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Includes audio/video content: AV special element video. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming. Includes audio/video content: AV special element video. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.Subjects

MATLAB | MATLAB | numerical analysis | numerical analysis | programming | programming | physical modeling | physical modeling | calculus | calculus | linear algebra | linear algebra | Monte Carlo Method | Monte Carlo Method | differential equations | differential equations | nonlinear systems | nonlinear systems | variable types | variable types | data structure | data structure | flow control | flow control | probability | probability | statistics | statistics | robotics | roboticsLicense

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See all metadata2.670 Mechanical Engineering Tools (MIT) 2.670 Mechanical Engineering Tools (MIT)

Description

This course introduces the fundamentals of machine tool and computer tool use. Students work with a variety of machine tools including the bandsaw, milling machine, and lathe. Instruction given on MATLAB®, MAPLE®, XESS™, and CAD. Emphasis is on problem solving, not programming or algorithmic development. Assignments are project-oriented relating to mechanical engineering topics. It is recommended that students take this subject in the first IAP after declaring the major in Mechanical Engineering. This course was co-created by Prof. Douglas Hart and Dr. Kevin Otto. This course introduces the fundamentals of machine tool and computer tool use. Students work with a variety of machine tools including the bandsaw, milling machine, and lathe. Instruction given on MATLAB®, MAPLE®, XESS™, and CAD. Emphasis is on problem solving, not programming or algorithmic development. Assignments are project-oriented relating to mechanical engineering topics. It is recommended that students take this subject in the first IAP after declaring the major in Mechanical Engineering. This course was co-created by Prof. Douglas Hart and Dr. Kevin Otto.Subjects

fundamentals of machine tool and computer tool use | fundamentals of machine tool and computer tool use | bandsaw | bandsaw | milling machine | milling machine | lathe | lathe | MATLAB | MATLAB | MAPLE | MAPLE | XESS | XESS | CAD | CAD | problem solving | problem solving | project-oriented | project-oriented | machine tool use | machine tool use | computer tool use | computer tool use | mechanical engineering projects | mechanical engineering projects | Inter Activities Period | Inter Activities Period | IAP | IAP | engine design | engine design | engine construction | engine construction | Stirling engines | Stirling enginesLicense

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See all metadataMATLAB aplicado a la ingenierÃa telemÃ¡tica MATLAB aplicado a la ingenierÃa telemÃ¡tica

Description

La IngenierÃa telemÃ¡tica, en los Ãºltimos tiempos, ha superado ampliamente los lÃmites tradicionales que se le asociaban. La apariciÃ³n de nuevos conceptos como Big Data, Internet de las Cosas, dispositivos llevables, etc. ha provocado que sea necesario diseÃ±ar nuevos paradigmas y marcos de trabajo, asÃ como que sea obligada una revisiÃ³n de conceptos tan tradicionales del campo como la teorÃa de la informaciÃ³n. Por su parte, el programa MATLAB ha evolucionado en los Ãºltimos tiempos hasta convertirse en una completa suite de simulaciÃ³n y procesado, bastante alejada de sus orÃgenes de modesta liberarÃa de cÃ¡lculo numÃ©rico matricial. En este curso se introduce al alumno en las funcionalidades que este programa (MATLAB) ofrece en relaciÃ³n con las nuevas tendencias de la ingeni La IngenierÃa telemÃ¡tica, en los Ãºltimos tiempos, ha superado ampliamente los lÃmites tradicionales que se le asociaban. La apariciÃ³n de nuevos conceptos como Big Data, Internet de las Cosas, dispositivos llevables, etc. ha provocado que sea necesario diseÃ±ar nuevos paradigmas y marcos de trabajo, asÃ como que sea obligada una revisiÃ³n de conceptos tan tradicionales del campo como la teorÃa de la informaciÃ³n. Por su parte, el programa MATLAB ha evolucionado en los Ãºltimos tiempos hasta convertirse en una completa suite de simulaciÃ³n y procesado, bastante alejada de sus orÃgenes de modesta liberarÃa de cÃ¡lculo numÃ©rico matricial. En este curso se introduce al alumno en las funcionalidades que este programa (MATLAB) ofrece en relaciÃ³n con las nuevas tendencias de la ingeniSubjects

