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Author details

Ming Li

Publications

63

Citations

969

Publications by Year

20162025

Co-Authors:

Stephanie AelmoreSamson AggelopoulosAli Al-HashimiSeth AndrewsOlga Borić-Lubecke

Show All Co-Authors (94)

Ming Li

Affiliation

Department of Electrical

Computer Engineering

University of Arizona, Tucson, AZ, USA

Publication Topics

Frequency Band,
Multi-armed Bandit,
Successive Interference Cancellation,
Threat Model,
Adversary Model,
Bandit Problem,
Bit Error Rate,
Channel Access,
Collision Probability,
Decoding,
Differential Privacy,
Directional Antenna

Biography

Ming Li (M'11) received the PhD degree in ECE from the Worcester Polytechnic Institute, in 2011. He is an associate professor in the Department of Electrical and Computer Engineering of University of Arizona. He was an assistant professor in the Computer Science Department, Utah State University from 2011 to 2015. His main research interests include wireless networks and security, with current emphases on wireless network optimization, wireless security and privacy, and cyber-physical system security. He received the NSF Early Faculty Development (CAREER) Award in 2014, and the ONR Young Investigator Program (YIP) Award in 2016. He is a member of both the IEEE and the ACM.(Based on document published on 13 September 2017).

Publications

63

Citations

969

Publications by Year

20162025

Co-Authors:

Stephanie Aelmore
Samson Aggelopoulos
Ali Al-Hashimi
Seth Andrews
Olga Borić-Lubecke

Show All Co-Authors (94)

Author's Published Works

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Showing 1-25 of 10,401 resultsfor "Index Terms":Decomposition Approach

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Results

Generalizing discriminant analysis using the generalized singular value decomposition

P. Howland;H. Park

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2004 | Volume: 26, Issue: 8 | Journal Article |

Cited by: Papers (259) | Patents (1)

Discriminant analysis has been used for decades to extract features that preserve class separability. It is commonly defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular limits its application to data sets with certain relative dimensions. We examine a number of optimizatio...Show More

Generalizing discriminant analysis using the generalized singular value decomposition

P. Howland;H. Park

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2004 | Volume: 26, Issue: 8 | Journal Article |

A delay decomposition approach to stability and H∞ control of linear time-delay systems — part I: Stability

2008 7th World Congress on Intelligent Control and Automation

Year: 2008 | Conference Paper |

Cited by: Papers (3)

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This paper is concerned with the problem of stability of linear time-delay systems. Firstly, a new approach, i.e. a delay decomposition approach, is proposed to deal with the problem. The idea of the approach is that the delay interval is uniformly divided into N segments with N a positive integer, and a proper Lyapunov-Krasovskii functional is chosen with different weighted matrices corresponding...Show More

A delay decomposition approach to stability and H∞ control of linear time-delay systems — part I: Stability

2008 7th World Congress on Intelligent Control and Automation

Year: 2008 | Conference Paper |

Automotive Radar Interference Mitigation Using Two-Stage Signal Decomposition Approach

Ashwin Bhobani Baral;Bhaskar Raj Upadhyay;Murat Torlak

2023 IEEE Radar Conference (RadarConf23)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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The mutual interference between automotive radar sensors is inevitable due to their increasing demand in automotive applications. To reliably estimate the target parameters, this interference needs to be detected and mitigated. This paper proposes a two-stage approach for suppressing the mutual interference between frequency modulated continuous wave (FMCW) radars. In the first stage, the signals ...Show More

Automotive Radar Interference Mitigation Using Two-Stage Signal Decomposition Approach

Ashwin Bhobani Baral;Bhaskar Raj Upadhyay;Murat Torlak

2023 IEEE Radar Conference (RadarConf23)

Year: 2023 | Conference Paper |

An Incremental Tensor-Train Decomposition for Cyber-Physical-Social Big Data

Huazhong Liu;Laurence T. Yang;Yimu Guo;Xia Xie;Jianhua Ma

IEEE Transactions on Big Data

Year: 2021 | Volume: 7, Issue: 2 | Journal Article |

Cited by: Papers (47)

