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6.003 Signals and Systems (MIT) 6.003 Signals and Systems (MIT)

Description

This course covers fundamentals of signal and system analysis, with applications drawn from filtering, audio and image processing, communications, and automatic control. Topics include convolution, Fourier series and transforms, sampling and discrete-time processing of continuous-time signals, modulation, Laplace and Z-transforms, and feedback systems. This course covers fundamentals of signal and system analysis, with applications drawn from filtering, audio and image processing, communications, and automatic control. Topics include convolution, Fourier series and transforms, sampling and discrete-time processing of continuous-time signals, modulation, Laplace and Z-transforms, and feedback systems.Subjects

signal and system analysis | signal and system analysis | filtering | filtering | audio | audio | audio processing | audio processing | image processing | image processing | communications | communications | automatic control | automatic control | convolution | convolution | Fourier series | Fourier series | fourier transforms | fourier transforms | sampling | sampling | discrete-time processing | discrete-time processing | modulation | modulation | Laplace transforms | Laplace transforms | Z-transforms | Z-transforms | feedback systems | feedback systemsLicense

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See all metadata6.438 Algorithms for Inference (MIT) 6.438 Algorithms for Inference (MIT)

Description

This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference. This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.Subjects

inference | inference | algorithm | algorithm | graphical model | graphical model | factor graph | factor graph | markov chain | markov chain | Gaussian model | Gaussian model | loopy belief propagation | loopy belief propagation | EM algorithm | EM algorithm | statistical inference | statistical inference | probabilistic graphical model | probabilistic graphical model | Hidden Markov model | Hidden Markov model | linear dynamical systems | linear dynamical systems | Sum-product algorithm | Sum-product algorithm | junction tree algorithm | junction tree algorithm | Forward-backward algorithm | Forward-backward algorithm | Kalman filtering | Kalman filtering | smoothing | smoothing | Variational method | Variational method | mean-field theory | mean-field theory | Min-sum algorithm | Min-sum algorithm | Viterbi algorithm | Viterbi algorithm | parameter estimation | parameter estimation | learning structure | learning structureLicense

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Includes audio/video content: AV lectures. An introduction to several fundamental ideas in electrical engineering and computer science, using digital communication systems as the vehicle. The three parts of the course—bits, signals, and packets—cover three corresponding layers of abstraction that form the basis of communication systems like the Internet. The course teaches ideas that are useful in other parts of EECS: abstraction, probabilistic analysis, superposition, time and frequency-domain representations, system design principles and trade-offs, and centralized and distributed algorithms. The course emphasizes connections between theoretical concepts and practice using programming tasks and some experiments with real-world communication channels. Includes audio/video content: AV lectures. An introduction to several fundamental ideas in electrical engineering and computer science, using digital communication systems as the vehicle. The three parts of the course—bits, signals, and packets—cover three corresponding layers of abstraction that form the basis of communication systems like the Internet. The course teaches ideas that are useful in other parts of EECS: abstraction, probabilistic analysis, superposition, time and frequency-domain representations, system design principles and trade-offs, and centralized and distributed algorithms. The course emphasizes connections between theoretical concepts and practice using programming tasks and some experiments with real-world communication channels.Subjects

digital communication | digital communication | communication systems | communication systems | information | information | entropy | entropy | compression | compression | error correction | error correction | Fourier analysis | Fourier analysis | filtering | filtering | signals | signals | media access protocols | media access protocols | networks | networks | packets | packets | data transport | data transport | internet | internetLicense

