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Mini Project Communication Link Simulation Channels And Noise Lecture

Description

The objective of this module is to have built communication links using existing AM modulation, PSK modulation and demodulation blocks, constructed AM modulators and constructed PSK modulators using operational function blocks based on their mathematical expressions, and conducted simulations of the links and modulators, all in Simulink®.Subjects

2ele0064 | additive white gaussian noise | channel | communication link simulation | communication systems | communications | demodulation | digital modulation | electronics | engineering | engsc | engscoer | errors performance degradation | expressions | fading and delay of channels | johnson noise | links | mathematical expressions | matlab | measures of system performance | mini project | modulation | noise | operational function blocks | school of electronic communications and electrical | sources of noise | system performance | thermal noise | ukoer | uniofhertsoer | university of hertfordshire | Engineering | H000License

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Includes audio/video content: AV special element audio. This class explores sound and what can be done with it. Sources are recorded from students' surroundings - sampled and electronically generated (both analog and digital). Assignments include composing with the sampled sounds, feedback, and noise, using digital signal processing (DSP), convolution, algorithms, and simple mixing. The class focuses on sonic and compositional aspects rather than technology, math, or acoustics, though these are examined in varying detail. Students complete weekly composition and listening assignments; material for the latter is drawn from sound art, experimental electronica, conventional and non-conventional classical electronic works, popular music, and previous students' compositions. Includes audio/video content: AV special element audio. This class explores sound and what can be done with it. Sources are recorded from students' surroundings - sampled and electronically generated (both analog and digital). Assignments include composing with the sampled sounds, feedback, and noise, using digital signal processing (DSP), convolution, algorithms, and simple mixing. The class focuses on sonic and compositional aspects rather than technology, math, or acoustics, though these are examined in varying detail. Students complete weekly composition and listening assignments; material for the latter is drawn from sound art, experimental electronica, conventional and non-conventional classical electronic works, popular music, and previous students' compositions.Subjects

computer music | computer music | sound | sound | music | music | audio | audio | listening | listening | electronic music | electronic music | new music | new music | electronica | electronica | sound art | sound art | noise | noise | noise music | noise music | avant-garde | avant-garde | contemporary music | contemporary music | modern music | modern music | composition | composition | recording | recording | music production | music production | recording studio | recording studio | audio software | audio software | recording software | recording software | sampling | sampling | synthesis | synthesis | audio engineering | audio engineering | mixing | mixing | Radiohead | RadioheadLicense

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See all metadataII "Junior Lab" (MIT) II "Junior Lab" (MIT)

Description

Junior Lab consists of two undergraduate courses in experimental physics. The courses are offered by the MIT Physics Department, and are usually taken by Juniors (hence the name). Officially, the courses are called Experimental Physics I and II and are numbered 8.13 for the first half, given in the fall semester, and 8.14 for the second half, given in the spring. The purposes of Junior Lab are to give students hands-on experience with some of the experimental basis of modern physics and, in the process, to deepen their understanding of the relations between experiment and theory, mostly in atomic and nuclear physics. Each term, students choose 5 different experiments from a list of 21 total labs. Junior Lab consists of two undergraduate courses in experimental physics. The courses are offered by the MIT Physics Department, and are usually taken by Juniors (hence the name). Officially, the courses are called Experimental Physics I and II and are numbered 8.13 for the first half, given in the fall semester, and 8.14 for the second half, given in the spring. The purposes of Junior Lab are to give students hands-on experience with some of the experimental basis of modern physics and, in the process, to deepen their understanding of the relations between experiment and theory, mostly in atomic and nuclear physics. Each term, students choose 5 different experiments from a list of 21 total labs.Subjects

Junior Lab | Junior Lab | experimental | experimental | atomic | atomic | nuclear | nuclear | physics | physics | optics | optics | photoelectric effect | photoelectric effect | poisson | poisson | statistics | statistics | electromagnetic pulse | electromagnetic pulse | compton scattering | compton scattering | Franck-Hertz experiment | Franck-Hertz experiment | relativistic dynamics | relativistic dynamics | nuclear magnetic resonance | nuclear magnetic resonance | spin echoes | spin echoes | cosmic-ray muons | cosmic-ray muons | Rutherford Scattering | Rutherford Scattering | emission spectra | emission spectra | neutron physics | neutron physics | Johnson noise | Johnson noise | shot noise | shot noise | quantum mechanics | quantum mechanics | alpha decay | alpha decay | radio astrophysics | radio astrophysics | Zeeman effect | Zeeman effect | rubidium | rubidium | M?ssbauer | M?ssbauer | spectroscopy | spectroscopy | X-Ray physics | X-Ray physics | superconductivity | superconductivity | Doppler-free | Doppler-free | laser | laserLicense