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Cyber-physical-social big data generated from ubiquitous devices and diverse spaces generally are multi-source, heterogeneous, and deeply intertwined. To efficiently analyze and handle the ubiquitous cyber-physical-social big data, tensor is considered as an effective tool, but the curse of dimensionality is still the main bottleneck of tensor-based big data analysis. Tensor networks can considera...Show More

An Incremental Tensor-Train Decomposition for Cyber-Physical-Social Big Data

Huazhong Liu;Laurence T. Yang;Yimu Guo;Xia Xie;Jianhua Ma

IEEE Transactions on Big Data

Year: 2021 | Volume: 7, Issue: 2 | Journal Article |

Image Compression via Alternating Least Squares

Muhammad Zeeshan

2024 International Conference on IT and Industrial Technologies (ICIT)

Year: 2024 | Conference Paper |

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In numerous application areas such as medical imaging and remote sensing, image compression methods play an important role in managing data storage and transmission costs. In this paper, we present an empirical study on a low-rank approximation using the alternating least squares (ALS) method for image compression. For high-dimensional data, we demonstrate the advantage of a matrix decomposition-b...Show More

Image Compression via Alternating Least Squares

Muhammad Zeeshan

2024 International Conference on IT and Industrial Technologies (ICIT)

Year: 2024 | Conference Paper |

A Fast and Efficient Approach for Human Action Recovery From Corrupted 3-D Motion Capture Data Using QR Decomposition-Based Approximate SVD

M. S. Subodh Raj;Sudhish N. George

IEEE Transactions on Human-Machine Systems

Year: 2024 | Volume: 54, Issue: 4 | Journal Article |

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In this article, we propose a robust algorithm for the fast recovery of human actions from corrupted 3-D motion capture (mocap) sequences. The proposed algorithm can deal with misrepresentations and incomplete representations in mocap data simultaneously. Fast convergence of the proposed algorithm is ensured by minimizing the overhead associated with time and resource utilization. To this end, we ...Show More

A Fast and Efficient Approach for Human Action Recovery From Corrupted 3-D Motion Capture Data Using QR Decomposition-Based Approximate SVD

M. S. Subodh Raj;Sudhish N. George

IEEE Transactions on Human-Machine Systems

Year: 2024 | Volume: 54, Issue: 4 | Journal Article |

Probabilistic Tensor Train Decomposition

Jesper L. Hinrich;Morten Mørup

2019 27th European Signal Processing Conference (EUSIPCO)

Year: 2019 | Conference Paper |

Cited by: Papers (4)

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The tensor train decomposition (TTD) has become an attractive decomposition approach due to its ease of inference by use of the singular value decomposition and flexible yet compact representations enabling efficient computations and reduced memory usage using the TTD representation for further analyses. Unfortunately, the level of complexity to use and the order in which modes should be decompose...Show More

Probabilistic Tensor Train Decomposition

Jesper L. Hinrich;Morten Mørup

2019 27th European Signal Processing Conference (EUSIPCO)

Year: 2019 | Conference Paper |

A stable model-based three-component decomposition approach for polarimetric SAR data

Zhihao Jiao;Jiong Chen;Jian Yang

2011 IEEE International Geoscience and Remote Sensing Symposium

Year: 2011 | Conference Paper |

Cited by: Papers (3)

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A stable model-based three-component decomposition approach for polarimetric SAR data is proposed in this paper. Two problems in the standard Freeman decomposition: (1) the instability of the decomposition; (2) the emergence of negative powers, have been stated. The proposed approach uses the regularization method to solve the problems, by minimizing a continuous objective function consisting two ...Show More

A stable model-based three-component decomposition approach for polarimetric SAR data

Zhihao Jiao;Jiong Chen;Jian Yang

2011 IEEE International Geoscience and Remote Sensing Symposium

Year: 2011 | Conference Paper |

Data-Driven Fault Detection in Gas Regulators Using Empirical Mode Decomposition

Yang Song;Yijia Lu;Tianyong Hu

2024 4th International Conference on Electrical Engineering and Control Science (IC2ECS)

Year: 2024 | Conference Paper |

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This paper proposes a data-driven approach for fault detection in gas regulators using empirical mode decomposition (EMD). The structure of direct gas regulators is first explained. Next, the paper introduces data-driven residual generators and presents an optimization problem for designing the parameter matrix of residuals. Based on this, a set of EMD-based evaluation function is developed, and t...Show More

