The Cooper Union for the Advancement of Science and Art · Courses
ECE
33 courses with the subject ECE, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
ECE 150Digital Logic Design3
Theoretical and practical issues concerning design with combinational and sequential logic circuits, and programmable logic devices. Number systems, Boolean algebra, representation and simplification of Boolean functions, universal logic families. Finite-state machines, state tables and state diagrams, flip-flops, counters, registers. Adders, decoders, comparators, mltiplexers, memories and applications. Programmable devices: PLA, PLD, etc. Principles of analog circuits are presented in the context of real world problems, such as ‘glitches,’ power and ground bounce, contact bounce, tri-state logic and bus interfacing, timing circuits, asynchronous versus synchronous circuit components. Characterization of electronic and logical properties of digital circuits. Course work involves individual and team projects in which: digital circuits are designed and prototypes are constructed and tested on breadboards; designs involving programmable logic devices are developed using CAD tools. The projects, approximately 50 percent of the course grade, are used to assess technical writing, oral presentation, teamwork and project management skills. Open to all students. Credits: 3 199
Circuit components, dependent and independent sources, Kirchhoff’s laws, loop and nodal analysis. Superposition, Thevenin and Norton equivalent circuits, and other techniques for circuit simplification. Time-domain analysis of RLC circuits, initial conditions, transient response and steady-state. Phasor analysis, complex power. Ideal op-amps. Prerequisite: Ma 113. Ma 240 is a suggested corequisite. Credits: 3
An introduction to fundamental data structures and algorithms, with an emphasis on practical implementation issues and good programming methodology. Topics include lists, stacks, queues, trees, hash tables and sorting algorithms. Also an introduction to analysis of algorithms with big-O notation. Assignments include programming projects and problem sets.
This course focuses on one particular complex system (e.g., music synthesizer, wireless transceiver, radar) to introduce a wide range of electrical engineering principles such as frequency response, noise, feedback, loading and interfacing. In a laboratory setting, students investigate the design of subsystems that may include amplifiers, oscillators, RF or opto-electronic circuits, A/D and D/A converters, and power circuits. By measuring the impact of the operating conditions on circuit performance, students learn the principles of systems engineering, development of a testbench, and proper documentation. By the end of the semester, the class will have developed a complete functioning system through reverse engineering. Prerequisite or corequisite: ECE150. Corequisite: ECE240.
Information theory: entropy, information, channel capacity, rate-distortion functions, theoretical limits to data transmission and compression. Error control coding: block, cyclic and convolutional codes, Viterbi algorithm. Baseband and bandpass signals, signal constellations, noise and channel models. Analog and digital modulation formats (amplitude, phase and frequency), MAP and ML receivers, ISI and equalization. Coherent and non- coherent detection, carrier recovery and synchronization. Performance: computation of SNR, BER, power and bandwidth requirements. Multiple access schemes. OFDM. Prerequisites: Ma 224 and ECE 211. Credits: 3 200
Review of Laplace and z-transforms. Minimum-phase and all-pass functions. Multidimensional signals, systems and Fourier analysis. Analog filter design, digital IIR and FIR filter design. Sampling, multirate systems and filterbanks, A/D and D/A converter models. Discrete-time state-space. Filter structures, quantization effects and design to mitigate quantization effects. DFT and FFT. Spectral analysis of deterministic and random signals. Introduction to adaptive filters. Differential coding, transform coding. Speech, audio and video signals. Extensive use of MATLAB.
Development methodologies for signal processing hardware systems: RTL, HDL, synthesis and verification. Special processors including FPGA, multicore, ARM and GPU. ADC and DAC, interchip and intrachip communication, mixed-signal systems, clock and power distribution, loading, sensors and actuators, embedded systems. PCB and surface mount devices. Systems engineering. Course work including projects involving hardware realizations, simulation and emulation, and software tools for system design. Prerequisite: ECE 211, ECE 241, ECE 251. Credits: 3
MOS circuits: DC operation and analysis. Single stage MOS amplifiers, circuit design, DC and small signal analysis. Cascode amplifier. Current mirrors, active loads. BJT and MOS differential amplifiers. Monolithic operational amplifiers. Output stages. Frequency response. Introduction to feedback theory, amplifier topologies. Circuit design and analysis are supplemented with industry standard CAD software.
