Foundations of Analog Electronics

Foundations of Analog Electronics: Analog Signals, Signal Parameters & Decibels, Circuit Analysis for Analog Design, Network Theorems, Real Resistors, Capacitors & Inductors, RC, RL & RLC Transients, Resonance and Q.

• Analog Signals, Signal Parameters & Decibels — What analog electronics is and the language of signals. Learn analog vs digital, continuous time and amplitude, real-world signals from sensors, DC/AC/transient/periodic signals, sinusoids (amplitude, frequency, period, phase, ω = 2πf), peak, peak-to-peak, average, RMS, crest factor, duty cycle and DC offset, noise, offset, headroom and dynamic range, decibels (dB, dBm, dBW, dBV, dBu) and cascaded gain, and the complete analog signal chain from sensor to ADC — with Python computing RMS, averages, dB conversions and dynamic range.
• Circuit Analysis for Analog Design — The tools every analog designer uses daily. Learn voltage, current, resistance, conductance, power and energy, Ohm's law, KCL and KVL, nodal and mesh analysis, voltage and current dividers (and why loading matters), source transformation, Thevenin and Norton equivalents, maximum power transfer, superposition and linearity, dependent sources (the basis of transistor models), practical source and load impedance, and the small-signal idea — with worked LED-resistor, bias-divider, sensor-divider, loaded-divider and Thevenin examples and a NumPy nodal solver.
• Network Theorems — Thevenin, Norton, superposition, source transformation and maximum power transfer — the shortcuts that reduce a complex linear network to something you can reason about in one step.
• Real Resistors, Capacitors & Inductors — Passive parts are not ideal. Learn resistor types, power rating, tolerance, temperature coefficient, noise and parasitics; capacitor construction and dielectrics (C0G, X7R, electrolytic, film), charge and energy, polarisation, leakage, ESR, ESL, DC-bias derating, decoupling, AC coupling and energy storage; inductors (magnetic energy, DCR, saturation, core loss, self-resonance) and how each part's impedance changes with frequency — with Python computing an MLCC's impedance curve, self-resonance, stored energy and resistor noise.
• RC, RL & RLC Transients, Resonance and Q — How circuits respond in time and frequency. Learn RC charging and discharging, RL current rise and decay, time constants (τ = RC, τ = L/R), natural and forced responses, the second-order RLC step response with under-, critical and over-damping, damping ratio ζ and overshoot, series and parallel resonance, quality factor Q and bandwidth, practical parasitic resonances (ringing), and first-order RC filters as the bridge to frequency response — with Python computing charge curves, damping, Q and bandwidth.