Data-Driven Fault Detection in Gas Regulators Using Empirical Mode Decomposition

Yang Song;Yijia Lu;Tianyong Hu

2024 4th International Conference on Electrical Engineering and Control Science (IC2ECS)

Year: 2024 | Conference Paper |

Leaderless Consensus of Linear Multi-agent Systems: Matrix Decomposition Approach

Shaolei Zhou;Wei Liu;Qingpo Wu;Gaoyang Yin

2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics

Year: 2015 | Conference Paper |

Cited by: Papers (20)

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This paper considers the leaderless consensus problem of linear multi-agent systems with static and dynamic consensus controllers. The communication topology is modeled by a directed graph which contains a spanning tree. A special type of matrix decomposition is performed on the graph Laplacian matrix which can be factored into the product of two specific matrices. Base on this property of graph L...Show More

Leaderless Consensus of Linear Multi-agent Systems: Matrix Decomposition Approach

Shaolei Zhou;Wei Liu;Qingpo Wu;Gaoyang Yin

2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics

Year: 2015 | Volume: 2 | Conference Paper |

Intrinsic image decomposition by hierarchical L0 sparsity

Xuecheng Nie;Wei Feng;Liang Wan;Haipeng Dai;Chi-Man Pun

2014 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2014 | Conference Paper |

Cited by: Papers (3)

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This paper presents a hierarchical approach to single image intrinsic decomposition based on non-local L0 sparsity. In contrast to previous studies using heuristic methods to well-define the ill-posed problem, our approach is able to effectively construct sparse, non-local and multiscale reflectance dependencies in an unsupervised manner, thus is less dependent on the chromaticity feature and more...Show More

Intrinsic image decomposition by hierarchical L0 sparsity

Xuecheng Nie;Wei Feng;Liang Wan;Haipeng Dai;Chi-Man Pun

2014 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2014 | Conference Paper |

A Multi-Objective Minimum Matrix Search Algorithm Applied to Large-Scale Bi-Objective TSP

Michael Manuel Smith;Yun Shiow Chen

2018 5th International Conference on Computational Science/ Intelligence and Applied Informatics (CSII)

Year: 2018 | Conference Paper |

Cited by: Papers (1)

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The well-known NP-hard traveling salesman problem (TSP) primarily considers distance as its single objective. However, applications modeled from real world systems repeatedly involve more than one objective giving rise to multi-objective optimization. Fusing ideas of dimension reduction, decomposition approaches, and genetic algorithms, this paper presents a multiobjective minimum matrix search al...Show More

A Multi-Objective Minimum Matrix Search Algorithm Applied to Large-Scale Bi-Objective TSP

Michael Manuel Smith;Yun Shiow Chen

2018 5th International Conference on Computational Science/ Intelligence and Applied Informatics (CSII)

Year: 2018 | Conference Paper |

Comparative research on noise reduction of transient electromagnetic signals based on empirical mode decomposition and variational mode decomposition

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Year: 2021 | Volume: 56, Issue: 10 | Journal Article |

Cited by: Papers (7)

Transient electromagnetic (TEM) detection has been widely used in the fields of mineral resources exploration, hydrogeological survey and engineering survey, but electromagnetic interference can lead to late signal distortion, so it has become an urgent problem to suppress the electromagnetic noise of TEM signal and improve the interpretation accuracy of TEM signal. For the first time, this paper ...Show More

Comparative research on noise reduction of transient electromagnetic signals based on empirical mode decomposition and variational mode decomposition

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Year: 2021 | Volume: 56, Issue: 10 | Journal Article |

Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach

Jennifer J. Schnur;Ryan Karl;Angélica García-Martínez;Meng Jiang;Nitesh V. Chawla

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2020 | Conference Paper |

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In this paper, we discuss a tensor decomposition method for imputing similarity scores between individual clinical pictures at predefined patient age intervals in order to construct a dynamic similarity network of patients with respect to early childhood anthropomorphic development. The method leverages Canonical Polyadic Decomposition (or PARAFAC) to compute missing Euclidean similarity scores be...Show More

Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach

Jennifer J. Schnur;Ryan Karl;Angélica García-Martínez;Meng Jiang;Nitesh V. Chawla

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2020 | Conference Paper |

eOTD: An Efficient Online Tucker Decomposition for Higher Order Tensors

Houping Xiao;Fei Wang;Fenglong Ma;Jing Gao

2018 IEEE International Conference on Data Mining (ICDM)

Year: 2018 | Conference Paper |

Cited by: Papers (5)

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A tensor (i.e., an N-mode array) is a natural representation for multidimensional data. Tucker Decomposition (TD) is one of the most popular methods, and a series of batch TD algorithms have been extensively studied and widely applied in signal/image processing, bioinformatics, etc. However, in many applications, the large-scale tensor is dynamically evolving at all modes, which poses significant ...Show More

eOTD: An Efficient Online Tucker Decomposition for Higher Order Tensors

Houping Xiao;Fei Wang;Fenglong Ma;Jing Gao

2018 IEEE International Conference on Data Mining (ICDM)

Year: 2018 | Conference Paper |

Randomized methods for higher-order subspace separation

Michele N. da Costa;Renato R. Lopes;João Marcos T. Romano

2016 24th European Signal Processing Conference (EUSIPCO)

Year: 2016 | Conference Paper |

Cited by: Papers (2)

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This paper presents an algorithm for signal subspace separation in the context of multidimensional data. The proposal is an extension of the randomized Singular Value Decomposition (SVD) for higher-order tensors. From a set derived from random sampling, we construct an orthogonal basis associated with the range of each mode-space of the input data tensor. Multilinear projection of the input data o...Show More

Randomized methods for higher-order subspace separation

Michele N. da Costa;Renato R. Lopes;João Marcos T. Romano

2016 24th European Signal Processing Conference (EUSIPCO)

Year: 2016 | Conference Paper |

A linguistic approach to user interface design

Interacting with Computers

Year: 2001 | Volume: 13, Issue: 5 | Journal Article |

This paper considers user interface design world (UIDW) as a system of communication lines. It necessitates the adoption of a UI design method, centered on language application. For that reason, this approach to UI design is called linguistic approach. The basic characteristics of communication require the presence of a balanced model of UIDW. The integration approach applied to the proposed balan...Show More

A linguistic approach to user interface design

Interacting with Computers

Year: 2001 | Volume: 13, Issue: 5 | Journal Article |

Wind speed prediction using hybrid long short-term memory neural network based approach

G. Rakesh Yadav;E. Muneender;M. Santhosh

2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET)

Year: 2021 | Conference Paper |

Cited by: Papers (5)

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Accurate wind speed prediction is a essential for enhanced wind energy integration with grid. A hybrid forecasting model is implemented to improve prediction accuracy. Decomposition technique is utilized to separate the input training wind speed data into intrinsic mode functions (IMFs). Deep neural network is used for the feature learning from each sub-series signal. Thus, the developed approach ...Show More

Wind speed prediction using hybrid long short-term memory neural network based approach

G. Rakesh Yadav;E. Muneender;M. Santhosh

2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET)

Year: 2021 | Conference Paper |

A Regularized Iterative Adaptive Approach Based for Radar Forward-Looking Imaging

Yongwei Zhang;Jie Li;Yongchao Zhang;Fanyun Xu;Yulin Huang;Jianyu Yang

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Year: 2021 | Conference Paper |

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Iterative adaptive approach (IAA) is an effective super-resolution method to improve the resolution of airborne forward-looking radar imaging. Regretfully, the noise sensitivity caused by the non-full rank of matrix lead to the poor performance under low signal-to-noise ratio condition in the forward-looking imaging process. In response to this problem, a regularized IAA method (RIAA) based on sin...Show More

A Regularized Iterative Adaptive Approach Based for Radar Forward-Looking Imaging

Yongwei Zhang;Jie Li;Yongchao Zhang;Fanyun Xu;Yulin Huang;Jianyu Yang

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Year: 2021 | Conference Paper |

Fast Co-clustering Using Matrix Decomposition

Yun Ling;Chongyi Ye

2009 Asia-Pacific Conference on Information Processing

Year: 2009 | Conference Paper |

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Co-clustering is a powerful data mining technique with varied applications such as text clustering, web-log mining and microarray analysis. Simultaneously clustering rows and columns (co-clustering) of large data matrix is an important problem with these wide applications. Current co-clustering techniques such as information-theoretic and Bayesian based methods provide good accuracy, but are compu...Show More

