Definition and history of Automation. Social and economic dimension of Automation. Types and principles of automation systems, the hierarchy of control and instrumentation, career opportunities and scope of control and instrumentation profession. Skills, ethics, and design process. Case studies: Analysis of representative automation systems in the process industry, manufacturing, management, home and transportation.

Binary arithmetic. Boolean Algebra. Boolean functions and their simplification. Implementation of Boolean functions using logical gates, SSI, MSI, and LSI chips. Analysis and Design of Combinational circuits. Sequential Logic: Flip-Flops, Counters, and Registers. Analysis and Design of sequential circuits, Programmable Logic Devices, FPGA/PLD hardware. Analysis and Design using CAD software. Interfacing of digital electronics to control and instrumentation elements, such as relays, 24-volt signals, analog switches, and proximity switches.

This course introduces the fundamentals of information technology and systems; their structure, and components. The course emphasizes the Enterprise applications of IT in improving the performance of business and industrial systems. In addition, the course introduces the current trends related to information technology, such as the Internet, Ecommerce, and wireless communication. The course also gives an insight into security and ethical issues related to information exchange.

Roots of nonlinear equations. Solutions of systems of linear algebraic equations. Numerical differentiation and integration. Interpolation. Extrapolation and approximation. Least squares approximation and regression analysis. Numerical solution of ordinary and partial differential equations. Introduction to error analysis. Engineering case studies.

Linear systems, Modeling of physical systems, Ordinary Differential equations models, Laplace Transform, transfer functions, block diagram manipulation. Open loop and closed loop systems, time domain analysis, response of systems to different test signals, Steady state analysis. Concept of stability, Routh-Hurwitz criteria, controller design. Laboratory activities include modeling, analysis and simulation of physical processes.

General measurement systems; SI units, errors in measurement systems, static and dynamic modeling of measurement systems, environmental effects, loading effects, noise in measurement systems, calibration, design of experiment, reliability of instrumentation systems. Typical measurement systems, displacement, velocity, and acceleration measurement. Pressure/Force measurement (capacitive, elastic, strain gauges, piezoelectric, electronic, weight scales, load cells). Temperature (elastic, expansion, resistive, thermocouple, IR, electronic). Analog signal conditioning and DC/AC bridges.

This is the first level of instrumentation and mechatronics. The course introduces the basic concepts of switching input and output devices, sensing devices, and how they are used in real life automation systems. The course is a
**Prerequisite** for the mechatronics course and for the advanced instrumentation course.

Basic models of continuous and discrete-time signals and systems. Basic characteristics of signals (energy, power, peak amplitude). Fourier analysis of continuous and discrete-time signals and systems. Basic concepts of signal sampling and reconstruction. Basic properties of Laplace and Z-transforms and concept of transfer function. Applications of signals and systems concepts to linear control systems and digital signal processing.

Transient and Steady State analysis and design specifications. Root locus, Design using Root locus. Frequency Response Techniques, Bode plot, Nyquist plot, principle of Specifications and controller Design in the Frequency domain. State-space model, analysis of the state-space model, Controllability and Observability, pole placement, and robust Control.

Elements of Computer Control Systems, A/D and D/A, Sampling theorem, signal conditioning, anti-alias filters, sensors, actuators. Discrete time systems, digital control design, digital PID control. Programmable logic controllers, computer control technology including distributed computer control, Fieldbus technology, and OLE for process control.

The Cooperative Work Program accounts for nine (9) credit hours, involves either a team-based or a single studentbased project that is geared toward an integrated application of several pieces of Systems Engineering knowledge learned by the student in his undergraduate education thus far. The co-op project must address technical aspects of the practice of Systems Engineering, including analysis, experimentation and design, by utilizing the problem-solving techniques covered in the various required (core) and elective courses offered at the Systems Engineering Department.

Improve students’ ability in presenting technical work and introduce them to the knowledge of contemporary issues in their field of studies. The course features students’ attendance to seminars, workshops, industrial visits, professional societal meetings, governmental agencies’ conferences. Each student is required to present a written evidence for attending each of an adequate number of seminars and industrial visits at the end of the semester.

Students spend eight weeks in the industry, and submit a report and a presentation at the end of his summer training work.

Mechatronics is the synergistic integration of mechanism, electronics, and computer control to achieve a functional system. Fundamentals of interfacing of modern mixed electrical, mechanical, and computers systems. Sensors, Signal Conditioning, Electro-Mechanical Actuation, Basic System Modeling, Essentials of Dynamic Systems, Data Acquisition and Virtual Instrumentation, and PC-Based and Embedded Controllers. Physical properties, mathematical modeling for computer simulation. Applications illustrated by numerically and experimentally generated results.