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Maneuvering motions of surface and underwater vehicles. Derivation of equations of motion, hydrodynamic coefficients. Memory effects. Linear and nonlinear forms of the equations of motion. Control surfaces modeling and design. Engine, propulsor, and transmission systems modeling and simulation during maneuvering. Stability of motion. Principles of multivariable automatic control. Optimal control, Kalman filtering, loop transfer recovery. Applications chosen from autopilots for surface vehicles; towing in open seas; remotely operated vehicles. Maneuvering motions of surface and underwater vehicles. Derivation of equations of motion, hydrodynamic coefficients. Memory effects. Linear and nonlinear forms of the equations of motion. Control surfaces modeling and design. Engine, propulsor, and transmission systems modeling and simulation during maneuvering. Stability of motion. Principles of multivariable automatic control. Optimal control, Kalman filtering, loop transfer recovery. Applications chosen from autopilots for surface vehicles; towing in open seas; remotely operated vehicles.Subjects

remotely operated vehicles | remotely operated vehicles | loop transfer recovery | loop transfer recovery | Kalman filtering | Kalman filtering | Optimal control | Optimal control | multivariable automatic control | multivariable automatic control | Undersea vehicles | Undersea vehiclesLicense

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See all metadata6.161 Modern Optics Project Laboratory (MIT) 6.161 Modern Optics Project Laboratory (MIT)

Description

6.161 explores modern optics through lectures, laboratory exercises, and projects. Topics covered include: polarization properties of light, reflection and refraction, coherence and interference, Fraunhofer and Fresnel diffraction, imaging and transforming properties of lenses, spatial filtering, coherent optical processors, holography, optical properties of materials, lasers, nonlinear optics, electro-optic and acousto-optic materials and devices, optical detectors, fiber optics, and optical communication. This course is worth 12 Engineering Design Points. 6.161 explores modern optics through lectures, laboratory exercises, and projects. Topics covered include: polarization properties of light, reflection and refraction, coherence and interference, Fraunhofer and Fresnel diffraction, imaging and transforming properties of lenses, spatial filtering, coherent optical processors, holography, optical properties of materials, lasers, nonlinear optics, electro-optic and acousto-optic materials and devices, optical detectors, fiber optics, and optical communication. This course is worth 12 Engineering Design Points.Subjects

modern optics lab | modern optics lab | modern optics | modern optics | laboratory | laboratory | polarization | polarization | light | light | reflection | reflection | refraction | refraction | coherence | coherence | interference | interference | Fraunhofer diffraction | Fraunhofer diffraction | Fresnel diffraction | Fresnel diffraction | imaging | imaging | transforming | transforming | lenses | lenses | spatial filtering | spatial filtering | coherent optical processors | coherent optical processors | holography | holography | optical properties of materials | optical properties of materials | lasers | lasers | nonlinear optics | nonlinear optics | electro-optic | electro-optic | acousto-optic | acousto-optic | optical detectors | optical detectors | fiber optics | fiber optics | optical communication | optical communicationLicense

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See all metadata6.161 Modern Optics Project Laboratory (MIT) 6.161 Modern Optics Project Laboratory (MIT)

Description

6.161 offers an introduction to laboratory optics, optical principles, and optical devices and systems. This course covers a wide range of topics, including: polarization properties of light, reflection and refraction, coherence and interference, Fraunhofer and Fresnel diffraction, holography, imaging and transforming properties of lenses, spatial filtering, two-lens coherent optical processor, optical properties of materials, lasers, electro-optic, acousto-optic and liquid-crystal light modulators, optical detectors, optical waveguides and fiber-optic communication systems. Students engage in extensive oral and written communication exercises. There are 12 engineering design points associated with this subject. 6.161 offers an introduction to laboratory optics, optical principles, and optical devices and systems. This course covers a wide range of topics, including: polarization properties of light, reflection and refraction, coherence and interference, Fraunhofer and Fresnel diffraction, holography, imaging and transforming properties of lenses, spatial filtering, two-lens coherent optical processor, optical properties of materials, lasers, electro-optic, acousto-optic and liquid-crystal light modulators, optical detectors, optical waveguides and fiber-optic communication systems. Students engage in extensive oral and written communication exercises. There are 12 engineering design points associated with this subject.Subjects

modern optics lab | modern optics lab | modern optics | modern optics | laboratory | laboratory | polarization | polarization | light | light | reflection | reflection | refraction | refraction | coherence | coherence | interference | interference | Fraunhofer diffraction | Fraunhofer diffraction | Fresnel diffraction | Fresnel diffraction | imaging | imaging | transforming | transforming | lenses | lenses | spatial filtering | spatial filtering | coherent optical processors | coherent optical processors | holography | holography | optical properties of materials | optical properties of materials | lasers | lasers | nonlinear optics | nonlinear optics | electro-optic | electro-optic | acousto-optic | acousto-optic | optical detectors | optical detectors | fiber optics | fiber optics | optical communication | optical communicationLicense