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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6.777J / 2.372J is an introduction to microsystem design. Topics covered include: material properties, microfabrication technologies, structural behavior, sensing methods, fluid flow, microscale transport, noise, and amplifiers feedback systems. Student teams design microsystems (sensors, actuators, and sensing/control systems) of a variety of types, (e.g., optical MEMS, bioMEMS, inertial sensors) to meet a set of performance specifications (e.g., sensitivity, signal-to-noise) using a realistic microfabrication process. There is an emphasis on modeling and simulation in the design process. Prior fabrication experience is desirable. The course is worth 4 Engineering Design Points. 6.777J / 2.372J is an introduction to microsystem design. Topics covered include: material properties, microfabrication technologies, structural behavior, sensing methods, fluid flow, microscale transport, noise, and amplifiers feedback systems. Student teams design microsystems (sensors, actuators, and sensing/control systems) of a variety of types, (e.g., optical MEMS, bioMEMS, inertial sensors) to meet a set of performance specifications (e.g., sensitivity, signal-to-noise) using a realistic microfabrication process. There is an emphasis on modeling and simulation in the design process. Prior fabrication experience is desirable. The course is worth 4 Engineering Design Points.Subjects

microsystem design | microsystem design | material properties | material properties | microfabrication technologies | microfabrication technologies | structural behavior | structural behavior | sensing methods | sensing methods | fluid flow | fluid flow | microscale transport | microscale transport | noise | noise | amplifiers feedback systems | amplifiers feedback systems | sensors | sensors | actuators | actuators | sensing/control systems | sensing/control systems | optical MEMS | optical MEMS | bioMEMS | bioMEMS | inertial sensors | inertial sensors | sensitivity | sensitivity | signal-to-noise | signal-to-noise | realistic microfabrication process | realistic microfabrication processLicense

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See all metadataII "Junior Lab" (MIT) II "Junior Lab" (MIT)

Description

Junior Lab consists of two undergraduate courses in experimental physics. The courses are offered by the MIT Physics Department, and are usually taken by Juniors (hence the name). Officially, the courses are called Experimental Physics I and II and are numbered 8.13 for the first half, given in the fall semester, and 8.14 for the second half, given in the spring.The purposes of Junior Lab are to give students hands-on experience with some of the experimental basis of modern physics and, in the process, to deepen their understanding of the relations between experiment and theory, mostly in atomic and nuclear physics. Each term, students choose 5 different experiments from a list of 21 total labs. Junior Lab consists of two undergraduate courses in experimental physics. The courses are offered by the MIT Physics Department, and are usually taken by Juniors (hence the name). Officially, the courses are called Experimental Physics I and II and are numbered 8.13 for the first half, given in the fall semester, and 8.14 for the second half, given in the spring.The purposes of Junior Lab are to give students hands-on experience with some of the experimental basis of modern physics and, in the process, to deepen their understanding of the relations between experiment and theory, mostly in atomic and nuclear physics. Each term, students choose 5 different experiments from a list of 21 total labs.Subjects

Junior Lab | Junior Lab | experimental | experimental | atomic | atomic | nuclear | nuclear | physics | physics | optics | optics | photoelectric effect | photoelectric effect | poisson | poisson | statistics | statistics | electromagnetic pulse | electromagnetic pulse | compton scattering | compton scattering | Franck-Hertz experiment | Franck-Hertz experiment | relativistic dynamics | relativistic dynamics | nuclear magnetic resonance | nuclear magnetic resonance | spin echoes | spin echoes | cosmic-ray muons | cosmic-ray muons | Rutherford Scattering | Rutherford Scattering | emission spectra | emission spectra | neutron physics | neutron physics | Johnson noise | Johnson noise | shot noise | shot noise | quantum mechanics | quantum mechanics | alpha decay | alpha decay | radio astrophysics | radio astrophysics | Zeeman effect | Zeeman effect | rubidium | rubidium | M?ssbauer | M?ssbauer | spectroscopy | spectroscopy | X-Ray physics | X-Ray physics | superconductivity | superconductivity | Doppler-free | Doppler-free | laser | laserLicense

