minor change

_config.yml

@@ -24,7 +24,7 @@ author:
   avatar           : "batuhan1.jpeg"
   name             : "Batuhan Avci"
   pronouns         : # example: "she/her"  
-  bio              : "I am a second-year MSc student in the Control, Robotics, and Autonomous Systems at Aalto University. I am interested in machine learning, control, and optimization."
+  bio              : "I am a second-year MSc student in Control, Robotics, and Autonomous Systems at Aalto University. I am interested in machine learning, control, and optimization."
   location         : "Helsinki, Finland"
   employer         : 
   uri              : # URL

_portfolio/portfolio-2.md

@@ -1,10 +1,10 @@
 ---
 title: "Recognizing and Tracking Person of Interest: A Real-Time Efficient Deep Learning based Method for Quadcopters"
-excerpt: "Participated in MATLAB and Simulink Challenge Projects program. Extended to a conference paper. <br/><img src='/images/drone.jpeg' width='auto' height='800px'>"
+excerpt: "Participated in MATLAB and Simulink Challenge Projects program. Extended to a conference paper. <br/><img src='/images/drone.jpg' width='auto' height='800px'>"
 collection: portfolio
 ---
 
-The recognition and tracking of a person of interest is a crucial task in many applications, including search and rescue, security, and surveillance. This paper presents a distributed system architecture that leverages the asynchronous threading and communication property of ROS2 to develop and implement a real-time efficient Deep Learning (DL) based method for recognizing and tracking a person of interest. The DL model receives snapshots from the quadcopter's camera and sends back an information vector, which includes all recognized persons and their corresponding position information within the camera frame of the quadcopter. The person of interest tracking control system receives face set information about the person of interest and generates reference velocity signals to be tracked by low-level controllers embedded within the drone. Experiments conducted in a cluttered and complex environment demonstrate the efficiency of the DL-based architecture for quadcopters. The presented real- world results validate the effectiveness of the proposed approach in recognizing and tracking a person of interest. 
+This work presents a distributed system architecture that leverages the asynchronous threading and communication property of ROS2 to develop and implement a real-time efficient Deep Learning (DL) based method for recognizing and tracking a person of interest. The DL model receives snapshots from the quadcopter's camera and sends back an information vector, which includes all recognized persons and their corresponding position information within the camera frame of the quadcopter. The person of interest tracking control system receives face set information about the person of interest and generates reference velocity signals to be tracked by low-level controllers embedded within the drone. Experiments conducted in a cluttered and complex environment demonstrate the efficiency of the DL-based architecture for quadcopters.
 
 
 ## Video Demonstration