NOMA for IoT – multiple access and SDR
Non-orthogonal multiple access: collisions, limits of TDM/FDM, simultaneous radio access, SIC at the receiver, and practical signal processing with software-defined radio.
Read article →Electrical engineering articles
Here you will find selected electrical-engineering articles from my school and work projects and from this website. The focus is on clear theory, practical implementation, and real applications that I feed directly into my own tools—for example VisuKey.
Topics like AI, quantum computing, or blockchain are everywhere—but understanding often stays at the surface.
👉 Everyone knows the buzzwords; few people know the technology behind them.
That is why I write my articles differently: not oversimplified to the point of being meaningless, but so you can really understand the systems—from the idea to the technical implementation.
RF, IoT, and wireless systems
Multiple access, low-power communication, and measurements with SDR.
Backscattering modulator
Energy-efficient wireless communication with ambient backscattering, including component choice, measurement setup, BER analysis, and optimization.
Read article →Digital signal processing (DSP)
From theory to discrete implementation: filter design, coefficient calculation, and error correction on noisy measurements.
Deriving a 2nd-order digital low-pass filter
From the analog transfer function via bilinear transformation to the discrete difference equation for DSP and microcontrollers.
Read article →Designing digital filters (IIR / biquad)
From cutoff frequency and sample rate to coefficients b0–b2, a1, a2: bilinear transformation, Python example, and an interactive calculator for a 2nd-order low-pass.
Read article →Error-correcting codes in biometric authentication systems
BCH codes, stable bits, and signal-to-noise analysis for robust key reconstruction from noisy biometric measurements.
Read article →Machine learning
Neural networks, language models, and mathematical models for patterns, language, and technical systems.
Understanding neural networks
Foundations with hand calculations, real numbers, activation functions, deep learning, backpropagation, and how this relates to LLMs.
Read article →Embeddings and vector spaces in neural networks
How discrete tokens become geometric representations and why embeddings are the foundation of LLMs and modern retrieval systems.
Read article →How does a Transformer work?
In-depth technical article on self-attention, QKV, multi-head attention, LayerNorm, residuals, and causal masking in modern language models.
Read article →How does a large language model (LLM) work?
Explained simply and analyzed in depth: next token, Transformer architecture, training, limitations, and typical use cases.
Read article →Autoencoders for anomaly detection
Reconstruction-based error detection with neural networks, a worked example, and a minimal PyTorch implementation for technical signals.
Read article →Cryptography and IT security
Algorithms, protocols, and migration—from symmetric encryption to post-quantum cryptography.
Key derivation functions – protection against brute-force attacks
Principles of KDFs, salt, time and memory cost, and Argon2 as a modern standard for password-based security.
Read article →Authenticated encryption – data security with AES-GCM
How AES-GCM combines confidentiality and integrity, including the authentication tag and additional authenticated data (AAD).
Read article →Quantum computers and post-quantum cryptography
Report on quantum threat models: Shor/Grover, NIST standards, hybrid TLS, PQC migration, and practical examples.
Read article →