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 broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation. This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation.Subjects

system identification; estimation; least squares estimation; Kalman filter; noise dynamics; system representation; function approximation theory; neural nets; radial basis functions; wavelets; volterra expansions; informative data sets; persistent excitation; asymptotic variance; central limit theorem; model structure selection; system order estimate; maximum likelihood; unbiased estimates; Cramer-Rao lower bound; Kullback-Leibler information distance; Akaike?s information criterion; experiment design; model validation. | system identification; estimation; least squares estimation; Kalman filter; noise dynamics; system representation; function approximation theory; neural nets; radial basis functions; wavelets; volterra expansions; informative data sets; persistent excitation; asymptotic variance; central limit theorem; model structure selection; system order estimate; maximum likelihood; unbiased estimates; Cramer-Rao lower bound; Kullback-Leibler information distance; Akaike?s information criterion; experiment design; model validation. | system identification | system identification | estimation | estimation | least squares estimation | least squares estimation | Kalman filter | Kalman filter | noise dynamics | noise dynamics | system representation | system representation | function approximation theory | function approximation theory | neural nets | neural nets | radial basis functions | radial basis functions | wavelets | wavelets | volterra expansions | volterra expansions | informative data sets | informative data sets | persistent excitation | persistent excitation | asymptotic variance | asymptotic variance | central limit theorem | central limit theorem | model structure selection | model structure selection | system order estimate | system order estimate | maximum likelihood | maximum likelihood | unbiased estimates | unbiased estimates | Cramer-Rao lower bound | Cramer-Rao lower bound | Kullback-Leibler information distance | Kullback-Leibler information distance | Akaike?s information criterion | Akaike?s information criterion | experiment design | experiment design | model validation | model validationLicense

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See all metadata9.04 Neural Basis of Vision and Audition (MIT) 9.04 Neural Basis of Vision and Audition (MIT)

Description

Examines the neural bases of visual and auditory processing for perception and sensorimotor control. Focuses on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization. Offered alternate years. Examines the neural bases of visual and auditory processing for perception and sensorimotor control. Focuses on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization. Offered alternate years.Subjects

visual system | visual system | eye-movement control | eye-movement control | retina | retina | lateral geniculate nucleus | lateral geniculate nucleus | visual cortex | visual cortex | the parallel channels | the parallel channels | color | color | motion | motion | depth | depth | form | form | neural control | neural control | visually guided eye movements | visually guided eye movements | middle ear | middle ear | cochlear | cochlear | otoacoustic emissions | otoacoustic emissions | cochlear ultrastructure and neuroanatomy | cochlear ultrastructure and neuroanatomy | cochlear ion homeostasis and synaptic transmission | cochlear ion homeostasis and synaptic transmission | noise-induced and age-related hearing loss | noise-induced and age-related hearing loss | neural degeneration | neural degeneration | neurophysiological | neurophysiological | ascending | ascending | descending | descending | auditory pathways auditory nerve | auditory pathways auditory nerve | cochlear nucleus | cochlear nucleus | inferior colliculus | inferior colliculus | olivocochlear system | olivocochlear system | functional brain imaging | functional brain imaging | tinnitus | tinnitusLicense

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See all metadata16.881 Robust System Design (MIT) 16.881 Robust System Design (MIT)

Description

This course was created for the "product development" track of MIT's System Design and Management Program (SDM) in conjunction with the Center for Innovation in Product Development. After taking this course, a student should be able to: Formulate measures of performance of a system or quality characteristics. These quality characteristics are to be made robust to noise affecting the system. Sythesize and select design concepts for robustness. Identify noise factors whose variation may affect the quality characteristics. Estimate the robustness of any given design (experimentally and analytically). Formulate and implement methods to reduce the effects of noise (parameter design, active control, adjustment). Select rational tolerances for a design. Explain the role of robust design This course was created for the "product development" track of MIT's System Design and Management Program (SDM) in conjunction with the Center for Innovation in Product Development. After taking this course, a student should be able to: Formulate measures of performance of a system or quality characteristics. These quality characteristics are to be made robust to noise affecting the system. Sythesize and select design concepts for robustness. Identify noise factors whose variation may affect the quality characteristics. Estimate the robustness of any given design (experimentally and analytically). Formulate and implement methods to reduce the effects of noise (parameter design, active control, adjustment). Select rational tolerances for a design. Explain the role of robust designSubjects

robust system design | robust system design | quality characteristics | quality characteristics | product development | product development | noise factors | noise factors | parameter design | parameter design | active control | active control | rational tolerances | rational tolerancesLicense

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See all metadata6.002 Circuits and Electronics (MIT) 6.002 Circuits and Electronics (MIT)