TelemÃ¡tica | TelemÃ¡tica | Big Data | Big Data | Pruebas unitarias | Pruebas unitarias | CodificaciÃ³n de fuente | CodificaciÃ³n de fuente | Control de versiones | Control de versiones | IngenierÃa Software | IngenierÃa Software | MATLAB | MATLAB | Lenguaje M | Lenguaje M | CÃ³digos cÃclicos | CÃ³digos cÃclicos | TeorÃa de la informaciÃ³n | TeorÃa de la informaciÃ³n | CÃ³digos lineales | CÃ³digos linealesLicense

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This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.Subjects

MATLAB | MATLAB | numerical analysis | numerical analysis | programming | programming | physical modeling | physical modeling | calculus | calculus | linear algebra | linear algebra | Monte Carlo Method | Monte Carlo Method | differential equations | differential equations | nonlinear systems | nonlinear systemsLicense

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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Includes audio/video content: AV special element video. This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data. Enrollment preference is given to juniors and seniors. Includes audio/video content: AV special element video. This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data. Enrollment preference is given to juniors and seniors.Subjects

DNA analysis | DNA analysis | Fourier analysis | Fourier analysis | FFT | FFT | DNA melting | DNA melting | electronics | electronics | microscopy | microscopy | microscope | microscope | probes | probes | biology | biology | atomic force microscope | atomic force microscope | AFM | AFM | scanning probe microscope | scanning probe microscope | image processing | image processing | MATLAB | MATLAB | convolution | convolution | optoelectronics | optoelectronics | rheology | rheology | fluorescence | fluorescence | noise | noise | detector | detector | optics | optics | diffraction | diffraction | optical trap | optical trap | 3D | 3D | 3-D | 3-D | three-dimensional imaging | three-dimensional imaging | visualization | visualizationLicense

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See all metadata6.094 Introduction to MATLAB (MIT) 6.094 Introduction to MATLAB (MIT)

Description

This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with students doing sample MATLAB problems in real time. Problem-based MATLAB assignments are given which require significant time on MATLAB. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month. Acknowledgements The 6.094 course materials were developed by Danilo Å Ä‡epanoviÄ‡, Sourav R. Dey, Ankit Patel, and Patrick Ho. This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with students doing sample MATLAB problems in real time. Problem-based MATLAB assignments are given which require significant time on MATLAB. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month. Acknowledgements The 6.094 course materials were developed by Danilo Å Ä‡epanoviÄ‡, Sourav R. Dey, Ankit Patel, and Patrick Ho.Subjects

introduction to MATLAB | introduction to MATLAB | scripts | scripts | making variables | making variables | manipulating variables | manipulating variables | functions | functions | flow control | flow control | line plots | line plots | surface plots | surface plots | vectorization | vectorization | linear algebra | linear algebra | optimization | optimization | differential equations | differential equations | data structures | data structures | debugging | debugging | animation | animation | symbolic math | symbolic math | Simulink | Simulink | file input/output | file input/output | graphical user interfaces | graphical user interfacesLicense

