Hello world! Or Habr in English, v1.0
Real-time edge detection using FPGA
Introduction
Our project implements a real-time edge detection system based on capturing image frames from an OV7670 camera and streaming them to a VGA monitor after applying a grayscale filter and Sobel operator. Our design is built on a Cyclone IV FPGA board which enables us to optimize the performance using the powerful features of the low-level hardware and parallel computations which is important to meet the requirements of the real-time system.
We used ZEOWAA FPGA development board which is based on Cyclone IV (EP4CE6E22C8N). Also, we used Quartus Prime Lite Edition as a development environment and Verilog HDL as a programming language. In addition, we used the built-in VGA interface to drive the VGA monitor, and GPIO (General Pins for Input and Output) to connect the external hardware with our board.
Stack-based calculator on the Cyclone IV FPGA board
Introduction
As first-year students of Innopolis University, we had an opportunity to make our own project in computer architecture. University suggested us several projects and we have chosen to make a stack-based calculator with reverse polish notation. One of the requirements for the project is to use FPGA board provided by the university.
As our board, we have chosen Cyclon IV. Therefore, we had to write code on hardware description language. In the course we have studied Verilog, so we have chosen it. Also, the university has additional modules for FPGA, such as numpad, thus we decided to use it in our project.
In this article, we want to share our knowledge about FPGA and Verilog, also provide you with a tutorial to repeat our project.
How linear algebra is applied in machine learning
When you study an abstract subject like linear algebra, you may wonder: why do you need all these vectors and matrices? How are you going to apply all this inversions, transpositions, eigenvector and eigenvalues for practical purposes?
Well, if you study linear algebra with the purpose of doing machine learning, this is the answer for you.
In brief, you can use linear algebra for machine learning on 3 different levels:
- application of a model to data;
- training the model;
- understanding how it works or why it does not work.
«Чемодан из крокодиловой кожи» или «мешок с аллигатором»: сравнение подключенных к Lokalise онлайн-переводчиков
Google Machine Translate/Google Neural Translate
Около полугода назад компания Google заявила о подключении очередного набора языков к нейронной сети своего сервиса Google Translate, в том числе и русского. Событие это стало знаковым для всего русскоязычного интернет-пространства: ежедневно тысячи человек пользуются встроенным в Chrome переводчиком Google или идут на сайт Google Translate за переводом иностранного текста на родной язык.