Description

6.002 introduces the fundamentals of the lumped circuit abstraction. Topics covered include: resistive elements and networks; independent and dependent sources; switches and MOS transistors; digital abstraction; amplifiers; energy storage elements; dynamics of first- and second-order networks; design in the time and frequency domains; and analog and digital circuits and applications. Design and lab exercises are also significant components of the course. 6.002 is worth 4 Engineering Design Points. 6.002 introduces the fundamentals of the lumped circuit abstraction. Topics covered include: resistive elements and networks; independent and dependent sources; switches and MOS transistors; digital abstraction; amplifiers; energy storage elements; dynamics of first- and second-order networks; design in the time and frequency domains; and analog and digital circuits and applications. Design and lab exercises are also significant components of the course. 6.002 is worth 4 Engineering Design Points.Subjects

circuit | circuit | electronic | electronic | abstraction | abstraction | lumped circuit | lumped circuit | digital | digital | amplifier | amplifier | differential equations | differential equations | time behavior | time behavior | energy storage | energy storage | semiconductor diode | semiconductor diode | field-effect | field-effect | field-effect transistor | field-effect transistor | resistor | resistor | source | source | inductor | inductor | capacitor | capacitor | diode | diode | series-parallel reduction | series-parallel reduction | voltage | voltage | current divider | current divider | node method | node method | linearity | linearity | superposition | superposition | Thevenin-Norton equivalent | Thevenin-Norton equivalent | power flow | power flow | Boolean algebra | Boolean algebra | binary signal | binary signal | MOSFET | MOSFET | noise margin | noise margin | singularity functions | singularity functions | sinusoidal-steady-state | sinusoidal-steady-state | impedance | impedance | frequency response curves | frequency response curves | operational amplifier | operational amplifier | Op-Amp | Op-Amp | negative feedback | negative feedback | positive feedback | positive feedbackLicense

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See all metadata6.050J Information and Entropy (MIT) 6.050J Information and Entropy (MIT)

Description

Includes audio/video content: AV selected lectures. This course explores the ultimate limits to communication and computation, with an emphasis on the physical nature of information and information processing. Topics include: information and computation, digital signals, codes and compression, applications such as biological representations of information, logic circuits, computer architectures, and algorithmic information, noise, probability, error correction, reversible and irreversible operations, physics of computation, and quantum computation. The concept of entropy applied to channel capacity and to the second law of thermodynamics. Includes audio/video content: AV selected lectures. This course explores the ultimate limits to communication and computation, with an emphasis on the physical nature of information and information processing. Topics include: information and computation, digital signals, codes and compression, applications such as biological representations of information, logic circuits, computer architectures, and algorithmic information, noise, probability, error correction, reversible and irreversible operations, physics of computation, and quantum computation. The concept of entropy applied to channel capacity and to the second law of thermodynamics.Subjects

information and entropy | information and entropy | computing | computing | communications | communications | thermodynamics | thermodynamics | digital signals and streams | digital signals and streams | codes | codes | compression | compression | noise | noise | probability | probability | reversible operations | reversible operations | irreversible operations | irreversible operations | information in biological systems | information in biological systems | channel capacity | channel capacity | maximum-entropy formalism | maximum-entropy formalism | thermodynamic equilibrium | thermodynamic equilibrium | temperature | temperature | second law of thermodynamics quantum computation | second law of thermodynamics quantum computationLicense

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See all metadata2.160 Identification, Estimation, and Learning (MIT)

Description

This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation.Subjects

system identification; estimation; least squares estimation; Kalman filter; noise dynamics; system representation; function approximation theory; neural nets; radial basis functions; wavelets; volterra expansions; informative data sets; persistent excitation; asymptotic variance; central limit theorem; model structure selection; system order estimate; maximum likelihood; unbiased estimates; Cramer-Rao lower bound; Kullback-Leibler information distance; Akaike?s information criterion; experiment design; model validation. | system identification | estimation | least squares estimation | Kalman filter | noise dynamics | system representation | function approximation theory | neural nets | radial basis functions | wavelets | volterra expansions | informative data sets | persistent excitation | asymptotic variance | central limit theorem | model structure selection | system order estimate | maximum likelihood | unbiased estimates | Cramer-Rao lower bound | Kullback-Leibler information distance | Akaike?s information criterion | experiment design | model validationLicense

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 metadata8.044 Statistical Physics I (MIT) 8.044 Statistical Physics I (MIT)

Description

This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices.Subjects

probability | probability | statistical mechanics | statistical mechanics | thermodynamics | thermodynamics | random variables | random variables | joint and conditional probability densities | joint and conditional probability densities | functions of a random variable | functions of a random variable | macroscopic variables | macroscopic variables | thermodynamic equilibrium | thermodynamic equilibrium | fundamental assumption of statistical mechanics | fundamental assumption of statistical mechanics | microcanonical and canonical ensembles | microcanonical and canonical ensembles | First | First | second | second | and third laws of thermodynamics | and third laws of thermodynamics | magnetism | magnetism | polyatomic gases | polyatomic gases | hermal radiation | hermal radiation | thermal radiation | thermal radiation | electrons in solids | electrons in solids | and noise in electronic devices | and noise in electronic devices | First | second | and third laws of thermodynamics | First | second | and third laws of thermodynamicsLicense