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La InstrumentaciÃ³n electrÃ³nica, en los Ãºltimos tiempos, ha superado ampliamente los lÃmites tradicionales que se le asociaban. La apariciÃ³n de nuevas tÃ©cnicas de integraciÃ³n, dispositivos cada vez mÃ¡s precisos y el creciente avance de la digitalizaciÃ³n han propiciado que a la instrumentaciÃ³n se hayan agregado tÃ©cnicas, como el tratamiento digital de seÃ±ales, en principio propias de otros campos de actividad. Este hecho ha obligado a los profesionales de este ramo a comenzar a utilizar herramientas hasta ahora desconocidas en la instrumentaciÃ³n. Por su parte, el programa MATLAB ha evolucionado en los Ãºltimos tiempos hasta convertirse en una completa suite de adquisiciÃ³n y procesado de datos, bastante alejada de sus orÃgenes de modesta liberarÃa de cÃ¡lculo numÃ©rico matric La InstrumentaciÃ³n electrÃ³nica, en los Ãºltimos tiempos, ha superado ampliamente los lÃmites tradicionales que se le asociaban. La apariciÃ³n de nuevas tÃ©cnicas de integraciÃ³n, dispositivos cada vez mÃ¡s precisos y el creciente avance de la digitalizaciÃ³n han propiciado que a la instrumentaciÃ³n se hayan agregado tÃ©cnicas, como el tratamiento digital de seÃ±ales, en principio propias de otros campos de actividad. Este hecho ha obligado a los profesionales de este ramo a comenzar a utilizar herramientas hasta ahora desconocidas en la instrumentaciÃ³n. Por su parte, el programa MATLAB ha evolucionado en los Ãºltimos tiempos hasta convertirse en una completa suite de adquisiciÃ³n y procesado de datos, bastante alejada de sus orÃgenes de modesta liberarÃa de cÃ¡lculo numÃ©rico matricSubjects

InstrumentaciÃ³n | InstrumentaciÃ³n | ElectrÃ³nica | ElectrÃ³nica | Microcontroladores | Microcontroladores | Puertos COM | Puertos COM | AdquisiciÃ³n de datos | AdquisiciÃ³n de datos | Tipos de datos | Tipos de datos | MATLAB | MATLAB | Lenguaje M | Lenguaje MLicense

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This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.Subjects

HST.582 | HST.582 | 6.555 | 6.555 | 16.456 | 16.456 | signal processing | signal processing | medicine | medicine | biological signal | biological signal | diagnosis | diagnosis | diagnostic tool | diagnostic tool | physiology | physiology | cardiology | cardiology | speech recognition | speech recognition | speech processing | speech processing | imaging | imaging | medical imaging | medical imaging | MRI | MRI | ultrasound | ultrasound | ECG | ECG | electrocardiogram | electrocardiogram | fourier | fourier | FFT | FFT | applications of probabilitym | applications of probabilitym | noise | noise | MATLAB | MATLAB | digital filter | digital filter | DSP | DSPLicense

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This course is a project-based introduction to manipulating and characterizing cells and biological molecules using microfabricated tools. It is designed for first year undergraduate students. In the first half of the term, students perform laboratory exercises designed to introduce (1) the design, manufacture, and use of microfluidic channels, (2) techniques for sorting and manipulating cells and biomolecules, and (3) making quantitative measurements using optical detection and fluorescent labeling. In the second half of the term, students work in small groups to design and test a microfluidic device to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building. This course is a project-based introduction to manipulating and characterizing cells and biological molecules using microfabricated tools. It is designed for first year undergraduate students. In the first half of the term, students perform laboratory exercises designed to introduce (1) the design, manufacture, and use of microfluidic channels, (2) techniques for sorting and manipulating cells and biomolecules, and (3) making quantitative measurements using optical detection and fluorescent labeling. In the second half of the term, students work in small groups to design and test a microfluidic device to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building.Subjects

HST.410 | HST.410 | 6.07 | 6.07 | cell manipulation | cell manipulation | microchips | microchips | lithography | lithography | rapid prototyping | rapid prototyping | optical imaging of cells | optical imaging of cells | cell sorting | cell sorting | microfluidics | microfluidics | osmosis | osmosis | diffusion | diffusion | microfabrication | microfabrication | models of diffusion | models of diffusion | laminar flow | laminar flow | MATLAB data analysis | MATLAB data analysis | cell traps | cell traps | experimental design | experimental design | cytometry techniques | cytometry techniques | computer simulation of neural behavior | computer simulation of neural behavior | casting PDMS | casting PDMS | coulter counter | coulter counter | plasma bonding | plasma bondingLicense

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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Includes audio/video content: AV special element video. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Includes audio/video content: AV special element video. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis.Subjects

