no code implementations • 17 Mar 2024 • Alireza Hariri, Hadi Zayyani, Mehdi Korki
This paper presents a novel sparse signal detection scheme designed for a correlated Markovian Bernoulli-Gaussian sparse signal model, which can equivalently be viewed as a block sparse signal model.
no code implementations • 17 Mar 2024 • Hadi Zayyani, Mehdi Korki
In this paper, an algorithm for estimation and compensation of second-order nonlinearity in wireless sensor setwork (WSN) in distributed estimation framework is proposed.
no code implementations • 17 Mar 2024 • Mehdi Korki, Fatemehsadat Hosseiniamin, Hadi Zayyani, Mehdi Bekrani
In this brief, we present an enhanced privacy-preserving distributed estimation algorithm, referred to as the ``Double-Private Algorithm," which combines the principles of both differential privacy (DP) and cryptography.
no code implementations • 20 Sep 2022 • Razieh Torkamani, Hadi Zayyani, Mehdi Korki
The first algorithm is the Proportionate-type Graph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursion process of the LMS algorithm and accelerates the convergence of the Pt-GLMS algorithm compared to the LMS algorithm.
no code implementations • 19 Sep 2022 • Razieh Torkamani, Hadi Zayyani, Farokh Marvasti
In addition, some synthetic and real-world experiments show the advantage of the proposed algorithm in comparison to some other adaptive algorithms in the literature of adaptive graph signal recovery.
no code implementations • 10 Apr 2022 • Seyed Hossein Mosavi Azarang, Roozbeh Rajabi, Hadi Zayyani, Amin Zehtabian
Spectral unmixing is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember.
no code implementations • 16 Oct 2020 • Razieh Torkamani, Hadi Zayyani
In this paper, the elements of the weighted adjacency matrix is statistically related to normal distribution and the graph signal is assumed to be Gaussian Markov Random Field (GMRF).
no code implementations • 16 Oct 2020 • Razieh Torkamani, Hadi Zayyani, Ramazan Ali Sadeghzadeh
Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as well as the inter-signal correlations.
no code implementations • 16 May 2019 • Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani
In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery.
no code implementations • 20 Feb 2019 • Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani
Each pixel in the hyperspectral images is considered as a node in this network.
no code implementations • 27 Dec 2018 • Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani
In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing.
1 code implementation • 12 Apr 2018 • Amirhossein Javaheri, Hadi Zayyani, Mario A. T. Figueiredo, Farrokh Marvasti
In this paper, we exploit a Continuous Mixed Norm (CMN) for robust sparse recovery instead of $\ell_p$-norm.
no code implementations • 3 Nov 2017 • Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani
Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.
no code implementations • 28 Jun 2017 • Amirhossein Javaheri, Hadi Zayyani, Farokh Marvasti
In this paper, we study the missing sample recovery problem using methods based on sparse approximation.
no code implementations • 25 Jan 2017 • Amirhossein Javaheri, Hadi Zayyani, Farokh Marvasti
The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal.
no code implementations • 2 Jul 2016 • Hadi Zayyani, Farzan Haddadi, Mehdi Korki
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection.
no code implementations • 3 Jan 2016 • Hadi Zayyani, Mehdi Korki, Farrokh Marvasti
A diffusion strategy is suggested for distributive learning of the sparse vector.
no code implementations • 30 Aug 2015 • Hadi Zayyani, Mehdi Korki, Farrokh Marvasti
In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain.
no code implementations • 22 Aug 2015 • Mehdi Korki, Hadi Zayyani, Jingxin Zhang
The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal.
no code implementations • 7 Dec 2014 • Mehdi Korki, Jingxin Zhang, Cishen Zhang, Hadi Zayyani
Unlike the existing algorithms for block sparse signal recovery which assume the cluster structure of the nonzero elements of the unknown signal to be independent and identically distributed (i. i. d.