Basic features microcontrollers, organization & architectural Features of Microprocessor & microcontroller, Basic organization, high level and assembly language conversion to machine level instruction. Basic fetch & execute cycle of a program. Instruction Set, basic operations and addressing modes, Assembly language programming, fast prototyping using high level languages. Typical Bus structure, I/O Control & interfacing to digital systems, Interfacing to various power switching devices. Interfacing Protocols. Sensors, A/D & D/A Converters, Analog signal conditioning Circuits. Pulse Width Modulation. Applications to Industrial Automation.

Modeling of processes, Mass balance, and Energy balance, Models of representative processes, Dynamic response, and Linearization. Process identification using time and frequency domain techniques. Time delay, Smith predictor. Basic and advanced control strategies, e.g. PID, Feed forward, Internal model, and supervisory control. Time domain controller design, Controller tuning. Controller design in the frequency domain, Optimization Techniques and Supervisory Control. Case studies.

Review of the Fundamental laws, mathematical modeling; model and simulation of typical processes. Computer simulation tools, Virtual Instruments, MMI. Systems identification, IMC, Predictive control, DMC, Neural Network modeling and control. Students will work out simulation and control projects, using DYNSIM process dynamic simulation and SIMULINK, of typical processes, e.g., CSTR, Gas Surge Drum, Isothermal Chemical Reactor, Vaporizer, Binary Column, Heat Exchanger, etc.

The course offers an introductory material to advanced control strategies such as fuzzy and neural network based controllers. The need for model–free control, Linguistic based control, foundations of fuzzy set theory. Main approaches of fuzzy control, design issues, fundamental of neural networks, neural networks architecture, neural networks based controller design. Application examples.

The course introduces the concept of model predictive control (MPC), their importance in process industry, implementation issues and application examples. The course covers: model based predictive control, generalized MPC, constrained MPC, some commercial MPC, issues in implementation in industrial control systems and case studies.

Dynamical systems and their mathematical models, random variables and signals, The system identification procedure. Guiding principles behind least-squares parameter estimation, statistical properties of estimates. Identification of the transfer function of linear systems in continuous time. Models for discrete-time linear systems: FIR, AR, ARX, ARMA. Various methods for recursive estimation. Experiments for data acquisition and their design.

Industrial instrumentation: measurement techniques in industrial processes. Computer data acquisition. NC and CNC machine tools. Computer process interfacing and control. Feedback control systems. Group technology. Flexible manufacturing systems. Automated assembly. Industrial robots. Computer-aided inspection and testing. Automated factories. Case studies.

Need for, advantages and basic structure of DSP systems. Basic concepts of discrete-time signals and systems. ZTransform, discrete Fourier Transform (DFT) and frequency analysis of signals and systems. Efficient implementation of DFT: Fast Fourier Transform (FFT) algorithms. Implementation issues of discrete-time systems. Digital filter design techniques. Applications of DSP systems.

Condition-based maintenance process. Data collection and Analysis process. Decision making. Condition-based monitoring components sensors and software programs. CMMS. Hazard and reliability functions. Models for CBM. Reliability improvement. Integration of CBM into the control design and operation. Engineering case studies.

Review of DC motors, optical encoders, precision control of DC motors, Stepper motors, control of stepper motors, micro-step control, gearboxes, belts, motor torque and power sizing, programming motion using G-code. Basic structure and functions of milling machines and lathes. Motion simulation, CAD/CAM system. Robot arms construction, analysis, and motion programming. Case study of retrofitting conventional machines with Computer Numerical Control.

Hierarchy of plant communication systems, field equipment, DCS systems, SCADA systems, Supervisory control and production control, Man-Machine Interface (MMI). Local area networks, OSI network architectures, serial communications, IEEE 802.xx standards, Local area networks for industrial applications, Field buses, Hart protocol, Foundation Field Bus, Profibus, CAN bus, etc. Smart instruments. Examples of industrial DCS systems.

Signal conditioning: 4-20 mA circuits, E/I transducers, bridges (AC and DC), design of bridges, operational amplifier circuits, filters (LP & HP), power supplies, reference voltages, analog multiplexer/ de-multiplexers. Data acquisition systems, SCADA Systems, interface cards, isolations, intrinsic safety, Nondestructive testing. LABVIEW, virtual instrumentation, Visual programming, and HMI, Plant network hierarchy, DCS, Data communications, smart transmitters, Field buses, and OPC. Process and Instrumentation diagrams.

