Course Learning Outcomes:

1. Understand the basic circuit components and the fundamental laws of circuit theories (KCL, KVL, Ohm's law,...).
2. Derive the mathematical model of resistive, and first and second order circuits.
3. Solve resistive circuits using techniques such as current voltage divider, mesh-current, node-voltage, Thevenin and Norton, superposition...).
4. Solve the initial condition and step responses of RC, RL and RLC circuits.
5. Solve sinusoidal steady-state response of RL, RC, and RLC circuits using techniques such as mesh-current, node-voltage, Thevenin and Norton, superposition.
6. Calculate power consumption in RL, RC and RLC circuits under steady-state sinusoidal excitation.
7. Investigate the initial and step response of RL, RC and RLC circuit.
8. Investigate the sinusoidal steady-state response of RL, RC and RLC circuits and power consummation is such circuits.

Course Learning Outcomes:

1. Classify systems based on their properties: in particular, to understand and exploit the implications of linearity, time-invariance, causality, memory, and bounded-input, bounded-out (BIBO) stability.
2. Understand the concepts of convolution, impulse response and transfer function and how they apply to continuous-time linear time-invariant systems.
3. Determine Fourier transforms for continuous-time, and understand how to interpret and plot Fourier transform magnitude and phase functions.
4. Use Laplace transform and its inverse to solve differential equations and to determine the response of linear time-invariant systems to known inputs.
5. Derive the Fourier Transform and use it as a tool for frequency-domain analysis.
6. Simulate signals and systems using modern computer software packages.
7. Use linear systems tools, especially transform analysis and convolution, to analyze and predict the behavior of linear systems.

Course Learning Outcomes:

1. Describe semiconductor behaviour and terminal characteristics of junction diodes and transistors.
2. Construct linearized models of nonlinear semiconductor devices, including diodes and transistors, and apply them to analysis of circuits.
3. Conduct large signal analysis of circuits using operational amplifiers, diodes and transistors.
4. Design circuits for practical applications including power conversion, and digital and analog signal.
5. Use computer-aided design tools to analyze and optimize practical electronic circuits.
6. Implement and characterize electronic circuits using fundamental test equipment.

Course Learning Outcomes:

1. Identify when a mathematical proposition is a tautology, contradiction, or logical equivalence.
2. Determine whether a relation is an equivalence relation or whether a relation is a partial order.
3. Use mathematical induction as a proof technique to prove a result.
4. Apply the Pigeonhole Principle in mathematical proofs.
5. Be able to compute an inverse in modular arithmetic and know how to use it to solve linear congruences.
6. Apply counting techniques such as inclusion-exclusion, combinations and permutations to count large groups of objects.

Course Learning Outcomes:

1. Represent combinational logic building blocks such as multiplexers, encoders, and decoders in algebraic form, in schematic form, and in the syntax of a hardware design language for computer-aided logic synthesis.
2. Represent latches and flip-flops for storing information in schematic form and in hardware-design-language syntax and describe the timing behavior of flip-flops.
3. Optimize a combinational logic function with Karnaugh maps, both with and without don’t-care valuations.
4. Design a finite-state machine from a given state diagram by directly deriving and optimizing the next-state and output logic, and by generating a behavioural description in a hardware design language for computer- aided logic synthesis.

Course Learning Outcomes:

1. Understand instruction set architecture to support arithmetic, memory-access, and program branching operations.
2. Understand internal semiconductor memory architecture and the design of basic cache and memory organizations.
3. Use register-transfer notation to specify cycle-by-cycle logic behavior of instruction execution in a basic five-stage processing unit.
4. Write a subroutine-based assembly-language program for specified data processing and input/output operations.

Course Learning Outcomes:

1. Identify and describe the standard data structures and algorithms.
2. Describe fundamental techniques for comparing alternative data structures and algorithms.
3. Implement fundamental data structures using the C programming language.
4. Select the appropriate data structure or algorithm to efficiently solve a given computing problem.
5. Analyze a given computing problem and solve it using suitable data structures and algorithms.
6. Examine solutions using critical thinking to increase efficiency and robustness of a given computing problem solution.

Course Learning Outcomes:

1. Demonstrate understanding of fundamental concepts of object-oriented programming.
2. Design and implement object-oriented programs.
3. Debug and test object-oriented programs.
4. Develop basic programming skills in Java.
5. Implement programs with graphical user interface.

Course Learning Outcomes:

1. Determine electrostatic fields using Coulomb's law and Gauss's law.
2. Determine magnetostatic fields using Biot-Savart's law and Ampere's law.
3. Calculate voltage or current across transverse electromagnetic (TEM) transmission lines.
4. Calculate the induced voltage in time-varying electromagnetic fields using Maxwell's equations.
5. Analyze the transmission line parameters using various techniques including Smith chart, and calculate transient voltage and current of the transmission line.
6. Calculate the gradient, divergence, and curl of various scalar and vector fields.
7. Calculate the gradient, divergence, and curl of various scalar and vector fields.

