Jing Yan - IEEE Xplore Author Profile

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Cart
- Create Account
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Author details

Jing Yan

Publications

97

Citations

1,636

Publications by Year

20112025

Co-Authors:

Hongjun BanYifan CaoWenqiang CaoCailian ChenChuanzhi Chen

Show All Co-Authors (80)

Jing Yan

Affiliation

Department of Automation and the Key Laboratory of Intelligent Control and Neural Information Processing

Ministry of Education

Yanshan University, Qinhuangdao, China

Publication Topics

Autonomous Underwater Vehicles,
Undersea,
Inertial Reference Frame,
Position Vector,
Formation Control,
Linear Velocity,
Underwater Communication,
Value Function,
Formal Errors,
Optimal Policy,
Speed Of Sound,
Tracking Error

Biography

Jing Yan (Senior Member, IEEE) received the B.Eng. degree in automation from Henan University, Kaifeng, China, in 2008, and the Ph.D. degree in control theory and control engineering from Yanshan University, Qinhuangdao, China, in 2014.,From January 2016 to September 2016, he was a Postdoctoral Fellow with the University of North Texas, Denton, TX, USA. From October 2016 to January 2017, he was a Research Associate with the University of Texas at Arlington, Arlington, TX, USA. He is currently a Full Professor with Yanshan University. He has authored over 40 referred international journal and conference papers. Meanwhile, he has published two books, and he is also the inventor of 15 patents. His research interests cover in underwater acousti...

Publications

97

Citations

1,636

Publications by Year

20112025

Co-Authors:

Hongjun Ban
Yifan Cao
Wenqiang Cao
Cailian Chen
Chuanzhi Chen

Show All Co-Authors (80)

Author's Published Works

Search History

Showing 1-25 of 99 results

Conferences (58)

Journals (40)

Magazines (1)

Sort

Filter Results

Show

Open Access Only

Range
Single Year
M. Gori(63)
Marco Gori(36)
M. Maggini(19)
Stefano Melacci(18)
F. Scarselli(14)
Dipartimento di Ingegneria dell'Informazione, Università di Siena, Siena, Italy(7)
Dipartimento di Sistemi e Informatica, Università di Firenze, Florence, Italy(6)
Department of Information Engineering and Mathematics, University of Siena, Siena, Italy(6)
Dept. of Information Engineering, University of Florence, Florence, Italy(4)
Department of Information Engineering, University of Sienna, Siena, Italy(4)
IEEE Transactions on Neural Networks(18)
IEEE Transactions on Pattern Analysis and Machine Intelligence(10)
IEEE Transactions on Knowledge and Data Engineering(5)
IEEE Transactions on Neural Networks and Learning Systems(5)
2020 International Joint Conference on Neural Networks (IJCNN)(3)
IEEE(99)
Media(2)
Seattle, WA, USA(4)
Washington, DC, USA(4)
Glasgow, UK(3)
Honolulu, HI, USA(3)
Bangalore, India(2)
Neural Network(34)
Feed-forward Network(20)
Recurrent Neural Network(20)
Training Set(20)
Artificial Neural Network(16)

Select All on Page

Sort By

Results

Graph Neural Networks for Missing Data Imputation in Time Series from Meteorological Sensors

Giovanna Maria Dimitri;Irene Cappelli;Franco Scarselli;Ada Fort;Marco Gori

2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

Year: 2024 | Conference Paper |

HTML

Application of Artificial Neural Networks methods for the analysis of sensors data has become a priority nowadays. In this paper, Graph Neural Networks are exploited for the imputation of missing data in meteorological time series. To construct the graphs, a Horizontal Visibility Graphs approach is used. The dataset is populated with the acquisitions of five sensor nodes, deployed for several mont...Show More

Graph Neural Networks for Missing Data Imputation in Time Series from Meteorological Sensors