A course in an area of instrumentation reflecting current theory and practice.

Review of state variable models, Review of basic matrix algebra, Static optimization, Formulation of optimal control problems, Principle of optimality. The linear quadratic regulator problem, properties of the algebraic Riccati equation (ARE) The minimum principle and time optimal control problems. Output feedback design. Homework assignments include design and simulation using MATLAB or other similar software packages.

Probability, Random Variables and distributions, correlation, MA, AR, and ARMA systems, power spectrum, Spectral factorization, Weiner-Hopf filter. Stochastic control systems, Minimum variance control, State-variable forms, Kalman filter, LQG feedback systems. Cases studies from published work.

This course introduces the concepts of uncertainty and Modeling Error in Control System Analysis and Design. Review the basic methods and tools of Classical Control. Robust stabilization, Loop shaping, Introduction to H? Optimal Control Analysis and Synthesis. Design examples.

A course in an area of control reflecting current theory and practice.

Basics of anatomy and biological science. Fundamentals of engineering applications in biomedicine. Biomedical instrumentation and information technology, control and communication in biomedicine. eHealth and telemedicine.

Review of basic Probability, Statistical Independence, Conditional Expectation and Characteristic Function. Introduction to Stochastic Processes, Stationarity and Ergodicity. Markov Chains and Poisson Processes. Linear Models of Continuous and Discrete Stochastic Processes. Engineering Applications.

An overview of large-scale problems and the framework for Systems Engineering. Graphic tools for Systems Engineering. Interaction matrices and graphs, interpretive structure modeling. Spare matrix and decomposition techniques. Model reduction techniques. Case studies.

High volume discrete parts production systems. Fundamentals of CAD/CAM. Computers in manufacturing. Computer process monitoring. Systems for manufacturing support. Group technology and integrated manufacturing systems. Case studies for robots in industry. CAD/CAM using computer graphics laboratory.

Micro-machined sensors, Fiber optical sensors, Gas chromatography, Gas detectors, Environment monitoring systems, NMR, Soft-sensing techniques.

DCS systems, Intrinsic safety, Emergency shutdown ESD systems, reliability of instruments and control systems, MTBF, Redundant systems, Safety standards,. Classification of industrial process, Safety integrity levels (SIL), Quantitative risk assessment (QRA), Safety and control networks, Fieldbus for safety systems, Cost benefit analysis, Best practices.

The course introduces the students to the latest trends in industrial communications systems in a practical theme. The course starts by previewing the main topics in communications systems such as modulation and coding. The course then covers the main communication network standards used in industry. The course covers mainly all data layers from the field instruments to the TCP/IP and world-wide web and even latest wireless data exchange techniques. Case studies of industrial DCS and CIM and their integration with the enterprise networks.

A course in an area of automation reflecting current theory and practice.

A design course that draws upon various components of the undergraduate curriculum. The project typically contains problem definition, analysis, evaluation and selection of alternatives. Real life applications are emphasized where appropriate constraints are considered. Oral presentation and a report are essential for course completion. The work should be supervised by faculty member(s). Team projects are acceptable wherever appropriate.

CISE 201 Introduction to Control and Instrumentation (1-0-1)

CISE 204 Digital Systems Design (2-3-3)

CISE 209 Introduction to Information Technology (2-0-2)

CISE 301 Numerical Methods (3-0-3)

CISE 302 Linear Control Systems (3-3-4)

CISE 312 Instrumentation Engineering (2-3-3)

CISE 313 Automation Devices and Electronics (2-3-3)

CISE 315 Signals and Systems (3-0-3)

CISE 316 Control Systems Design (2-3-3)

CISE 318 Computer Control Systems (2-3-3)

CISE 350 Beginning Coop Program (0-0-0)

CISE 351 Cooperative Work Program (0-0-9)

CISE 390 Seminars (0-0-0)

CISE 399 Summer Training (0-0-0)

CISE 412 Mechatronics (2-3-3)

CISE 414 Embedded Control Systems (2-3-3)

CISE 418 Industrial Process Control (3-0-3)

CISE 421 Simulation and Control for Process Industry (3-0-3)

CISE 422 Intelligent Controllers (3-0-3)

CISE 423 Model Predictive Control (3-0-3)

CISE 424 Identification of Linear Systems (3-0-3)

