This course covers the principles, technologies, methods and applications of biosensors and bioinstrumentation beginning with an examination of the ethical, legal, cultural, religious, and social implications of nanotechnologies. The objective of this course is to link engineering principles to understanding of biosystems in sensors and bioelectronics. The course provides students with detail of methods and procedures used in the design, fabrication, and application of biosensors and bioelectronic devices. The fundamentals of measurement science are applied to optical, electrochemical, mass, and pressure signal transduction. Upon successful completion of this course, students are expected to be able to explain biosensing and transducing techniques; design and construct biosensors instrumentation.
This course focuses on fundamentals of electronics theory and design. The topics covered include semiconductor physics, diodes, diode circuits such as limiters, clamps; bipolar junction transistors; small-signal models; cut-off, saturation, and active regions; common emitter, common base and emitter-follower amplifier configurations; field-effect transistors (MOSFET and JFET); biasing; small-signal models; common-source and common gate amplifiers; and integrated circuit MOS amplifiers. The laboratory experiments include the design, building and testing of diode circuits, including rectifiers, BJT biasing, large signal operation and FET characteristics, providing hands-on experience of design, theory and applications, with emphasis on small signal analysis and amplifier design. The course also covers the design and analysis of small-signal bipolar junction transistor and field-effect transistor amplifiers; and, diode circuits. The students are introduced to designing and analyzing circuits using the LTPSpice or Cadence simulation tool.
This course introduces students to the fundamentals of data analysis. The course starts with tools used to summarize and visualize data. The focus then shifts to fitting and parameter estimation. The derivation of estimators of parameters using both maximum likelihood and least-squares techniques are covered. Analysis of the statistical properties of estimators is also covered. The course includes hands-on exercises using MATLAB.
This course builds on the foundations of the Circuits Fundamentals Course. The topics covered include sinusoidal steady-state response, complex voltage, current and the phasor concept; impedance, admittance; average, apparent and reactive power; polyphase circuits; node and mesh analysis for AC circuits; frequency response; parallel and series resonance; and, operational amplifier circuits.
This course covers topics related to security and trustworthiness of electronic hardware. Lectures and in-class discussions on recent research papers cover the following topics: Trustworthiness of integrated circuits; counterfeit chips, hardware Trojans, reverse engineering and IP piracy. Design-for-Trust; hardware metering, logic encryption, split manufacturing, IC camouflaging. Encryption hardware; AES, DES, etc. Testability vs Security; misuse of test infrastructure to attack encryption hardware and countermeasures. Encrypted architectures; homomorphic encryption, privacy-preserving computation. Signal processing in the encrypted domain. Malware detection through hardware structures, side channel attacks, cyber-security for the smart grid. Lectures are complemented by hands-on lab exercises.
An important goal of artificial intelligence (AI) is to equip computers with the capability of interpreting visual inputs. Computer vision is an area in AI that deals with the construction of explicit, meaningful descriptions of physical objects from images. It includes the techniques for image processing, pattern recognition, geometric modeling, and cognitive processing. This course introduces students to the fundamental concepts and techniques used in computer vision, which includes image representation, image pre-processing, edge detection, image segmentation, object recognition and detection, and neural networks and deep learning. In addition to learning about the most effective machine learning techniques, students will gain the practical implementation of applying these techniques to real engineering problems.
This course introduces students to the basic concepts of thermodynamics and their applications to engineering problems. The following topics are covered in this course: properties of pure substances; concepts of work and heat; closed and open systems; the fundamental laws of thermodynamics; Carnot and Clausius statements of the 2nd law; entropy and entropy production; heat engines, refrigerators, heat pumps; efficiencies, coefficients of performance.
The course introduces students to fundamental concepts that underlie highway design, traffic operations and control, and transportation systems. The course begins with vehicle performance and the role it has on road design. We later cover the fundamentals of traffic flow theory and operations. In combination with such fundamentals we also discuss the use and collection of traffic data, as well as more advanced concepts on traffic safety, public transportation, and traffic management and control. Moreover, we look at clear applications of the concepts covered in class with a real-world student led project.
