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| − | + | = <u>'''ECE 301: Applications in Biomedical Engineering'''</u> = | |
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| − | + | == Examples of Signals and Systems in Biomedical Engineering: == | |
| − | + | *'''Physiological Systems Modeling''' – Physiological processes can often be modeled mathematically. An organism's environment (or the output from another body system) can be viewed as a signal, x(t). The physiological system being modeled, h(t), will convert this signal into output, y(t). For example, the blood clotting mechanism can be illustrated as a system [[Video Tutorial on How to Cascade Transformations of the Independent Variable|cascade]]: <br>x(t) = the amount of protein released by damaged endothelium (skin) cells<br>h1(t) = amplifying system <br>w1(t) = the amount of clotting factor VII<br>h2(t) = amplifying system<br>w2(t) = the amount of factor XI converted to XIa<br>and so forth until the final output...<br>y(t) = amount of cross-linked fibrin (that makes the blood clot)<br>Would you consider this system [[Memoryless system question ECE301S11|memoryless?]] [[Causal system question ECE301S11|Causal?]] | |
| − | * | + | *'''Image and Image Processing''' – X-ray, ultrasound, MRI (magnetic resonance imaging), and CT (computerized tomography) scans all convert analog physical signals to discrete computer signals. Systems (maybe even [[Fourier_Transform_Video |Fourier]]/Laplace/[[Compute z-transform u n ECE301S11|Z transforms]] from [[2011 Spring ECE 301 Boutin|ECE301]]!) are utilized to convert, filter, and combine these signals and produce the images used in diagnostics. |
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| − | + | *'''Bioinformatics''' – Gene sequencing requires techniques to translate DNA from chromosomes to digitally recordable information. Think of each nucleic acid as n = 0, 1, 2, ... for the discrete signal x[n] of the entire DNA sequence. (Maybe x[n] = 1, 2, 3, and 4 corresponding to adenine, guanine, cytosine, and thymine.) Furthermore, this signal must be broken into distinct genes and decoded. These genes can be matched to their potential phenotypes (physical traits/functions) using databases of sequences. | |
| + | *'''Proteomics''' – A proteome is the set of proteins produced by a species, similar to a set of genes in a genome. The study of proteomes is important in understanding cellular processes, infection, and patterns related to disease. In proteomics, engineers must develop hardware that can measure protein levels rapidly and accurately. In this case, the protein levels can be thought of as a CT signal, sampled and stored as a DT signal. Think about what we have learned about [[2011 Spring ECE 301 Bouton Notes|sampling and conversion between CT/DT signals]]. Filters are also used to sort the main trend of the signal from the noise (common in biological systems). | ||
| + | *'''Wireless and Mobile Technologies''' – Just as phones and computers have gone wireless, now medical equipment, sensors, and surgical devices are going wireless. These critical signals must be properly transmitted, which means the use of sampling, signal carriers, CT/DT converters, etc. Athena GTX, a company described in the Biomedical Industry section below, focuses on this exact subject. | ||
| − | + | *'''BioSIGNALS''' – The electrical signals produced by the body are extremely useful in diagnostics:<br>'' Electrocardiogram (ECG)'' – used to measure the electrical activity of the heart and diagnose conditions including blockages and arrhythmias. For more detailed information on diagnosis with ECGs, check out this website: http://www.bem.fi/book/19/19.htm<br>'' Electromyogram (EMG)'' – used to measure the electrical activity of muscles and nerves to diagnose conditions including carpal tunnel syndrome and sciatica. One technique, called targeted muscle reinnervation (TMR), uses these signals to allow amputees to control mechanical limbs with their natural nerve signals. Purdue professor Pedro Irazoqui, research link below, is involved in this research with DARPA and Northwestern University.<br>'' Electroencephalogram (EEG)'' – used to measure the electrical activity of the brain to diagnose conditions including epilepsy and comas. Biomedical research attempts to use these signals to allow people with paralysis to control a computer. <br> All of these signals require filtering of background noise and, often times, conversion from continuous time (CT) to discrete time (DT).<br> | |
| − | + | These and more applications of biomedical engineering can be found at http://www.embs.org/docs/careerguide.pdf | |
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| − | + | == Uses for Signals and Systems in Biomedical Industry == | |
| − | + | Listed below are biomedical companies that apply what we are learning in ECE301. I have started the list with two companies I know, but feel free to add on! | |
