Electrical Engineering (EE) Undergraduate Courses (2015-16)
Ph/APh/EE/BE 118 ab.
Physics of Measurement.
9 units (3-0-6):
first and second terms.
Prerequisites: Ph127, APh 105, or equivalent, or permission from instructor.
This course focuses on exploring the fundamental underpinnings of experimental measurements from the perspectives of responsivity, noise, backaction, and information. Its overarching goal is to enable students to critically evaluate real measurement systems, and to determine the ultimate fundamental and practical limits to information that can be extracted from them. Topics will include physical signal transduction and responsivity, fundamental noise processes, modulation, frequency conversion, synchronous detection, signal-sampling techniques, digitization, signal transforms, spectral analyses, and correlations. The first term will cover the essential fundamental underpinnings, while topics in second term will include examples from optical methods, high-frequency and fast temporal measurements, biological interfaces, signal transduction, biosensing, and measurements at the quantum limit.
Instructor: Roukes.
Ph/APh/EE/BE 118 c.
Physics of Measurement.
9 units (3-0-6):
third terms.
Prerequisites: Ph127, APh 105, or equivalent, or permission from instructor.
Ph118c will focus on the physical principles and applications of several important measurement techniques to modern condensed matter physics research. The course will begin with an introduction of the concept of self-energy and Green function techniques in the descriptions of many-body interactions. Several representative experimental techniques for investigating important physical properties of many-body systems will be discussed, followed by explicit examples for their applications to condensed matter physics research. The measurement techniques will include scanning probe microscopy, angle-resolved photoemission spectroscopy, optical measurements, and thermodynamic and electrical transport measurements.
Instructor: Yeh.
EE/Ma/CS 126 ab.
Information Theory.
9 units (3-0-6):
first, second terms.
Prerequisites: Ma 2.
Shannon's mathematical theory of communication, 1948-present. Entropy, relative entropy, and mutual information for discrete and continuous random variables. Shannon's source and channel coding theorems. Mathematical models for information sources and communication channels, including memoryless, first- order Markov, ergodic, and Gaussian. Calculation of capacity and rate-distortion functions. Kolmogorov complexity and universal source codes. Side information in source coding and communications. Network information theory, including multiuser data compression, multiple access channels, broadcast channels, and multiterminal networks. Discussion of philosophical and practical implications of the theory. This course, when combined with EE 112, EE/Ma/CS 127, EE 161, and/or EE 167 should prepare the student for research in information theory, coding theory, wireless communications, and/or data compression.
Instructor: Effros.
EE/Ma/CS 127.
Error-Correcting Codes.
9 units (3-0-6):
second term.
Prerequisites: Ma 2.
This course develops from first principles the theory and practical implementation of the most important techniques for combating errors in digital transmission or storage systems. Topics include algebraic block codes, e.g., Hamming, BCH, Reed-Solomon (including a self-contained introduction to the theory of finite fields); and the modern theory of sparse graph codes with iterative decoding, e.g. LDPC codes, turbo codes, fountain coding. Emphasis will be placed on the associated encoding and decoding algorithms, and students will be asked to demonstrate their understanding with a software project.
Instructor: Kostina.
CS/EE/Ma 129 abc.
Information and Complexity.
9 units (3-0-6), first and second terms:
(1-4-4) third term.
Prerequisites: basic knowledge of probability and discrete mathematics.
A basic course in information theory and computational complexity with emphasis on fundamental concepts and tools that equip the student for research and provide a foundation for pattern recognition and learning theory. First term: what information is and what computation is; entropy, source coding, Turing machines, uncomputability. Second term: topics in information and complexity; Kolmogorov complexity, channel coding, circuit complexity, NP-completeness. Third term: theoretical and experimental projects on current research topics. Not offered 2015-16.