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This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection. This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | Input-output | state-space models | state-space models | linear systems | linear systems | deterministic and random signals | deterministic and random signals | time- and transform-domain representations | time- and transform-domain representations | sampling | sampling | discrete-time processing | discrete-time processing | continuous-time signals | continuous-time signals | state feedback | state feedback | observers | observers | probabilistic models | probabilistic models | stochastic processes | stochastic processes | correlation functions | correlation functions | power spectra | power spectra | whitening filters | whitening filters | Detection | Detection | matched filters | matched filters | Least-mean square error estimation | Least-mean square error estimation | Wiener filtering | Wiener filteringLicense

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See all metadata6.551J Acoustics of Speech and Hearing (MIT) 6.551J Acoustics of Speech and Hearing (MIT)

Description

The Acoustics of Speech and Hearing is an H-Level graduate course that reviews the physical processes involved in the production, propagation and reception of human speech. Particular attention is paid to how the acoustics and mechanics of the speech and auditory system define what sounds we are capable of producing and what sounds we can sense. Areas of discussion include: the acoustic cues used in determining the direction of a sound source, the acoustic and mechanical mechanisms involved in speech production and the acoustic and mechanical mechanism used to transduce and analyze sounds in the ear. The Acoustics of Speech and Hearing is an H-Level graduate course that reviews the physical processes involved in the production, propagation and reception of human speech. Particular attention is paid to how the acoustics and mechanics of the speech and auditory system define what sounds we are capable of producing and what sounds we can sense. Areas of discussion include: the acoustic cues used in determining the direction of a sound source, the acoustic and mechanical mechanisms involved in speech production and the acoustic and mechanical mechanism used to transduce and analyze sounds in the ear.Subjects

HST.714 | HST.714 | sound | sound | speech communication | speech communication | human anatomy | human anatomy | speech production | speech production | sound production | sound production | airflow | airflow | filtering | filtering | vocal tract | vocal tract | auditory physiology | auditory physiology | acoustical waves | acoustical waves | mechanical vibrations | mechanical vibrations | cochlear structures | cochlear structures | sound perception | sound perception | spatial hearing | spatial hearing | masking | masking | auditory frequency selectivity | auditory frequency selectivity | physical processes | physical processes | sound propagation | sound propagation | human speech | human speech | acoustics | acoustics | speech mechanics | speech mechanics | auditory system | auditory system | sound direction | sound direction | ear | ear | 6.551 | 6.551License

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See all metadata12.864 Inference from Data and Models (MIT) 12.864 Inference from Data and Models (MIT)

Description

The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themesLinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.Standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc. The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themesLinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.Standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc.Subjects

kinematical and dynamical models | kinematical and dynamical models | Basic statistics | Basic statistics | linear algebra | linear algebra | inverse methods | inverse methods | singular value decompositions | singular value decompositions | control theory | control theory | sequential estimation | sequential estimation | Kalman filters | Kalman filters | smoothing algorithms | smoothing algorithms | adjoint/Pontryagin principle methods | adjoint/Pontryagin principle methods | model testing | model testing | stationary processes | stationary processes | Fourier methods | Fourier methods | z-transforms | z-transforms | sampling theorems | sampling theorems | spectra | spectra | multi-taper methods | multi-taper methods | coherences | coherences | filtering | filtering | quantitative combinations of models | quantitative combinations of modelsLicense

