/NSU-FIT

🎓 NSU FIT laboratory works

Primary LanguageC++MIT LicenseMIT

Novosibirsk State University

Faculty of Information Technologies

1st year bachelor:

  • Digital Design (Verilog HDL)
  • The C Programming Language and Basic Algorithms

2nd year bachelor:

  • Computer Architecture and Peripheral devices:
    • x86/x86-64 assembly language (laboratory work â„–3)
    • ARM assembly language (laboratory work â„–4)
    • OpenCV (laboratory work â„–5)
    • LibUSB (laboratory work â„–6)
    • SIMD/BLAS (laboratory work â„–7)
  • OpenMP and MPI programming

3rd year bachelor:

  • Architecture of modern microprocessors and multiprocessors:
    • ARM vs x86 reorder buffer size (laboratory work â„–2)
    • Performance Application Programming Interface (PAPI) (laboratory work â„–3)
  • Coding theory:
    • MTF-algorithm (C++)
  • Data bases:
    • MS Access
    • Oracle SQL
    • Oracle PL/SQL (theater_info_system project)
  • Effective programming of modern microprocessors and multiprocessors:
    • Intel VTune Profiler (all laboratory works)
    • Intel Advisor (all laboratory works)
    • AVX instructions (laboratory work â„–2, 3, 4)
    • OpenMP (laboratory work â„–4)
  • Network programming:
    • POSIX TCP/IP, UDP Stack (all laboratory works, C/C++)
    • C-ares (laboratory works 5, SIMPLE SOCKS5 PROXY SERVER(more info in README.md of the project))
    • Protobuf, XML (laboratory works 4, Distributed Snake Game(more info in README.md of the project))

4th year bachelor:

  • Information security:
    • RC4, AES and AES-based hash function (laboratory work â„–3)
    • LSB injection to WAV audio (laboratory work â„–4)
  • Introduction to the organization of distributed calculations:
    • Multithreaded mutual exception in distributed systems (laboratory work â„–2)
    • Simple multithreaded HTTP server (laboratory work â„–4)
    • Distributed Files Storage (laboratory work â„–5)

1st year master:

  • Distributed systems.
  • Modern programming approaches.
    • Basic operations with data structures (laboratory work â„–1)
    • Numerical integration (laboratory work â„–2)
    • Parallel sequences processing (laboratory work â„–3)
    • DNF (laboratory work â„–4)

2nd year master:

  • Neural Networks and Machine Learning.
    • KNN (laboratory work â„–1, 2)
    • Polynomial regression (laboratory work â„–3, 4)
    • Elementary perceptron (laboratory work â„–5)
    • Multilayer perceptron (laboratory work â„–5, 6)