H
Hadi S. Aghdasi
Researcher at University of Tabriz
Publications - 44
Citations - 498
Hadi S. Aghdasi is an academic researcher from University of Tabriz. The author has contributed to research in topics: Wireless sensor network & Humanoid robot. The author has an hindex of 11, co-authored 40 publications receiving 330 citations. Previous affiliations of Hadi S. Aghdasi include Shahid Beheshti University.
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Energy-Aware Virtual Machine Consolidation Algorithm Based on Ant Colony System
TL;DR: A new algorithm based on Ant Colony System to solve Virtual Machine Consolidation problem aims to save the energy consumption of cloud data centers and significantly reduces the number of migrations and the active physical machines that result in the reduction of totalEnergy consumption of data centers.
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An Energy-Efficient and High-Quality Video Transmission Architecture in Wireless Video-Based Sensor Networks
TL;DR: A thorough architecture is presented for video transmission over WVSN called Energy-efficient and high-Quality Video transmission Architecture (EQV-Architecture), which influences three layers of communication protocol stack and considers wireless video sensor nodes constraints like limited process and energy resources while video quality is preserved in the receiver side.
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Community detection in social networks with node attributes based on multi-objective biogeography based optimization
TL;DR: A multi-objective discrete Biogeography Based Optimization (BBO) algorithm to find communities in social networks with node attributes using the Pareto-based approach and introduces a method for mutation probability approximation and uses a chaotic mechanism to dynamically tune the mutation probability in each iteration.
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A comprehensive survey on humanoid robot development
TL;DR: This review paper is presented to show variations in the requirements, design, and development process and also proposes a taxonomy of existing humanoid robots, aimed at demonstrating a general perspective ofexisting humanoid robots’ characteristics and applications.
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An energy-efficient artificial bee colony-based clustering in the internet of things
TL;DR: A novel cluster-head selection and clustering mechanism on IoT that improves energy consumption, lifetime, and transmission delay and group devices into some clusters based on Euclidean distance between each cluster- head and its members, and the data volume generated by clusters.