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This course is about maneuvering motions of surface and underwater vehicles. Topics covered include: derivation of equations of motion, hydrodynamic coefficients, memory effects, linear and nonlinear forms of the equations of motion, control surfaces modeling and design, engine, propulsor, and transmission systems modeling and simulation during maneuvering. The course also deals with stability of motion, principles of multivariable automatic control, optimal control, Kalman filtering, and loop transfer recovery. We will also explore applications chosen from autopilots for surface vehicles; towing in open seas; and remotely operated vehicles. This course is about maneuvering motions of surface and underwater vehicles. Topics covered include: derivation of equations of motion, hydrodynamic coefficients, memory effects, linear and nonlinear forms of the equations of motion, control surfaces modeling and design, engine, propulsor, and transmission systems modeling and simulation during maneuvering. The course also deals with stability of motion, principles of multivariable automatic control, optimal control, Kalman filtering, and loop transfer recovery. We will also explore applications chosen from autopilots for surface vehicles; towing in open seas; and remotely operated vehicles.Subjects

Maneuvering | Maneuvering | motion | motion | surface and underwater vehicles | surface and underwater vehicles | Derivation of equations of motion | Derivation of equations of motion | hydrodynamic coefficients | hydrodynamic coefficients | Memory effects | Memory effects | Linear and nonlinear forms | Linear and nonlinear forms | Control surfaces | Control surfaces | modeling and design | modeling and design | Engine | Engine | propulsor | propulsor | transmission systems modeling | transmission systems modeling | simulation | simulation | Stability of motion | Stability of motion | multivariable automatic control | multivariable automatic control | Optimal control | Optimal control | Kalman filtering | Kalman filtering | loop transfer recovery | loop transfer recovery | autopilots for surface vehicles | autopilots for surface vehicles | towing in open seas | towing in open seas | remotely operated vehicles | remotely operated vehicles | 2.154 | 2.154License

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See all metadata6.801 Machine Vision (MIT) 6.801 Machine Vision (MIT)

Description

Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed. Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.Subjects

machine vision | machine vision | image formation physics | image formation physics | image analysis | image analysis | binary image processing | binary image processing | image filtering | image filtering | recovering | recovering | shape | shape | lightness | lightness | orientation | orientation | motion | motion | photometric stereo | photometric stereo | extended Gaussian | extended Gaussian | environment interaction | environment interaction | motion vision | motion vision | shape from shading | shape from shading | photogrammetry | photogrammetry | stereo | stereo | object representation alignment | object representation alignment | analog VLSI | analog VLSI | computational vision | computational vision | robot vision | robot visionLicense

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See all metadataMAS.622J Pattern Recognition and Analysis (MIT) MAS.622J Pattern Recognition and Analysis (MIT)

Description

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class. This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.Subjects

MAS.622 | MAS.622 | 1.126 | 1.126 | pattern recognition | pattern recognition | feature detection | feature detection | classification | classification | probability theory | probability theory | pattern analysis | pattern analysis | conditional probability | conditional probability | bayes rule | bayes rule | random vectors | decision theory | random vectors | decision theory | ROC curves | ROC curves | likelihood ratio test | likelihood ratio test | fisher discriminant | fisher discriminant | template-based recognition | template-based recognition | feature extraction | feature extraction | eigenvector and multilinear analysis | eigenvector and multilinear analysis | linear discriminant | linear discriminant | perceptron learning | perceptron learning | optimization by gradient descent | optimization by gradient descent | support vecotr machines | support vecotr machines | K-nearest-neighbor classification | K-nearest-neighbor classification | parzen estimation | parzen estimation | unsupervised learning | unsupervised learning | clustering | clustering | vector quantization | vector quantization | K-means | K-means | Expectation-Maximization | Expectation-Maximization | Hidden markov models | Hidden markov models | viterbi algorithm | viterbi algorithm | Baum-Welch algorithm | Baum-Welch algorithm | linear dynamical systems | linear dynamical systems | Kalman filtering | Kalman filtering | Bayesian networks | Bayesian networks | decision trees | decision trees | reinforcement learning | reinforcement learning | genetic algorithms | genetic algorithmsLicense

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See all metadata6.661 Receivers, Antennas, and Signals (MIT) 6.661 Receivers, Antennas, and Signals (MIT)

