Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Korki, Mehdi"'
Autor:
Zandi, Sajad, Korki, Mehdi
We investigate the distributed multi-agent sharing optimization problem in a directed graph, with a composite objective function consisting of a smooth function plus a convex (possibly non-smooth) function shared by all agents. While adhering to the
Externí odkaz:
http://arxiv.org/abs/2406.14011
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. Despite the inherent complexity of the model, our
Externí odkaz:
http://arxiv.org/abs/2403.11072
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. The proposed algorithm enhanc
Externí odkaz:
http://arxiv.org/abs/2403.11064
Autor:
Zayyani, Hadi, Korki, Mehdi
In this paper, an algorithm for estimation and compensation of second-order nonlinearity in wireless sensor setwork (WSN) in distributed estimation framework is proposed. First, the effect of second-order nonlinearity on the performance of Diffusion
Externí odkaz:
http://arxiv.org/abs/2403.11059
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.
Externí odkaz:
http://arxiv.org/abs/2303.10309
This paper generalizes the proportionate-type adaptive algorithm to the graph signal processing and proposes two proportionate-type adaptive graph signal recovery algorithms. The gain matrix of the proportionate algorithm leads to faster convergence
Externí odkaz:
http://arxiv.org/abs/2209.09501
Autor:
Zayyani, Hadi, Korki, Mehdi
Publikováno v:
In Digital Signal Processing January 2025 156 Part A
Autor:
Sadeghi, Parastoo, Korki, Mehdi
The Gaussian distribution is widely used in mechanism design for differential privacy (DP). Thanks to its sub-Gaussian tail, it significantly reduces the chance of outliers when responding to queries. However, it can only provide approximate $(\epsil
Externí odkaz:
http://arxiv.org/abs/2110.06412