Course Learning Outcomes:

1. Develop and apply excellent written communication skills.
2. Develop and apply excellent verbal communication skills.
3. Develop and apply excellent presentation skills.
4. Use graphics and figures to effectively support written and verbal communication.
5. Reflect on project activities and provide insight related to project and learning scenarios.
6. Apply information research, assessment, and management concepts in engineering design.
7. Design creative solution(s) for open-ended, complex problems, applying engineering principles and theories from courses in other disciplines where applicable.
8. Apply design processes and tools for problem definition, idea generation, and decision making.
9. Make design decisions using financial factors, environmental factors, social factors, and public interests.
10. Consider equity, diversity, inclusion, and indigenization during the design process.
11. Incorporate the core principles of project management into the development of design solutions (including frameworks, objectives, scheduling, work breakdown, milestones, and life cycle).
12. Discuss engineering as a regulated profession, including reference to relevant engineering regulations/codes/standards, ethics, equity, health and safety.
13. Discuss professional/technical associations in engineering and discipline.
14. Discuss the role of ethics in a project with reference to real-world engineering applications.
15. Demonstrate effective teaming skills.
16. Demonstrate ability to identify and to address personal educational needs.

Course Learning Outcomes:

1. CLOs coming soon; please refer to your course syllabus in the meantime.

Course Learning Outcomes:

1. Interface low voltage motors and sensors to a microcontroller’s pins.
2. Program in a mid-level C-like language to sense/control microcontroller pins.
3. Design complete programs and algorithms for controlling a robotic vehicle to achieve a specified set of tasks.
4. Use basic electrical and electronic test equipment, perform testing and debugging of software and hardware, and write documentation.
5. Recognize areas of interest in the fields of electrical and computer engineering, including electronics, sensors, motors, software, and wireless systems.
6. As a team, design a robotic system to achieve certain tasks with specified performance in which team members lead, plan tasks and schedules, make decisions, and assume responsibilities fairly for certain aspects of the design.
7. Work effectively in a team.

Course Learning Outcomes:

1. Classify systems based on their properties: in particular, to understand and exploit the implications of linearity, time-invariance, causality, memory, and bounded-input, bounded-out (BIBO) stability.
2. Understand the concepts of convolution, impulse response and transfer function and how they apply to continuous-time linear time-invariant systems.
3. Determine Fourier transforms for continuous-time, and understand how to interpret and plot Fourier transform magnitude and phase functions.
4. Use Laplace transform and its inverse to solve differential equations and to determine the response of linear time-invariant systems to known inputs.
5. Derive the Fourier Transform and use it as a tool for frequency-domain analysis.
6. Simulate signals and systems using modern computer software packages
7. Use linear systems tools, especially transform analysis and convolution, to analyze and predict the behavior of linear systems.

Course Learning Outcomes:

1. Relate analog and discrete-time signals in terms of the sampling theorem.Represent and analyse discrete-time signals in time and frequency domains
2. Represent and analyse discrete-time signals in time and frequency domains.
3. Characterize signal transformations as systems and in terms of system properties.
4. Characterize LTI systems in time, frequency, and z domains.
5. Experimentally investigate the effects of sampling, choice of sampling frequency, and practical reconstruction.
6. Apply discrete Fourier transform to generate spectrograms for spectral analysis of complex signals.

Course Learning Outcomes:

1. Describe different types of random variables and identify important distributions.
2. Discuss the law of large numbers and asymptotic behaviours, and the concept of information and entropy.
3. Characterize and identify distribution of functions of multiple random variables and well-known random and point processes.
4. Apply probability theory for modeling uncertainties involved in engineering problems.
5. Solve problems involving probably with conditional probabilities.
6. Calculate different statistics of random variables by manipulating one and two random variables and working with joint distributions.

Course Learning Outcomes:

1. Understand magnetic materials and their application in energy transfer devices / equipment, including transformers, DC machines, AC induction machines and as synchronous machines.
2. Understand the equivalent circuits for transformers, DC motors, DC generators, AC induction motor and AC synchronous generators and motors.
3. Analyze the performance of a power system comprising the transformers, DC generators and AC synchronous generators.
4. Evaluate the performance of a driving system comprising DC motors and / or AC synchronous generators.
5. Perform the transformer testing and transformer equivalent parameters measurement (PA)
6. Use three phase induction motor and determine the inductor motor equivalent parameters by no load and blocked load tests. (PA)

Course Learning Outcomes:

1. Explain the basic transduction mechanismsin different types of sensors, and the evolution of emerging sensor and actuator technologies.
2. Explain the concepts behind converting electrical power into a mechanical output (actuators), and describe different types of motors.
3. Explain the operation of commonly used sensors and actuators, recognizing their limitations.
4. Test and calibrate different sensors and actuators, and be able to read and understand their datasheets.
5. Analyze and identify the most appropriate sensors and actuators for an application in a mechatronic system.
6. Work collaboratively on team tasks to design, build and test an integrated system involving sensors and actuators, and demonstrate system operation.

Course Learning Outcomes:

1. Explain the basic transduction mechanisms in different types of sensors.
2. Explain the operation of commonly used sensors.
3. Explain the different methods and approaches of microfabrication.
4. Explain and understand how the design of the sensors affect the performance and operation of those.
5. Analyze and identify the appropriate design of different sensors.
6. Work collaboratively on team tasks to partially fabricate and test.