MATLAB | MATLAB | numerical analysis | numerical analysis | programming | programming | physical modeling | physical modeling | calculus | calculus | linear algebra | linear algebra | Monte Carlo Method | Monte Carlo Method | differential equations | differential equations | nonlinear systems | nonlinear systems | variable types | variable types | data structure | data structure | flow control | flow control | probability | probability | statistics | statistics | robotics | roboticsLicense

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 focuses on computational and experimental analysis of biological systems across a hierarchy of scales, including genetic, molecular, cellular, and cell population levels. The two central themes of the course are modeling of complex dynamic systems and protein design and engineering. Topics include gene sequence analysis, molecular modeling, metabolic and gene regulation networks, signal transduction pathways and cell populations in tissues. Emphasis is placed on experimental methods, quantitative analysis, and computational modeling. This course focuses on computational and experimental analysis of biological systems across a hierarchy of scales, including genetic, molecular, cellular, and cell population levels. The two central themes of the course are modeling of complex dynamic systems and protein design and engineering. Topics include gene sequence analysis, molecular modeling, metabolic and gene regulation networks, signal transduction pathways and cell populations in tissues. Emphasis is placed on experimental methods, quantitative analysis, and computational modeling.Subjects

biological engineering | biological engineering | kinase | kinase | PyMOL | PyMOL | PyRosetta | PyRosetta | MATLAB | MATLAB | Michaelis-Menten | Michaelis-Menten | bioreactor | bioreactor | bromodomain | bromodomain | protein-ligand interactions | protein-ligand interactions | titration analysis | titration analysis | fractional separation | fractional separation | isothermal titration calorimetry | isothermal titration calorimetry | ITC | ITC | mass spectrometry | mass spectrometry | MS | MS | co-immunoprecipitation | co-immunoprecipitation | Co-IP | Co-IP | Forster resonance energy transfer | Forster resonance energy transfer | FRET | FRET | primary ligation assay | primary ligation assay | PLA | PLA | surface plasmon resonance | surface plasmon resonance | SPR | SPR | enzyme kinetics | enzyme kinetics | kinase engineering | kinase engineering | competitive inhibition | competitive inhibition | epidermal growth factor receptor | epidermal growth factor receptor | mitogen-activated protein kinase | mitogen-activated protein kinase | MAPK | MAPK | genome editing | genome editing | Imatinib | Imatinib | Gleevec | Gleevec | Glivec | Glivec | drug delivery | drug delivery | kinetics of molecular processes | kinetics of molecular processes | dynamics of molecular processes | dynamics of molecular processes | kinetics of cellular processes | kinetics of cellular processes | dynamics of cellular processes | dynamics of cellular processes | intracellular scale | intracellular scale | extracellular scale | extracellular scale | and cell population scale | and cell population scale | biotechnology applications | biotechnology applications | gene regulation networks | gene regulation networks | nucleic acid hybridization | nucleic acid hybridization | signal transduction pathways | signal transduction pathways | cell populations in tissues | cell populations in tissues | cell populations in bioreactors | cell populations in bioreactors | experimental methods | experimental methods | quantitative analysis | quantitative analysis | computational modeling | computational modelingLicense

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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Includes audio/video content: AV special element video. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming. Includes audio/video content: AV special element video. This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.Subjects

MATLAB | MATLAB | numerical analysis | numerical analysis | programming | programming | physical modeling | physical modeling | calculus | calculus | linear algebra | linear algebra | Monte Carlo Method | Monte Carlo Method | differential equations | differential equations | nonlinear systems | nonlinear systemsLicense