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| − | + | *'''Cyberonics, Inc.''' – This company focuses on Vagus Nerve Stimulation (VNS Therapy) to treat refractory epilepsy. In this therapy, an electronic device is implanted next to the vagus nerve (located in the chest, but leading to the brain) to prevent seizures through intermittent stimulation. Current research (including the research of Purdue's Pedro Irazoqui) aims to measure and understand brain activity before seizures, so that the device can predict seizures based on the changes and only stimulate when necessary. This work requires filters for the biological signals as well systems to interpret these signals. As of 2005, the company has also moved into the use of VNS for treatment-resistant depression. (After this summer, I should be able to add on to this description!) | |
| − | * | + | *'''Athena GTX''' – This company makes wireless monitoring devices for hospitals, emergency medical services, and even military soldiers. This work requires filtering and measurement of various physiological signals, as well as the challenges of sending and receiving wireless signals. (I know of one ECE301 student who could elaborate more on this company...)<br> |
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| − | + | == Purdue Biomedical Research with Signals and Systems<br> == | |
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| − | + | *'''Biomedical Imaging and Sensing''' - This research aims to improve accuracy and and safety of current medical/diagnostic equipment. Research includes: | |
| + | **Less expensive acoustical instruments for the home or physician offices | ||
| + | **Acoustics to guide the placement of breathing tubes in infants | ||
| + | **Signal processing and filtering algorithms to create stethoscopes that function in high-noise environments such as helicopters and ambulances | ||
| + | **Stereo imaging for visualization for mammography diagnostics and acoustic imaging of the lung. | ||
| + | https://engineering.purdue.edu/ECE/Research/Areas/BiomedIS.whtml | ||
| − | * | + | *'''VACCINE''' - This research group creates methods and tools to analyze and manage information for homeland security, but some [[Vaccine Posters|past research]] has focused on emergency response with mobile devices. This research has direct implications on the field of Emergency Medicine.<br> |
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| − | + | *'''Biophotonics and Medical Imaging''' - This research uses atomic and nano-scale imaging technologies to locate, track, and explore human tissues, as well as individual cells and molecules, using sophisticated algorithms to analyze numerical images. Research also uses non-invasive optical systems to provide real-time imaging of drug dispersal and interaction with target cells. | |
| + | https://engineering.purdue.edu/BME/Research/BIO | ||
| − | * | + | *'''Neuroengineering''' - This research employs interdisciplinary engineering approaches to examine and manipulate the function and behavior of the nervous system. Concepts used include computational biology, neuroscience, electrical engineering, ''signal processing'', and chemistry. The goal of this research is to prevent and treat paralysis, as well as to help victims of degenerative disease to communicate using brain-computer interfaces. |
| − | + | https://engineering.purdue.edu/BME/Research/NE | |
| − | + | *'''Systems Science and Engineering''' - The goal of this research is to improve healthcare efficiency, which will reduce costs, improve treatments and outcomes, and eliminate other problems. This research requires the use of complex mathematical modeling and system analysis. | |
| − | + | https://engineering.purdue.edu/BME/Research/HCS<br> | |
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| − | + | == Other Purdue courses that use Signals and Systems related to Biomedical Engineering == | |
| − | + | These descriptions were borrowed from the myPurdue course catalog and Purdue BME course resources (with a few added comments from me). Listed in order of relevance: | |
| − | + | *'''BME 595/511 - Biosignal Processing '''(Fall)'''- '''An introduction to the application of digital signal processing to practical problems involving biomedical signals and systems. Topic include: examples of biomedical signals; analysis of concurrent, coupled, and correlated processes; filtering for removal of artifacts; event detection, analysis of waveshape and waveform complexity; frequency domain characterization of signals and systems; modeling biomedical signal-generating processes and systems; analysis of nonstationary signals; pattern classification and diagnostic decision. MATLAB will be used throughout to provide numerous opportunities for hands-on application of the theory and techniques discussed to real-life biomedical signals. | |
| − | + | *'''BME 495/430 - Biomedical Imaging Modalities''' (Spring) - This course covers basic principles and modes of bioimaging methods for biomedical sciences. Topics include interaction of electromagnetic radiation with tissue, basic concepts in imaging and detection, basic modes of imaging modalities (e.g. reflection, transmission, absorption, and emission), and basic image processing/analysis. Model systems to be used to teach the topics include conventional imaging modalities such as optical imaging, optical microscopy, X-ray, computed tomography, ultrasound, magnetic resonance imaging, etc. This course also includes hands-on exercise that reinforces important concepts. | |