Giovanna Maria Dimitri;Irene Cappelli;Franco Scarselli;Ada Fort;Marco Gori

2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

Year: 2024 | Conference Paper |

Building Bridges of Knowledge: Innovating Education with Automated Crossword Generation

Kamyar Zeinalipour;Tommaso Iaquinta;Giovanni Angelini;Leonardo Rigutini;Marco Maggini;Marco Gori

2023 International Conference on Machine Learning and Applications (ICMLA)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

HTML

Educational crossword puzzles enhance critical thinking, vocabulary development, and concept reinforcement. They encourage independent learning, improve memorization, and foster problem-solving skills. With their multisensory approach, crossword puzzles offer a valuable educational experience. With the help of AI technology, creating high-quality, diverse crosswords is now easier, promoting enjoya...Show More

Building Bridges of Knowledge: Innovating Education with Automated Crossword Generation

Kamyar Zeinalipour;Tommaso Iaquinta;Giovanni Angelini;Leonardo Rigutini;Marco Maggini;Marco Gori

2023 International Conference on Machine Learning and Applications (ICMLA)

Year: 2023 | Conference Paper |

Continual Learning with Pretrained Backbones by Tuning in the Input Space

Simone Marullo;Matteo Tiezzi;Marco Gori;Stefano Melacci;Tinne Tuytelaars

2023 International Joint Conference on Neural Networks (IJCNN)

Year: 2023 | Conference Paper |

HTML

The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, in...Show More

Continual Learning with Pretrained Backbones by Tuning in the Input Space

Simone Marullo;Matteo Tiezzi;Marco Gori;Stefano Melacci;Tinne Tuytelaars

2023 International Joint Conference on Neural Networks (IJCNN)

Year: 2023 | Conference Paper |

PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks

Enrico Meloni;Lapo Faggi;Simone Marullo;Alessandro Betti;Matteo Tiezzi;Marco Gori;Stefano Melacci

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

Year: 2022 | Conference Paper |

HTML

In this paper, we present PARTIME, a software library written in Python and based on PyTorch, designed specifically to speed up neural networks whenever data is continuously streamed over time, for both learning and inference. Existing libraries are designed to exploit data-level parallelism, assuming that samples are batched, a condition that is not naturally met in applications that are based on...Show More

PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks

Enrico Meloni;Lapo Faggi;Simone Marullo;Alessandro Betti;Matteo Tiezzi;Marco Gori;Stefano Melacci

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

Year: 2022 | Conference Paper |

Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers

Stefano Melacci;Gabriele Ciravegna;Angelo Sotgiu;Ambra Demontis;Battista Biggio;Marco Gori;Fabio Roli

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 12 | Journal Article |

Cited by: Papers (10)

HTML

Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-label classification, where the availability of domain knowledge on the relationships among the considered classes may offer a natural way to spot incoherent predictions, i.e., predi...Show More

Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers

Stefano Melacci;Gabriele Ciravegna;Angelo Sotgiu;Ambra Demontis;Battista Biggio;Marco Gori;Fabio Roli

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 12 | Journal Article |

Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects

Enrico Meloni;Matteo Tiezzi;Luca Pasqualini;Marco Gori;Stefano Melacci

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

HTML

In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds. On one hand, these environments could also become a mean to study the weaknesses of Machine Learning algorithms, or to simulate training settings that allow Machine Learning models to gain robust...Show More

Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects

Enrico Meloni;Matteo Tiezzi;Luca Pasqualini;Marco Gori;Stefano Melacci

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

Year: 2021 | Conference Paper |

HeMoG: A White-Box Model to Unveil the Connection between Saliency Information and Human Head Motion in Virtual Reality

Miguel Fabian Romero Rondon;Dario Zanca;Stefano Melacci;Marco Gori;Lucile Sassatelli

2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

HTML

Immersive environments such as Virtual Reality (VR) are now a main area of interactive digital entertainment. The challenge to design personalized interactive VR systems is specifically to guide and adapt to the user's attention. Understanding the connection between the visual content and the human attentional process is therefore key. In this article, we investigate this connection by first propo...Show More

