Signal Exams 2023 Class 8 Pdf Download [REPACK]

To provide a Tactical Communications Systems management exercises designed to demonstrate knowledge of the BCT/BN Signal-DMG in the installation, planning and management of BCT/BN signal communications. Students will apply network planning and managing skills acquired in previous instruction in a training lab environment. Scenario-based practical exercises will enable students to demonstrate their understanding installation and management of Signal Support, Data Support, MCIS Support, BCCS Support, COMSEC, Internetwork Connectivity, Restoration of Communication Services and Network Operations.

The Signal Digital Master Gunner (S-DMG) Course is 5 weeks in duration and is programmed to train 90 students annually (7) iterations with optimum class size of 12 students. The S-DMG Course prepares Signal Non-commissioned Officers with standardized instructions required for assignment as a Signal Digital Master Gunner in a Brigade Combat Team (BCT). This course is structured in modules and lessons. The S-DMG Course consists of seven modules that train students in tasks determined by the Signal Critical Task Site Selection Board (CTSSB) and training directed by TRADOC's Combined Arms Center (CAC). The seven modules are: Signal Digital Master Gunner (S-DMG) Introduction (Module A), Warfighter formation Network-Tactical (WIN-T) (Module B), Integrated Tactical Networking Environment (ITNE) (Module C), Battle Command Common Services (BCCS) System Administration (Module D), Mission Command Information Systems (MCIS) (Module E), Capstone (Module F), and the Administrative (Module G).

Signal Exams 2023 Class 8 Pdf Download

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Introduction to discrete-time systems and discrete-time signal processing: discrete-time linear systems, difference equations, z-transform, discrete convolution, stability, discrete-time Fourier transform, analog-to-digital and digital-to-analog conversion, interpolation and decimation, digital filter design, discrete Fourier transform, fast Fourier transform, spectral analysis, applications of digital signal processing.

E E 201 Computer Hardware Skills (1) RSN

An exclusively lab-based class focused on basic hands-on skills for electrical and computer engineers. Topics include soldering, PCB layout, basic microcontroller coding, 3D printing, use of basic test and measurement equipment, file management and version control. Prerequisite: CSE 122, CSE 123, CSE 142, or CSE 143, any of which may be taken concurrently

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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 241 Programming for Signal and Information Processing Applications (2)

Introduction to programming for signal and information processing. Basic syntax and data types. Packages for data manipulation and visualization. Handling a variety of data formats. Prerequisite: either CSE 122, CSE 123, CSE 142, CSE 143, or CSE 160.

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E E 242 Signals, Systems, and Data I (5)

Introduction to signal processing, including both continuous- and discrete-time signals and systems. Basic signals including impulses, unit steps, periodic signals and complex exponentials. Convolution of signals. Fourier series and transforms. Linear, time-invariant filters. Computer laboratory. Prerequisite: either MATH 135, MATH 207, or AMATH 351, any of which may be taken concurrently; and either E E 241, which may be taken concurrently, or CSE 163.

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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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E E 371 Design of Digital Circuits and Systems (5)

Provides a theoretical background in, and practical experience with, tools, and techniques for modeling complex digital systems with the Verilog hardware description language, maintaining signal integrity, managing power consumption, and ensuring robust intra- and inter-system communication. Prerequisite: either E E 205 or E E 215; either E E 271 or CSE 369. Offered: jointly with CSE 371.

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E E 391 Probability for Information and Communication Engineering (4)

Introduces probabilistic concepts for Electrical and Computer Engineering majors with applications to information/data science, signal processing, and communication systems. Includes accompanying Python labs that apply probabilistic concepts to these application domains. Prerequisite: E E 235 or E E 241; and MATH 126 or MATH 136.

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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 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 445 Fundamentals of Optimization and Machine Learning (4)

Introduction to optimization and machine learning models motivated by their application in areas including statistics, decision-making and control, and communication and signal processing. Topics include convex sets and functions, convex optimization problems and properties, convex modeling, duality, linear and quadratic programming, with emphasis on usage in machine learning problems including regularized linear regression and classification. Prerequisite: either MATH 224 or MATH 324; either MATH 136, MATH 208, MATH 308, or AMATH 352; and either E E 235, E E 242, or CSE 163.

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E E 460 Neural Engineering (3)

Introduces the field of Neural Engineering: overview of neurobiology, recording and stimulating the nervous system, signal processing, machine learning, powering and communicating with neural devices, invasive and non-invasive brain-machine interfaces, spinal interfaces, smart prostheses, deep-brain stimulators, cochlear implants and neuroethics. Heavy emphasis on primary literature. Prerequisite: either BIOL 130, BIOL 162, or BIOL 220; and either MATH 208, AMATH 301, or AMATH 352. Offered: jointly with BIOEN 460; A.

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E E 466 Neural Computation and Engineering Laboratory (4) NSc

Introduces neural recording and quantitative analysis techniques to students with a background in quantitative methods. Prerequisite: either BIOL 130, BIOL 162, BIOL 220, AMATH 342; and either MATH 208, AMATH 301, or AMATH 352.; recommended: courses in scientific computing and matrix manipulations in Matlab; and courses in neural signal processing and data analysis. Offered: jointly with BIOEN 466.