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 subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for EnvironmSubjects

probability | probability | statistics | statistics | events | events | random variables | random variables | univariate distributions | univariate distributions | multivariate distributions | multivariate distributions | uncertainty propagation | uncertainty propagation | Bernoulli trials | Bernoulli trials | Poisson processed | Poisson processed | conditional probability | conditional probability | Bayes rule | Bayes rule | random sampling | random sampling | point estimation | point estimation | interval estimation | interval estimation | hypothesis testing | hypothesis testing | analysis of variance | analysis of variance | linear regression | linear regression | computational analysis | computational analysis | data analysis | data analysis | environmental engineering | environmental engineering | applications | applications | MATLAB | MATLAB | numerical modeling | numerical modeling | probabilistic concepts | probabilistic concepts | statistical methods | statistical methods | field data | field data | laboratory data | laboratory data | numerical techniques | numerical techniques | Monte Carlo simulation | Monte Carlo simulation | variability | variability | sampling | sampling | data sets | data sets | computer | computer | uncertainty | uncertainty | interpretation | interpretation | quantitative data | quantitative dataLicense

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See all metadata6.245 Multivariable Control Systems (MIT) 6.245 Multivariable Control Systems (MIT)

Description

This course uses computer-aided design methodologies for synthesis of multivariable feedback control systems. Topics covered include: performance and robustness trade-offs; model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; and nonlinear effects. The assignments for the course comprise of computer-aided (MATLAB®) design problems. This course uses computer-aided design methodologies for synthesis of multivariable feedback control systems. Topics covered include: performance and robustness trade-offs; model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; and nonlinear effects. The assignments for the course comprise of computer-aided (MATLAB®) design problems.Subjects

multivariable control systems | multivariable control systems | computer-aided design | computer-aided design | MATLAB | MATLAB | multivariable feedback control systems | multivariable feedback control systems | model-based compensators | model-based compensators | Q-parameterization | Q-parameterization | optimization | optimization | dynamic augmentation | dynamic augmentation | linear-quadratic optimization | linear-quadratic optimization | H-infinity controller design | H-infinity controller design | Mu-synthesis | Mu-synthesis | nonlinear systems | nonlinear systems | engineering design | engineering designLicense

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See all metadata18.S997 Introduction To MATLAB Programming (MIT) 18.S997 Introduction To MATLAB Programming (MIT)

Description

Includes audio/video content: AV lectures. This course is intended to assist undergraduates with learning the basics of programming in general and programming MATLAB® in particular. Includes audio/video content: AV lectures. This course is intended to assist undergraduates with learning the basics of programming in general and programming MATLAB® in particular.Subjects

MATLAB | programming | MATLAB | programming | variables | variables | plotting | plotting | scripts | scripts | functions | functions | flow control | flow control | statistics | statistics | data structures | data structures | images | images | vectors | vectors | matrices | matrices | root-finding | root-finding | Newton's Method | Newton's Method | Secant Method | Secant Method | Basins of Attraction | Basins of Attraction | Conway Game of Life | Conway Game of Life | Game of Life | Game of Life | vectorization | vectorization | debugging | debugging | scope | scope | function block | function blockLicense

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 provides a solid theoretical foundation for the analysis and processing of experimental data, and real-time experimental control methods. Topics covered include spectral analysis, filter design, system identification, and simulation in continuous and discrete-time domains. The emphasis is on practical problems with laboratory exercises. This course provides a solid theoretical foundation for the analysis and processing of experimental data, and real-time experimental control methods. Topics covered include spectral analysis, filter design, system identification, and simulation in continuous and discrete-time domains. The emphasis is on practical problems with laboratory exercises.Subjects

analysis and processing of experimental data; real-time experimental control methods; spectral analysis; filter design; system identification; simulation in continuous and discrete-time domains; MATLAB | analysis and processing of experimental data; real-time experimental control methods; spectral analysis; filter design; system identification; simulation in continuous and discrete-time domains; MATLAB | fast Fourier transform | fast Fourier transform | correlation function | correlation function | sampling | sampling | op-amps | op-amps | Chebyshev | Chebyshev | Laplace transform | Laplace transform | Butterworth | Butterworth | convolution | convolution | frequency response | frequency response | windowing | windowing | low-pass | low-pass | poles | poles | zeros | zerosLicense

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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See all metadata9.17 Systems Neuroscience Lab (MIT) 9.17 Systems Neuroscience Lab (MIT)

