Hi, I’m Rami. These are my electrical engineering notes from Queen’s University — a public Zettelkasten that grows as I take more courses, made for any student who gets stuck on the same things I did. Hopefully I’ll be able to publish the rest of my past courses here over time too.
Search by concept name, or use the MOCs below as a guided tour through a course’s material.
Maps of content
Each MOC groups atomic notes by topic, in pedagogical order (foundations first, applications later).
Digital logic
Course: Digital Systems. Covers the foundations of digital circuits: transistors, Boolean algebra, logic gates, combinational circuits (adders, multiplexers, decoders), CMOS implementation, programmable logic (PLAs, FPGAs, LUTs), sequential circuits (latches, flip-flops, registers, counters), and finite state machines. Also includes VHDL for hardware description.
Computer architecture
Course: Computer Architecture. Builds on digital logic to design programmable processors. Covers the instruction set architecture (ISA), Nios II as a worked example, processor internals (datapath, control unit, instruction execution cycle), the memory system (cache, virtual memory, DRAM), I/O (polling and interrupts), and the software toolchain (compiler, assembler, linker, loader).
Data structures
Course: Information Structures. Algorithmic thinking and data organization. Covers C foundations (pointers, structs, dynamic memory), linear structures (linked lists, stacks, queues, deques), trees (BSTs, AVL, heaps), hash tables, sorting algorithms (selection, bubble, quick, merge, heap, radix), searching, and graph algorithms (BFS, DFS, Dijkstra’s, Prim’s).
Mathematical methods
Course: Vector Calculus and Complex Analysis. The applied math foundation underneath the rest of EE — vector calculus and complex analysis. Covers complex numbers and phasors, vectors and coordinate systems (Cartesian, cylindrical, spherical), vector-valued functions and curves, vector fields with the gradient/divergence/curl operators, line and surface integrals, the integral theorems (Green’s, Stokes’, divergence), and complex analysis (analytic functions, Cauchy-Riemann, contour integration, Cauchy’s theorem and integral formula, Taylor and Laurent series, residues, conformal mapping and the Smith chart). This is the math that Differential equations, Signals and systems, and Electromagnetics all lean on — Laplace and Fourier inversions are contour integrals, phasor analysis is Euler’s formula in steady state, and Maxwell’s equations are vector calculus PDEs.
Differential equations
Course: Differential Equations. The math of change. Covers first-order solution methods (separable, integrating factor, exact), second-order linear ODEs (constant coefficients, undetermined coefficients, variation of parameters), the Laplace transform (with discontinuous and impulsive forcing), systems of ODEs (eigenvalue methods, complex/repeated cases), and stability theory (phase plane analysis, linearization, Lyapunov’s method).
Signals and systems
Course: Continuous-Time Signals and Systems. How to describe signals as functions of time and the systems that process them. Covers the standard signal zoo (sinusoids, exponentials, impulses, steps, rectangles, triangles), LTI system properties (linearity, time-invariance, causality, BIBO stability, memory), impulse response and convolution, Fourier series for periodic signals, the Fourier transform for aperiodic signals, the Laplace transform with poles/zeros and the s-plane, sampling and the Nyquist theorem, and filter analysis (lowpass/highpass/bandpass/bandstop, decibels, Bode plots).
Microelectronic circuits
Course: Electronics I. The analog half of EE hardware: how semiconductor physics produces devices, and how those devices become amplifiers. Covers semiconductor fundamentals (energy bands, doping, carriers, drift and diffusion), the pn junction, diodes and their models, diode application circuits (rectifiers, filters, clampers, limiters, Zener regulators), the MOSFET and BJT (structure, regions of operation, DC biasing), small-signal modelling (hybrid-pi and T-models, transconductance), the single-transistor amplifier configurations (common-source/emitter, common-gate/base, followers, cascode, degeneration), operational amplifiers and negative feedback (inverting, non-inverting, summing, difference, instrumentation, integrator/differentiator) with their non-idealities, and amplifier frequency response including the Miller effect and gain-bandwidth product.
Electromagnetics
Course: Electromagnetics. The unified theory of electric and magnetic fields and their propagation as waves. Covers electrostatics (Coulomb’s law, Gauss’s law, electric potential, Poisson’s and Laplace’s equations, capacitance), magnetostatics (Biot-Savart, Ampère’s law, magnetic flux, inductance, Lorentz force), time-varying fields (Faraday’s law, Lenz’s law, displacement current, the full Maxwell equations), electromagnetic waves (plane waves, Poynting vector), transmission lines (telegrapher’s equations, characteristic impedance, reflection coefficient, SWR, input impedance), transients on transmission lines (bounce diagrams, step response), and impedance matching (quarter-wave transformer, single-stub matching, the Smith chart as a graphical design tool).
Data science
Course: Introduction to Data Science. The end-to-end data science workflow for engineers. Covers Python tooling (NumPy, Pandas, scikit-learn, Matplotlib), sensor-based data collection (IMU, EEG, ECG), labelling and data ethics (GDPR, HIPAA, PIPEDA), storage formats (CSV, JSON, HDF5) and relational databases (SQL, SQLite), big-data infrastructure (Hadoop, HDFS, MapReduce, YARN), visualization, cleaning (missing data, imputation, noise filtering, scaling), feature extraction and dimensionality reduction (PCA, t-SNE), regression and classification (linear/logistic regression, gradient descent), and model evaluation (cross-validation, confusion matrix, ROC/AUC).
Engineering economics
Course: Engineering Economics. The economic side of engineering decisions plus the surrounding business context. Covers cost concepts (fixed/variable, direct/indirect, sunk, opportunity, life-cycle), cost estimation (parametric, power-sizing, learning curves), the time value of money and the standard compound-interest factors, cash flow analysis with annuities and gradients, comparison methods (present/future/annual worth, IRR, ERR, payback), inflation and real rates, depreciation (straight-line, declining balance) and the Canadian CCA tax system, replacement decisions via equivalent annual cost, risk and uncertainty (sensitivity, break-even, decision trees, expected value), opportunity identification and feasibility analysis, the three financial statements with ratio analysis, business planning, and strategic management (SWOT, levels of strategy, change management).