Ashkan Panahi

Works (8)

Updated: March 13th, 2024 08:24

2021 article

Dynamic Graph Learning: A Structure-Driven Approach

Jiang, B., Huang, Y., Panahi, A., Yu, Y., Krim, H., & Smith, S. L. (2021, January 15). Mathematics, Vol. 9.

By: B. Jiang n, Y. Huang n, A. Panahi*, Y. Yu *, H. Krim n  & S. Smith *

author keywords: dynamic graph learning; graph signal processing; sparse signal; convex optimization
topics (OpenAlex): Functional Brain Connectivity Studies; Neural dynamics and brain function; Advanced Neuroimaging Techniques and Applications
TL;DR: The purpose of this paper is to infer a dynamic graph as a global model of time-varying measurements at a set of network nodes, which captures both pairwise as well as higher order interactions among the nodes. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 15, 2021

2020 article

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Ghanem, S., Panahi, A., Krim, H., & Kerekes, R. A. (2020, June 2). IEEE Sensors Journal, Vol. 20, pp. 12307–12316.

By: S. Ghanem n, A. Panahi n, H. Krim n  & R. Kerekes *

author keywords: Sensor fusion; Data integration; Data models; Sparse matrices; Magnetic sensors; Sensor phenomena and characterization; Sparse learning; unsupervised classification; data fusion; multimodal data
topics (OpenAlex): Speech and Audio Processing; Blind Source Separation Techniques; Structural Health Monitoring Techniques
TL;DR: The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that the method is competitive with other state of the art subspace clustering methods. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: October 12, 2020

2019 journal article

Analysis Dictionary Learning Based Classification: Structure for Robustness

IEEE Transactions on Image Processing, 28(12), 6035–6046.

By: W. Tang n, A. Panahi n, H. Krim n  & L. Dai*

author keywords: Discriminate analysis dictionary learning; distributed analysis dictionary learning; structured mapping; supervised learning
topics (OpenAlex): Remote-Sensing Image Classification; Image Processing Techniques and Applications; Sparse and Compressive Sensing Techniques; Face and Expression Recognition; Advanced Image and Video Retrieval Techniques; Domain Adaptation and Few-Shot Learning
TL;DR: A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. (via Semantic Scholar)
Sources: Web Of Science, Crossref, NC State University Libraries
Added: October 19, 2020

2019 article

Community Detection and Improved Detectability in Multiplex Networks

Huang, Y., Panahi, A., Krim, H., & Dai, L. (2019, October 23). IEEE Transactions on Network Science and Engineering, Vol. 7, pp. 1697–1709.

By: Y. Huang n, A. Panahi*, H. Krim n  & L. Dai*

author keywords: Multiplexing; Stochastic processes; Belief propagation; Correlation; Periodic structures; Computational modeling; Bayes methods; Network theory (graphs); graphical models; belief propagation
topics (OpenAlex): Complex Network Analysis Techniques; Opinion Dynamics and Social Influence; Data Visualization and Analytics; Advanced Graph Neural Networks; Bayesian Modeling and Causal Inference
TL;DR: A generative model that leverages the multiplicity of a single community in multiple layers, with no prior assumption on the relation of communities among different layers is proposed, which shows a better detection performance over a certain correlation and signal to noise ratio (SNR) range. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: September 21, 2020

2019 article

Deep Dictionary Learning: A PARametric NETwork Approach

Mahdizadehaghdam, S., Panahi, A., Krim, H., & Dai, L. (2019, May 8). IEEE Transactions on Image Processing, Vol. 28, pp. 4790–4802.

By: S. Mahdizadehaghdam n, A. Panahi n, H. Krim n  & L. Dai*

author keywords: Image classification; deep learning; sparse representation
topics (OpenAlex): Image Processing Techniques and Applications; Image and Signal Denoising Methods; Digital Media Forensic Detection; Generative Adversarial Networks and Image Synthesis; Sparse and Compressive Sensing Techniques
TL;DR: The performance of the proposed hierarchical method increases by adding more layers, which consequently makes this model easier to tune and adapt and shows a remarkably lower fooling rate in the presence of adversarial perturbation. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 26, 2019

2019 article

Sparse Generative Adversarial Network

Mahdizadehaghdam, S., Panahi, A., & Krim, H. (2019, October 1). 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), pp. 3063–3071.

By: S. Mahdizadehaghdam n, A. Panahi n & H. Krim n 

topics (OpenAlex): Advanced Image Processing Techniques; Image and Signal Denoising Methods; Image Processing Techniques and Applications; Digital Media Forensic Detection
TL;DR: A new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well-recognized mode collapse, by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the structure. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: September 7, 2020

2018 article

Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm

Panahi, A., Bian, X., Krim, H., & Dai, L. (2018, March 1). 2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018), pp. 1302–1311.

By: A. Panahi n, X. Bian  n, H. Krim n  & L. Dai *

topics (OpenAlex): Sparse and Compressive Sensing Techniques; Face and Expression Recognition; Remote-Sensing Image Classification
TL;DR: This work considers subspace clustering under sparse noise, and provides an analysis of this optimization problem demonstrating that this approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2017 article

Performance Analysis of Sparsity-Based Parameter Estimation

Panahi, A., & Viberg, M. (2017, September 21). IEEE Transactions on Signal Processing.

By: A. Panahi n & M. Viberg  n

author keywords: Superresolution theory; performance bounds; error analysis; LASSO; atomic norm regularization; atomic decomposition; continuous LASSO; off-grid estimation
topics (OpenAlex): Sparse and Compressive Sensing Techniques; Direction-of-Arrival Estimation Techniques; Advanced Adaptive Filtering Techniques
TL;DR: A novel analysis of the LASSO as an estimator of continuous parameters by providing a novel framework for the analysis by studying nearly ideal sparse solutions and quantifying the error in the high signal-to-noise ratio regime. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

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