As part of our school course we are doing a project with Yolov3 and OpenCV. We are a group of 4 and two of us are working with YOLOv3 and rest are working with OpenCV. Our goal is to detect snooker balls from live video and count statistics, as potting percent from overall hits, from it. We started this project with about minimal coding experience with python and zero experience with artificial intelligence. We like to take the deep end from the pool, that is where you learn to swim.
It has a lot of information but I’m interested in people who travels for business frequently. How old are they, is it men or females who travels more, marital status, job satisfaction and stuff like that. Continue reading →
We are working on a school project which is initiated by our course Monialaprojekti.
Our goal is to build a program which detects snooker balls and counts statistics from games. We are using open source libraries like YOLOv3, CUDA and OpenCV for live video object detecting. Our progress is going to be posted on https://github.com/kristiansyrjanen/billystat
As a school assignment we did a program which reads information and timestamps from RFID cards and sends them to Excel workbook into our virtual private server (VPS) across the Internet. Then it automatically calculates work time between timestamps. We did set up the VPS for creating a account to each “employee”. With this we accomplished a personal work time Excel workbook, which only the employee and administrator can access with any device which has Internet connection. Continue reading →
Osana Tietoturvan hallinta-kurssia kuului suunnitella ja toteuttaa projekti, jonka aiheeksi valitsimme suojatut yhteydet ja varmenteet. Projekti tehtiin Ciscon Packet Tracer simulaattori ohjelmassa.Continue reading →
I thought that it would be important first step to plan the addressing scheme. I segmented every department in their own VLAN and every VLAN has it’s own host range. For getting known a little better with subnetting math, I didn’t assign easiest /24 bit masks for every subnet. For sake of complexity and training, subnets has /25, /26 or /30 bit long masks. Continue reading →