scholarly journals Novel eye-blink artefact detection algorithm from raw EEG signals using FCN-based semantic segmentation method

2020 ◽  
Vol 14 (8) ◽  
pp. 489-494
Author(s):  
Mustafa Tosun ◽  
Ömer Kasım
Keyword(s):  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Segmentation Method ◽  
Eeg Signals ◽  
Eye Blink ◽  
Artefact Detection

scholarly journals A Remote Sensing Image Segmentation Method Based on Fusion Mechanism

2021 ◽  
Vol 2138 (1) ◽  
pp. 012016
Author(s):  
Shuangling Zhu ◽  
Guli Nazi·Aili Mujiang ◽  
Huxidan Jumahong ◽  
Pazi Laiti·Nuer Maiti
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Input Layer ◽  
Basic Network

Abstract A U-Net convolutional network structure is fully capable of completing the end-to-end training with extremely little data, and can achieve better results. When the convolutional network has a short link between a near input layer and a near output layer, it can implement training in a deeper, more accurate and effective way. This paper mainly proposes a high-resolution remote sensing image change detection algorithm based on dense convolutional channel attention mechanism. The detection algorithm uses U-Net network module as the basic network to extract features, combines Dense-Net dense module to enhance U-Net, and introduces dense convolution channel attention mechanism into the basic convolution unit to highlight important features, thus completing semantic segmentation of dense convolutional remote sensing images. Simulation results have verified the effectiveness and robustness of this study.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

An adaptive coarse-fine semantic segmentation method for the attachment recognition on marine current turbines

2021 ◽  
Vol 93 ◽  
pp. 107182
Author(s):  
Haiyang Peng ◽  
Dingding Yang ◽  
Tianzhen Wang ◽  
Shreya Pandey ◽  
Lisu Chen ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Segmentation Method ◽  
Marine Current

scholarly journals A semantic segmentation method for exposed rebar on dam concrete based on Unet

2020 ◽  
Vol 1651 ◽  
pp. 012169
Author(s):  
Jun Zhang ◽  
Jian Mo ◽  
Huajun Xu ◽  
Zixing Liu
Keyword(s):  
Semantic Segmentation ◽  
Segmentation Method ◽  
Dam Concrete
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