职称：副教授



电子邮件：yuanye@stu.edu.cn
办公地址：工东303


办公电话：**


主要研究方向：信号与信息处理、混沌理论与应用、医学信号与图像处理
开设的主要课程：电路基础、通信网基础
科研情况：






教育背景
工作背景
科研项目
翻转课堂五步渐进学习方法的理论研究与实践

2015广东省优秀青年教师培养计划

适用于心脑血管专科诊断的中高端彩色多普勒超声诊断系统核心技术研发及产业化

基于无线传感网络的建筑火灾耦合映像格子模型研究与应用

基于属性加权聚类分析算法的医学信息挖掘及健康评估平台开发

进化智能与机器人联合研究中心

基于SCADA视频监控系统的研究及其实现

先进信号处理技术及其应用研究

彩色医学超声乳腺肿瘤病灶自动检测系统的研制

多模态乳腺癌早期筛查成像诊断仪

基于复合总线的智能家电一体化接口的设计与标准化

Duffing振子新特性及其在癫痫脑电分类中的应用研究

基于无线传感网络的早期火灾信息分布式压缩感知识别

光伏发电控制系统的研究

光伏发电控制算法与控制系统的研究

基于无线传感网络的早期火灾信息分布式压缩感知识别

基于非线性程度的癫痫发作检测新方法研究

癫痫发作自动检测方法研究

科研论文
Study on New Information Theory Based Cooperative Clustering Algorithm

Application of medical image segmentation based on optimized FCM

Estimating Gas Source Location Based on Distributed Adaptive Deflection Projected Subgradient Method

SAR image denoising using local properties analysis and discrete non-separable shearlet transform

Research on Video Target Tracking Technology Based on Improved SIFT Algorithm

Compressed sensing video processing based on stagewise weak selection and backtracking

基于目标特征提取的改进型压缩跟踪算法

Estimating gas source location based on distributed adaptive deflection projected subgradient method

Research on video target tracking technology based on improved SIFT algorithm

Compressed sensing video processing based on stagewise weak selection and backtracking

A Survey of Video Object Tracking.

A Survey of Video Object Tracking

Target Detection for SAR Images Based on Beamlet Transform

基于自适应阈值的curvelet医学超声图像去噪算法

基于稀疏表示和PCNN的多模态图像融合

A novel algorithm for image fusion based on orthogonal grouplet transform and pulse-coupled neural network

Algorithm for Image Fusion Based on Orthogonal Grouplet Transform and Pulse Coupled Neural Network

A method for analyzing the determinism of a signal

V-Test for Determining Degree of Nonlinearity of Time Series

An Efficient Method for Electric Meter Readings Automatic Location and Recognition

Classification of multi-types of EEG time series based on embedding dimension characteristic parameter

Embedding parameters based distinguish between normal and epileptic EEG using artificial neural network

正常和癫痫脑电信号之间非线性程度差异

科研成果