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Electives

Mechatronics Engineering

Every elective Zooky offers for this major.

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Year 3 · Semester 1 · choose 1 of 2

  • Ready to learn · Level 4 · 6 points

    Power Electronics and Converters

    How power is switched and shaped: rectifiers, device losses, buck and boost converters, magnetics, isolation, inverters, harmonics and control.

    Prerequisites: Electrical Fundamentals, Analogue Electronics, Control Engineering

    What you'll learn (11 topics)
    • Rectifiers
    • Switching devices and their losses
    • The buck converter
    • The boost and buck-boost converters
    • Discontinuous conduction
    • Converter magnetics
    • Isolated converters
    • Inverters and PWM
    • Harmonics and power quality
    • Converter control
    • Power-factor correction with a boost stage

  • Ready to learn · Level 4 · 6 points

    Electrical Drives

    How machines are driven and controlled: drive mechanics and quadrants, brushless drives, Clarke and Park transforms, the permanent-magnet machine in dq, vector control, field weakening, induction vector control, servo loops and regeneration.

    Prerequisites: Electrical Fundamentals, Control Engineering, Signals and Systems

    What you'll learn (23 topics)
    • DC machines: generators, speed control and starting
    • Induction motor performance
    • Synchronous machines
    • Balanced three-phase circuits
    • Drive mechanics and the four quadrants
    • DC motor and actuator models
    • Gear trains
    • Brushless DC and six-step commutation
    • Actuators and drives: DC motors, PWM, steppers and gearmotors
    • Clarke and Park transforms
    • Inverters and PWM
    • The permanent-magnet machine in dq
    • Vector control of a permanent-magnet motor
    • PID control
    • Bode plots
    • Stability margins and Nyquist
    • Field weakening and maximum torque per amp
    • Variable-speed drives
    • Vector control of an induction motor
    • Servo cascade control
    • Trajectory planning: cubic, quintic and trapezoidal moves
    • Regeneration and braking
    • Choosing a motor

Year 3 · Semester 2 · choose 1 of 1

  • Ready to learn · Level 4 · 6 points

    Industrial Automation and Process Instrumentation

    Sensors, PLCs, networks, SCADA and safety: how a plant measures, decides and acts, and how you keep it safe.

    Prerequisites: Electrical Fundamentals, Digital Systems Design, Fluid Mechanics

    What you'll learn (9 topics)
    • Process measurement and transmitters
    • Final elements and actuators
    • PLC hardware and the scan cycle
    • Ladder logic and IEC 61131
    • Sequential control and SFC
    • Industrial networks and fieldbus
    • SCADA, HMI and alarms
    • Loop tuning in practice
    • Safety instrumented systems and machine safety

Year 4 · Semester 1 · choose 1 of 2

  • Ready to learn · Level 4 · 6 points

    Advanced Control and Optimisation

    Control run by a computer: sampling and the zero-order hold, z-domain stability, digital equivalents and the digital current loop, then state feedback, observers, LQR, the Kalman filter and LQG, predictive control with input limits, and Lyapunov analysis.

    Prerequisites: Mathematical Methods, Control Engineering, Multivariable Calculus

    What you'll learn (18 topics)
    • Stability margins and Nyquist
    • z-transform: ROC, transfer functions, stability and inversion
    • Sampling and the zero-order hold
    • Analysis in the z-domain
    • Lead and lag compensators
    • IIR filter design: the bilinear transform, Butterworth filters and notches
    • Digital equivalents and PID
    • The delay and the digital current loop
    • State-space models
    • Discrete state space
    • State feedback and pole placement
    • Observers and output feedback
    • Maxima, minima and saddle points
    • Linear quadratic regulation
    • The Kalman filter, linear and extended
    • Kalman filter and LQG
    • Model predictive control, a first look
    • Lyapunov analysis

  • Ready to learn · Level 4 · 6 points

    Computer Vision and Perception

    Camera models and calibration, corners, optical flow and robust matching, two-view geometry and stereo depth, classical detection and its scoring, visual servoing and depth images.

    Prerequisites: Multivariable Calculus, Numerical Methods and Modelling, Mathematical Methods

    What you'll learn (23 topics)
    • Rotation matrices and homogeneous transforms
    • Reflection, refraction and lenses
    • Camera models and image formation
    • Least-squares regression
    • Calibration and homography
    • Image filtering and edges
    • Eigenvalues and eigenvectors
    • Corners and optical flow
    • Events and probability
    • Matching and robust fitting
    • Epipolar geometry and triangulation
    • Range sensors: ultrasonic, time-of-flight and triangulation
    • Stereo and depth
    • Thresholding, morphology and segmentation
    • Hough transform, templates and cascades
    • Learning from data and evaluation
    • Convolutional networks
    • Detection scoring
    • The Jacobian and singularities
    • Visual servoing
    • Robot programming with ROS: nodes, topics and a motion pipeline
    • Features, PCA and clustering
    • Depth images and point clouds

Year 4 · Semester 2 · choose 1 of 1

  • Ready to learn · Level 4 · 6 points

    Machine Learning and Vision

    Learning from data and honest evaluation, linear and logistic models, classifiers, PCA and clustering, neural networks and backpropagation, 2-D image filtering, segmentation, convolutional networks, deep learning for signals, and squeezing a model onto a microcontroller.

    Prerequisites: Embedded Systems, Multivariable Calculus, Mathematical Methods

    What you'll learn (22 topics)
    • Events and probability
    • Point estimation: moments, maximum likelihood, bias and variance
    • Learning from data and evaluation
    • Least-squares regression
    • Gradient and directional derivatives
    • Linear and logistic models
    • Detection in noise: matched filter and bit error rate
    • Bayes, neighbour and tree classifiers
    • Joint distributions and correlation
    • Eigenvalues and eigenvectors
    • Features, PCA and clustering
    • The multivariable chain rule
    • Neural networks and backpropagation
    • Discrete convolution and impulse copies
    • Image filtering and edges
    • Thresholding, morphology and segmentation
    • Convolutional networks
    • Windowing and spectral estimation
    • Deep learning for signals
    • Low-power design: duty cycling, battery life, CMOS power and sleep break-even
    • Real-time basics: polling, utilisation, response time and state machines
    • Models on microcontrollers