. Extensive experiments demonstrate that the cross-domain design is capable of better eliminating the ocular artifact when implemented over several representative denoising networks. For example, RRMSE is reduced by 4%–26% for semisimulated data. These results verify the effectiveness of the proposed framework, which also indicates the importance of combining domain knowledge into the deep-learning models for EEG denoising.

Frequency Information Enhanced Deep EEG Denoising Network for Ocular Artifact Removal

Inferring brain connectivity networks has been increasingly important for understanding brain functioning. It is suggested that brain is inherently non-stationary and the dynamic patterns of brain networks may provide deeper insights into brain function. However, the majority of current models assume that brain connectivity networks have time invariant structures, neglecting the variability in brain interactions over time. To investigate time varying brain connectivity networks, a stick time varying model is presented in this paper. Simulation results demonstrate that the proposed method could improve the accuracy in estimating time-dependent connectivity patterns. It is also applied to real fMRI data set for studying time-varying resting-state brain connectivity networks.

Time varying brain connectivity modeling using FMRI signals

In this study, we proposed a multi-muscle contraction force estimation framework and implemented it on the elbow flexion task to explore the contributions of different muscles to integrated force at different force levels. High density surface electromyography (HD-sEMG) signals were collected from four muscle groups and the wrist pulling force was measured synchronously. The deep belief network (DBN) was adopted to establish the relationship between HD-sEMG and force. The representative signals of four primary areas, which were considered as the input signal of the force estimation model, were extracted from HD-sEMG by principle component analysis (PCA) algorithm, then fed separately or in common to the DBN to estimate the generated force. And the contributions of different muscle groups to the generated force was analyzed with an index called mean impact value (MIV). The experimental result demonstrates that in multi-muscle contraction task, not all muscles are suitable for force estimation, the force estimation accuracy obtained using only one muscle approximates even exceeds that obtained using multiple muscles, and the relative contributions of different muscle groups to the force can be obtained according to the ranking of MIVs. This scheme provides an effective method for muscle force estimation in multi-muscle contraction tasks, and can be further applied to biomechanics, sports and rehabilitation medicine.

Xun Chen

Papers

Intelligent text input method

Frequency Information Enhanced Deep EEG Denoising Network for Ocular Artifact Removal

Time varying brain connectivity modeling using FMRI signals

Investigation on the Contributions of Different Muscles to the Generated Force based on HD-sEMG and DBN *

Spatio-temporal MLP network for seizure prediction using EEG signals