Description

Systems Neuroscience Laboratory consists of a series of laboratories designed to give students experience with basic techniques for conducting systems neuroscience research. It includes sessions on anatomical, neurophysiological, and data acquisition and analysis techniques, and the ways these techniques are used to study nervous system function. Training is provided in the art of scientific writing with feedback designed to improve writing skills. Assignments include weekly preparation for lab sessions, two major research reports and a series of basic computer programming tutorials (MATLAB®). The class involves the use of experimental animals. Enrollment is limited. Systems Neuroscience Laboratory consists of a series of laboratories designed to give students experience with basic techniques for conducting systems neuroscience research. It includes sessions on anatomical, neurophysiological, and data acquisition and analysis techniques, and the ways these techniques are used to study nervous system function. Training is provided in the art of scientific writing with feedback designed to improve writing skills. Assignments include weekly preparation for lab sessions, two major research reports and a series of basic computer programming tutorials (MATLAB®). The class involves the use of experimental animals. Enrollment is limited.Subjects

laboratory | laboratory | experiment | experiment | protocol | protocol | neuroscience | neuroscience | nerves | nerves | nervous system | nervous system | electrophysiology | electrophysiology | action potential | action potential | neurophysiology | neurophysiology | rat barrel | rat barrel | MATLAB | MATLAB | frog | frog | fly | fly | vision | vision | physiology | physiology | human | human | MRI | MRI | EEG | EEG | electroencephalography | electroencephalography | ablation | ablation | computer modeling techniques | computer modeling techniques | brain function | brain function | histology | histology | neural tissue | neural tissue | surgery | surgery | laboratory notebook | laboratory notebook | scientific writing | scientific writingLicense

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See all metadata2.003J Dynamics and Control I (MIT) 2.003J Dynamics and Control I (MIT)

Description

Introduction to the dynamics and vibrations of lumped-parameter models of mechanical systems. Kinematics. Force-momentum formulation for systems of particles and rigid bodies in planar motion. Work-energy concepts. Virtual displacements and virtual work. Lagrange's equations for systems of particles and rigid bodies in planar motion. Linearization of equations of motion. Linear stability analysis of mechanical systems. Free and forced vibration of linear multi-degree of freedom models of mechanical systems; matrix eigenvalue problems. Introduction to numerical methods and MATLAB® to solve dynamics and vibrations problems. Introduction to the dynamics and vibrations of lumped-parameter models of mechanical systems. Kinematics. Force-momentum formulation for systems of particles and rigid bodies in planar motion. Work-energy concepts. Virtual displacements and virtual work. Lagrange's equations for systems of particles and rigid bodies in planar motion. Linearization of equations of motion. Linear stability analysis of mechanical systems. Free and forced vibration of linear multi-degree of freedom models of mechanical systems; matrix eigenvalue problems. Introduction to numerical methods and MATLAB® to solve dynamics and vibrations problems.Subjects

dynamics and vibrations of lumped-parameter models | dynamics and vibrations of lumped-parameter models | mechanical systems | mechanical systems | Kinematics | Kinematics | Force-momentum formulation | Force-momentum formulation | systems of particles | systems of particles | rigid bodies in planar motion | rigid bodies in planar motion | Work-energy concepts | Work-energy concepts | Virtual displacements | Virtual displacements | virtual work | virtual work | Lagrange's equations | Lagrange's equations | Linearization of equations of motion | Linearization of equations of motion | Linear stability analysis | Linear stability analysis | Free vibration | Free vibration | forced vibration | forced vibration | linear multi-degree of freedom models | linear multi-degree of freedom models | matrix eigenvalue problems | matrix eigenvalue problems | numerical methods | numerical methods | MATLAB | MATLABLicense