Course Learning Outcomes:

1. Describe which circuit components determine the lower 3-dB cutoff frequency of an amplifier's gain response and which circuit components determine the upper 3-dB cutoff frequency of the gain response.
2. Identify which type of negative feedback arrangement (e.g. shunt sampling/series mixing, etc.) to use for a voltage amplifier, a current amplifier, a transconductance amplifier and a transimpedance amplifier.
3. Calculate the differential gain (Ad), the common mode gain (Acm) and common mode rejection ratio (CMRR) of a differential amplifier.
4. Calculate the gain of an amplifier with negative feedback.
5. Measurement of differential amplifiers and amplifiers with/without feedback as a function of frequency.
6. Students as a group discuss, explain and identify issues and results in the laboratory and report on them.

Course Learning Outcomes:

1. Describe the organization and behavior of hardware for supporting interrupts, and write appropriate code sequences in assembly language to initialize hardware for and respond to interrupt requests.
2. Describe concepts and design issues related to embedded systems and system-on-chip implementation involving microcontrollers and field-programmable logic chips, highlighting similarities and differences.
3. Design the address space and the address decoding logic for specified memory and input/output components in an embedded system, and analyze the memory-interface timing for execution of load/store instructions.
4. Write a program in the C language for a specified embedded application involving the use of parallel input/output ports and a hardware timer with interrupt capability.

Course Learning Outcomes:

1. Show numbers in binary and decimal as scientific representation (e.g., IEEE 754 Format) and compute error analysis.
2. Solve a system of linear equations numerically using different methods (e.g., Gaussian elimination, Gaussian-Seidel).
3. Find an approximation of unknown values that fall between known pairs of values using various numerical interpolation methods (e.g., Vandermonde method and Lagrange interpolating polynomials).
4. Find mathematical relationships between given data pairs as input-output using linear and non-linear regression methods.
5. Approximate numerical differentiation and integration using various methods.
6. Solve Initial-Value Problems (IVP) (i.e., an ordinary differential equation (ODE) together with an initial condition) numerically.
7. Solve Single-Variable Optimization problems (e.g., roots finding) as well as Multi-Variable Optimization problems using various numerical methods.
8. Describe different (practical) optimization problems through linear programming (LP) optimization problems, and solve those problems graphically and/or using the simplex method.
9. Use MATLAB to represent that data format and to implement related methods and algorithms learned during the course (e.g., interpolating methods, solving the system of linear equations and roots finding).

Course Learning Outcomes:

1. CLOs coming soon; please refer to your course syllabus in the meantime.

Course Learning Outcomes:

1. Write the behavioral and structural description of combinational and sequential circuits using Verilog or VHDL .
2. Describe the internal organization and the logical/timing interface of various memory subsystems including asynchronous/synchronous static, dynamic, and flash memory, as well as understand computer bus protocols and I/O interfaces.
3. Describe the concepts of data-level parallelism, CUDA/OpenCL kernel functions and threading for GPU architectures.
4. Analyze/design testable combinational and sequential circuits using techniques such as D-algorithm, Design for Testability (DFT), Built-In-Self-Test (BIST) and Boundary-Scan Architecture.
5. Analyze/design various high-performance digital circuits for fixed-point and floating-point arithmetic operations such as array multipliers, Booth algorithm, array dividers and multiplicative division, etc.
6. Analyze/design race-free asynchronous sequential circuits using the flow table, merger diagram and transition diagram.
7. Design, simulate, implement, and verify the datapath and control unit of a processor using Verilog/VHDL, with/without schematic design.
8. Effectively communicate the outcome of the lab CPU design team project through a final report and design documentation.

Course Learning Outcomes:

1. Develop software requirements specification for a medium-sized project.
2. Develop System Design Document for a medium-sized project.
3. Recall software development methodology concepts.
4. Use C++ features from basic to more advanced such as inheritance and polymorphic functions.
5. Implement medium-sized console and GUI programs in both procedural and object-oriented paradigms.
6. Implement a larger software project as a member of an agile programming team.
7. Apply software project management best practices in the collaborative development of large software project.

Course Learning Outcomes:

1. Describe and analyze scheduling algorithms. Describe criteria for evaluating scheduling algorithms.
2. Describe process synchronization techniques. Describe the requirements of synchronization and synchronization primitives used modern languages and libraries.
3. Recognize and describe invasive techniques to exploit a system, types and levels of security and security defenses. Exploit a simple security vulnerability. 
4. Define deadlocks and the four criteria that lead to deadlock. Able to draw Resource allocation graphs, and identify prevention, avoidance, and detection.
5. Write shell scripts and system programming. Prepare non-trivial shell scripts and system programs using standard Linux utilities, temporary files and pipes to meet a requirement.
6. Describe the use of Process Control Blocks to contain information about processes. Describe the operating systems concepts of process and thread, explain the various states a process or  thread may be in at any instant of time, and explain the transitions between these states and the reasons why those transitions occur.
7. Describe the organization of secondary storage, file systems, and error recovery.
8. Explain how a program’s logical address space is bound to physical memory addresses, the concept of virtual memory, and various memory allocation and paging techniques.