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 metadata8.044 Statistical Physics I (MIT) 8.044 Statistical Physics I (MIT)

Description

Introduction to probability, statistical mechanics, and thermodynamics. Random variables, joint and conditional probability densities, and functions of a random variable. Concepts of macroscopic variables and thermodynamic equilibrium, fundamental assumption of statistical mechanics, microcanonical and canonical ensembles. First, second, and third laws of thermodynamics. Numerous examples illustrating a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. Concurrent enrollment in 8.04, Quantum Physics I, is recommended. Introduction to probability, statistical mechanics, and thermodynamics. Random variables, joint and conditional probability densities, and functions of a random variable. Concepts of macroscopic variables and thermodynamic equilibrium, fundamental assumption of statistical mechanics, microcanonical and canonical ensembles. First, second, and third laws of thermodynamics. Numerous examples illustrating a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. Concurrent enrollment in 8.04, Quantum Physics I, is recommended.Subjects

probability | probability | statistical mechanics | statistical mechanics | thermodynamics | thermodynamics | random variables | random variables | joint and conditional probability densities | joint and conditional probability densities | functions of a random variable | functions of a random variable | macroscopic variables | macroscopic variables | thermodynamic equilibrium | thermodynamic equilibrium | fundamental assumption of statistical mechanics | fundamental assumption of statistical mechanics | microcanonical and canonical ensembles | microcanonical and canonical ensembles | First | First | second | second | and third laws of thermodynamics | and third laws of thermodynamics | magnetism | magnetism | polyatomic gases | polyatomic gases | hermal radiation | hermal radiation | thermal radiation | thermal radiation | electrons in solids | electrons in solids | and noise in electronic devices | and noise in electronic devices | First | second | and third laws of thermodynamics | First | second | and third laws of thermodynamicsLicense

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See all metadata22.058 Principles of Medical Imaging (MIT) 22.058 Principles of Medical Imaging (MIT)

Description

An introduction to the principles of tomographic imaging and its applications. It includes a series of lectures with a parallel set of recitations that provide demonstrations of basic principles. Both ionizing and non-ionizing radiation are covered, including x-ray, PET, MRI, and ultrasound. Emphasis on the physics and engineering of image formation. An introduction to the principles of tomographic imaging and its applications. It includes a series of lectures with a parallel set of recitations that provide demonstrations of basic principles. Both ionizing and non-ionizing radiation are covered, including x-ray, PET, MRI, and ultrasound. Emphasis on the physics and engineering of image formation.Subjects

general imaging principles | | general imaging principles | | linear optics | | linear optics | | ray tracing | | ray tracing | | Linear Imaging Systems | | Linear Imaging Systems | | Space Invariance | | Space Invariance | | Pin-hole camera | | Pin-hole camera | | Fourier Transformations | | Fourier Transformations | | Modulation Transfer Functions | | Modulation Transfer Functions | | Fourier convolution | | Fourier convolution | | Sampling | | Sampling | | Nyquist | | Nyquist | | counting statistics | | counting statistics | | additive noise | | additive noise | | optical imaging | | optical imaging | | Radiation types | | Radiation types | | Radiation detection | | Radiation detection | | photon detection | | photon detection | | spectra | | spectra | | attenuation | | attenuation | | Planar X-ray imaging | | Planar X-ray imaging | | Projective Imaging | | Projective Imaging | | X-ray CT | | X-ray CT | | Ultrasound | | Ultrasound | | microscopy | k-space | | microscopy | k-space | | NMR pulses | | NMR pulses | | f2-D gradient | | f2-D gradient | | spin echoes | | spin echoes | | 3-D methods of MRI | | 3-D methods of MRI | | volume localized spectroscopy | volume localized spectroscopyLicense

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See all metadata6.441 Transmission of Information (MIT) 6.441 Transmission of Information (MIT)

Description

6.441 offers an introduction to the quantitative theory of information and its applications to reliable, efficient communication systems. Topics include: mathematical definition and properties of information; source coding theorem, lossless compression of data, optimal lossless coding; noisy communication channels, channel coding theorem, the source-channel separation theorem, multiple access channels, broadcast channels, Gaussian noise, and time-varying channels. 6.441 offers an introduction to the quantitative theory of information and its applications to reliable, efficient communication systems. Topics include: mathematical definition and properties of information; source coding theorem, lossless compression of data, optimal lossless coding; noisy communication channels, channel coding theorem, the source-channel separation theorem, multiple access channels, broadcast channels, Gaussian noise, and time-varying channels.Subjects

transmission of information | transmission of information | quantitative theory of information | quantitative theory of information | efficient communication systems | efficient communication systems | mathematical definition of information | mathematical definition of information | properties of information | properties of information | source coding theorem | source coding theorem | lossless compression of data | lossless compression of data | optimal lossless coding | optimal lossless coding | noisy communication channels | noisy communication channels | channel coding theorem | channel coding theorem | the source-channel separation theorem | the source-channel separation theorem | multiple access channels | multiple access channels | broadcast channels | broadcast channels | gaussian noise | gaussian noise | time-varying channels | time-varying channels | lossless data compression | lossless data compression | telecommunications | telecommunications | data transmission | data transmissionLicense