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Includes audio/video content: AV lectures. Learn Differential Equations: Up Close with Gilbert Strang and Cleve Moler is an in-depth series of videos about differential equations and the MATLAB® ODE suite. These videos are suitable for students and life-long learners to enjoy.About the Instructors Gilbert Strang is the MathWorks Professor of Mathematics at MIT. His research focuses on mathematical analysis, linear algebra and PDEs. He has written textbooks on linear algebra, computational science, finite elements, wavelets, GPS, and calculus.Cleve Moler is chief mathematician, chairman, and cofounder of MathWorks. He was a professor of math and computer science for almost 20 years at the University of Michigan, Stanford University, and the University of New Mexico. These videos w Includes audio/video content: AV lectures. Learn Differential Equations: Up Close with Gilbert Strang and Cleve Moler is an in-depth series of videos about differential equations and the MATLAB® ODE suite. These videos are suitable for students and life-long learners to enjoy.About the Instructors Gilbert Strang is the MathWorks Professor of Mathematics at MIT. His research focuses on mathematical analysis, linear algebra and PDEs. He has written textbooks on linear algebra, computational science, finite elements, wavelets, GPS, and calculus.Cleve Moler is chief mathematician, chairman, and cofounder of MathWorks. He was a professor of math and computer science for almost 20 years at the University of Michigan, Stanford University, and the University of New Mexico. These videos wSubjects

differential equations | differential equations | ODE | MATLAB | ODE | MATLAB | first order equations | first order equations | second order equations | second order equations | matrices | matrices | Laplace transform | Laplace transform | linear algebra | linear algebra | eigenvalues | eigenvalues | eigenvectors | eigenvectors | Fourier series | Fourier series | Runge-Kutta | Runge-Kutta | Tumbling box | Tumbling box | predator-prey equations | predator-prey equations | Lorenz Attractor | Lorenz AttractorLicense

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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Are you interested in building and testing your own imaging radar system? MIT Lincoln Laboratory offers this 3-week course in the design, fabrication, and test of a laptop-based radar sensor capable of measuring Doppler, range, and forming synthetic aperture radar (SAR) images. You do not have to be a radar engineer but it helps if you are interested in any of the following; electronics, amateur radio, physics, or electromagnetics. It is recommended that you have some familiarity with MATLAB®. Teams of three students will receive a radar kit and will attend a total of 5 sessions spanning topics from the fundamentals of radar to SAR imaging. Experiments will be performed each week as the radar kit is implemented. You will bring your radar kit into the field and perform additional experi Are you interested in building and testing your own imaging radar system? MIT Lincoln Laboratory offers this 3-week course in the design, fabrication, and test of a laptop-based radar sensor capable of measuring Doppler, range, and forming synthetic aperture radar (SAR) images. You do not have to be a radar engineer but it helps if you are interested in any of the following; electronics, amateur radio, physics, or electromagnetics. It is recommended that you have some familiarity with MATLAB®. Teams of three students will receive a radar kit and will attend a total of 5 sessions spanning topics from the fundamentals of radar to SAR imaging. Experiments will be performed each week as the radar kit is implemented. You will bring your radar kit into the field and perform additional experiSubjects

applied electromagnetics | applied electromagnetics | RF design | RF design | signal processing | signal processing | analog design | analog design | radar system design | radar system design | practical electronics | practical electronics | MATLAB | MATLABLicense

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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See all metadataón de Cálculo Numérico (2009) ón de Cálculo Numérico (2009)

Description

La asignatura de Ampliación de Cálculo Numérico es el complemento ideal a las asignaturas de Cálculo Numérico y Ecuaciones Diferenciales que se cursan en segundo curso. En ella se aplican los conocimientos y técnicas de la primera para resolver los problemas de la segunda. Tiene un interés indudable en Ingeniería Química pues muchos procesos químicos (cinética química, evolución de tanques de reacción, etc) están gobernados por ecuaciones diferenciales que en la mayoría de los casos no se pueden resolver analíticamente y hay que recurrir a métodos númericos para obtener uns descripción precisa del proceso. Del mismo modo, los procesos de transferencia de calor y mecánica de fluidos están gobernados por ecuaciones en derivadas parciales que también serán consideradas La asignatura de Ampliación de Cálculo Numérico es el complemento ideal a las asignaturas de Cálculo Numérico y Ecuaciones Diferenciales que se cursan en segundo curso. En ella se aplican los conocimientos y técnicas de la primera para resolver los problemas de la segunda. Tiene un interés indudable en Ingeniería Química pues muchos procesos químicos (cinética química, evolución de tanques de reacción, etc) están gobernados por ecuaciones diferenciales que en la mayoría de los casos no se pueden resolver analíticamente y hay que recurrir a métodos númericos para obtener uns descripción precisa del proceso. Del mismo modo, los procesos de transferencia de calor y mecánica de fluidos están gobernados por ecuaciones en derivadas parciales que también serán consideradasSubjects

