Loading [MathJax]/extensions/MathMenu.js

R. A. Chapman - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Author details

R. A. Chapman

Also published under: Richard A. Chapman, R. Chapman, Richard Chapman

Publications

53

Citations

909

Publications by Year

19782020

Co-Authors:

R. AggarwalH. N. AlshareefA. AmerasekeraB. AmirhekmatD. Anderson

Show All Co-Authors (180)

R. A. Chapman

Also published under: Richard A. Chapman, R. Chapman, Richard Chapman

Affiliation

The University of Texas at Dallas

Richardson

TX, USA

Publication Topics

Atomic Layer Deposition,
Chemical Vapor Deposition,
Bias Stress,
Fabrication Process,
Gate Dielectric,
Grain Boundaries,
Interface States,
Pulsed Laser Deposition,
Threshold Voltage,
Transfer Characteristics,
Active Layer,
Amorphous

Biography

Richard A. Chapman (M’78–SM’93–F’98) received the Ph.D. degree in physics from Rice University, Houston, TX, USA. In 1998, he retired from Texas Instruments in Dallas, TX, USA, as a TI Senior Fellow specializing in scaling of silicon MOSFETs. He worked part time for Texas Instruments until 2007. Since 2008, he has been a Research Scientist with UT Dallas.,Dr. Chapman is a Fellow of the American Physical Society.(Based on document published on 27 March 2018).

Publications

53

Citations

909

Publications by Year

19782020

Co-Authors:

R. Aggarwal
H. N. Alshareef
A. Amerasekera
B. Amirhekmat
D. Anderson

Show All Co-Authors (180)

Author's Published Works

Search History

Showing 1-25 of 1,017 resultsfor "Index Terms":Accurate Segmentation Results

Conferences (677)

Journals (338)

Early Access Articles (1)

Magazines (1)

Sort

Filter Results

Show

Open Access Only

Range
Single Year
Yong Xia(6)
Yan Wang(6)
Yang Liu(6)
Changick Kim(5)
Zexuan Ji(5)
University of Chinese Academy of Sciences, Beijing, China(10)
School of Electrical and Information Engineering, Tianjin University, Tianjin, China(6)
Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China(5)
Image Processing Center, School of Astronautics, Beihang University, Beijing, China(4)
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China(4)
IEEE Access(55)
IEEE Transactions on Geoscience and Remote Sensing(34)
IEEE Transactions on Image Processing(29)
IEEE Transactions on Medical Imaging(27)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(24)
IEEE(1,010)
CIE(4)
IET(3)
Media(26)
Beijing, China(24)
Chongqing, China(16)
Guangzhou, China(16)
Shanghai, China(16)
Chengdu, China(14)
Segmentation Results(568)
Image Segmentation(379)
Accurate Segmentation Results(340)
Segmentation Accuracy(272)
Convolutional Neural Network(215)

Select All on Page

Sort By

Results

Fast two-stage segmentation based on local correntropy-based K-means clustering

Yangyang Song;Xiaozhen Xie

2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN)

Year: 2017 | Conference Paper |

Cited by: Papers (1)

HTML

A novel two-stage segmentation model is proposed for fast segmentation of intensity inhomogeneity images. In the first stage, the image is down-sampled and then segmented by local correntropy-based K-means clustering (LCK) method to get a coarse segmentation contour fast. In the second stage, the image is further segmented using an improved LCK method, which takes the up-sampled coarse segmentatio...Show More

Fast two-stage segmentation based on local correntropy-based K-means clustering

Yangyang Song;Xiaozhen Xie

2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN)

Year: 2017 | Conference Paper |

Accurate Semi-Supervised Medical Image Segmentation Using DECTNet Combined with DSST Framework

Boliang Li;Yan Wang;Yaming Xu;Xiaoyang Li

2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL)

Year: 2024 | Conference Paper |

HTML

Since medical image pixel-level annotations are expensive and difficult to obtain, semi-supervised learning methods could reduce the reliance of deep learning models on annotated data. Therefore, the segmentation methods based on semi-supervised learning are widely utilized in medical image analysis. However, most current semi-supervised learning methods primarily employ UNet or VNet as the segmen...Show More

