research.html

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  <h3>Victim Detection and 3D Pose Estimation from 2D Images for a Rescue Robot</h3>
  <img class="floating" src="images/victimpose.png" width="120" height="120" alt="vitctimpose">
  <p>
   2 phase Victim Detection and 3D pose estimation system implemented for a rescue robot.<br>
- In the first stage an Object Detection method used to detect the lying-down human bodies(victims).<br>
- In second stage a CNN algorithm is used to find body joints information then this joints are used to reconstruct a 3D model of human
    body and camera pose by applying matching pursuit algorithm in order to estimate the sparse representation of 3D pose and the relative
    camera from only 2D image evidence.<br> 

This Project is going to be submmited as: M. TaherAhmadi, S. Azami, S. Shiry, ”2 Stage Victim Detection and Pose Estimation from 2D Images 
    for a Rescue Robot” In Intelligent Robots and Systems, IROS. IEEE/RSJ International Conference. IEEE, 2017. <p>
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  <h3>Community Detection in Social Networks</h3>
  <img class="floating" src="images/communitis-graph.png" width="120	" height="120" alt="communitis-graph">
  <p>
    In this project we used Louvain Method implementation from pyhton NetworkX library for community detection in social networks.
    Modularity is a scale value between -1 and 1 that measures the density of edges inside communities to edges outside communities.
    Optimizing this value theoretically results in the best possible grouping of the nodes of a given network.
    This method is a greedy optimization method that appears to run in time O(n log n). <p>
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<h2>Recent Projects</h2>



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  <h3>Realistic Simulation of Wireless Communication in Multi-Robot Scenarios</h3>
  <img class="floating" src="images/01.jpg" width="128" height="110" alt="Bloodcells">
  <p>
<i><h5><span style="color: #1f6400; "><a href="http://aut-crlab.github.io">CRLab</a> and <a
                        href="http://airlab.deib.polimi.it/">AIRLab</a> Joint Project, July 2017-Present</span></h5></i>
                <p>Development of a communication node that interacts both with the ROS nodes of the robots and with
                    a simulator(Gazebo), in order to receive requests and uses various models of signal propagation
                    to decide if two robots can communicate or not. We also evaluate this node's impact on tasks like
                    SLAM or multi-agent exploration.</div>

  <div class="row">
  <h3>Comparing Corresponding fMRI, ECOG and EEG data in Human VTC</h3>
  <img class="floating" src="images/ccfmri.jpg" width="128" height="110" alt="Bloodcells">
  <p>
                <i><h5><span style="color: #1f6400; "><a href="http://www.royaninstitute.org/cmsen/index.php">Royan Institute of Research</a>, August 2017-Present</span>
                </h5></i>
                <p> This research is focused on validating the correlation between corresponding ECOG, EEG and fMRI
                    data. The data are collected from epilepsy patients and we will design a task for showing same
                    active regions in the brain by Blood-oxygen-level dependent and related signals. This research is
                    supported by <a
                            href="http://www.royaninstitute.org/cmsen/index.php"> Royan institute</a> under supervision
                    of <a href="http://smahdirazavi.wixsite.com/skhaligh"> Dr. Khaligh-Razavi</a>.

                    </div>
  <div class="row">
  <h3>Victim Detection and 3D Pose Estimation from 2D Images for a Rescue Robot</h3>
  <img class="floating" src="images/victimpose.png" width="128" height="110" alt="Bloodcells">
  <p>
   2 phase Victim Detection and 3D pose estimation system implemented for a rescue robot.<br>
- In the first stage an Object Detection method used to detect the lying-down human bodies(victims).<br>
- In second stage a CNN algorithm is used to find body joints information then this joints are used to reconstruct a 3D model of human
    body and camera pose by applying matching pursuit algorithm in order to estimate the sparse representation of 3D pose and the relative
    camera from only 2D image evidence.<br> 
    This Project is going to be submmited as: M. TaherAhmadi, S. Azami, S. Shiry, ”2 Stage Victim Detection and Pose Estimation from 2D Images 
    for a Rescue Robot” In Intelligent Robots and Systems, IROS. IEEE/RSJ International Conference. IEEE, 2017. <p>
</div>


<hr>
<h2>Past Projects</h2>
<div class="row">
<h3>Robocup Rescue Simulation Base Code</h3>
  <img class="floating" src="images/husky-rviz.png" width="148" height="108" alt="trich">
We all know that natural disasters and accidents take a huge toll on us and the people we love. Although we cannot prevent 
  these kind of accidents (Atleast not yet), we can use our tools and technology to aid us in these situations.This is our 
  contribution on Robot Rescue Simulation  platform as a base  code for starters to develop new high-level algorithms 
  faster and without wasting time on low-level  implementation of basestructures. That’s what we are trying to achieve here 
  specifically by using robots. Our main goal  is to develop basic modules that can later be used in a bigger framework and 
  interact with each other. The team description paper briefly explains these  modules and the framework  that we are
  prototyping to optimize them with respect to the tasks in the challenge.  Therefore, as discussed in the “future of robot
  rescue simulation” workshop, our team has designed a new  structure of ros  framework and Gazebo simulation environment
  that we’re going to explain its details in the following.We the ”S.O.S VR” are  participating in RoboCup Rescue Virtual 
  Robot for the first time, yet our team has already been the winner of agent simulation league in two tournaments. 
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