CISE 431 Industrial Automation (3-0-3)

CISE 432 Digital Signal Processing (3-0-3)

CISE 433 Condition-based Maintenance (3-0-3)

CISE 434 Computer Numerical Control (3-0-3)

CISE 435 Distributed Computer Control Systems (3-0-3)

CISE 438 Instrumentation for Process Control (2-3-3)

CISE 439 Special Topics in Instrumentation (3-0-3)

CISE 441 Linear Optimal Control (3-0-3)

CISE 442 Stochastic Control (3-0-3)

CISE 443 Introduction to Robust Control (3-0-3)

CISE 449 Special Topics in Control (3-0-3)

CISE 451 Introduction to Biomedical Engineering (3-0-3)

CISE 452 Theory of Stochastic Systems (3-0-3)

CISE 453 Methodology for Large Scale Systems (3-0-3)

CISE 454 Computer-Aided Manufacturing and Robotics (2-3-3)

CISE 455 Advanced Instrumentation (3-0-3)

CISE 456 Safety and Reliability of Control Systems (3-0-3)

CISE 457 Industrial Communication Systems (3-0-3)

CISE 459 Special Topics in Automation (3-0-3)

CISE 490 Senior Design Project (0-9-3)

Definition and history of Automation. Social and economic dimension of Automation. Types and principles of automation systems, the hierarchy of control and instrumentation, career opportunities and scope of control and instrumentation profession. Skills, ethics, and design process. Case studies: Analysis of representative automation systems in the process industry, manufacturing, management, home and transportation.

Binary arithmetic. Boolean Algebra. Boolean functions and their simplification. Implementation of Boolean functions using logical gates, SSI, MSI, and LSI chips. Analysis and Design of Combinational circuits. Sequential Logic: Flip-Flops, Counters, and Registers. Analysis and Design of sequential circuits, Programmable Logic Devices, FPGA/PLD hardware. Analysis and Design using CAD software. Interfacing of digital electronics to control and instrumentation elements, such as relays, 24-volt signals, analog switches, and proximity switches.

This course introduces the fundamentals of information technology and systems; their structure, and components. The course emphasizes the Enterprise applications of IT in improving the performance of business and industrial systems. In addition, the course introduces the current trends related to information technology, such as the Internet, Ecommerce, and wireless communication. The course also gives an insight into security and ethical issues related to information exchange.

Roots of nonlinear equations. Solutions of systems of linear algebraic equations. Numerical differentiation and integration. Interpolation. Extrapolation and approximation. Least squares approximation and regression analysis. Numerical solution of ordinary and partial differential equations. Introduction to error analysis. Engineering case studies.

Linear systems, Modeling of physical systems, Ordinary Differential equations models, Laplace Transform, transfer functions, block diagram manipulation. Open loop and closed loop systems, time domain analysis, response of systems to different test signals, Steady state analysis. Concept of stability, Routh-Hurwitz criteria, controller design. Laboratory activities include modeling, analysis and simulation of physical processes.

General measurement systems; SI units, errors in measurement systems, static and dynamic modeling of measurement systems, environmental effects, loading effects, noise in measurement systems, calibration, design of experiment, reliability of instrumentation systems. Typical measurement systems, displacement, velocity, and acceleration measurement. Pressure/Force measurement (capacitive, elastic, strain gauges, piezoelectric, electronic, weight scales, load cells). Temperature (elastic, expansion, resistive, thermocouple, IR, electronic). Analog signal conditioning and DC/AC bridges.

This is the first level of instrumentation and mechatronics. The course introduces the basic concepts of switching input and output devices, sensing devices, and how they are used in real life automation systems. The course is a
**Prerequisite** for the mechatronics course and for the advanced instrumentation course.

Basic models of continuous and discrete-time signals and systems. Basic characteristics of signals (energy, power, peak amplitude). Fourier analysis of continuous and discrete-time signals and systems. Basic concepts of signal sampling and reconstruction. Basic properties of Laplace and Z-transforms and concept of transfer function. Applications of signals and systems concepts to linear control systems and digital signal processing.

Transient and Steady State analysis and design specifications. Root locus, Design using Root locus. Frequency Response Techniques, Bode plot, Nyquist plot, principle of Specifications and controller Design in the Frequency domain. State-space model, analysis of the state-space model, Controllability and Observability, pole placement, and robust Control.

Elements of Computer Control Systems, A/D and D/A, Sampling theorem, signal conditioning, anti-alias filters, sensors, actuators. Discrete time systems, digital control design, digital PID control. Programmable logic controllers, computer control technology including distributed computer control, Fieldbus technology, and OLE for process control.

