phd in computer science (signal processing for short-range radar measurement system)
Repositories
MVRonkin/Turtle-CV-Textbook
The Turtle CV Textbook is more about computer vision than about turtles.
MVRonkin/dsatools
Digital signal analysis library for python. The library includes such methods of the signal analysis, signal processing and signal parameter estimation as ARMA-based techniques; subspace-based techniques; matrix-pencil-based methods; singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD); empirical mode decomposition; variational mode decomposition (EMD); empirical wavelet transform (EWT); Hilbert vibration decomposition (HVD) and many others.
MVRonkin/TimeSeriesCourse
MVRonkin/Computer-Vision-Course_lec-practice
Курс Компьютерное зрение (глубокое обучение в компьютерном зрении) для баколавров 09.03.04 Программная инженерия
MVRonkin/MLSystemDesignCourse
MVRonkin/CVCourse
MVRonkin/Deep-Learning-Foundation-Course
MVRonkin/TSATextbook
MVRonkin/DLCVCourseEn
DLCVCourse English version
MVRonkin/Deep-Learning-in-Image-Processing-Lectures
MVRonkin/utilits4tsc
utilits for time series course
MVRonkin/Classical-computer-vision
This repo contains implementations of some of the classical computer vision algorithms/techniques for feature extraction, feature matching, image transformation, color image reconstruction, image denoising, image classification, and image segmentation.
MVRonkin/portfolio-cv
https://github.com/MVRonkin/portfolio-cv/blob/main/EN.md
MVRonkin/Passive-Fingerprinting-of-Same-Model-Electrical-Devices-by-Current-Consumption
One of the possible device authentication methods is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e. current consumption from the electrical network. The key insight is that small hardware discrepancies naturally exist even between same-electrical-circuit devices, making it feasible to identify small variations in the consumed current. An experimental database of current consumption signals was collected. The resulting signals we classified within several modern time series classification methods, including \texttt{tsfel}, deep neural network (DNN), ROCKET and empirical wavelet decomposition-based manual feature extraction technique. We have successfully identified 40 similar (same-model) electrical devices with about 94\% precision.
MVRonkin/Basic_ML_Alg
Supplementary repository for Basic Algorithms of ML in Python
MVRonkin/for_course_DLCV
MVRonkin/arch_comp_sys
lec, workshops, questions
MVRonkin/SOMPY
A Python Library for Self Organizing Map (SOM)
MVRonkin/Time-Series-Analysis-Lectures-and-Workshops
MVRonkin/AI-for-IS-Course
MVRonkin/gost732
Преамбула и другие части LaTeX-документа для соответствия ГОСТ 7.32-2017
MVRonkin/ASBEST_VEINS_LABELING
MVRonkin/asbestos_labeling
MVRonkin/cd-diagram
Critical difference diagram with Wilcoxon-Holm post-hoc analysis.
MVRonkin/stylegan2-pytorch
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
MVRonkin/CvPytorch
CvPytorch is an open source COMPUTER VISION toolbox based on PyTorch.