HeMoG: A White-Box Model to Unveil the Connection between Saliency Information and Human Head Motion in Virtual Reality

Miguel Fabian Romero Rondon;Dario Zanca;Stefano Melacci;Marco Gori;Lucile Sassatelli

2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

Year: 2021 | Conference Paper |

Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier

Simone Marullo;Matteo Tiezzi;Marco Gori;Stefano Melacci

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

HTML

In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective. When focussing on the way in which the training data are provided to the learning machine, we can distinguish between the classic random selection of stochastic gradient-based optimization and more involved techniques ...Show More

Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier

Simone Marullo;Matteo Tiezzi;Marco Gori;Stefano Melacci

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

SAILenv: Learning in Virtual Visual Environments Made Simple

Enrico Meloni;Luca Pasqualini;Matteo Tiezzi;Marco Gori;Stefano Melacci

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

HTML

Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world. However, most of the existing platforms to interface algorithms with 3D environments are often designed to setup navigation-related experiments, to st...Show More

SAILenv: Learning in Virtual Visual Environments Made Simple

Enrico Meloni;Luca Pasqualini;Matteo Tiezzi;Marco Gori;Stefano Melacci

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Local Propagation in Constraint-based Neural Networks

Giuseppe Marra;Matteo Tiezzi;Stefano Melacci;Alessandro Betti;Marco Maggini;Marco Gori

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

HTML

In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints, learning from the available supervisions. The computational structure of the proposed Local Propagation (LP) algorithm is based on ...Show More

Local Propagation in Constraint-based Neural Networks

Giuseppe Marra;Matteo Tiezzi;Stefano Melacci;Alessandro Betti;Marco Maggini;Marco Gori

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Developing Constrained Neural Units Over Time

Alessandro Betti;Marco Gori;Simone Marullo;Stefano Melacci

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

HTML

In this paper we present a foundational study on a constrained method that defines learning problems with Neural Networks in the context of the principle of least cognitive action, which very much resembles the principle of least action in mechanics. Starting from a general approach to enforce constraints into the dynamical laws of learning, this work focuses on an alternative way of defining Neur...Show More

Developing Constrained Neural Units Over Time

Alessandro Betti;Marco Gori;Simone Marullo;Stefano Melacci

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach

Dario Zanca;Stefano Melacci;Marco Gori

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

HTML

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another...Show More

Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach

Dario Zanca;Stefano Melacci;Marco Gori

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Gravitational Laws of Focus of Attention

Dario Zanca;Stefano Melacci;Marco Gori

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 12 | Journal Article |

Cited by: Papers (27)

HTML

The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer vision. In this paper, we describe a model of scanpath as a dynamic process which can be interpreted as a variational law somehow related to mechanics, where the focus of attention is subject to a gravitational field. The distributed virtual mass that dri...Show More

Gravitational Laws of Focus of Attention

Dario Zanca;Stefano Melacci;Marco Gori

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 12 | Journal Article |

Cognitive Action Laws: The Case of Visual Features

Alessandro Betti;Marco Gori;Stefano Melacci

IEEE Transactions on Neural Networks and Learning Systems

Year: 2020 | Volume: 31, Issue: 3 | Journal Article |

Cited by: Papers (1)

HTML

This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on time. They are used to minimize the cognitive action, an appropriate functional index that measures the agent interactions with the environment. The ...Show More

Cognitive Action Laws: The Case of Visual Features

Alessandro Betti;Marco Gori;Stefano Melacci

IEEE Transactions on Neural Networks and Learning Systems

Year: 2020 | Volume: 31, Issue: 3 | Journal Article |

On a Convex Logic Fragment for Learning and Reasoning

Francesco Giannini;Michelangelo Diligenti;Marco Gori;Marco Maggini

IEEE Transactions on Fuzzy Systems

Year: 2019 | Volume: 27, Issue: 7 | Journal Article |

Cited by: Papers (8)

