Dmitry Safonov (@juvus)
github

juvus

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  • Stars
    star
    13
  • Global Rank 834,067 (Top 29 %)
  • Followers 2
  • Following 2
  • Registered almost 8 years ago
  • Most used languages
    C
         57.1 %
    Python
         28.6 %
    C++
         14.3 %
  • Location 🇫🇮 Finland
  • Country Total Rank 3,143
  • Country Ranking
    C
         341
    C++
         613
    Python
         1,551

Top repositories

1

JEMA_Project

Juvus Engine for Multimedia Applications (JEMA) is a simple engine for creation of graphical applications under Windows platform. It is written in C and could be used, for example, to build simple 2D games.
C
3
star
2

Smooth_polygon_algorithm

The application demonstates the work of the algorithm for constructing an arbitrary polygon with smoothed vertices. Determination of the points needed to smooth vertices is based on utilizing the de Casteljan algorithm.
C++
3
star
3

Traffic_light_simulator

A graphical program that is written in C (using Win32API) with purpose of simulation of the single traffic light work
C
1
star
4

PSD_Dv_values_determination

Program is designed for determination of the cumulative distribution parameters Dv(x), which can be used to describe the cumulative particle size distribution.
Python
1
star
5

Circles_packaging_simulation

A graphical program that is written in C (using Win32API) that solves the problem of optimal packaging of circles using particle swarm optimization (PSO) algorithm
C
1
star
6

Particle_System_Generator

Developing an application with the main task of creating artificial images with generated 2D particles. An important feature of such particle generation is that particles are generated in accordance with desired and predefined distribution of sizes and shape parameters (PSD, Circularity, Convexity and Elongation).
Python
1
star
7

Food_searching_agent_simulation

Simulation of the agent's learning behavior in a dynamically changing environment. The goal of the agent's behavior is to find and consume food. Search efficiency increases in the process of continuous interaction of the agent with the environment. A model-free reinforcement learning algorithm, Q-learning, is used as the learning algorithm.
C
1
star