Description

This course explores the detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Topics covered include: statistical analysis of signal processing systems, including radiometers, spectrometers, interferometers, and digital correlation systems; matched filters and ambiguity functions; communications channel performance; measurement of random electromagnetic fields, angular filtering properties of antennas, interferometers, and aperture synthesis systems; and radiative transfer and parameter estimation. This course explores the detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Topics covered include: statistical analysis of signal processing systems, including radiometers, spectrometers, interferometers, and digital correlation systems; matched filters and ambiguity functions; communications channel performance; measurement of random electromagnetic fields, angular filtering properties of antennas, interferometers, and aperture synthesis systems; and radiative transfer and parameter estimation.Subjects

receiver | receiver | antenna | antenna | signal | signal | radio | radio | optical | optical | detection | detection | communications | communications | astronomy | astronomy | remote sensing | instrumentation | remote sensing | instrumentation | radar | radar | statistics | statistics | signal processing | signal processing | radiometer | radiometer | spectrometer | spectrometer | interferometer | interferometer | digital correlation | digital correlation | matched filter | matched filter | ambiguity function | ambiguity function | channel performance | channel performance | electromagnetic | electromagnetic | angular filtering | angular filtering | aperture synthesis | aperture synthesis | radiative transfer | radiative transfer | parameter estimation | parameter estimation | remote sensing | remote sensing | instrumentation | instrumentation | radio signals | radio signals | optical signals | optical signals | statistical analysis | statistical analysisLicense

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See all metadata12.864 Inference from Data and Models (MIT) 12.864 Inference from Data and Models (MIT)

Description

The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc. The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc.Subjects

observation | observation | kinematical models | kinematical models | dynamical models | dynamical models | basic statistics | basic statistics | linear algebra | linear algebra | inverse methods | inverse methods | singular value decompositions | singular value decompositions | control theory | control theory | sequential estimation | sequential estimation | Kalman filters | Kalman filters | smoothing algorithms | smoothing algorithms | adjoint/Pontryagin principle methods | adjoint/Pontryagin principle methods | model testing | model testing | stationary processes | stationary processes | Fourier methods | Fourier methods | z-transforms | z-transforms | sampling theorems | sampling theorems | spectra | spectra | multi-taper methods | multi-taper methods | coherences | coherences | filtering | filtering | quantitative combinations | quantitative combinations | realistic observations | realistic observations | data assimilations | data assimilations | deduction | deduction | regression | regression | objective mapping | objective mapping | time series analysis | time series analysis | inference | inferenceLicense

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See all metadata12.864 Inference from Data and Models (MIT) 12.864 Inference from Data and Models (MIT)

Description

This course covers the fundamental methods used for exploring the information content of observations related to kinematical and dynamical models. This course covers the fundamental methods used for exploring the information content of observations related to kinematical and dynamical models.Subjects

kinematical and dynamical models | kinematical and dynamical models | Basic statistics | Basic statistics | linear algebra | linear algebra | inverse methods | inverse methods | singular value decompositions | singular value decompositions | control theory | control theory | sequential estimation | sequential estimation | Kalman filters | Kalman filters | smoothing algorithms | smoothing algorithms | adjoint/Pontryagin principle methods | adjoint/Pontryagin principle methods | model testing | model testing | stationary processes | stationary processes | Fourier methods | Fourier methods | z-transforms | z-transforms | sampling theorems | sampling theorems | spectra | spectra | multi-taper methods | multi-taper methods | coherences | coherences | filtering | filtering | quantitative combinations of models | quantitative combinations of modelsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course is about maneuvering motions of surface and underwater vehicles. Topics covered include: derivation of equations of motion, hydrodynamic coefficients, memory effects, linear and nonlinear forms of the equations of motion, control surfaces modeling and design, engine, propulsor, and transmission systems modeling and simulation during maneuvering. The course also deals with stability of motion, principles of multivariable automatic control, optimal control, Kalman filtering, and loop transfer recovery. We will also explore applications chosen from autopilots for surface vehicles; towing in open seas; and remotely operated vehicles. This course was originally offered in Course 13 (Department of Ocean Engineering) as 13.49. In 2005, ocean engineering subjects became part of Co This course is about maneuvering motions of surface and underwater vehicles. Topics covered include: derivation of equations of motion, hydrodynamic coefficients, memory effects, linear and nonlinear forms of the equations of motion, control surfaces modeling and design, engine, propulsor, and transmission systems modeling and simulation during maneuvering. The course also deals with stability of motion, principles of multivariable automatic control, optimal control, Kalman filtering, and loop transfer recovery. We will also explore applications chosen from autopilots for surface vehicles; towing in open seas; and remotely operated vehicles. This course was originally offered in Course 13 (Department of Ocean Engineering) as 13.49. In 2005, ocean engineering subjects became part of CoSubjects