Course Learning Outcomes:

1. Understand and apply the mathematics for order of growth and recurrences for characterizing algorithm running times.
2. Understand different algorithmic techniques, characterize their requirements for obtaining optimal solutions, and describe their application to representative problems.
3. Describe aspects of complexity theory and NP-completeness, including decision algorithms, verification algorithms, complexity classes, and reductions.
4. Describe graph algorithms, characterize their running times, and apply them to sample problems.
5. Design/develop an appropriate algorithm for a specified problem, consider issues related to correctness, and characterize running time.

Course Learning Outcomes:

1. Understand the fundamentals of unguided electromagnetic wave propagation in lossless and lossy media (normal and oblique incidence, reflection and transmission, state of polarization, power flow).
2. Understand the fundamentals of guided electromagnetic wave propagation in rectangular metallic waveguides and optical fibers and the factors that impact wave propagation (e.g. dispersion, attenuation).
3. Understand the fundamentals of half-wave dipole antennas, dipole antenna arrays, horn antennas, planar patch antennas.
4. Use 2D and 3D full-wave electromagnetic simulators to design multiple types of electromagnetic structures.
5. Design waveguide resonant cavities and waveguide filters.
6. Design microwave and optical gratings.
7. Design and simulate dipole, horn and planar patch antennas.

Course Learning Outcomes:

1. Demonstrate and apply knowledge of engineering design theory and methodology through the analysis of a problem and framing of relevant objectives, constraints, and metrics.
2. Document and compare multiple strategies to motivate the selected solution.
3. Evaluate the performance of solutions with respect to criteria and metrics which include relevant health and safety risks, and societal considerations.
4. Develop software algorithms to meet project requirements.
5. Apply relevant software tools to create, simulate, evaluate, compare, and verify solutions.
6. Identify and comment on potential ethical issues of using artificial intelligence in engineering projects, using ethical principles and codes, and reflecting on personal ethical and moral compass.
7. Take initiative within a team to plan, organize, and complete tasks, contribute to a strong team culture, effectively manage conflicts, assign clear accountability and roles, and distribute work fairly. Contribute to a collaborative learning community.
8. Independently acquire the knowledge of the tools and skills required for success.
9. Organize and evaluate information from online sources and apply them in creating the solutions and troubleshooting issues.
10. Produce well-organized written engineering reports outlining the design process, and commentaries discussing complementary considerations, with clear, concise language.
11. Create visuals, figures, and tables that effectively communicate design decision, strategies, and results.
12. Effectively plan projects, including mitigating risk and managing change, to complete project on-time.

Course Learning Outcomes:

1. Describe the generation of biopotentials and the characteristics of the major biological signals.
2. Describe and perform standard and advanced signal processing of biosignals.
3. Explain how medical images are obtained for a number of medical imaging technologies.
4. Recognize and explain image characteristics and how image processing tools are applied to medical images.
5. Using appropriate processing tools, analyze and extract information from biosignal and/or a medical image data.

Course Learning Outcomes:

1. Understand pertinent bioinformatics terms (DNA, Genes, Amino Acids, proteins etc.).
2. Understand how to find datasets for bioinformatics research.
3. Understand the challenges and open issues in bioinformatics.
4. Understand techniques for preparation of datasets for processing.
5. Understand how to build a clustering or classification algorithm for the dataset.

Course Learning Outcomes:

1. Analyze pole-zero filters using z-transforms and relate to their time- and frequency-domain properties.
2. Analyze, design, and specify computational algorithms for real-time realizable digital filters.
3. Understand the theory of adaptive finite impulse response filters.
4. Understand the principle of modeling random signals using digital filters and the theory of linear prediction.
5. Write software to implement adaptive filtering algorithms for noise cancellation and test the theory of LMS adaptive filters.
6. Write software to test linear prediction for all-pole modeling of random processesand apply that to speech synthesis.

Course Learning Outcomes:

1. Understanding of wide sense stationary random processes including definition, input/output characterization for linear time-invariant systems (filtering) as well as in frequency domain application to quantizer design.
2. Understanding of minimum phase systems and minimum phase / all pass decompositions with application to inverse systems.
3. Understanding of minimum mean squared error optimum linear filtering principles.
4. Understanding of different methods of sampling, quantization, and reconstruction of baseband and bandpass signals including their design tradeoffs.
5. Understanding of digital filtering design principles of finite impulse response (FIR) filters including linear phase response, windowing, and transformations.
6. Understanding of multi-rate signal processing principles with application to computation reduction and parallel processing tradeoffs.

Course Learning Outcomes:

1. Demonstrate understanding of basic supervised and unsupervised machine learning models.
2. Demonstrate learning of regression, classification, clustering, and time series modelling.
3. Demonstrate the understanding of basic architectures of deep learning models.
4. Develop skills in designing and implementing basic machine learning and deep learning models.
5. Develop the basic ability to use popular machine learning and deep learning environments.