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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 metadata9.04 Neural Basis of Vision and Audtion (MIT) 9.04 Neural Basis of Vision and Audtion (MIT)

Description

This course is designed to ground the undergraduate student in the fields of vision and audition, which includes both speech and hearing. The neural bases of visual and auditory processing for perception and sensorimotor control is examined. Topics focus on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies in visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization are also covered. This course is designed to ground the undergraduate student in the fields of vision and audition, which includes both speech and hearing. The neural bases of visual and auditory processing for perception and sensorimotor control is examined. Topics focus on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies in visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization are also covered.Subjects

visual system | visual system | eye-movement control | eye-movement control | retina | retina | lateral geniculate nucleus | lateral geniculate nucleus | visual cortex | visual cortex | the parallel channels | the parallel channels | color | color | motion | motion | depth | depth | form | form | neural control | neural control | visually guided eye movements | visually guided eye movements | middle ear | middle ear | cochlear | cochlear | otoacoustic emissions | otoacoustic emissions | cochlear ultrastructure and neuroanatomy | cochlear ultrastructure and neuroanatomy | cochlear ion homeostasis and synaptic transmission | cochlear ion homeostasis and synaptic transmission | noise-induced and age-related hearing loss | noise-induced and age-related hearing loss | neural degeneration | neural degeneration | neurophysiological | neurophysiological | ascending | ascending | descending | descending | auditory pathways auditory nerve | auditory pathways auditory nerve | cochlear nucleus | cochlear nucleus | inferior colliculus | inferior colliculus | olivocochlear system | olivocochlear system | functional brain imaging | functional brain imaging | tinnitus | tinnitusLicense

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See all metadata14.127 Behavioral Economics and Finance (MIT) 14.127 Behavioral Economics and Finance (MIT)

Description

This course surveys research which incorporates psychological evidence into economics. Topics include: prospect theory, biases in probabilistic judgment, self-control and mental accounting with implications for consumption and savings, fairness, altruism, and public goods contributions, financial market anomalies and theories, impact of markets, learning, and incentives, and memory, attention, categorization, and the thinking process. This course surveys research which incorporates psychological evidence into economics. Topics include: prospect theory, biases in probabilistic judgment, self-control and mental accounting with implications for consumption and savings, fairness, altruism, and public goods contributions, financial market anomalies and theories, impact of markets, learning, and incentives, and memory, attention, categorization, and the thinking process.Subjects

behavioral economics | behavioral economics | finance | finance | psychology | psychology | prospect theory | prospect theory | bias | bias | probabilistic judgment | probabilistic judgment | self-control | self-control | mental accounting | mental accounting | fairness | fairness | altruism | altruism | public goods | public goods | market anomalies | market anomalies | market theories | market theories | heuristics | heuristics | noise | noise | confusion | confusion | competition | competition | bounded rationality | bounded rationality | learning | learning | games | games | neuroeconomics | neuroeconomics | hyperbolic discounting | hyperbolic discounting | consumption | consumption | hyperbolics | hyperbolics | temptation | temptation | assets | assets | puzzles | puzzles | bubbles | bubbles | Gul-Pesendorfer | Gul-PesendorferLicense

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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Junior Lab consists of two undergraduate courses in experimental physics. The courses are offered by the MIT Physics Department, and are usually taken by Juniors (hence the name). Officially, the courses are called Experimental Physics I and II and are numbered 8.13 for the first half, given in the fall semester, and 8.14 for the second half, given in the spring.The purposes of Junior Lab are to give students hands-on experience with some of the experimental basis of modern physics and, in the process, to deepen their understanding of the relations between experiment and theory, mostly in atomic and nuclear physics. Each term, students choose 5 different experiments from a list of 21 total labs.Subjects

Junior Lab | experimental | atomic | nuclear | physics | optics | photoelectric effect | poisson | statistics | electromagnetic pulse | compton scattering | Franck-Hertz experiment | relativistic dynamics | nuclear magnetic resonance | spin echoes | cosmic-ray muons | Rutherford Scattering | emission spectra | neutron physics | Johnson noise | shot noise | quantum mechanics | alpha decay | radio astrophysics | Zeeman effect | rubidium | M?ssbauer | spectroscopy | X-Ray physics | superconductivity | Doppler-free | laserLicense