Accurate Semi-Supervised Medical Image Segmentation Using DECTNet Combined with DSST Framework

Boliang Li;Yan Wang;Yaming Xu;Xiaoyang Li

2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL)

Year: 2024 | Conference Paper |

An unsupervised method for flotation froth image segmentation evaluation base on image gray-level distribution

Jinping Liu;Weihua Gui;Qing Chen;Zhaohui Tang;Chunhua Yang

Proceedings of the 32nd Chinese Control Conference

Year: 2013 | Conference Paper |

HTML

In the machine vision based flotation process monitoring and control, it well accepted that the accurate froth image segmentation acts as the foundation of morphological features extraction of flotation bubbles, whose important roles of closely relating to the flotation production states have long been acknowledged. However, automatic and unsupervised froth image segmentation evaluation far has be...Show More

An unsupervised method for flotation froth image segmentation evaluation base on image gray-level distribution

Jinping Liu;Weihua Gui;Qing Chen;Zhaohui Tang;Chunhua Yang

Proceedings of the 32nd Chinese Control Conference

Year: 2013 | Conference Paper |

A Watershed-Based Intelligent Scissors Approach for Interactive Semi-Automated Pulmonary Lobes Segmentation

QIANG LI;YAN KANG

2020 International Conference on Machine Learning and Cybernetics (ICMLC)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

HTML

The computational detection of lung lobes from computed tomography (CT) images is a challenging segmentation problem with important respiratory healthcare applications, including emphysema, chronic bronchitis, and asthma. This paper proposes a watershed-based intelligent scissors (IS) approach for interactive semi-automated pulmonary lobes segmentation. First, our model performs automated segmenta...Show More

A Watershed-Based Intelligent Scissors Approach for Interactive Semi-Automated Pulmonary Lobes Segmentation

QIANG LI;YAN KANG

2020 International Conference on Machine Learning and Cybernetics (ICMLC)

Year: 2020 | Conference Paper |

Hierarchical DNN-Based Image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions

Cheng-Hsuan Yu;Jian-Jiun Ding

2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

HTML

Image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is har...Show More

Hierarchical DNN-Based Image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions

Cheng-Hsuan Yu;Jian-Jiun Ding

2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)

Year: 2022 | Conference Paper |

Graphic Image Segmentation Method based on High-Precision and Fast Algorithm

2023 Global Conference on Information Technologies and Communications (GCITC)

Year: 2023 | Conference Paper |

HTML

The role of graphic image segmentation in image processing is very important, but there is a problem that the segmentation results are not satisfactory. The ordinary segmentation method cannot solve the problem of graphic image segmentation in image processing, and the results of graphic image segmentation do not meet expectations. Therefore, this paper proposes a high-precision and fast algorithm...Show More

Graphic Image Segmentation Method based on High-Precision and Fast Algorithm

2023 Global Conference on Information Technologies and Communications (GCITC)

Year: 2023 | Conference Paper |

An Accurate Segmentation Method for Experimental Image of Cells

Shoucong Li;Weiyang Chen

2021 7th International Conference on Computer and Communications (ICCC)

Year: 2021 | Conference Paper |

HTML

Image segmentation is not only a basic problem in the field of image processing, but also in the field of biological data processing. In recent years, people have made a lot of efforts to improve the accuracy of segmentation. This paper proposes an image segmentation method based on generative adversarial networks to segment the images of human HT29 colon cancer cells. The network uses Unet as the...Show More

An Accurate Segmentation Method for Experimental Image of Cells

Shoucong Li;Weiyang Chen

2021 7th International Conference on Computer and Communications (ICCC)

Year: 2021 | Conference Paper |

Comparative Evaluation of Marker-Controlled Method and Gradient Distance Transformation through Watershed Segmentation

Nilam Choudhary;Pramod Singh Rathore;Dinesh Kumar

2024 IEEE 9th International Conference for Convergence in Technology (I2CT)

Year: 2024 | Conference Paper |

HTML

Image segmentation is a fundamental process in computer vision and image analysis, with applications in various fields. By dividing digital images into distinct regions or objects based on their characteristics, such as color, intensity, or texture, image segmentation allows for more efficient object recognition, accurate medical image analysis, and improved computer vision algorithms. This paper ...Show More