The course introduces the principles of computer organization and basic architecture concepts. It discusses the basic structure of a digital computer and study in details formal descriptions, machine instruction sets design, formats and data representation, addressing structures, mechanization of procedure calls, memory management, arithmetic and logical unit, virtual and cache memory organization, I/O processing and interrupts, fundamental of reliability aspects. The course also covers performance and distributed system models. The labs emphasize experiential learning of computer organization and architecture concepts, and require students to use learned knowledge to create and build prototypes and evaluate their performance.
This course covers the principles, technologies, methods and applications of biosensors and bioinstrumentation beginning with an examination of the ethical, legal, cultural, religious, and social implications of nanotechnologies. The objective of this course is to link engineering principles to understanding of biosystems in sensors and bioelectronics. The course provides students with detail of methods and procedures used in the design, fabrication, and application of biosensors and bioelectronic devices. The fundamentals of measurement science are applied to optical, electrochemical, mass, and pressure signal transduction. Upon successful completion of this course, students are expected to be able to explain biosensing and transducing techniques; design and construct biosensors instrumentation.
This course introduces students to the field of mechanics through study of rigid bodies in static equilibrium. Knowledge and understanding of static equilibrium is essential for future study of topics as diverse as dynamics, solid mechanics, structures, robotics, and fluid mechanics. The methods, techniques, theory, and application of equilibrium in the solution of engineering problems are presented for two-dimensional systems. Topics covered include collinear forces, coincident forces, general equilibrium, moments and torques, analysis of trusses, frames and machines, Coulomb friction, centroid, center of mass, and moments of inertia.
Students learn about the process of design with measurable metrics, and how to incorporate appropriate engineering standards and multiple realistic constraints in the design process. Students learn how to clearly frame the design problem and follow the design process to result in an optimized solution. Students perform a review of the relevant literature, develop a preliminary design, generate solution concepts and selection criteria, and review and evaluate the chosen design. Students must consider social, economic, lifecycle, environmental, ethical, and other constraints, and must document the design process and the evolution of their design. This project culminates with a final report and presentation that proposes the actual design selected for further development and/or prototyping and testing in the subsequent semester.
The objective of the course is for students to acquire the fundamental knowledge of computer programming, develop transferable programming skills, and learn to solve engineering problems via programming. The course is primarily based on the C programming language and an introduction to another programming language such as MATLAB (to demonstrate transferring programming knowledge from one language to another). The course explores the application of engineering computation in various engineering domains including mechanical, civil, computer, and electrical engineering. The following topics are covered: introduction to computer systems, standard input/output, file input/output, decision structures, loop structures, functions, arrays, addressing, dynamically allocated memory, structures, introduction to object oriented programming, problem solving via programming algorithm design, and applications in another programming language such as MATLAB.
Machine Learning is the basis for the most exciting careers in data analysis today. This course introduces students to the concepts of machine learning and deep learning. This course covers a broad introduction to machine learning techniques, which include both supervised learning and unsupervised learning techniques such as classification, support vector machines, decision trees, ensemble learning and random forests, dimensionality reduction, and neural networks and deep learning. In addition to learning about the most effective machine learning techniques, you will gain the practical implementation of applying these techniques to real engineering problems.
This introductory course to Bioimaging is designed to provide an understanding on how images of organs, tissues, cells and molecules can be obtained using different forms of penetrating radiation and waves. Students will learn the imaging techniques used for soft and hard tissue visualization such as X-ray, Computed Tomography (CT), Ultrasound (US), Magnetic Resonance Imaging (MRI), Spectroscopy and Optical Imaging. The course will give students an insight into the theoretical physics of imaging, real-life clinical applications of these modalities and demonstration of post-processing of the images using high-level programming.
This module provides an introduction to electrical circuits. The topics covered include DC circuits, passive DC circuit elements, Kirchoff’s laws, electric power calculations, analysis of DC circuits, nodal and loop analysis techniques, voltage and current division, Thevenin’s and Norton’s theorems, and source free and forced responses of RL, RC and RLC circuits.
