检索结果-内蒙古大学图书馆

Detection of Bio-signals from Body Movement Based on High-Dynamic-Range Doppler Radar Sensor (Invited)

Detection of Bio-signals from Body Movement Based on High-Dy...

IEEE MTT-S International Microwave Workshop on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO)

作者： Lv, Qinyi Dong, Yazhou Sun, Yongzhi Li, Changzhi Ran, Lixin Zhejiang Univ Lab Appl Res Electromagnet ARE Hangzhou 310027 Zhejiang Peoples R China Natl Key Lab Sci & Technol Space Microwave Xian 710100 Peoples R China Nanjing Inst Elect Equipment Nanjing 210007 Jiangsu Peoples R China Texas Tech Univ Dept Elect & Comp Engn Lubbock TX 79424 USA

ISBN: (纸本)9781479985432

Doppler radar sensor has been widely used in noncontact bio-signal monitoring. This paper aims at recovering bio-signals from body movement. To solve the severe phase wrapping and saturation problems in large-scale body movement, as well as unwanted DC offsets and gradual changes of received microwave power problems, curve fitting technology is employed to compensate for the large-scale body movement to recover small-scale bio-signal based on a digital-IF structured, high-dynamic-range Doppler radar sensor and linearized demodulated algorithms. Experimental validations show weak bio-signal hidden in the strong body movement noise can be well extracted.

关键词： Doppler radar curve fitting DACM algorithm gradient descent algorithm bio-signal

来源：评论

学校读者我要写书评

暂无评论

Maximum Likelihood Detection of Nonlinearly Distorted OFDM Signal 58

Maximum Likelihood Detection of Nonlinearly Distorted OFDM S...

引用

IEEE Global Telecommunications Conference (GLOBECOM)

作者： Regev, Nir Iofedov, Ilia Wulich, Dov Ben Gurion Univ Negev Dept Elect & Comp Engn IL-84105 Beer Sheva Israel

ISBN: (纸本)9781479959525

This paper deals with a Maximum Likelihood receiver for a nonlinearly distorted OFDM signal over a flat channel with AWGN. The nonlinearity destroys the orthogonality between subcarriers, consequently, a per subcarrier decision, used when the linear PA is considered, is no longer optimal. We propose a sub-optimal receiver based on the Maximum Likelihood (ML) criterion. The ML receiver has to find the minimum Euclidean distance between the received vector and a set of all possible OFDM symbols passed through the same nonlinearity. This approach has exponential complexity. To reduce the complexity, we propose a sub-optimal receiver that minimizes the Euclidean distance, seen as a cost function, by the gradient descent algorithm. Unfortunately, due to the nonlinearity, the cost function is non-convex. In order to overcome this obstacle, we propose a method to classify the solution, i.e., to decide if the achieved minimum is local or global. We modify the gradient descent algorithm to avoid convergence to a local minimum. It is shown that the proposed receiver outperforms the simple OFDM and iterative receivers in terms of symbol error rate (SER) performance.

关键词： OFDM Nonlinear Power Amplifier Maximum Likelihood gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Control Implementation of Compliant Composite Material Actuators for Wearable Robotic Exoskeleton 14

Control Implementation of Compliant Composite Material Actua...

引用

14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR)

作者： Dinh, Binh Khanh Cappello, Leonardo Masia, Lorenzo Nanyang Technol Univ Sch Aerosp & Mech Engn Singapore 639798 Singapore Ist Italian Tecnog Dept Robot Brain & Cognit Sci Genoa Italy

ISBN: (纸本)9781479918089

In previous work [1], [2], a novel actuator is presented that merges traditional electromechanical motors and multistable composite structures. It has been shown that these structures are able to arrange themselves in multi-stable configurations corresponding to local minima of their strain enery. When this composite structure is connected with the electromechanical motor as proposed, the resulting actuator shows significant benefits in terms of safety, energy saving and control implementation using the compliant property of the overall structure, the particular shape of the strain energy landscape, and the accurately predictable non-linear behavior. Hence the proposed actuator is well-suited for many robotic applications requiring continuous assistance and robust stability. In this paper, the structure's multistability property is exploited for energy saving purpose. In order to do that, a supervised learning method named Extreme Learning Machine is introduced to approximate the elastic force applied by the structure and gradient descent algorithm is used to compute the local minimum points equivalent to local minima of structure's strain energy.