Maneuvering | Maneuvering | motion | motion | surface and underwater vehicles | surface and underwater vehicles | Derivation of equations of motion | Derivation of equations of motion | hydrodynamic coefficients | hydrodynamic coefficients | Memory effects | Memory effects | Linear and nonlinear forms | Linear and nonlinear forms | Control surfaces | Control surfaces | modeling and design | modeling and design | Engine | Engine | propulsor | propulsor | transmission systems modeling | transmission systems modeling | simulation | simulation | Stability of motion | Stability of motion | multivariable automatic control | multivariable automatic control | Optimal control | Optimal control | Kalman filtering | Kalman filtering | loop transfer recovery | loop transfer recovery | autopilots for surface vehicles | autopilots for surface vehicles | towing in open seas | towing in open seas | remotely operated vehicles | remotely operated vehiclesLicense

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See all metadata6.551J Acoustics of Speech and Hearing (MIT)

Description

The Acoustics of Speech and Hearing is an H-Level graduate course that reviews the physical processes involved in the production, propagation and reception of human speech. Particular attention is paid to how the acoustics and mechanics of the speech and auditory system define what sounds we are capable of producing and what sounds we can sense. Areas of discussion include: the acoustic cues used in determining the direction of a sound source, the acoustic and mechanical mechanisms involved in speech production and the acoustic and mechanical mechanism used to transduce and analyze sounds in the ear.Subjects

HST.714 | sound | speech communication | human anatomy | speech production | sound production | airflow | filtering | vocal tract | auditory physiology | acoustical waves | mechanical vibrations | cochlear structures | sound perception | spatial hearing | masking | auditory frequency selectivity | physical processes | sound propagation | human speech | acoustics | speech mechanics | auditory system | sound direction | ear | 6.551License

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See all metadata6.161 Modern Optics Project Laboratory (MIT)

Description

6.161 offers an introduction to laboratory optics, optical principles, and optical devices and systems. This course covers a wide range of topics, including: polarization properties of light, reflection and refraction, coherence and interference, Fraunhofer and Fresnel diffraction, holography, imaging and transforming properties of lenses, spatial filtering, two-lens coherent optical processor, optical properties of materials, lasers, electro-optic, acousto-optic and liquid-crystal light modulators, optical detectors, optical waveguides and fiber-optic communication systems. Students engage in extensive oral and written communication exercises. There are 12 engineering design points associated with this subject.Subjects

modern optics lab | modern optics | laboratory | polarization | light | reflection | refraction | coherence | interference | Fraunhofer diffraction | Fresnel diffraction | imaging | transforming | lenses | spatial filtering | coherent optical processors | holography | optical properties of materials | lasers | nonlinear optics | electro-optic | acousto-optic | optical detectors | fiber optics | optical communicationLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.Subjects

machine vision | image formation physics | image analysis | binary image processing | image filtering | recovering | shape | lightness | orientation | motion | photometric stereo | extended Gaussian | environment interaction | motion vision | shape from shading | photogrammetry | stereo | object representation alignment | analog VLSI | computational vision | robot visionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadataMAS.622J Pattern Recognition and Analysis (MIT)