ía Química | ía Química | álisis Matemático | álisis Matemático | Ecuaciones en Derivadas Parciales | Ecuaciones en Derivadas Parciales | étodo de Euler | étodo de Euler | étodo de Runge-Kutta | étodo de Runge-Kutta | MATLAB | MATLAB | ón Numérica de Ecuaciones Diferenciales | ón Numérica de Ecuaciones Diferenciales | ática Aplicada | ática AplicadaLicense

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See all metadata22.15 Essential Numerical Methods (MIT) 22.15 Essential Numerical Methods (MIT)

Description

Includes audio/video content: AV special element video. This half-semester course introduces computational methods for solving physical problems, especially in nuclear applications. The course covers ordinary and partial differential equations for particle orbit, and fluid, field, and particle conservation problems; their representation and solution by finite difference numerical approximations; iterative matrix inversion methods; stability, convergence, accuracy and statistics; and particle representations of Boltzmann's equation and methods of solution such as Monte-Carlo and particle-in-cell techniques. Includes audio/video content: AV special element video. This half-semester course introduces computational methods for solving physical problems, especially in nuclear applications. The course covers ordinary and partial differential equations for particle orbit, and fluid, field, and particle conservation problems; their representation and solution by finite difference numerical approximations; iterative matrix inversion methods; stability, convergence, accuracy and statistics; and particle representations of Boltzmann's equation and methods of solution such as Monte-Carlo and particle-in-cell techniques.Subjects

MATLAB | MATLAB | Octave | Octave | numerical methods | numerical methods | numerical analysis | numerical analysis | computational methods | computational methods | differential equations | differential equations | approximation | approximation | finite difference | finite difference | iteration | iterationLicense

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See all metadata16.323 Principles of Optimal Control (MIT) 16.323 Principles of Optimal Control (MIT)

Description

This course studies the principles of deterministic optimal control. It uses variational calculus and Pontryagin's maximum principle. It focuses on applications of the theory, including optimal feedback control, time-optimal control, and others. Dynamic programming and numerical search algorithms are introduced briefly. This course studies the principles of deterministic optimal control. It uses variational calculus and Pontryagin's maximum principle. It focuses on applications of the theory, including optimal feedback control, time-optimal control, and others. Dynamic programming and numerical search algorithms are introduced briefly.Subjects

nonlinear optimization | nonlinear optimization | linear quadratic regulators | linear quadratic regulators | MATLAB implementation | MATLAB implementation | dynamic programming | dynamic programming | calculus of variations | calculus of variations | LQR | LQR | LQG | LQG | stochastic optimization | stochastic optimization | on-line optimization and control | on-line optimization and control | constrained optimization | constrained optimization | signals | signals | system norms | system norms | Model Predictive Behavior | Model Predictive Behavior | quadratic programming | quadratic programming | mixed-integer linear programming | mixed-integer linear programming | linear programming | linear programmingLicense

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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See all metadata6.094 Introduction to MATLAB (MIT)

Description

This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with students doing sample MATLAB problems in real time. Problem-based MATLAB assignments are given which require significant time on MATLAB. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month. Acknowledgements The 6.094 course materials were developed by Danilo ??epanovi?, Sourav R. Dey, Ankit Patel, and Patrick Ho.Subjects

introduction to MATLAB | scripts | making variables | manipulating variables | functions | flow control | line plots | surface plots | vectorization | linear algebra | optimization | differential equations | data structures | debugging | animation | symbolic math | Simulink | file input/output | graphical user interfacesLicense

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 metadata1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for EnvironmSubjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative dataLicense

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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