| − | ECE | + | *'''ECE 438 - Digital Signal Processing with Applications '''- The course is presented in five units. Foundations: the review of continuous-time and discrete-time signals and spectral analysis; design of finite impulse response and infinite impulse response digital filters; processing of random signals. Speech processing; vocal tract models and characteristics of the speech waveform; short-time spectral analysis and synthesis; linear predictive coding. Image processing: two-dimensional signals, systems and spectral analysis; image enhancement; image coding; and image reconstruction. The laboratory experiments are closely coordinated with each unit. Throughout the course, the integration of digital signal processing concepts in a design environment is emphasized. Prof. Mimi teaches [[2010 Fall ECE 438 Boutin|this course]] sometimes. |
| − | + | *'''ECE 52100 - Acoustics In Engineering And Medicine''' - An introduction to the uses of acoustics in medical imaging, flaw detection, blood flow measurement, and signal processing. Topics include physical acoustics, bulk, surface and plate waves, transducer design, ultrasonic lenses and mirrors, pulse echo and ultrasonic doppler systems, bulk and surface wave signal processing devices, holographic imaging, clinical applications of ultrasonic imaging, and acoustic flaw detection. | |
| − | ECE | + | *'''ECE 51300 - Diffraction, Fourier Optics, And Imaging''' - Modern theories of diffraction and Fourier optics for imaging, optical communications, and networking. Imaging techniques involving diffraction and/or Fourier analysis with application to tomography, magnetic resonance imaging, synthetic aperture radar, and confocal microscopy. Additional topics in optical communications and networking, including wave propagation in free space, fiber, integrated optics, and related design issues. Simulation studies, using Matlab and other software packages for analysis and design. |
| − | + | *'''ECE 52200 - Problems In The Measurement Of Physiological Events''' - Lectures devoted to the methods used to measure physiological events with demonstrations and laboratory exercises to emphasize the practical aspects of quantitative measurements on living subjects. The systems covered are cardiovascular, respiratory, central and peripheral nervous, gastrointestinal, and renal. | |
| − | + | *'''BME 495/420 - Control for Biomedical and Healthcare Engineering''' (Fall) - This course will present modern control theory fundamentals from the biomedical engineering perspective. The concepts of feedback control and open loop control will be presented with an emphasis on biological and healthcare systems. Theory for linear state space models and feedback controller design will be taught. Examples will be drawn from physiological regulation of cardiac output and ventilation, pacemeaker design, automated insulin delivery, and patient scheduling. | |
| − | + | *'''BME 560 - Modeling and Analysis of Physiological and Healthcare Systems - '''Introduces students to healthcare engineering research through a variety of delivery decision problems that can be formulated and analyzed with engineering techniques such as simulation and linear programming. | |
| − | ECE | + | *'''BME 528/ ECE 528 - Measurement and Stimulation of the Nervous System''' (Spring) - Engineering principles addressing questions of clinical significance in the nervous system: neuroanatomy, fundamental properties of excitable tissues, hearing, vision, motor function, electrical and magnetic stimulation, functional neuroimaging, disorders of the nervous system, development and refinement of sensory prostheses. |
| − | + | *'''ECE 511/ PSY 511 - Psychophysics''' (Fall) - An examination of the relationship between physical stimuli and perception (visual, auditory, haptics, etc.). Includes a review of various methods for studying this relationship and of the mathematical and computational tools used in modeling perceptual mechanisms. | |
| − | + | *'''ECE 53800 - Digital Signal Processing I -''' Theory and algorithms for processing of deterministic and stochastic signals. Topics include discrete signals, systems, and transforms, linear filtering, fast Fourier transform, nonlinear filtering, spectrum estimation, linear prediction, adaptive filtering, and array signal processing.<br> | |
| − | + | *'''ECE 38200 - Feedback System Analysis And Design''' - In this course, classical concepts of feedback system analysis and associated compensation techniques are presented. In particular, the root locus, Bode diagram, and Nyquist criterion are used as determinants of stability. | |
| − | + | *'''BME 595/510 - Analog Integrated-Circuit Design (Fall) - '''This course will be specially offered next fall, taught by Professor Pedro Irazoqui. In this course, students will learn to make the circuits used to record biological signals.'''<br>''' | |