Description

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.Subjects

MAS.622 | 1.126 | pattern recognition | feature detection | classification | probability theory | pattern analysis | conditional probability | bayes rule | random vectors | decision theory | ROC curves | likelihood ratio test | fisher discriminant | template-based recognition | feature extraction | eigenvector and multilinear analysis | linear discriminant | perceptron learning | optimization by gradient descent | support vecotr machines | K-nearest-neighbor classification | parzen estimation | unsupervised learning | clustering | vector quantization | K-means | Expectation-Maximization | Hidden markov models | viterbi algorithm | Baum-Welch algorithm | linear dynamical systems | Kalman filtering | Bayesian networks | decision trees | reinforcement learning | genetic algorithmsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.Subjects

machine vision | image formation physics | image analysis | binary image processing | image filtering | recovering | shape | lightness | orientation | motion | photometric stereo | extended Gaussian | environment interaction | motion vision | shape from shading | photogrammetry | stereo | object representation alignment | analog VLSI | computational vision | robot visionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata12.864 Inference from Data and Models (MIT)

Description

The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themesLinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.Standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc.Subjects

kinematical and dynamical models | Basic statistics | linear algebra | inverse methods | singular value decompositions | control theory | sequential estimation | Kalman filters | smoothing algorithms | adjoint/Pontryagin principle methods | model testing | stationary processes | Fourier methods | z-transforms | sampling theorems | spectra | multi-taper methods | coherences | filtering | quantitative combinations of modelsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata13.49 Maneuvering and Control of Surface and Underwater Vehicles (MIT)

Description

This course is about maneuvering motions of surface and underwater vehicles. Topics covered include: derivation of equations of motion, hydrodynamic coefficients, memory effects, linear and nonlinear forms of the equations of motion, control surfaces modeling and design, engine, propulsor, and transmission systems modeling and simulation during maneuvering. The course also deals with stability of motion, principles of multivariable automatic control, optimal control, Kalman filtering, and loop transfer recovery. We will also explore applications chosen from autopilots for surface vehicles; towing in open seas; and remotely operated vehicles.Subjects

Maneuvering | motion | surface and underwater vehicles | Derivation of equations of motion | hydrodynamic coefficients | Memory effects | Linear and nonlinear forms | Control surfaces | modeling and design | Engine | propulsor | transmission systems modeling | simulation | Stability of motion | multivariable automatic control | Optimal control | Kalman filtering | loop transfer recovery | autopilots for surface vehicles | towing in open seas | remotely operated vehicles | 2.154License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata2.154 Maneuvering and Control of Surface and Underwater Vehicles (13.49) (MIT)

Description

This course is about maneuvering motions of surface and underwater vehicles. Topics covered include: derivation of equations of motion, hydrodynamic coefficients, memory effects, linear and nonlinear forms of the equations of motion, control surfaces modeling and design, engine, propulsor, and transmission systems modeling and simulation during maneuvering. The course also deals with stability of motion, principles of multivariable automatic control, optimal control, Kalman filtering, and loop transfer recovery. We will also explore applications chosen from autopilots for surface vehicles; towing in open seas; and remotely operated vehicles. This course was originally offered in Course 13 (Department of Ocean Engineering) as 13.49. In 2005, ocean engineering subjects became part of CoSubjects

Maneuvering | motion | surface and underwater vehicles | Derivation of equations of motion | hydrodynamic coefficients | Memory effects | Linear and nonlinear forms | Control surfaces | modeling and design | Engine | propulsor | transmission systems modeling | simulation | Stability of motion | multivariable automatic control | Optimal control | Kalman filtering | loop transfer recovery | autopilots for surface vehicles | towing in open seas | remotely operated vehiclesLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadataManage Excel Data Effectively Using Lists - Learning Package

Description

This is the packaged learning material. Discover how to use Lists to organise, summarise, and analyse your Excel data to reveal its meanings. This learning activity contains practical advice and demonstrations of data analysis techniques. It includes a set of interactive tasks and questions to use as a test so you can be sure you understand the material.Subjects

employability | ms excel | data lists | data analysis | filtering | sorting | sort | subtotal | analyse | uclan | e-evolve | ukoer | excel | management | administrative studies | N000License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/Site sourced from

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