关键词： Multistable Composite Transmission Compliant Actuator Extreme Learning Machine gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

gradient descent based linear regression approach for modeling PID parameters

Gradient descent based linear regression approach for modeli...

引用

International Conference on Power Signals Control and Computations (EPSCICON)

作者： Parvathy, R. Devi, Rani Rajini Fed Inst Sci & Technol Dept Elect & Elect Engn Ernakulam 683577 Kerala India UKF Coll Engn & Technol Dept Mat Kollam 691302 Kerala India

ISBN: (纸本)9781479936120

The paper proposes a method for developing a linear model for Proportional Integral Derivative (PID) parameters of a PID controller. The approach is based on linear regression and it uses gradient descent algorithm for parameter optimization. A deep perusal of the literature shows that there have been many improvements made in developing PID tuning methods, and improving its performance. But a mention of a linear modeling of PID parameters is not noticed in any of the literature. The highlight of this method is the dependency of the tuning parameters on certain selected transient specifications. The speed control system of a dc motor is identified for the present study.

关键词： PID linear modeling regression gradient descent algorithm transient parameters

来源：评论

学校读者我要写书评

暂无评论

A Novel Interactively Recurrent Self-Evolving Fuzzy CMAC and Its Classification Applications

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2015年第3期14卷 1550019-1550019页

作者： Wang, Jyun-Guo Tai, Shen-Chuan Lin, Cheng-Jian Natl Cheng Kung Univ Inst Comp & Commun Engn Tainan 701 Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn 57Sec 2Zhongshan Rd Taichung 41170 Taiwan

In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

关键词： Recurrent network cerebellar model articulation controller fuzzy theory gradient descent algorithm classification

来源：评论

学校读者我要写书评

暂无评论

Micro-IMU-Based Motion Tracking System for Virtual Training

Micro-IMU-Based Motion Tracking System for Virtual Training

引用

第三十四届中国控制会议

作者： Yang Zhang Yunfeng Fei Lin Xu Guangyi Sun Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Engineering Design and Research Institute of the Second Artillery Corps.

ISBN: (纸本)9781467374439

This paper presents the development of a low cost wireless real-time inertial body tracking system for virtual training. The system is designed to provide highly accurate human body motion capture and interactive three-dimensional(3-D) avatar steering, by combining low cost MEMS inertial measurement units(IMUs), wireless body sensor network(BSN), and Unity 3D virtual reality game engine. First, several wearable MEMS IMU sensors are placed on user's body and limbs according to human skeletal action, and each sensor performs a 9 degrees of freedom(DOF) tracking at a high-speed update rate. Second, a Zigbee-based BSN is designed to support up to 20 MEMS IMU sensor nodes data transmission at 50 Hz sampling frequency. All collected sensors' data are loaded to a Matlab-based PC program by means of serial port. In order to accurately estimate the local orientation of each IMU sensor, an optimized gradient descent algorithm is implemented. The algorithm uses a quaternion representation, which allows accelerometer and magnetometer data to be fused to compute the gyroscope measurement error as a quaternion derivative. Finally, the estimated orientation data by fusion algorithm are imported to a virtual environment, consisting of the 3-D virtual skeletal representation and the virtual scene for specific training. Experimental results indicate that the system achieves < 1o static RMS error and <2o dynamic RMS error. The systems further expand the usability of low cost body tracking solution to virtual training in virtual environments.

关键词： micro inertial measurement unit(IMU) motion capture virtual training gradient descent algorithm wireless sensor network

来源：评论

学校读者我要写书评

暂无评论

Adaptive On-Line Optimizing the Gaussian Kernel for Classification based on the Kernel Target Alignment

Adaptive On-Line Optimizing the Gaussian Kernel for Classifi...