| − | <br> | + | *'''ECE 473 - Intro to Artificial Intelligence '''(Spring) - The course introduces fundamental areas of artificial intelligence: knowledge representation and reasoning; machine learning; planning; game playing; natural language processing; and vision.'''<br>''' |
| − | ECE | + | *'''ECE 48300 - Digital Control Systems Analysis And Design''' - The course introduces feedback computer controlled systems, the components of digital control systems, and system models on the z-domain (z-transfer functions) and on the time domain (state variable representations.) The objectives for system design and evaluation of system performance are considered. Various discrete-time controllers are designed including PID-controllers, state and output feedback controllers, and reconstruction of states using observers. The systems with the designated controllers are tested by simulations.'''<br>''' |
| − | + | *'''ECE 52600 - Fundamentals Of MEMS And Micro-Integrated Systems (BME 58100)''' - Key topics in micro-electro-mechanical systems (MEMS) and biological micro-integrated systems; properties of materials for MEMS; microelectronic process modules for design and fabrication. Students will prepare a project report on the design of a biomedical MEMS-based micro-integrated system.'''<br>''' | |
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| − | ECE 57000 - Artificial Intelligence | + | *'''ECE 57000 - Artificial Intelligence''' - Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how, in some cases, it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamental issues in all aspects of artificial intelligence'''<br>''' |
Latest revision as of 09:53, 6 May 2012
ECE 301: Applications in Biomedical Engineering
Examples of Signals and Systems in Biomedical Engineering:
- Physiological Systems Modeling – Physiological processes can often be modeled mathematically. An organism's environment (or the output from another body system) can be viewed as a signal, x(t). The physiological system being modeled, h(t), will convert this signal into output, y(t). For example, the blood clotting mechanism can be illustrated as a system cascade:
x(t) = the amount of protein released by damaged endothelium (skin) cells
h1(t) = amplifying system
w1(t) = the amount of clotting factor VII
h2(t) = amplifying system
w2(t) = the amount of factor XI converted to XIa
and so forth until the final output...
y(t) = amount of cross-linked fibrin (that makes the blood clot)
Would you consider this system memoryless? Causal?
- Image and Image Processing – X-ray, ultrasound, MRI (magnetic resonance imaging), and CT (computerized tomography) scans all convert analog physical signals to discrete computer signals. Systems (maybe even Fourier/Laplace/Z transforms from ECE301!) are utilized to convert, filter, and combine these signals and produce the images used in diagnostics.
- Bioinformatics – Gene sequencing requires techniques to translate DNA from chromosomes to digitally recordable information. Think of each nucleic acid as n = 0, 1, 2, ... for the discrete signal x[n] of the entire DNA sequence. (Maybe x[n] = 1, 2, 3, and 4 corresponding to adenine, guanine, cytosine, and thymine.) Furthermore, this signal must be broken into distinct genes and decoded. These genes can be matched to their potential phenotypes (physical traits/functions) using databases of sequences.
- Proteomics – A proteome is the set of proteins produced by a species, similar to a set of genes in a genome. The study of proteomes is important in understanding cellular processes, infection, and patterns related to disease. In proteomics, engineers must develop hardware that can measure protein levels rapidly and accurately. In this case, the protein levels can be thought of as a CT signal, sampled and stored as a DT signal. Think about what we have learned about sampling and conversion between CT/DT signals. Filters are also used to sort the main trend of the signal from the noise (common in biological systems).
- Wireless and Mobile Technologies – Just as phones and computers have gone wireless, now medical equipment, sensors, and surgical devices are going wireless. These critical signals must be properly transmitted, which means the use of sampling, signal carriers, CT/DT converters, etc. Athena GTX, a company described in the Biomedical Industry section below, focuses on this exact subject.
- BioSIGNALS – The electrical signals produced by the body are extremely useful in diagnostics:
Electrocardiogram (ECG) – used to measure the electrical activity of the heart and diagnose conditions including blockages and arrhythmias. For more detailed information on diagnosis with ECGs, check out this website: http://www.bem.fi/book/19/19.htm
Electromyogram (EMG) – used to measure the electrical activity of muscles and nerves to diagnose conditions including carpal tunnel syndrome and sciatica. One technique, called targeted muscle reinnervation (TMR), uses these signals to allow amputees to control mechanical limbs with their natural nerve signals. Purdue professor Pedro Irazoqui, research link below, is involved in this research with DARPA and Northwestern University.