HTML

In this paper, we introduce the convex fragment of Łukasiewicz logic and discuss its possible applications in different learning schemes. The provided theoretical results are highly general because they can be exploited in any learning framework involving logical constraints. The method is of particular interest since the fragment guarantees to deal with convex constraints, which are shown to be e...Show More

On a Convex Logic Fragment for Learning and Reasoning

Francesco Giannini;Michelangelo Diligenti;Marco Gori;Marco Maggini

IEEE Transactions on Fuzzy Systems

Year: 2019 | Volume: 27, Issue: 7 | Journal Article |

A Constrained-Based Approach to Machine Learning

Alessandro Betti;Marco Gori;Giuseppe Marra

2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)

Year: 2018 | Conference Paper |

Cited by: Papers (1)

HTML

This paper promotes a constrained-based approach to machine learning as a natural evolution to classic distinction between supervised, unsupervised and semi-supervised learning. In particular, in addition to the unification of symbolic and sub-symbolic processing, it is shown that the notion of constraint opens the doors to a truly new way of discovering the solution that relies on the Lagrangian ...Show More

A Constrained-Based Approach to Machine Learning

Alessandro Betti;Marco Gori;Giuseppe Marra

2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)

Year: 2018 | Conference Paper |

Learning in Variable-Dimensional Spaces

Michelangelo Diligenti;Marco Gori;Claudio Saccà

IEEE Transactions on Neural Networks and Learning Systems

Year: 2016 | Volume: 27, Issue: 6 | Journal Article |

Cited by: Papers (2)

HTML

This paper proposes a unified approach to learning in environments in which patterns can be represented in variable-dimension domains, which nicely includes the case in which there are missing features. The proposal is based on the representation of the environment by pointwise constraints that are shown to model naturally pattern relationships that come out in problems of information retrieval, c...Show More

Learning in Variable-Dimensional Spaces

Michelangelo Diligenti;Marco Gori;Claudio Saccà

IEEE Transactions on Neural Networks and Learning Systems

Year: 2016 | Volume: 27, Issue: 6 | Journal Article |

Online learning as an LQG optimal control problem with random matrices

Giorgio Gnecco;Alberto Bemporad;Marco Gori;Rita Morisi;Marcello Sanguineti

2015 European Control Conference (ECC)

Year: 2015 | Conference Paper |

Cited by: Papers (1)

HTML

In this paper, we combine optimal control theory and machine learning techniques to propose and solve an optimal control formulation of online learning from supervised examples, which are used to learn an unknown vector parameter modeling the relationship between the input examples and their outputs. We show some connections of the problem investigated with the classical LQG optimal control proble...Show More

Online learning as an LQG optimal control problem with random matrices

Giorgio Gnecco;Alberto Bemporad;Marco Gori;Rita Morisi;Marcello Sanguineti

2015 European Control Conference (ECC)

Year: 2015 | Conference Paper |

Learning With Mixed Hard/Soft Pointwise Constraints

Giorgio Gnecco;Marco Gori;Stefano Melacci;Marcello Sanguineti

IEEE Transactions on Neural Networks and Learning Systems

Year: 2015 | Volume: 26, Issue: 9 | Journal Article |

Cited by: Papers (12)

HTML

A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples ...Show More

Learning With Mixed Hard/Soft Pointwise Constraints

Giorgio Gnecco;Marco Gori;Stefano Melacci;Marcello Sanguineti

IEEE Transactions on Neural Networks and Learning Systems

Year: 2015 | Volume: 26, Issue: 9 | Journal Article |

On-Line Video Motion Estimation by Invariant Receptive Inputs

Marco Gori;Marco Lippi;Marco Maggini;Stefano Melacci

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Year: 2014 | Conference Paper |

Cited by: Papers (1)