Course Learning Outcomes:

1. Describe the fundamental characteristics of power semiconductor devices (Diode, SCR, MOSFET, IGBT, BJT, GTO).
2. Describe and explain the fundamental operation of uncontrolled and controlled rectifiers.
3. Describe and explain the fundamental operation of DC/DC converters, DC/AC inverters, and resonant converters.
4. Analyze the behavior/performance of uncontrolled and controlled rectifiers.
5. Analyze the behavior/performance of DC/DC converters, DC/AC inverters and resonant converters.
6. Work in a group to apply theoretical learning in conducting experimental work on uncontrolled and controlled rectifiers, DC/DC converters, DC/AC inverters, and resonant converters.
7. Investigate the effect of non-ideal characteristics of circuit components on the operation of uncontrolled and controlled rectifiers, DC/DC converters, DC/AC inverters, and resonant converters and relate their experimental findings with the theory taught in the class.
8. Design buck, boost, buck boost, resonant converters by calculating the current and voltage ratings of semiconductor components and other passive components.
9. Design the HVDC transmission line for long distance power distribution by applying resonant converters.
10. Design a renewable PV energy source by applying DC/DC converters and DC/AC inverters.

Course Learning Outcomes:

1. Describe the fundamentals of energy resources and distributed energy conversion, power systems modeling and matrix manipulations related to power systems, modern power systems and power electronics converters, DC/AC, AC/DC converters and DC transmission.
2. Describe optimization methods in power systems operations; economic dispatch, optimal power flow.
3. Perform load flow analyses, symmetrical component calculations, and analyze balanced and unbalanced three-phase systems, single and three phase transformers, balanced and unbalanced faults.
4. Using software, carry out the preliminary design and analysis of the different aspects in the power systems.

Course Learning Outcomes:

1. n/a

Course Learning Outcomes:

1. Understand the operating principles of the single-phase induction motor and be able to analyze the motor characteristics (electrical and mechanical) using equivalent circuits.
2. Understand the operating principles of the single-phase series motor and be able to analyze the motor characteristics (electrical and mechanical) using equivalent circuits.
3. Be familiar with single-phase synchronous motors.
4. Understand the speed control technologies for single-phase motors.
5. Understand the operating principles of three-phase induction motors, three-phase synchronous motors, and three-phase salient pole synchronous motors.
6. Be able to analyze the motor characteristics (electrical and mechanical) using equivalent circuitsUnderstand the principles of commonly used motor speed control methods for three-phase induction motors and three-phase synchronous motorsKnow the differences among these control methods.
7. Be able to draw the block diagrams and explain the functions of these block diagram.
8. Be familiar with operating principles of Brushless DC (BLDC) motors and switched reluctance motors (SRM), servomotors, stepper motors.
9. Be familiar with the impact of time and space harmonics.
10. Be familiar with machine operation under transient conditions.

Course Learning Outcomes:

1. Understand how to model electrical and mechanical systems.
2. Understand how the position of poles and zeros impact the transient response of LTI systems.
3. Determine transfer function of LTI systems and determine the pole and zero locations to meet certain transient performance specifications.
4. Determine the system stability using locations of the poles.
5. Use PowerSim, MATLAB Simulink, and experiments to analyze the impacts of proportional, integral and derivative control.
6. As a group, communicate technical material through the use of calculations and plots and express meaningful conclusions in a concise way.

Course Learning Outcomes:

1. Describe dynamic models for mechanical, electrical, thermal and fluid systems, and know how to linearize the nonlinear dynamics associated with these systems.
2. Describe the effect of sampling rate and quantization on the stability and performance of discrete-time systems.
3. Analyze the stability, transient response and steady-state response of discrete-time feedback systems.
4. Design continuous-time controllers for the continuous-time plant and derive their discrete-time equivalents using various available techniques, including Euler rectangular methods, Tustin method, impulse invariance, and matched zero-pole.
5. Design a discrete-time controller for the discrete-time equivalent of the plant using root locus or frequency-domain methods, such as Bode or Nyquist plots.

Course Learning Outcomes:

1. CLOs coming soon; please refer to your course syllabus in the meantime.

Course Learning Outcomes:

1. Derive minimal representation of rotation matrices and transform coordinates.
2. Assign coordinate frames to robot manipulators according to DH convention and derive their kinematic equations.
3. Derive geometric Jacobian of robot manipulators and analyze the manipulator singularity.
4. Derive the dynamics of robot manipulators and simulate them in MATLAB.
5. Design and evaluate position and force controllers for robot manipulators.

Course Learning Outcomes:

1. Describe the steps of integrated-circuit fabrication processes to form NMOS/PMOS transistors and interconnections using polysilicon and metal.
2. Describe robust CMOS circuit implementation of flip-flop behavior and relevant considerations for clock signals to ensure reliable operation.
3. Describe cell, sense-amplifier, and address-decoding circuits for implementation of CMOS-based memory arrays.
4. Develop a standard-cell physical layout for a schematic CMOS circuit representation.
5. Characterize the parasitic and load capacitances for a CMOS circuit, and use that characterization to estimate delays for switching behavior.
6. Design static and dynamic CMOS circuits in schematic representation to implement combinational logic functions.

Course Learning Outcomes:

1. Describe and explain Class A, B, AB, C and D amplifiers, and simple Op Amp design.
2. Understand the principles of op amps, Comparator design, Digital to Analogue Converter design, Analogue to Digital Converter design.
3. Design and analysis of Active Filters using bilinear and Biquads.
4. Analyse the noise figure and equivalent input noise density of linear electronic circuits.
5. Analyse and design Colpitts, Crystal Oscillator, VCOs and Phase Looked Loops.
6. Design of power amps, filters, an Op Amp or Comparator Circuit (CO).
7. Design of a Data Converter (CO).

