scholarly journals Low-Rank Linear Dynamical Systems for Motor Imagery EEG

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wenchang Zhang ◽  
Fuchun Sun ◽  
Chuanqi Tan ◽  
Shaobo Liu
Keyword(s):  
Feature Extraction ◽  
Dynamical Systems ◽  
Motor Imagery ◽  
Feature Fusion ◽  
Low Cost ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Common Spatial Pattern ◽  
Decomposition Approach ◽  
Linear Dynamical Systems

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP.

Improved SfM-Based Indoor Localization with Occlusion Removal

2018 ◽  
Vol 10 (3) ◽  
pp. 24-40
Author(s):  
Yushi Li ◽  
George Baciu ◽  
Yu Han ◽  
Chenhui Li
Keyword(s):  
Indoor Localization ◽  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Data Sets ◽  
Indoor Environments ◽  
Decomposition Approach ◽  
Localization Accuracy ◽  
Occlusion Removal ◽  
Accuracy Increase

This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.

scholarly journals Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3766
Author(s):  
Behnood Rasti ◽  
Pedram Ghamisi ◽  
Peter Seidel ◽  
Sandra Lorenz ◽  
Richard Gloaguen
Keyword(s):  
Feature Extraction ◽  
Sensor Fusion ◽  
Optical Sensor ◽  
Feature Fusion ◽  
Heterogeneous Data ◽  
Hyperspectral Data ◽  
Low Rank ◽  
Svm Classifier ◽  
Imaging Data ◽  
Rank Analysis

Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.

scholarly journals Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongxia Wang ◽  
Bo Wang ◽  
Min Li ◽  
Peng Luo ◽  
Hengrui Ma ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Meteorological Parameters ◽  
Feature Fusion ◽  
Low Cost ◽  
Infrared Image ◽  
Weather Conditions ◽  
Power Grids ◽  
Pollution Level ◽  
Stable Operation ◽  
Fusion Model

Polluted insulators seriously threaten the safe and stable operation of power grids, which attaches great significance to insulator contamination perception. Among the present methods, the non-contact approaches based on infrared images have gradually been widely used, as they are much more safe and are of low cost. However, the thermal effect of insulators is largely affected by meteorological conditions, which makes the infrared image-based methods less accurate. To solve the above problem, we take infrared image and meteorological parameters including humidity and temperature as input, and propose a feature fusion model to perceive insulator contamination in different weather conditions. Firstly, different feature extraction networks are used to perform feature extraction on the two types of data; secondly, the two features are concatenated to fuse together; thirdly, further feature extraction is performed and contamination is classified according to the pollution severity. Case studies show that the proposed method can better explore the relationship between humidity, temperature and pollution level of the insulators, thus can better separate the contamination grades and outperform the conventional infrared image based methods.

Musical noise suppression using a low-rank and sparse matrix decomposition approach

2020 ◽  
Vol 125 ◽  
pp. 41-52
Author(s):  
Jishnu Sadasivan ◽  
Jitendra K. Dhiman ◽  
Chandra Sekhar Seelamantula
Keyword(s):  
Noise Suppression ◽  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Decomposition Approach

scholarly journals Discriminating three motor imagery states of the same joint for brain-computer interface

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12027
Author(s):  
Shan Guan ◽  
Jixian Li ◽  
Fuwang Wang ◽  
Zhen Yuan ◽  
Xiaogang Kang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Cloud Model ◽  
Brain Computer Interface ◽  
Laboratory Data ◽  
Computer Interface ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Shoulder Abduction ◽  
Common Spatial Pattern

The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.

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