A continuation of ECE 264, also with an emphasis on practical implementation issues and good programming methodology. Topics include graphs, graph-related algorithms and dynamic programming techniques. Also an introduction to some advanced topics such as Turing machines, computability and NP-complete systems. Assignments include programming projects and problem sets. Prerequisite: ECE 264. Credits: 2
ECE 395 and ECE 396 constitute the year-long senior design project. Students work in small groups on projects chosen with the advice and consent of the faculty adviser. Projects may be oriented towards research or product development, and may be in any area of electrical and computer engineering, such as in: computer engineering, signal processing (imaging, sensor arrays, multimedia), telecommunications, computer networks, microwaves, optics, advanced electronics, VLSI chip design, or an interdis- ciplinary area such as robotics or bioengineering. Students perform all aspects of project management, such as scheduling, budgeting, system design and developing milestones, as well as technical work including hardware and software implementation, testing and performance evaluation. Students also give several spontaneous and rehearsed oral presentations and prepare written reports. Students attend weekly lectures covering: social, economic, legal and ethical issues; safety and laboratory practice; design methodologies; technical writing; preparation of multimedia presentations and tailoring presentations to target audiences.
In addition to the courses listed above, the following courses are taken by all students, regardless of the chosen track. These courses (with the exception of the general studies electives noted below) should be completed during the first two years of study: Basic Math, Science & Engineering Courses (32.5 credits)
ECE 399Selected Topics in Electrical & Computer Engineering1-3
Subjects may include seminars on topics related to advances in technology, current research areas. Also individual research, design and development or study of subjects in electrical and computer engineering.
This course will provide an introduction to optical propagation and diffraction using a scalar wave approach and Fourier theory of imaging. Topics introduced will include pupil function, point spread function and line spread function, optical transfer function, image formation with coherent and incoherent light, holography and diffractive optical elements. Requisites: Complete Ph 214, Ma 223, Ma 224, and ECE 211 or ME 251. Must be completed prior to taking this course.
This course focuses on the top-down, automated digital system design flow using CMOS logic: RTL design/simulation, timing/power driven circuit synthesis, automated place-and-route, and post-layout simulation with emphasis on test/manufacturability in deep sub-micron technologies. The course culminates with the tape-out of a large design project covering functional specification to sign-off layout. 203
This course focuses on computer applications that involve the processing of written or spoken human languages. The exact content may vary from year to year. The course is divided into three parts. Topics from conventional, statistical natural language processing will likely include text normalization, N-grams, part-of-speech tagging, information retrieval, and text categorization. Topics from conventional computational linguistics will likely include grammars, parsing, and semantic representations. Topics from deep learning and NLP will likely include word embeddings, feed-forward neural networks, recurrent neural networks, sequence-to-sequence models, attention, and transformers. Course work will include programming projects and quizzes.
Differentiable directed acyclic graphs covering applications in unsupervised learning, as well as generative and discriminative modeling. Gradient-based methods for optimization (stochastic gradient descent, Nesterov momentum, adam). Fast gradient computation for arbitrary computational graphs (automatic differentiation). Exploding and vanishing gradient problems. Convolutional networks. Arbitrary graphs for regression, classification and ranking. Autoencoders, adversarial networks and variations for unsupervised representation learning, generative modeling and other applications. Focus on applications in computer vision, speech processing and research problems in communication theory.
MA223, MA224 and either ECE211, ChE352 or ME251. Credits: 3
ECE 474Bayesian Machine Learning
Machine learning from a primarily Bayesian perspective. Conjugate priors. Bayesian linear regression, model evidence, linear classification using generative models, logistic regression and the Laplace approximation. Kernel methods and Gaussian process regression. Mixture models, expectation maximization, hidden Markov models, sampling methods and Markov chain Monte Carlo. 204
