Search

Your search keyword '"Wang, Anhe"' showing total 290 results
Start Over Author "Wang, Anhe"
290 results on '"Wang, Anhe"'

1. Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG

Author

Han, Siqi, Zhang, Chao, Lei, Jiaxin, Han, Qingquan, Du, Yuhui, Wang, Anhe, Bai, Shuo, and Zhang, Milin

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the artifact detection task and a 82.79% accuracy on the 6-category eye movement classification task. A statistical value-based artifact removal method is proposed and evaluated on a public EEG database, where an accuracy improvement of 3.46% is obtained on the 3-category emotion classification task. In order to make a confident decision of each 5s EEG segment, the algorithm requires only 0.66M multiplication operations. Compared to the state-of-the-art approaches in artifact detection and removal, the proposed method features higher detection accuracy and lower computational cost, which makes it a more suitable solution to be integrated into a real-time and artifact robust Brain-Machine Interface (BMI)., Comment: 5 pages, 4 figures, published by TCAS-II

Published
2024
Full Text
View/download PDF

2. SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG

Author

Zhang, Chao, Tang, Zijian, Guo, Taoming, Lei, Jiaxin, Xiao, Jiaxin, Wang, Anhe, Bai, Shuo, and Zhang, Milin

Subjects
Computer Science - Hardware Architecture, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG). A bias-driven pruning method is proposed together with group convolution, global average pooling (GAP), near-zero pruning, weight clustering and quantization for the model compression, achieving a total compression ratio of 183.11x. The compressed SaleNet obtains a state-of-the-art subject-independent sustained attention level classification accuracy of 84.2% on the recorded 6-subject EEG database in this work. The SaleNet is implemented on a Artix-7 FPGA with a competitive power consumption of 0.11 W and an energy-efficiency of 8.19 GOps/W., Comment: 5 pages, 4 figures, to be published in IEEE International Symposium on Circuits and Systems (ISCAS) 2022

Published
2022

12. Hyperbranched Polyglycerol‐Induced Porous Silica Nanoparticles as Drug Carriers for Cancer Therapy In Vitro and In Vivo

Author

Yang, Yang, Wang, Anhe, Wei, Qiang, Schlesener, Cathleen, Haag, Rainer, Li, Qi, and Li, Junbai

Subjects
Organic Chemistry, Chemical Sciences, Bioengineering, Nanotechnology, Cancer, biodegradation, cancer therapy, drug delivery, hyperbranched polyglycerol, silica nanoparticles, Inorganic Chemistry, Physical Chemistry (incl. Structural), Organic chemistry
Abstract

Mesoporous silica-based nanoparticles are generally accepted as a potential platform for drug loading with a lot of advantages, except for their complex purification procedures and structures that are difficult to decompose. In this work, biocompatible hyperbranched polyglycerol is introduced to synthesize mesoporous silica nanoparticles (MSNs). The materials possess good biocompatibility, controlled release, and biodegradability. They also show passive targeting capability through the enhanced permeability and retention effect and can be excreted from the biological system. The method avoids the needs to employ traditional surfactants and complicated purified procedures, which make these MSNs an efficient delivery system for cancer therapy.

Published
2017

29. Hydrogel Nanoarchitectonics of a Flexible and Self‐Adhesive Electrode for Long‐Term Wireless Electroencephalogram Recording and High‐Accuracy Sustained Attention Evaluation

Author

Han, Qingquan, primary, Zhang, Chao, additional, Guo, Taoming, additional, Tian, Yajie, additional, Song, Wei, additional, Lei, Jiaxin, additional, Li, Qi, additional, Wang, Anhe, additional, Zhang, Milin, additional, Bai, Shuo, additional, and Yan, Xuehai, additional

Published
2023
Full Text
View/download PDF

44. Surface Self-Assembly of Dipeptides on Porous CaCO3Particles Promoting Cell Internalization

Author

Ma, Yuqi, Wang, Anhe, Li, Jieling, Li, Qi, Han, Qingquan, Jing, Yafeng, Zheng, Xuefang, Cao, Hongyu, Yan, Xuehai, and Bai, Shuo

Abstract

The self-assembling behavior of peptides and derivatives is crucial in the natural process to construct various architectures and achieve specific functions. However, the surface or interfacial self-assembly, in particular, on the surface of micro- or nanoparticles is even less systematically investigated. Here, uniform porous CaCO3microparticles were prepared with different charged, hydrophobic and hydrophilic surfaces to assess the self-assembling behavior of dipeptides composed of various sequences. Experimental results indicate that dipeptides with a negative charge in an aqueous solution preferred to self-assemble on the hydrophobic and positively charged surface of CaCO3particles, which can be ascribed to the electrostatic and hydrophobic interaction between dipeptides and CaCO3particles. Meanwhile, the Log p (lipid–water partition coefficient) of dipeptides has a significant effect on the self-assembling behavior of dipeptides on the surface of porous CaCO3; dipeptides with high Log p preferred to self-assemble on the surface of CaCO3particles, resulting in the improved cell internalization efficiency of particles with low cytotoxicity. After loading with a model drug (doxorubicin), the particles show obvious antitumor activity in animal experiments and can reduce Dox side effects effectively.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results