Comparative Evaluation of Marker-Controlled Method and Gradient Distance Transformation through Watershed Segmentation

Nilam Choudhary;Pramod Singh Rathore;Dinesh Kumar

2024 IEEE 9th International Conference for Convergence in Technology (I2CT)

Year: 2024 | Conference Paper |

Hierarchical segmentation for color images

2015 8th International Congress on Image and Signal Processing (CISP)

Year: 2015 | Conference Paper |

Cited by: Papers (1)

HTML

It is a challenging task in the field of computer vision to obtain the hierarchical segmentation for a color image. This paper focuses on the problem of efficiently performing hierarchical segmentation and improving the segmentation precision. We first apply multiscale normalized cut to image pre-segmentation step to replace Voronoi algorithm or watershed transformation. In addition, an improved u...Show More

Hierarchical segmentation for color images

2015 8th International Congress on Image and Signal Processing (CISP)

Year: 2015 | Conference Paper |

Segmentation by Fusion of Histogram-Based $K$-Means Clusters in Different Color Spaces

IEEE Transactions on Image Processing

Year: 2008 | Volume: 17, Issue: 5 | Journal Article |

Cited by: Papers (169) | Patents (1)

HTML

This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable and accurate segmentation result. The different label fields to be fused in our application are given by the same and simple (K-means based) clustering technique on an inp...Show More

Segmentation by Fusion of Histogram-Based $K$-Means Clusters in Different Color Spaces

IEEE Transactions on Image Processing

Year: 2008 | Volume: 17, Issue: 5 | Journal Article |

A Parking Space Detection Algorithm Based on Semantic Segmentation

Jiang Libiao;Li Xiaojun

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

Year: 2020 | Conference Paper |

HTML

In order to solve the problem that the current parking space detection relies on the fusion of ultrasonic sensors and millimeter wave radar multi-sensors, a parking space detection algorithm based on semantic segmentation is proposed. This algorithm only relies on the image of the vehicle's around view monitor system to complete the parking space detection. The algorithm mainly includes two module...Show More

A Parking Space Detection Algorithm Based on Semantic Segmentation

Jiang Libiao;Li Xiaojun

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

Year: 2020 | Volume: 9 | Conference Paper |

Image Segmentation Application of Conditional Convolution Restricted Boltzmann Machine

Yan Wang;Xili Wang

2019 2nd International Conference on Intelligent Autonomous Systems (ICoIAS)

Year: 2019 | Conference Paper |

Cited by: Papers (1)

HTML

The traditional image segmentation method is not effective in characterizing the specific features of the target and the representation of high-level information, while the deep learning model doing this job well. In the existing Conditional Restricted Boltzmann Machine (CRBM), Restricted Boltzmann Machine (RBM) is used to capture the correlation of the output space. The condition represents the m...Show More

Image Segmentation Application of Conditional Convolution Restricted Boltzmann Machine

Yan Wang;Xili Wang

2019 2nd International Conference on Intelligent Autonomous Systems (ICoIAS)

Year: 2019 | Conference Paper |

Multi-Region Texture Image Segmentation Based on Constrained Level-Set Evolution Functions

Asaad F. Said;Lina J. Karam

2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop

Year: 2009 | Conference Paper |

Cited by: Papers (2)

HTML

A multi-region texture image segmentation method based on level-set is proposed in this paper. In the proposed method, each region is represented by one level-set function and these functions evolve simultaneously based on a constraint. The constraint is used to keep a balance between competing regions and to guarantee disjoint and non-overlapping regions. To speed up the curve evolution functions...Show More

Multi-Region Texture Image Segmentation Based on Constrained Level-Set Evolution Functions

Asaad F. Said;Lina J. Karam

2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop

Year: 2009 | Conference Paper |

Retinal vessel segmentation based on U-Net network

Jing Zhang;Wen Wang

2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

HTML

To address the problems such as low segmentation accuracy due to small blood vessels in color fundus retinal images, a squeeze and excitation block is introduced in the encoding stage to focus on learning inter-channel connections to obtain more target features; an attention gate is introduced in the decoding stage to combine skip connections with an attention gate to eliminate the ambiguity of ir...Show More