The Cooperative Work Program accounts for nine (9) credit hours, involves either a team-based or a single studentbased project that is geared toward an integrated application of several pieces of Systems Engineering knowledge learned by the student in his undergraduate education thus far. The co-op project must address technical aspects of the practice of Systems Engineering, including analysis, experimentation and design, by utilizing the problem-solving techniques covered in the various required (core) and elective courses offered at the Systems Engineering Department.

Improve students’ ability in presenting technical work and introduce them to the knowledge of contemporary issues in their field of studies. The course features students’ attendance to seminars, workshops, industrial visits, professional societal meetings, governmental agencies’ conferences. Each student is required to present a written evidence for attending each of an adequate number of seminars and industrial visits at the end of the semester.

Students spend eight weeks in the industry, and submit a report and a presentation at the end of his summer training work.

Mechatronics is the synergistic integration of mechanism, electronics, and computer control to achieve a functional system. Fundamentals of interfacing of modern mixed electrical, mechanical, and computers systems. Sensors, Signal Conditioning, Electro-Mechanical Actuation, Basic System Modeling, Essentials of Dynamic Systems, Data Acquisition and Virtual Instrumentation, and PC-Based and Embedded Controllers. Physical properties, mathematical modeling for computer simulation. Applications illustrated by numerically and experimentally generated results.

Basic features microcontrollers, organization & architectural Features of Microprocessor & microcontroller, Basic organization, high level and assembly language conversion to machine level instruction. Basic fetch & execute cycle of a program. Instruction Set, basic operations and addressing modes, Assembly language programming, fast prototyping using high level languages. Typical Bus structure, I/O Control & interfacing to digital systems, Interfacing to various power switching devices. Interfacing Protocols. Sensors, A/D & D/A Converters, Analog signal conditioning Circuits. Pulse Width Modulation. Applications to Industrial Automation.

Modeling of processes, Mass balance, and Energy balance, Models of representative processes, Dynamic response, and Linearization. Process identification using time and frequency domain techniques. Time delay, Smith predictor. Basic and advanced control strategies, e.g. PID, Feed forward, Internal model, and supervisory control. Time domain controller design, Controller tuning. Controller design in the frequency domain, Optimization Techniques and Supervisory Control. Case studies.

Review of the Fundamental laws, mathematical modeling; model and simulation of typical processes. Computer simulation tools, Virtual Instruments, MMI. Systems identification, IMC, Predictive control, DMC, Neural Network modeling and control. Students will work out simulation and control projects, using DYNSIM process dynamic simulation and SIMULINK, of typical processes, e.g., CSTR, Gas Surge Drum, Isothermal Chemical Reactor, Vaporizer, Binary Column, Heat Exchanger, etc.

The course offers an introductory material to advanced control strategies such as fuzzy and neural network based controllers. The need for model–free control, Linguistic based control, foundations of fuzzy set theory. Main approaches of fuzzy control, design issues, fundamental of neural networks, neural networks architecture, neural networks based controller design. Application examples.

The course introduces the concept of model predictive control (MPC), their importance in process industry, implementation issues and application examples. The course covers: model based predictive control, generalized MPC, constrained MPC, some commercial MPC, issues in implementation in industrial control systems and case studies.

Dynamical systems and their mathematical models, random variables and signals, The system identification procedure. Guiding principles behind least-squares parameter estimation, statistical properties of estimates. Identification of the transfer function of linear systems in continuous time. Models for discrete-time linear systems: FIR, AR, ARX, ARMA. Various methods for recursive estimation. Experiments for data acquisition and their design.

Industrial instrumentation: measurement techniques in industrial processes. Computer data acquisition. NC and CNC machine tools. Computer process interfacing and control. Feedback control systems. Group technology. Flexible manufacturing systems. Automated assembly. Industrial robots. Computer-aided inspection and testing. Automated factories. Case studies.

Need for, advantages and basic structure of DSP systems. Basic concepts of discrete-time signals and systems. ZTransform, discrete Fourier Transform (DFT) and frequency analysis of signals and systems. Efficient implementation of DFT: Fast Fourier Transform (FFT) algorithms. Implementation issues of discrete-time systems. Digital filter design techniques. Applications of DSP systems.

Condition-based maintenance process. Data collection and Analysis process. Decision making. Condition-based monitoring components sensors and software programs. CMMS. Hazard and reliability functions. Models for CBM. Reliability improvement. Integration of CBM into the control design and operation. Engineering case studies.

