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Andrei Rusu
Cluj-Napoca, Romania
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103.4
CodersRank Rank
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Top 100
Java
Developer
Romania
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Kotlin
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Romania
Highest experience points: 0 points,
0 activities in the last year
Kotlin
35.3
exp.
Java
25.4
exp.
JavaScript
11.8
exp.
JSON
11.2
exp.
CSS
10.2
exp.
Python
5.9
exp.
Protocol Buffer
1.4
exp.
List your work history, including any contracts or internships
AirportLabs
Jul 2020 - Aug 2020 (1 month)
Machine Learning Intern
In charge of developing a hardware and software road/runway surface quality inspection system for detecting potholes and imperfections using depth cameras and image processing.
I worked with Intel D and L series depth and lidar cameras and a Jetson Nano dev kit.
We created our own dataset by collecting raw data from the streets and running it through a 10-stage image processing pipeline, that I designed and implemented, to produce bounding boxes of potholes and stitched sections of road.
The system containing the Jetson Nano and the cameras was mounted on a push-cart that I built.
My team consisted of 2 more software developers, one of whom worked on a React front-end application, and the other one worked on integrating a Flask backend with the pipeline that I had developed.
Key learning points that I developed:
- stereo depth processing and image acquisition
- 3D plane fitting (by linear regression)
- image filtering (smoothing, thresholding, morphological operations)
- sparse optical flow (Lucas-Kanade method)
- image stitching (and cropping)
I gained experience with the following tools and technologies:
- Python 3, OpenCV, Intel RealSense SDK, NumPy, PyCharm IDE, Git
Hardware devices used:
- Intel RealSense D435 and L415 Stereo and LiDAR cameras
- nVidia JetsonNano Development Kit running Ubuntu Linux and Intel RealSense SDK for interfacing the cameras
- Personal development machine running Windows
I worked with Intel D and L series depth and lidar cameras and a Jetson Nano dev kit.
We created our own dataset by collecting raw data from the streets and running it through a 10-stage image processing pipeline, that I designed and implemented, to produce bounding boxes of potholes and stitched sections of road.
The system containing the Jetson Nano and the cameras was mounted on a push-cart that I built.
My team consisted of 2 more software developers, one of whom worked on a React front-end application, and the other one worked on integrating a Flask backend with the pipeline that I had developed.
Key learning points that I developed:
- stereo depth processing and image acquisition
- 3D plane fitting (by linear regression)
- image filtering (smoothing, thresholding, morphological operations)
- sparse optical flow (Lucas-Kanade method)
- image stitching (and cropping)
I gained experience with the following tools and technologies:
- Python 3, OpenCV, Intel RealSense SDK, NumPy, PyCharm IDE, Git
Hardware devices used:
- Intel RealSense D435 and L415 Stereo and LiDAR cameras
- nVidia JetsonNano Development Kit running Ubuntu Linux and Intel RealSense SDK for interfacing the cameras
- Personal development machine running Windows
Fortech
Mar 2016 - Sep 2016 (6 months)
Mentee
Part of the Mentorship Program "Discover your passion in IT".
During a 7 month period I learned the basics of Android app development.
Together with my team, we implemented an app called "BusTracker", designed to find you the quickest bus route between two points in the city of Cluj-Napoca, using the routes of the public transportation system.
During a 7 month period I learned the basics of Android app development.
Together with my team, we implemented an app called "BusTracker", designed to find you the quickest bus route between two points in the city of Cluj-Napoca, using the routes of the public transportation system.
Add some compelling projects here to demonstrate your experience
iExperiment StartUp Competition - First Edition
Oct 2015 - Feb 2016
I took part in the first edition of iExperiment, a start-up accelerator for high-school students. During the time spent there, my team and I developed a startup which allowed high-school students to experience student life; we called it iUniversity.
While working on our project, we received many useful lectures and workshops from proeminent business figures in our area, such as CEOs of IT companies, which really helped us shape a mentality that allowed us to envision a great service and company.
While working on our project, we received many useful lectures and workshops from proeminent business figures in our area, such as CEOs of IT companies, which really helped us shape a mentality that allowed us to envision a great service and company.
Raspberry Pi toy for children with learning disabilities
Mar 2019 - Apr 2019
I designed, built and programmed a Raspberry Pi based system to help children with autism and other disorders identify emotions. The device was installed in a Retman toy doll and using RFID cards, different emotions could be selected. Upon selection, there would be a short spoken message of a psychologist coming from the doll, to help the child cope with the emotion, i.e. fear, anxiety etc.
The system consisted of a Raspberry Pi Zero controller main board, audio converters (DAC) and a battery pack. The code was written in Python.
The system consisted of a Raspberry Pi Zero controller main board, audio converters (DAC) and a battery pack. The code was written in Python.
BSc Thesis Project - Edge computing system for UAV crowd identification and monitoring.
Oct 2020 - Present
For my bachelor's thesis, I have decied to develop something that combines hardware and software.
For this purpouse, I have designed and built a quadcopter (designed in SolidWorks, printed frame with 3D printer), to which I fitted an array of sensors and actuators and a powerful nVidia JetsonNano computer.
You can see the fully-built drone here: https://imgur.com/a/Q2UoTaw
The aim of this project is to provide an edge-computing based method for identifying and monitoring crowds of people, and detecting possible health hazards using unmanned aerial vehicles (UAVs).
The system is built with ROS (Robot Operating System), the most widely-used robotics framework for control and communication.
For the image processing part I use the popular OpenCV framework, along with the SSD MobileNet V2 object detection network for identifying people on the ground from the air.
For this purpouse, I have designed and built a quadcopter (designed in SolidWorks, printed frame with 3D printer), to which I fitted an array of sensors and actuators and a powerful nVidia JetsonNano computer.
You can see the fully-built drone here: https://imgur.com/a/Q2UoTaw
The aim of this project is to provide an edge-computing based method for identifying and monitoring crowds of people, and detecting possible health hazards using unmanned aerial vehicles (UAVs).
The system is built with ROS (Robot Operating System), the most widely-used robotics framework for control and communication.
For the image processing part I use the popular OpenCV framework, along with the SSD MobileNet V2 object detection network for identifying people on the ground from the air.
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