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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Includes audio/video content: AV lectures. This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm. More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and Includes audio/video content: AV lectures. This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm. More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product andSubjects

coding techniques | coding techniques | the Shannon limit of additive white Gaussian noise channels | the Shannon limit of additive white Gaussian noise channels | performance analysis | performance analysis | Small signal constellations | Small signal constellations | coding gain | coding gain | Hard-decision and soft-decision decoding | Hard-decision and soft-decision decoding | Introduction to binary linear block codes | Introduction to binary linear block codes | Reed-Muller codes | Reed-Muller codes | finite fields | finite fields | Reed-Solomon and BCH codes | Reed-Solomon and BCH codes | binary linear convolutional codes | binary linear convolutional codes | Viterbi and BCJR algorithms | Viterbi and BCJR algorithms | Trellis representations of binary linear block codes | Trellis representations of binary linear block codes | trellis-based ML decoding | trellis-based ML decoding | Codes on graphs | Codes on graphs | sum-product | sum-product | max-product | max-product | decoding algorithms | decoding algorithms | Turbo codes | Turbo codes | LDPC codes and RA codes | LDPC codes and RA codes | Coding for the bandwidth-limited regime | Coding for the bandwidth-limited regime | Lattice codes. | Lattice codes. | Trellis-coded modulation | Trellis-coded modulation | Multilevel coding | Multilevel coding | Shaping | Shaping | Lattice codes | Lattice codesLicense

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See all metadata9.71 Functional MRI of High-Level Vision (MIT) 9.71 Functional MRI of High-Level Vision (MIT)

Description

We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory. The goals of this course are to help We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory. The goals of this course are to helpSubjects

functional magnetic resonance imaging (fMRI) | functional magnetic resonance imaging (fMRI) | neural activity | neural activity | human | human | brain | brain | noninvasive | noninvasive | resolution | resolution | high-level vision | high-level vision | object recognition | object recognition | visual attention | visual attention | perceptual awareness | perceptual awareness | visually guided action | visually guided action | visual memory | visual memory | voxelwise analysis | voxelwise analysis | conjugate mirroring | conjugate mirroring | interleaved stimulus presentation | interleaved stimulus presentation | magnetization following excitation | magnetization following excitation | active voxels | active voxels | scanner drift | scanner drift | trial sorting | trial sorting | collinear factors | collinear factors | different model factors | different model factors | mock scanner | mock scanner | scanner session | scanner session | visual stimulation task | visual stimulation task | hemoglobin signal | hemoglobin signal | labeling plane | labeling plane | nearby voxels | nearby voxels | shimming coils | shimming coils | bias field estimation | bias field estimation | conscious encoding | conscious encoding | spiral imaging | spiral imaging | functional resolution | functional resolution | hemodynamic activity | hemodynamic activity | direct cortical stimulation | direct cortical stimulation | physiological noise | physiological noise | refractory effects | refractory effects | independent statistical tests. | independent statistical tests.License

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See all metadata6.441 Information Theory (MIT) 6.441 Information Theory (MIT)

Description

6.441 offers an introduction to the quantitative theory of information and its applications to reliable, efficient communication systems. Topics include mathematical definition and properties of information, source coding theorem, lossless compression of data, optimal lossless coding, noisy communication channels, channel coding theorem, the source channel separation theorem, multiple access channels, broadcast channels, Gaussian noise, and time-varying channels. 6.441 offers an introduction to the quantitative theory of information and its applications to reliable, efficient communication systems. Topics include mathematical definition and properties of information, source coding theorem, lossless compression of data, optimal lossless coding, noisy communication channels, channel coding theorem, the source channel separation theorem, multiple access channels, broadcast channels, Gaussian noise, and time-varying channels.Subjects

properties of information | properties of information | source coding theorem | source coding theorem | lossless compression | lossless compression | noisy communication | noisy communication | channel coding theorem | channel coding theorem | source channel separation theorem | source channel separation theorem | multiple access channels | multiple access channels | broadcast channels | broadcast channels | Gaussian noise | Gaussian noise | time-varying channels | time-varying channelsLicense

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 metadataLecture 8: Strategy, Skill, and Chance, Part 1 Lecture 8: Strategy, Skill, and Chance, Part 1

