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20142025
most citedFederated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints

62 citations · 170 across the 44 of their papers we have counts for

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6 papers · 1 filter

eess.SP2020★ 5 cited

Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder

Ziqi Ke, Haris Vikalo

Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications inc…

eess.SP2019★ 3 cited

Submodular Observation Selection and Information Gathering for Quadratic Models

Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo +1

We study the problem of selecting most informative subset of a large observation set to enable accurate estimation of unknown parameters. This problem arises in a variety of settin…

eess.SP2018

Randomized Greedy Sensor Selection: Leveraging Weak Submodularity

Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo +1

We study the problem of estimating a random process from the observations collected by a network of sensors that operate under resource constraints. When the dynamics of the proces…

eess.SP2018

Near-Optimal Distributed Estimation for a Network of Sensing Units Operating Under Communication Constraints

Abolfazl Hashemi, Osman Fatih Kilic, Haris Vikalo

We study the problem of distributed state estimation in a network of sensing units that can exchange their measurements but the rate of communication between the units is constrain…

eess.SP2018

A Novel Scheme for Support Identification and Iterative Sampling of Bandlimited Graph Signals

Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo +1

We study the problem of sampling and reconstruction of bandlimited graph signals where the objective is to select a node subset of prescribed cardinality that ensures interpolation…

eess.SP2018

Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals

Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo +1

We study the problem of sampling and reconstructing spectrally sparse graph signals where the objective is to select a subset of nodes of prespecified cardinality that ensures inte…