Retinal vessel segmentation based on U-Net network

Jing Zhang;Wen Wang

2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)

Year: 2022 | Conference Paper |

Image Segmentation by Fusing Color and Depth Information for Region Merging

Chao Jia;Lei Guan;Yong Huang

2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC)

Year: 2023 | Conference Paper |

HTML

When there are occlusions and shadows in the image, it is difficult for the traditional algorithm to extract the object contour accurately by using only the appearance information. In this paper, an improved Mean-shift algorithm is used to obtain the initial set of oversegmentation subregions. Then the color contrast of the subregions is calculated, the depth contrast is calculated in the depth im...Show More

Image Segmentation by Fusing Color and Depth Information for Region Merging

Chao Jia;Lei Guan;Yong Huang

2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC)

Year: 2023 | Volume: 7 | Conference Paper |

Soil Image Segmentation Based on Mask R-CNN

Yida Chen;Kang Liu;Yi Xin;Xinru Zhao

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)

Year: 2023 | Conference Paper |

HTML

The complex background in the soil image collected in the field natural environment will affect the subsequent soil image recognition based on machine vision. Segmenting the soil center area from the soil image can eliminate the influence of the complex background, which is an important preprocessing work for subsequent soil image recognition. For the first time, the deep learning method was appli...Show More

Soil Image Segmentation Based on Mask R-CNN

Yida Chen;Kang Liu;Yi Xin;Xinru Zhao

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)

Year: 2023 | Conference Paper |

A Zero-Shot Method for 3D Medical Image Segmentation

Shiqiang Ma;Xuejian Li;Jijun Tang;Fei Guo

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Cited by: Papers (5)

HTML

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, existing methods based on outstanding 2.5D and 3D segmentation strategies are time-consumption and hardware-consumption while ensuring high accuracy. In order to reduce the high demand for automatic segmentation of tumor images and avoid the noise interference i...Show More

A Zero-Shot Method for 3D Medical Image Segmentation

Shiqiang Ma;Xuejian Li;Jijun Tang;Fei Guo

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Joint Optic Disc and Cup Segmentation with Parallel Cooperative Diffusion Model

Wenqing Li;Wenhui Huang

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

HTML

Glaucoma is a common eye disease that can lead to permanent vision loss. Accurate segmentation of the optic disc (OD) and optic cup (OC) plays a crucial role in glaucoma screening and assessment. However, existing methods predominantly focus on separate segmentation of the OD and OC, neglecting the interrelationship between the two structures. Furthermore, due to the optic cup’s indistinct boundar...Show More

Joint Optic Disc and Cup Segmentation with Parallel Cooperative Diffusion Model

Wenqing Li;Wenhui Huang

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

Segmentation of the Left Ventricle by SDD double threshold selection and CHT

WenFei Zhang;Xiufeng Li;ZhenZhou Wang

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

HTML

Automatic and robust segmentation of the left ventricle (LV) in magnetic resonance images (MRI) has remained challenging for many decades. With the great success of deep learning in object detection and classification, the research focus of LV segmentation has changed to convolutional neural network (CNN) in recent years. However, LV segmentation is a pixel-level classification problem and its cat...Show More

Segmentation of the Left Ventricle by SDD double threshold selection and CHT

WenFei Zhang;Xiufeng Li;ZhenZhou Wang

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization

Chanho Jung;Changick Kim

IEEE Transactions on Biomedical Engineering

Year: 2010 | Volume: 57, Issue: 10 | Journal Article |

Cited by: Papers (181) | Patents (4)

HTML

In this letter, we present a novel watershed-based method for segmentation of cervical and breast cell images. We formulate the segmentation of clustered nuclei as an optimization problem. A hypothesis concerning the nuclei, which involves a priori knowledge with respect to the shape of nuclei, is tested to solve the optimization problem. We first apply the distance transform to the clustered nucl...Show More

Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization

Chanho Jung;Changick Kim

IEEE Transactions on Biomedical Engineering

Year: 2010 | Volume: 57, Issue: 10 | Journal Article |

Accurate ship segmentation via ship contour prediction

Xiaowu Xiao;Changjun Ai;Weishen Wang;Zhiqiang Zhou;Linhao Li;Jun Chu

2020 39th Chinese Control Conference (CCC)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

HTML

Accurate ship segmentation in optical remote sensing images is challenging. In this paper, ship contour prediction network is introduced to improve the performance of ship segmentation network. Ship contour prediction network predicts the ship contour, which can promote the learning of ship segmentation network, producing more accurate segmentation results. The ship contour prediction network can ...Show More

Accurate ship segmentation via ship contour prediction

Xiaowu Xiao;Changjun Ai;Weishen Wang;Zhiqiang Zhou;Linhao Li;Jun Chu

2020 39th Chinese Control Conference (CCC)

Year: 2020 | Conference Paper |

3D fully convolutional networks for co-segmentation of tumors on PET-CT images

Zisha Zhong;Yusung Kim;Leixin Zhou;Kristin Plichta;Bryan Allen;John Buatti;Xiaodong Wu

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

Year: 2018 | Conference Paper |

Cited by: Papers (57)

HTML

Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the power...Show More

3D fully convolutional networks for co-segmentation of tumors on PET-CT images

Zisha Zhong;Yusung Kim;Leixin Zhou;Kristin Plichta;Bryan Allen;John Buatti;Xiaodong Wu

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

Year: 2018 | Conference Paper |

Medical Image Segmentation Using the Equivariance Under Diffeomorphisms Framework

Ming Ma;Jisui Huang;Wei Chen;Na Lei;Xianfeng Gu

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Year: 2024 | Conference Paper |

HTML

Medical image segmentation plays a crucial role in medical image analysis, computer-aided detection and diagnosis, treatment planning, and etc. However, it is still challenging to obtain an accurate segmentation result due to irregularity of organ contours or lack of labeled dataset. In this work, we formulate an important property for image segmentation: equivariance under diffeomorphisms, that i...Show More

Medical Image Segmentation Using the Equivariance Under Diffeomorphisms Framework

Ming Ma;Jisui Huang;Wei Chen;Na Lei;Xianfeng Gu

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Year: 2024 | Conference Paper |

Texture image segmentation using complex wavelet transform and Hidden Markov models

Xiao-Zhao Liu;Bin Fang;Zhao-Wei Shang

2009 International Conference on Wavelet Analysis and Pattern Recognition

Year: 2009 | Conference Paper |

Cited by: Papers (2)

HTML

Hidden Markov tree (HMT) is a tree-structure statistical model, which is used to capture the statistical structure information of smooth and singular regions. It works by modeling the relationship between the wavelet coefficients interscales. For the discrete wavelet transform (DWT) has its own drawbacks inherently, such as shift variance, lack of directionality, etc. The traditional HMT model bas...Show More

Texture image segmentation using complex wavelet transform and Hidden Markov models

Xiao-Zhao Liu;Bin Fang;Zhao-Wei Shang

2009 International Conference on Wavelet Analysis and Pattern Recognition

Year: 2009 | Conference Paper |

Sea-Land Clutter Segmentation Algorithm Based on Multi-measure Fusion with SVM Classifier

Kexin Li;Tao Shan;Yushi Zhang

2021 13th International Conference on Communication Software and Networks (ICCSN)

Year: 2021 | Conference Paper |

Cited by: Papers (3)

HTML

Effectively segmenting sea clutter and land clutter in the sea-land junction area is of great significance for target detection and recognition on the sea surface. Existing sea-land clutter segmentation algorithms are mostly based on a single measure, of which the segmentation effect is not very satisfactory. In view of this problem, this paper proposes a novel sea-land clutter segmentation algori...Show More

Sea-Land Clutter Segmentation Algorithm Based on Multi-measure Fusion with SVM Classifier

Kexin Li;Tao Shan;Yushi Zhang

2021 13th International Conference on Communication Software and Networks (ICCSN)

Year: 2021 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.