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 • 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 • 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 • 18 Mar 2023 • Mohammadjavad Mirzazadeh Moallem, Mehdi Korki
In this paper, we investigate the diffusion least mean square (DLMS) algorithm over fading channel, where in addition to channel noise and path-loss the inter-node-interference (INI) among neighboring nodes of a host node is also taken into account.
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 • 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.