Review of DC motors, optical encoders, precision control of DC motors, Stepper motors, control of stepper motors, micro-step control, gearboxes, belts, motor torque and power sizing, programming motion using G-code. Basic structure and functions of milling machines and lathes. Motion simulation, CAD/CAM system. Robot arms construction, analysis, and motion programming. Case study of retrofitting conventional machines with Computer Numerical Control.

Hierarchy of plant communication systems, field equipment, DCS systems, SCADA systems, Supervisory control and production control, Man-Machine Interface (MMI). Local area networks, OSI network architectures, serial communications, IEEE 802.xx standards, Local area networks for industrial applications, Field buses, Hart protocol, Foundation Field Bus, Profibus, CAN bus, etc. Smart instruments. Examples of industrial DCS systems.

Signal conditioning: 4-20 mA circuits, E/I transducers, bridges (AC and DC), design of bridges, operational amplifier circuits, filters (LP & HP), power supplies, reference voltages, analog multiplexer/ de-multiplexers. Data acquisition systems, SCADA Systems, interface cards, isolations, intrinsic safety, Nondestructive testing. LABVIEW, virtual instrumentation, Visual programming, and HMI, Plant network hierarchy, DCS, Data communications, smart transmitters, Field buses, and OPC. Process and Instrumentation diagrams.

A course in an area of instrumentation reflecting current theory and practice.

Review of state variable models, Review of basic matrix algebra, Static optimization, Formulation of optimal control problems, Principle of optimality. The linear quadratic regulator problem, properties of the algebraic Riccati equation (ARE) The minimum principle and time optimal control problems. Output feedback design. Homework assignments include design and simulation using MATLAB or other similar software packages.

Probability, Random Variables and distributions, correlation, MA, AR, and ARMA systems, power spectrum, Spectral factorization, Weiner-Hopf filter. Stochastic control systems, Minimum variance control, State-variable forms, Kalman filter, LQG feedback systems. Cases studies from published work.

This course introduces the concepts of uncertainty and Modeling Error in Control System Analysis and Design. Review the basic methods and tools of Classical Control. Robust stabilization, Loop shaping, Introduction to H? Optimal Control Analysis and Synthesis. Design examples.

A course in an area of control reflecting current theory and practice.

Basics of anatomy and biological science. Fundamentals of engineering applications in biomedicine. Biomedical instrumentation and information technology, control and communication in biomedicine. eHealth and telemedicine.

Review of basic Probability, Statistical Independence, Conditional Expectation and Characteristic Function. Introduction to Stochastic Processes, Stationarity and Ergodicity. Markov Chains and Poisson Processes. Linear Models of Continuous and Discrete Stochastic Processes. Engineering Applications.

An overview of large-scale problems and the framework for Systems Engineering. Graphic tools for Systems Engineering. Interaction matrices and graphs, interpretive structure modeling. Spare matrix and decomposition techniques. Model reduction techniques. Case studies.

High volume discrete parts production systems. Fundamentals of CAD/CAM. Computers in manufacturing. Computer process monitoring. Systems for manufacturing support. Group technology and integrated manufacturing systems. Case studies for robots in industry. CAD/CAM using computer graphics laboratory.

Micro-machined sensors, Fiber optical sensors, Gas chromatography, Gas detectors, Environment monitoring systems, NMR, Soft-sensing techniques.

DCS systems, Intrinsic safety, Emergency shutdown ESD systems, reliability of instruments and control systems, MTBF, Redundant systems, Safety standards,. Classification of industrial process, Safety integrity levels (SIL), Quantitative risk assessment (QRA), Safety and control networks, Fieldbus for safety systems, Cost benefit analysis, Best practices.

The course introduces the students to the latest trends in industrial communications systems in a practical theme. The course starts by previewing the main topics in communications systems such as modulation and coding. The course then covers the main communication network standards used in industry. The course covers mainly all data layers from the field instruments to the TCP/IP and world-wide web and even latest wireless data exchange techniques. Case studies of industrial DCS and CIM and their integration with the enterprise networks.

A course in an area of automation reflecting current theory and practice.

A design course that draws upon various components of the undergraduate curriculum. The project typically contains problem definition, analysis, evaluation and selection of alternatives. Real life applications are emphasized where appropriate constraints are considered. Oral presentation and a report are essential for course completion. The work should be supervised by faculty member(s). Team projects are acceptable wherever appropriate.