This course provides an introduction to computer-aided design (CAD) using solid modeling. Students learn to create solid object models using extrusions, revolutions, and swept paths, and learn to modify parts using cutting, patterns, fillets, chamfers, and other techniques. Assemblies of multiple parts are used to demonstrate the need for geometric tolerances, and students spend a large portion of class in hands-on use of software tools. The labs emphasize experiential learning of CAD concepts and applications using software tools.
The course introduces the principles of dynamic system modeling, analysis, and feedback control design with extensive, hands-on computer simulation. Modeling and analysis of dynamic systems. Description of interconnected systems via transfer functions and block/signal-flow diagrams. System response characterization as transient and steady-state responses and error considerations. Stability of dynamical systems: Routh-Hurwitz criterion. Controller design using root-locus and Bode-diagrams (frequency domain). Introduction to modern state-space controller designs. Computer-aided feedback control design for mechanical, aerospace, robotic, thermo-fluid, and other electrical systems.
Introductory course in probability and statistics with an emphasis on how these topics are relevant in engineering disciplines. Topics in probability theory include sample spaces, and counting, random variables (discrete and continuous), probability distributions, cumulative density functions, rules and theorems of probability, expectation, and variance. Topics in statistics include sampling, central limit theorem, and linear regression. The course emphasizes correct application of probability and statistics and highlights the limitations of each method presented. NOTE: This course may be replaced with MATH-UH 1003Q or MATH-UH 2011Q
This course provides an introduction to the methods, techniques, theory, and application of numerical methods in the solution of engineering problems. Topics to be covered include the following: finding roots of equations, numerical differentiation and integration, time marching methods in solving ordinary differential equations, and optimization. MATLAB software is the primary computing environment.
This intermediate-level programming course focuses on object oriented programming using C . Classes and objects including constructors, destructors, member functions and data members. Topics in this course include data representation, pointers, dynamic memory allocation and recursion, inheritance and templates, polymorphism, the process of compiling and linking using makefiles, memory management, exceptional control flow, introduction to performance evaluation, and optimization.
Conservation laws play a fundamental role in the analysis of engineering problems by providing a framework to derive the relationships between various physical properties of isolated systems. This course aims to introduce the students to these laws, namely, the conservation of mass, conservation of linear momentum, conservation of angular momentum, conservation of energy, and conservation of charge. These laws of conservation will be derived in integral forms and applied to selected case studies involving electrical, chemical, thermal, and fluid mechanical systems. In addition to the development of a unified framework for analysis of engineering problems, this course will also help the students develop a deeper understanding of the concepts of control volume and mass, work and heat, fluid pressure and hydrostatics, properties of pure substances, and the fundamental laws of thermodynamics.
This module provides a rigorous introduction to topics in digital logic design mostly focusing on combinational circuits but also touching upon basic concepts in sequential circuits. Introductory topics include: classification of digital systems, number systems and binary arithmetic, error detection and correction, and switching algebra. Combinational design analysis and synthesis topics include: logic function optimization, arithmetic units such as adders and subtractors, and control units such as decoders and multiplexers. A brief overview of sequential circuits by introducing basic memory elements such as flip-flops, and state diagrams concludes the module.
The course focuses on theory of measurement systems, selected electrical circuits and components for measurement, including passive and active filtering for signal conditioning, dynamic measurement system response characteristics, analog signal processing, analog to digital conversion, data acquisition, sensors, actuators and actuator characteristics. The laboratory involves topics related to the design of measurement systems pertaining to all disciplines of engineering such as data acquisition, operational amplifiers, sensors for the measurement of force, vibration, temperature etc. In addition, actuators will also be introduced, including electric motors and pneumatics. Design of virtual instrumentation systems using LabVIEW is also included.
ENGR-UH 3110-000 (17389)08/29/2023 – 12/15/2023 Mon2:00 PM – 4:00 PM (Early afternoon)at Abu DhabiInstructed by
A site for IMA NY Students to find equivalent courses outside of IMA NY
For most students joining IMA in Fall 2022 and beyond, there is a new program structure that affects the categorization of courses on this site:
Any class in any IMA major elective category (ie "Art & Design") refers to the IMA program structure previous to those entering in Fall 2022. If you are in the class of 2026 (most entering Fall 2022 or later), any course in an IMA elective category are generic IMA electives in the new structure.