Course Learning Outcomes:

1. Describe the basic components and operation of systems such as RFID transmit-receive system, RF-to-DC power conversion system, radar imagers and radiometers systems.
2. Describe the operation of various biological sensors, neurostimulators, and implantable medical devices and how biotelemetry systems using ASK, FSK, or BPSK modulation work.
3. Analyze the transient and steady-state operation of a phase locked loop.
4. Design and analyze passive lumped-element filtering networks, and high-frequency and tunable active filters.
5. Design and analyze Colpitts oscillators, and low phase-noise oscillators, and frequency mixer circuits.
6. Design a PLL to have a specified overshoot due to a step change in input phase or frequency.

Course Learning Outcomes:

1. Use random processes in systems.
2. Design digital communication systems.
3. Analyze the function of each building block of digital communication systems.
4. Use various signal spaces to represent information.
5. Apply information theory to different systems.
6. Discuss communication technologies.

Course Learning Outcomes:

1. Understanding of the physical principles of wireless signal propagation link budget analysis.
2. Understanding of wireless channel modelling, including multi path propagation, wideband and narrowband fading channel models, channel sounding principles.
3. Modulation techniques for signal transmission and their tradeoffs and performance analysis in wireless channels.
4. Diversity and equalization in wireless channels.
5. Random traffic modelling (queuing) with application to cellular networks and network planning.
6. Multiple access principles and applicationsWireless standards involving CDMA/TDMA/SDMA/OFDMA.

Course Learning Outcomes:

1. Understand quantitative design and analysis of computing systems, as well as the instruction set architecture design for RISC architectures.
2. Describe the concepts of hierarchical memory subsystems, including multi-level caches, advanced optimization techniques and integration with pipelined processors, as well as virtual memory.
3. Understand design trade-offs in processor design including pipelined processors, in-order vs. out-of-order execution, branch prediction techniques, different cache and TLB organizations, and through simulation.
4. Describe software multithreading and multicore computing by writing parallel programs using shared-memory and message-passing programming models.
5. Analyze/design single-issue pipelined datapath and control unit, realize how structural, data and control hazards can affect performance, and how they can be handled statically or dynamically at runtime.
6. Analyze/design advanced instruction level parallelism (ILP), including multiple-issue pipelined processors with static scheduling (VLIW), dynamic scheduling and speculation (superscalar), and hardware multithreading.
7. Analyze/design multicore architectures and shared memory multiprocessors with a focus on thread level parallelism (TLP) and snooping and directory-based cache coherency protocols; as well as data- level parallelism (DLP) and GPU architectures.

Course Learning Outcomes:

1. CLOs coming soon; please refer to your course syllabus in the meantime.

Course Learning Outcomes:

1. Discuss communication technologies.
2. Learn different methods of informed and uninformed search for problem solving and decision making.
3. Learn to use logic and inference for decision making.
4. Learn basic definitions, development, and applications of data preprocessing techniques.
5. Learn basic definitions, development, and applications supervised and unsupervised machine learning models.
6. Learn basic definitions, development, and applications of ensemble learning techniques.
7. Learn basic definitions, development, and applications of evolutionary models.

Course Learning Outcomes:

1. Identify the key security requirements of confidentiality, integrity, availability, authenticity, and accountability.
2. Identify cyber attacks and security threats, and formulate the problems to be addressed.
3. Understand design principles and mathematical background of cryptographic algorithms and protocols.
4. Describe how various cryptographic algorithms and secure network protocols work.
5. Analyze and evaluate the security of the cryptographic algorithms and protocols.
6. Apply the design principles to design new cryptographic algorithms that meet the security requirements.
7. Analyze, design, and implement security protocols and algorithms to achieve certain security goals.
8. Learn to configure and architect computer network with cryptographic algorithms for maximum wired and wireless security.

Course Learning Outcomes:

1. Describe the basis of computer vision, and its applicability to solving a range of pertinent problems.
2. Recognize and apply methods to enhance, smooth and sharpen an image, segment an image, and detect and extract edges, corners, lines and circles.
3. Describe the basic pinhole and thin lens camera models; the main steps of the SIFT feature detector; and how to calibrate a camera's intrinsic parameters.
4. Model the object recognition problem and apply appropriate methods for object recognition and image reconstruction.
5. Have knowledge of and experience with the OpenCV software library, and have the skill set to analyze and implement Machine Vision methods in OpenCV.

Course Learning Outcomes:

1. Describe the objectives of Computer Vision, and how these are addressed using Machine Learning methods.
2. Describe the fundamental Machine Learning structures, including Multi-Layer Perceptrons, Convolutional Neural Networks, and Transformers.
3. Identify the main problem areas of Machine Learning-based Computer Vision, including Autoencoders, Image Classification, Object Detection, Segmentation, Variational Autoencoders, Generative Adversarial Networks, and Visual Information Transformers.
4. Explain the major architectural innovations that have advanced the performance of Machine Learning-based Computer Vision.
5. Implement, train and test Machine Learning-based Computer Vision solutions.