Description

Description: Games contain various skill requirements, chance elements, and information availability, which guide strategy development. Changing the balance between these factors can create very different player experiences. Instructors/speakers: Philip Tan, Jason BegyKeywords: competition, strategy, game theory, roleplaying, vertigo, mimicry, ilinx, sports, alea, gameshows, randomness, games of skill, games of chance, luck, information theory, communication channel, noise, game state, card games, board games, determinism, probability, decision tree, utility, Nash equilibriumTranscript: PDFSubtitles: SRTAudio - download: Internet Archive (MP3)Audio - download: iTunes U (MP3)(CC BY-NC-SA) Description: Games contain various skill requirements, chance elements, and information availability, which guide strategy development. Changing the balance between these factors can create very different player experiences. Instructors/speakers: Philip Tan, Jason BegyKeywords: competition, strategy, game theory, roleplaying, vertigo, mimicry, ilinx, sports, alea, gameshows, randomness, games of skill, games of chance, luck, information theory, communication channel, noise, game state, card games, board games, determinism, probability, decision tree, utility, Nash equilibriumTranscript: PDFSubtitles: SRTAudio - download: Internet Archive (MP3)Audio - download: iTunes U (MP3)(CC BY-NC-SA)Subjects

competition | competition | strategy | strategy | game theory | game theory | roleplaying | roleplaying | vertigo | vertigo | mimicry | mimicry | ilinx | ilinx | sports | sports | alea | alea | gameshows | gameshows | randomness | randomness | games of skill | games of skill | games of chance | games of chance | luck | luck | information theory | information theory | communication channel | communication channel | noise | noise | game state | game state | card games | card games | board games | board games | determinism | determinism | probability | probability | decision tree | decision tree | utility | utility | Nash equilibrium | Nash equilibriumLicense

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 metadataDG2W35 Active Electronic Circuits

Description

This unit is designed to enable candidates to build on their knowledge of analogue electronic principles with regards to further understanding of electronic circuits. It will allow the candidate to gain an understanding of feedback and to develop this understanding with regards to a specified list of electronic amplifiers, filters and oscillator circuits. In addition, candidates will design a second-order filter using a reference table provided. The candidates will be required to perform practical tests on a circuit from the list. On completion of this unit the candidate should be able to: 1. analyse the effects of positive and negative feedback 2. analyse the circuits and properties of common operational amplifier circuits 3. outline the properties of filters 4. outline the operation of eSubjects

DG2W 35 | Feedback configurations | Series voltage feedback | Series current negative feedback | Shunt voltage negative feedback | Shunt current negative feedback | Frequency response | AC amplifiers | Distortion | Thermal noise | Signal to noise ratio | Integrator | Differentiator | Active filters | Sallen–Key filter | SCQF Level 8License

Licensed to colleges in Scotland only Licensed to colleges in Scotland only Except where expressly indicated otherwise on the face of these materials (i) copyright in these materials is owned by the Scottish Qualification Authority (SQA), and (ii) none of these materials may be Used without the express, prior, written consent of the Colleges Open Learning Exchange Group (COLEG) and SQA, except if and to the extent that such Use is permitted under COLEG's conditions of Contribution and Use of Learning Materials through COLEG’s Repository, for the purposes of which these materials are COLEG Materials, Except where expressly indicated otherwise on the face of these materials (i) copyright in these materials is owned by the Scottish Qualification Authority (SQA), and (ii) none of these materials may be Used without the express, prior, written consent of the Colleges Open Learning Exchange Group (COLEG) and SQA, except if and to the extent that such Use is permitted under COLEG's conditions of Contribution and Use of Learning Materials through COLEG’s Repository, for the purposes of which these materials are COLEG Materials, http://content.resourceshare.ac.uk/xmlui/bitstream/handle/10949/17761/LicenceSQAMaterialsCOLEG.pdf?sequence=1 http://content.resourceshare.ac.uk/xmlui/bitstream/handle/10949/17761/LicenceSQAMaterialsCOLEG.pdf?sequence=1 SQA SQASite sourced from

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See all metadata6.777J Design and Fabrication of Microelectromechanical Devices (MIT)

Description

6.777J / 2.372J is an introduction to microsystem design. Topics covered include: material properties, microfabrication technologies, structural behavior, sensing methods, fluid flow, microscale transport, noise, and amplifiers feedback systems. Student teams design microsystems (sensors, actuators, and sensing/control systems) of a variety of types, (e.g., optical MEMS, bioMEMS, inertial sensors) to meet a set of performance specifications (e.g., sensitivity, signal-to-noise) using a realistic microfabrication process. There is an emphasis on modeling and simulation in the design process. Prior fabrication experience is desirable. The course is worth 4 Engineering Design Points.Subjects

microsystem design | material properties | microfabrication technologies | structural behavior | sensing methods | fluid flow | microscale transport | noise | amplifiers feedback systems | sensors | actuators | sensing/control systems | optical MEMS | bioMEMS | inertial sensors | sensitivity | signal-to-noise | realistic microfabrication processLicense

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