引用

2015 IEEE International Conference on Computer and Communications(ICCC 2015)

作者： Yulin Xiao Shangping Zhong College of Mathematics and Computer Science Fuzhou University

Kernel target alignment is a very efficient evaluation criterion. It has been widely applied in kernel optimization. However the traditional kernel methods that based on the Kernel target alignment optimize the kernel function mainly with batch gradient descent algorithm. This kind of methods has to scan through the entire training set at each step, which is much too costly. The On-line learning algorithm exactly solve above problem. At each step, on-line learning algorithm only need one example then discarded after learning, which make on-line learning algorithm fast, simple, and often make few statistical assumptions. Thus, in this paper, we propose a novel method to optimize the Gaussian kernel with on-line learning. We formulate the objective criterion for kernel optimization based on kernel target alignment. The objective criterion can be proved to have a determined global minimum point. Then, we use the on-line learning algorithm to optimize the formulated kernel function. In addition, in order to get an appropriate learning rate for the algorithm to accelerate the convergence rate, we use an adaptive rate learning method to optimize the kernel function. Finally, we evaluate the empirical performance of the proposed kernel optimization method on ten diverse datasets. The experimental results show that the proposed method is more effective than the state-of-the-art kernel optimization algorithms.

关键词： kernel target alignment adaptive on-line learning kernel optimization kernel methods gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Maximum Likelihood Detection of Nonlinearly Distorted OFDM Signal

Maximum Likelihood Detection of Nonlinearly Distorted OFDM S...

引用

IEEE Global Communications Conference

作者： Nir Regev Ilia Iofedov Dov Wulich Ben-Gurion University of the Negev Dept. of Electrical and Computer Engineering

ISBN: (纸本)9781479959532

This paper deals with a Maximum Likelihood receiver for a nonlinearly distorted OFDM signal over a flat channel with AWGN. The nonlinearity destroys the orthogonality between subcarriers, consequently, a per subcarrier decision, used when the linear PA is considered, is no longer optimal. We propose a sub-optimal receiver based on the Maximum Likelihood (ML) criterion. The ML receiver has to find the minimum Euclidean distance between the received vector and a set of all possible OFDM symbols passed through the same non-linearity. This approach has exponential complexity. To reduce the complexity, we propose a sub-optimal receiver that minimizes the Euclidean distance, seen as a cost function, by the gradient descent algorithm. Unfortunately, due to the nonlinearity, the cost function is non-convex. In order to overcome this obstacle, we propose a method to classify the solution, i.e., to decide if the achieved minimum is local or global. We modify the gradient descent algorithm to avoid convergence to a local minimum. It is shown that the proposed receiver outperforms the simple OFDM and iterative receivers in terms of symbol error rate (SER) performance.

关键词： OFDM Nonlinear Power Amplifier Maximum Likelihood gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved Particle Swarm Optimization-Based Artificial Neural Network for Rainfall-Runoff Modeling

引用

JOURNAL OF HYDROLOGIC ENGINEERING 2014年第7期19卷 1320-1329页

作者： Asadnia, Mohsen Chua, Lloyd H. C. Qin, X. S. Talei, Amin Nanyang Technol Univ DHI NTU Water & Environm Res Ctr & Educ Hub Singapore 639798 Singapore Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Monash Univ Sch Engn Bandar Sunway 46150 Selangor Darul Malaysia

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui Watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate gradient, gradient descent, and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from the LM-NN, and these results were then compared with those from PSO-based ANNs, including the conventional PSO neural network (CPSONN) and the improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. The results show that the PSO-based ANNs performed better than the LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing data set for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multiparameter (rainfall and water level) inputs, the RMSE of the testing data set for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN. (C) 2014 American Society of Civil Engineers.

关键词： Rainfall-runoff modeling Passive particle swarm optimization Artificial neural network Levenberg-Marquardt algorithm Conjugate gradient algorithm gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Kernelization of matrix updates, when and how?

引用

THEORETICAL COMPUTER SCIENCE 2014年第C期558卷 159-178页

作者： Warmuth, Manfred K. Kotlowski, Wojciech Zhou, Shuisheng Univ Calif Santa Cruz Dept Comp Sci Santa Cruz CA 95064 USA Poznan Univ Tech Inst Comp Sci Poznan Poland Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

We define what it means for a learning algorithm to be kernelizable in the case when the instances are vectors, asymmetric matrices and symmetric matrices, respectively. We can characterize kernelizability in terms of an invariance of the algorithm to certain orthogonal transformations. If we assume that the algorithm's action relies on a linear prediction, then we can show that in each case, the linear parameter vector must be a certain linear combination of the instances. We give a number of examples of how to apply our methods. In particular we show how to kernelize multiplicative updates for symmetric instance matrices. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Kernelization Multiplicative updates Rotational invariance Exponentiated gradient algorithm gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：