Course Learning Outcomes:

1. CLOs coming soon; please refer to your course syllabus in the meantime.

Course Learning Outcomes:

1. Describe the fundamentals of socket programming and protocols used to communicate between systems.
2. Evaluate issues involved in consistency and replicaAon in distributed storage systems.
3. Explain algorithms used to distribute computaAon in distributed systems.
4. Identify issues in naming resources in distributed systems.
5. Identify issues in timing in distributed systems.
6. Explain issues in real time embedded systems.

Course Learning Outcomes:

1. Analyze the optical properties of metals, semiconductors and insulators using various physical models.
2. Determine the mode of propagation of electromagnetic waves in micro and nano structures using analytical, semi-analytical and numerical methods.
3. Examine the fundamentals of photovoltaic energy conversion, and materials. Compare different device technologies for photovoltaics.
4. Apply the concepts of spectroscopy and plasmonics for biosensing.
5. Determine reflection and transmission of light for periodic and multilayer structures.
6. Determine the effect of various design parameters on the performance of electro-optical modulators.
7. Design ridge, rib and slot waveguides for integrated optic applications for both high and low index contrast material systems.
8. Evaluate the performance of various optical waveguides (confinement ability, loss, dispersion).
9. Design photonic devices (Bragg mirrors, ring resonators) using electromagnetic design tools.
10. Design of optical photo detectors.

Course Learning Outcomes:

1. Describe and explain where parasitics in lumped element components come from and how to estimate them.
2. Describe and explain where matching networks are used and how they are implemented in RF/Microwave amplifiers oscillators and mixers.
3. Describe and explain path loss in Radar and Radio systems using antennae/wave theory.
4. Design and analyze microwave filters.
5. Design and analyze RF/microwave amplifiers, mixers and oscillators.
6. Design and analyze radio/radar systems.
7. Measure the S-parameters of RF/Microwave amplifiers and filters as a function of frequency.
8. Measure distortion of a RF/Microwave amplifier and mixer circuits.
9. Students as a group discuss, explain and identify issues and results in the laboratory and report on them.

Course Learning Outcomes:

1. Understand the fundamentals of generating modulated optical signals for optical fiber communications using directly modulated lasers and external modulators (semiconductor lasers, LiNbO3 Mach-Zehnder modulators, intensity modulation, amplitude modulation, phase modulation, chirp).
2. Understand the fundamentals of electromagnetic wave propagation in dielectric slab waveguides and single-mode optical fibers (solution to Maxwell's equations, modes, propagation constant, attenuation and dispersion).
3. Understand the fundamentals of detecting modulated optical signals for optical fiber communications using p-i-n photodiode, avalanche photodiode, and EDFA pre-amplified receivers (shot-noise, APD multiplication noise, beat noise, signal-to-noise ratio (SNR), bit error ratio (BER)).
4. Understand the similarities and differences between non-coherent and coherent optical fiber communication systems.
5. Be able to solve problems that relate to the generation of modulated optical signals (amplitude and phase) for optical fiber communications.
6. Be able to determine the propagation properties of dielectric slab waveguides and single-mode optical fibersBe able to assess the impact of dispersion on signal propagation.
7. Be able to evaluate the performance of basic optical fiber communication systems (SNR and BER).
8. Understand the operation of a vector network analyzer be able to perform S-parameter measurements of electrical devices and components.
9. Understand the operation of an optical spectrum analyzer to perform measurements of optical signals and amplified spontaneous emission noise.
10. Understand the operation of a BER analyzer to perform measurements of an optical fiber communications link.

Course Learning Outcomes:

1. Critically evaluates qualitative and quantitative information and draw conclusions based on major theories, concepts, and methodologies of the discipline.
2. Assesses the reasonableness and effectiveness of assumptions, methods and quality of results against appropriate standards, and draws conclusions and recommend further investigation.
3. Follows appropriate iterative design process involving knowledge, creativity, justifiable decision making, analysis, and tools.
4. Fully identifies problem and constraints including health and safety risks, applicable standards, economic, environmental, cultural, societal and ethical considerations.
5. Develops detailed specifications and metrics incorporating performance requirements, constraints, assumptions, and other stated and unstated factors from all stakeholders relevant to the specific application.
6. Applies creative approaches to identify and develop alternative concepts and procedures.
7. Uses appropriate calculations, models, simulations, analysis, and/or prototypes at various points in design with iteration and complexity appropriate to design stage.
8. Quantifies performance/yield/efficiency/output at appropriate stages through process to support design iteration and optimization.
9. Evaluates techniques, resources, and tools to identify their limitations with respect to needs.
10. Applies appropriate techniques, tools, and processes to accomplish a task.
11. Evaluates appropriateness of results from several engineering techniques and tools.
12. Shows respect for diversity in individuals and roles in a team.
13. Evaluates team effectiveness and plans for improvements.
14. Elicits and applies positive and effective feedback from mentors and peers in technical, communications, and/or team issues.
15. Demonstrates capacity for initiative and technical or team leadership while respecting others' roles.
16. Generates a traceable and defensible record of a technical project using an appropriate project records system.
17. Writes and revises documents using appropriate discipline-specific conventions.
18. Demonstrates conciseness, precision, and clarity of language in technical writing.
19. Demonstrates confidence in formal and informal oral communications.
20. Explains and interprets results for various audiences and purposes.
21. Uses graphics to explain, interpret, and assess information.
22. Analyzes and integrates professional factors* (see below) throughout the design process in reaching optimal solution (*Students should address professional factors that include reliability, risk, standards, codes of practice, legal, regulatory compliance, patents and intellectual property, economic, environmental and societal factors as appropriate to their projects.)
23. Demonstrates professional bearing.
24. Describes environmental issues, the environmental impact of decisions and actions, and incorporates sustainability into decision making.
25. Effectively plans and allocates resources to complete a project effectively using tools as appropriate.
26. Assesses reliability, risk, regulatory compliance and safety throughout design process and takes appropriate action to mitigate if issues identified.
27. Defines and articulates the needed information resulting from an assigned project using self-determined structures and processes.
28. Identifies and critically evaluates an appropriate range of information sources using defensible criteria.
29. Organizes and manages different types of disciplinary information using self-determined structures, processes, and tools (e.gdropbox, zotero, Google Docs, etc.).
30. Assesses project progress and outcomes by factors including technical, professional, and other relevant measurements.
31. Incorporates literacy related skills into learning plans. Identifies resources and professional associations that addresses own ongoing professional development.
32. Approach and solve problems in somewhat unfamiliar areas.
33. Enhance their knowledge of practical hardware and/or software implementation techniques, or increase their familiarity with research methodologies.
34. Increase their awareness of the tools and techniques related to prototype testing, evaluation, and documentation.
35. Improve their team-oriented work skills.
36. Refine their skills in report writing and technical presentations.

