Nikita Konkin
Technologies
The project showcase, filterable by the technologies and tools behind each one.
Diploma Supplement Automation Service
A service for validating Excel grade sheets, highlighting problematic data, and assembling XML files for diploma supplements.
- Automated validation and highlighting of problematic cells in Excel.
- XML generation in the target “CyberDiploma 3.5.1” format.
- Polyglot architecture with separate API, data-processing, and XML-generation layers.
Angry Tests — testing principles by Yegor Bugayenko
A practical collection of automated testing principles: short tests, bug-focused coverage, strict assertions, and stronger engineering feedback loops.
- Principles for fast and deep tests, flaky bug reproduction, and custom matchers.
- Practical guidance on making tests shorter, stricter, and more useful.
- References to tools and approaches such as jqwik, pitest, ArchUnit, and fuzzing.
ICT — an ionospheric sounding data-processing platform
An end-to-end GNSS data-processing pipeline: from RINEX archives to total electron content, coherence bands, maps, and analytics. A set of containerised services with a web orchestrator and an API for data access.
- An end-to-end data path: RINEX → TEC → coherence band → maps and statistics.
- A web orchestrator for containerised converters with streaming logs and a run audit.
- A DuckDB analytics backend over Parquet with JSON/CSV/XLSX export and plotting.
Tea Taste — a digital tea-tasting journal
A web app for structured tea tasting: it records brewing parameters and sensory impressions across infusions, turning a subjective evaluation into a reproducible record.
- A multi-step tasting form with a localStorage draft.
- An infusion model: aroma and taste are recorded in stages, each infusion has its own rating and timer.
- An Express and MongoDB REST API with JWT authentication and a public tasting feed.
Gradient Descent Playground — an interactive gradient-descent sandbox
A Streamlit app for learning gradient descent by sight: it generates synthetic data, trains polynomial models with three descent variants (Batch, SGD, Mini-batch) under MSE or MAE, and shows the loss curve, a step-by-step animation, and a 3D loss surface with the optimization trajectory.
- Three descent variants (Batch, SGD, Mini-batch) and two loss functions (MSE, MAE) over shared data — for direct comparison.
- An animation of the optimization steps and a 3D loss surface with the descent trajectory over two chosen parameters.
- Automatic divergence detection: an unstable history is truncated at the last stable iteration.
Similar projects
- TensorFlow Playground — Train a neural network right in the browser — the canonical interactive ML sandbox.
- Distill — Why Momentum Really Works — An interactive account of the dynamics of gradient descent with momentum.
- Setosa — Ordinary Least Squares — A visual explanation of least-squares fitting.
- 3Blue1Brown — Neural networks — A video series that explains gradient descent and learning visually.
HF-FEC GAN — adversarial robustness testing of error-correcting codes
Adversarial (GAN) generation of structured, fixed-power interference to probe the vulnerability of the FEC codes used in HF radio. It compares the BER of several FEC schemes (convolutional K=7 R=1/2, BCH(63,51), turbo) in an ideal channel, under Rayleigh and Rician fading, and against a trained noise generator.
- FEC to HF communication standards: a convolutional code with soft-decision Viterbi (MIL-STD-188-110, STANAG 4285), BCH(63,51) for ALE, a STANAG 4539 turbo-code stub, and 16×16 block interleaving.
- A noise generator (FC → Conv1D → LSTM → Conv1D) against a CNN discriminator that predicts a decoding failure — fixed-power interference learns to defeat the FEC specifically.
- AWGN, Rayleigh, and Rician channel models, plus spectral analysis (FFT, Welch PSD) of the most effective interference structures.
Similar projects
- O'Shea & Hoydis — Deep Learning for the Physical Layer — The foundational paper on learning the physical layer end-to-end as an autoencoder.
- Nachmani et al. — Learning to Decode Linear Codes — Neural-network improvement of linear-code decoding — ML on the decoder side.
- Sadeghi & Larsson — Adversarial Attacks on Radio Signal Classification — Adversarial perturbations in wireless — the same attack angle against the receiver.
- NVIDIA Sionna — A differentiable physical-layer link-level simulation library for research.
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