HTML

In this paper, we address the problem of estimating the optical flow in long-term video sequences. We devise a computational scheme that exploits the idea of receptive fields, in which the pixel flow does not only depends on the brightness level of the pixel itself, but also on neighborhood-related information. Our approach relies on the definition of receptive units that are invariant to affine t...Show More

On-Line Video Motion Estimation by Invariant Receptive Inputs

Marco Gori;Marco Lippi;Marco Maggini;Stefano Melacci

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Year: 2014 | Conference Paper |

Graph and Manifold Co-regularization

Claudio Saccá;Michelangelo Diligenti;Marco Gori

2013 12th International Conference on Machine Learning and Applications

Year: 2013 | Conference Paper |

HTML

Classical foundations of Statistical Learning Theory rely on the assumption that the input patterns are independently and identically distributed. However, in many applications, the inputs, represented as feature vectors, are also embedded into a network of pair wise relations. Transductive approaches like graph regularization rely on the network topology without considering the feature vectors. S...Show More

Graph and Manifold Co-regularization

Claudio Saccá;Michelangelo Diligenti;Marco Gori

2013 12th International Conference on Machine Learning and Applications

Year: 2013 | Conference Paper |

Collective Classification Using Semantic Based Regularization

Claudio Saccá;Michelangelo Diligenti;Marco Gori

2013 12th International Conference on Machine Learning and Applications

Year: 2013 | Conference Paper |

Cited by: Papers (1)

HTML

Semantic Based Regularization (SBR) is a framework for injecting prior knowledge expressed as FOL clauses into a semi-supervised learning problem. The prior knowledge is converted into a set of continuous constraints, which are enforced during training. SBR employs the prior knowledge only at training time, hoping that the learning process is able to encode the knowledge via the training data into...Show More

Collective Classification Using Semantic Based Regularization

Claudio Saccá;Michelangelo Diligenti;Marco Gori

2013 12th International Conference on Machine Learning and Applications

Year: 2013 | Conference Paper |

Learning with Box Kernels

Stefano Melacci;Marco Gori

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2013 | Volume: 35, Issue: 11 | Journal Article |

Cited by: Papers (13)

HTML

Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. ...Show More

Learning with Box Kernels

Stefano Melacci;Marco Gori

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2013 | Volume: 35, Issue: 11 | Journal Article |

Constraint Verification With Kernel Machines

Marco Gori;Stefano Melacci

IEEE Transactions on Neural Networks and Learning Systems

Year: 2013 | Volume: 24, Issue: 5 | Journal Article |

Cited by: Papers (19)

HTML

Based on a recently proposed framework of learning from constraints using kernel-based representations, in this brief, we naturally extend its application to the case of inferences on new constraints. We give examples for polynomials and first-order logic by showing how new constraints can be checked on the basis of given premises and data samples. Interestingly, this gives rise to a perceptual lo...Show More

Constraint Verification With Kernel Machines

Marco Gori;Stefano Melacci

IEEE Transactions on Neural Networks and Learning Systems

Year: 2013 | Volume: 24, Issue: 5 | Journal Article |

Unsupervised Learning by Minimal Entropy Encoding

Stefano Melacci;Marco Gori

IEEE Transactions on Neural Networks and Learning Systems

Year: 2012 | Volume: 23, Issue: 12 | Journal Article |

Cited by: Papers (18)

HTML

Following basic principles of information-theoretic learning, in this paper, we propose a novel approach to data clustering, referred to as minimal entropy encoding (MEE), which is based on a set of functions (features) projecting each input onto a minimum entropy configuration (code). Inspired by traditional parsimony principles, we seek solutions in reproducing kernel Hilbert spaces and then we ...Show More

Unsupervised Learning by Minimal Entropy Encoding

Stefano Melacci;Marco Gori

IEEE Transactions on Neural Networks and Learning Systems

Year: 2012 | Volume: 23, Issue: 12 | Journal Article |

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.