Course Learning Outcomes:

1. Generates a traceable and defensible record of a technical project using an appropriate project records system.
2. Explains and interprets results for various audiences and purposes.
3. Uses graphics to explain, interpret, and assess information.
4. Constructs appropriate hypotheses, designs experimental procedures to collect data and analyzes data to test hypotheses.

Course Learning Outcomes:

1. Critically evaluates qualitative and quantitative information and draw conclusions based on major theories, concepts, and methodologies of the discipline.
2. Assesses the reasonableness and effectiveness of assumptions, methods and quality of results against appropriate standards, and draws conclusions and recommend further investigation.
3. Follows appropriate iterative design process involving knowledge, creativity, justifiable decision making, analysis, and tools.
4. Fully identifies problem and constraints including health and safety risks, applicable standards, economic, environmental, cultural, societal and ethical considerations.
5. Develops detailed specifications and metrics incorporating performance requirements, constraints, assumptions, and other stated and unstated factors from all stakeholders relevant to the specific application.
6. Applies creative approaches to identify and develop alternative concepts and procedures.
7. Uses appropriate calculations, models, simulations, analysis, and/or prototypes at various points in design with iteration and complexity appropriate to design stage.
8. Quantifies performance/yield/efficiency/output at appropriate stages through process to support design iteration and optimization.
9. Evaluates techniques, resources, and tools to identify their limitations with respect to needs.
10. Applies appropriate techniques, tools, and processes to accomplish a task.
11. Evaluates appropriateness of results from several engineering techniques and tools.
12. Shows respect for diversity in individuals and roles in a team.
13. Evaluates team effectiveness and plans for improvements.
14. Elicits and applies positive and effective feedback from mentors and peers in technical, communications, and/or team issues.
15. Demonstrates capacity for initiative and technical or team leadership while respecting others' roles.
16. Generates a traceable and defensible record of a technical project using an appropriate project records system.
17. Writes and revises documents using appropriate discipline-specific conventions.
18. Demonstrates conciseness, precision, and clarity of language in technical writing.
19. Demonstrates confidence in formal and informal oral communications.
20. Explains and interprets results for various audiences and purposes.
21. Uses graphics to explain, interpret, and assess information.
22. Analyzes and integrates professional factors* (see below) throughout the design process in reaching optimal solution (*Students should address professional factors that include reliability, risk, standards, codes of practice, legal, regulatory compliance, patents and intellectual property, economic, environmental and societal factors as appropriate to their projects.)
23. Demonstrates professional bearing.
24. Describes environmental issues, the environmental impact of decisions and actions, and incorporates sustainability into decision making.
25. Effectively plans and allocates resources to complete a project effectively using tools as appropriate.
26. Assesses reliability, risk, regulatory compliance and safety throughout design process and takes appropriate action to mitigate if issues identified.
27. Defines and articulates the needed information resulting from an assigned project using self-determined structures and processes.
28. Identifies and critically evaluates an appropriate range of information sources using defensible criteria.
29. Organizes and manages different types of disciplinary information using self-determined structures, processes, and tools (e.gdropbox, zotero, Google Docs, etc.).
30. Assesses project progress and outcomes by factors including technical, professional, and other relevant measurements.
31. Incorporates literacy related skills into learning plans. Identifies resources and professional associations that addresses own ongoing professional development.
32. Approach and solve problems in somewhat unfamiliar areas.
33. Enhance their knowledge of practical hardware and/or software implementation techniques, or increase their familiarity with research methodologies.
34. Increase their awareness of the tools and techniques related to prototype testing, evaluation, and documentation.
35. Improve their team-oriented work skills.
36. Refine their skills in report writing and technical presentations.