Fast Co-clustering Using Matrix Decomposition

Yun Ling;Chongyi Ye

2009 Asia-Pacific Conference on Information Processing

Year: 2009 | Volume: 2 | Conference Paper |

Rational Value Sequence based Singular Value Decomposition for facial recognition

Ganeshayya I. Shidaganti;Satyadhyan Chickerur

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Year: 2013 | Conference Paper |

Cited by: Papers (2)

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Various prominent feature points of human faces play a critical role for designing a system that can perform facial recognition. Involvement of facial expression as precision parameter exponentially increases the challenges in developing a face recognition system. There exist abundant volume of studies in the area of face recognition, but majority of the techniques are either computationally not v...Show More

Rational Value Sequence based Singular Value Decomposition for facial recognition

Ganeshayya I. Shidaganti;Satyadhyan Chickerur

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Year: 2013 | Conference Paper |

Rank selection of CP-decomposed convolutional layers with variational Bayesian matrix factorization

Marcella Astrid;Seung-Ik Lee;Beom-Su Seo

2018 20th International Conference on Advanced Communication Technology (ICACT)

Year: 2018 | Conference Paper |

Cited by: Papers (6)

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Convolutional Neural Networks (CNNs) is one of successful method in many areas such as image classification tasks. However, the amount of memory and computational cost needed for CNNs inference obstructs them to run efficiently in mobile devices because of memory and computational ability limitation. One of the method to compress CNNs is compressing the layers iteratively, i.e. by layer-by-layer c...Show More

Rank selection of CP-decomposed convolutional layers with variational Bayesian matrix factorization

Marcella Astrid;Seung-Ik Lee;Beom-Su Seo

2018 20th International Conference on Advanced Communication Technology (ICACT)

Year: 2018 | Conference Paper |

An efficient approach to the computation of fast fourier transform(FFT) by Radix-3 algorithm

Syed Khairul Bashar

2013 International Conference on Informatics, Electronics and Vision (ICIEV)

Year: 2013 | Conference Paper |

Cited by: Papers (4)

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In this paper an efficient approach to compute Discrete Fourier Transform (DFT) using Radix-3 algorithm, which is a Fast Fourier Transform (FFT), has been presented. It takes less multiplication than the usual one. The key idea is that matrix formed by different powers of twiddle factor (phase factor) is decomposed into two matrices and it has been shown that it takes less complex multiplications ...Show More

An efficient approach to the computation of fast fourier transform(FFT) by Radix-3 algorithm

Syed Khairul Bashar

2013 International Conference on Informatics, Electronics and Vision (ICIEV)

Year: 2013 | Conference Paper |

A Tensor Decomposition Method for Unsupervised Feature Learning on Satellite Imagery

Golnoosh Dehghanpoor;Michael Frachetti;Brendan Juba

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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We introduce a tensor factorization approach to unsupervised feature learning of hyper-spectral imagery, and demonstrate its effectiveness on land type classification of publicly available datasets. The results show that this approach can produce state of the art accuracy, compared to other methods for feature learning in the classification task.Show More

A Tensor Decomposition Method for Unsupervised Feature Learning on Satellite Imagery

Golnoosh Dehghanpoor;Michael Frachetti;Brendan Juba

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Real-Time Nonlinear Facial Feature Extraction Using Cholesky Decomposition and QR Decomposition for Face Recognition

2009 International Conference on Electronic Computer Technology

Year: 2009 | Conference Paper |

Cited by: Papers (3)

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In this paper, we propose an efficient and effective method for extracting nonlinear discriminative facial features for real-time face recognition tasks. The optimal nonlinear discriminative features of face images are obtained by performing Cholesky decomposition and QR Decomposition only once respectively, which could be implemented using the existing fast algorithms. Since the proposed method d...Show More

Real-Time Nonlinear Facial Feature Extraction Using Cholesky Decomposition and QR Decomposition for Face Recognition

2009 International Conference on Electronic Computer Technology

Year: 2009 | Conference Paper |

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