Op-amps And Linear Integrated Circuits 4th Edition Pdf

[Kemal Allan]

Thistext covers the theory and application of operational amplifiers and other linear integrated circuits. It is appropriate for Associate and Bachelors degrees programs in Electrical and Electronic Engineering Technology, Electrical Engineering and similar areas of study. Topics include negative feedback, comparators, voltage amplifiers, summing and differencing amplifiers, high speed and high power devices, non-linear circuit applications, regulators, oscillators, integrators and differentiators, active filters and AD/DA conversion. A companion laboratory manual is available.

The companion laboratory manual features 22 separate exercises. It covers the theory and application of operational amplifiers and other linear integrated circuits. Exercises include discrete differential amplifier analysis; inverting, non-inverting and differential configurations; frequency response; slew rate; DC offset; OTA; oscillators; linear regulator; function synthesis; active filters; and integrators and differentiators.

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Weekly seminar featuring research primarily from within the Department of Electrical and Computer Engineering. Speakers include senior PhD students and postdocs as well as faculty from within the department. Provides students with an opportunity to connect with the broader research community in electrical and computer engineering. Credit/no-credit only.

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E E 233 Circuit Theory (5)

Electric circuit theory. Analysis of circuits with sinusoidal signals. Phasors, system functions, and complex frequency. Frequency response. Computer analysis of electrical circuits. Power and energy. Two port network theory. Laboratory in basic electrical engineering topics. Prerequisite: E E 215.

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E E 280 Exploring Devices (4)

Overview of modern electronic and photonic devices underlying modern electronic products including smartphones, traffic lights, lasers, solar cells, personal computers, and chargers. Introduction to modeling and principles of physics relevant to the analysis of electrical and optical/photonic devices. Prerequisite: either PHYS 122 or PHYS 142; recommended: either Python programming or Matlab; and Linux. Offered: AWSp.

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E E 331 Devices and Circuits I (5)

Physics, characteristics, applications, analysis, and design of circuits using semiconductor diodes and field-effect transistors with an emphasis on large-signal behavior and digital logic circuits. Classroom concepts are reinforced through laboratory experiments and design exercises. Prerequisite: 1.0 in E E 233.

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E E 332 Devices and Circuits II (5)

Characteristics of bipolar transistors, large- and small- signal models for bipolar and field effect transistors, linear circuit applications, including low and high frequency analysis of differential amplifiers, current sources, gain stages and output stages, internal circuitry of op-amps, op-amp configurations, op-amp stability and compensation. Weekly laboratory. Prerequisite: 1.0 in E E 331.

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E E 342 Signals, Systems, and Data II (4)

Review of basic signal processing concepts. Two-sided Laplace and z -transforms and connection to Fourier transforms. Modulation, sampling and the fast Fourier transform. Short-time Fourier transform. Multi-rate signal processing. Applications including inference and machine learning. Computer laboratory. Prerequisite: E E 242.

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Practical skills for day-to-day engineering communication as well as an advanced exploration of how to prepare persuasive documents and presentations for technical and non-technical audiences.

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E E 417 Modern Wireless Communications (4)

Introduction to wireless networks as an application of basic communication theorems. Examines modulation techniques for digital communications, signal space, optimum receiver design, error performance, error control coding for high reliability, mulitpath fading and its effects, RF link budget analysis, WiFi and Wimax systems. Prerequisite: E E 416

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E E 436 Medical Instrumentation (4)

Introductory course in the application of instrumentation to medicine. Topics include transducers, signal-conditioning amplifiers, electrodes and electrochemistry, ultrasound systems, electrical safety, and the design of clinical electronics. Laboratory included. For upper-division and first-year graduate students preparing for careers in bioengineering - both research and industrial. Prerequisite: E E 332.

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E E 437 Integrated Systems Capstone (5)

Team-based design experience to develop integrated electronic systems by constructing and validating, prototype integrated circuits (IC) and sensors using modern Computer Aided Design (CAD) tools. Systems are simulated using modern semiconductor, MEMs and nanophotonic technologies. Teams define requirements; investigate tradeoffs in performance, cost, power and size; design for both reliability and testability. Prerequisite: E E 331 and E E 473.

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E E 438 Instrumentation Design Project Capstone (5)

Team-based design for developing an electronic instrumentation system and constructing and validating a prototype using modern printed circuit board technology. Teams develop design requirements; investigate tradeoffs for miniaturization, integration, performance, and cost; and consider use cases, failure modes, manufacturability, and testability. Includes extensive laboratory. Prerequisite: either E E 433 or E E 436.

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E E 442 Digital Signals and Filtering (3)

Methods and techniques for digital signal processing. Review of sampling theorems, A/D and D/A converters. Demodulation by quadrature sampling. Z-transform methods, system functions, linear shift-invariant systems, difference equations. Signal flow graphs for digital networks, canonical forms. Design of digital filters, practical considerations, IIR and FIR filters. Digital Fourier transforms and FFT techniques. Prerequisite: a minimum grade of 1.0 in either E E 341 or E E 342.

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E E 443 Machine Learning for Signal Processing Applications (4)

Application of machine learning and deep learning algorithms to real-world signal, image, and video processing problems using cloud computing with central, graphics, and tensor processing units (CPU/GPU/TPU). Characteristics of multi-dimensional signals and systems. Unsupervised and supervised learning. Deep learning convolutional neural networks. Generative adversarial learning. Open long-tailed recognition. Object detection and segmentation. Prerequisite: a minimum grade of 1.0 in E E 242; MATH 136 or MATH 208; and either IND E 315, MATH 394/STAT 394, or STAT 390.

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E E 448 Systems, Controls, and Robotics Capstone (4-)

In-depth control engineering design experience in small design teams. Includes project planning and management, reporting, and technical communication. Student teams design, implement, test, and report on their project results, Includes lectures on selected topics, e.g., project management, intellectual property, and some control engineering topics. Prerequisite: E E 447.

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