Electroencephalogram (EEG) – used to measure the electrical activity of the brain to diagnose conditions including epilepsy and comas. Biomedical research attempts to use these signals to allow people with paralysis to control a computer.
All of these signals require filtering of background noise and, often times, conversion from continuous time (CT) to discrete time (DT).
These and more applications of biomedical engineering can be found at http://www.embs.org/docs/careerguide.pdf
Uses for Signals and Systems in Biomedical Industry
Listed below are biomedical companies that apply what we are learning in ECE301. I have started the list with two companies I know, but feel free to add on!
- Cyberonics, Inc. – This company focuses on Vagus Nerve Stimulation (VNS Therapy) to treat refractory epilepsy. In this therapy, an electronic device is implanted next to the vagus nerve (located in the chest, but leading to the brain) to prevent seizures through intermittent stimulation. Current research (including the research of Purdue's Pedro Irazoqui) aims to measure and understand brain activity before seizures, so that the device can predict seizures based on the changes and only stimulate when necessary. This work requires filters for the biological signals as well systems to interpret these signals. As of 2005, the company has also moved into the use of VNS for treatment-resistant depression. (After this summer, I should be able to add on to this description!)
- Athena GTX – This company makes wireless monitoring devices for hospitals, emergency medical services, and even military soldiers. This work requires filtering and measurement of various physiological signals, as well as the challenges of sending and receiving wireless signals. (I know of one ECE301 student who could elaborate more on this company...)
Purdue Biomedical Research with Signals and Systems
- Biomedical Imaging and Sensing - This research aims to improve accuracy and and safety of current medical/diagnostic equipment. Research includes:
- Less expensive acoustical instruments for the home or physician offices
- Acoustics to guide the placement of breathing tubes in infants
- Signal processing and filtering algorithms to create stethoscopes that function in high-noise environments such as helicopters and ambulances
- Stereo imaging for visualization for mammography diagnostics and acoustic imaging of the lung.
https://engineering.purdue.edu/ECE/Research/Areas/BiomedIS.whtml
- VACCINE - This research group creates methods and tools to analyze and manage information for homeland security, but some past research has focused on emergency response with mobile devices. This research has direct implications on the field of Emergency Medicine.
- Biophotonics and Medical Imaging - This research uses atomic and nano-scale imaging technologies to locate, track, and explore human tissues, as well as individual cells and molecules, using sophisticated algorithms to analyze numerical images. Research also uses non-invasive optical systems to provide real-time imaging of drug dispersal and interaction with target cells.
https://engineering.purdue.edu/BME/Research/BIO
- Neuroengineering - This research employs interdisciplinary engineering approaches to examine and manipulate the function and behavior of the nervous system. Concepts used include computational biology, neuroscience, electrical engineering, signal processing, and chemistry. The goal of this research is to prevent and treat paralysis, as well as to help victims of degenerative disease to communicate using brain-computer interfaces.
https://engineering.purdue.edu/BME/Research/NE
- Systems Science and Engineering - The goal of this research is to improve healthcare efficiency, which will reduce costs, improve treatments and outcomes, and eliminate other problems. This research requires the use of complex mathematical modeling and system analysis.
https://engineering.purdue.edu/BME/Research/HCS
These descriptions were borrowed from the myPurdue course catalog and Purdue BME course resources (with a few added comments from me). Listed in order of relevance:
- BME 595/511 - Biosignal Processing (Fall)- An introduction to the application of digital signal processing to practical problems involving biomedical signals and systems. Topic include: examples of biomedical signals; analysis of concurrent, coupled, and correlated processes; filtering for removal of artifacts; event detection, analysis of waveshape and waveform complexity; frequency domain characterization of signals and systems; modeling biomedical signal-generating processes and systems; analysis of nonstationary signals; pattern classification and diagnostic decision. MATLAB will be used throughout to provide numerous opportunities for hands-on application of the theory and techniques discussed to real-life biomedical signals.
- BME 495/430 - Biomedical Imaging Modalities (Spring) - This course covers basic principles and modes of bioimaging methods for biomedical sciences. Topics include interaction of electromagnetic radiation with tissue, basic concepts in imaging and detection, basic modes of imaging modalities (e.g. reflection, transmission, absorption, and emission), and basic image processing/analysis. Model systems to be used to teach the topics include conventional imaging modalities such as optical imaging, optical microscopy, X-ray, computed tomography, ultrasound, magnetic resonance imaging, etc. This course also includes hands-on exercise that reinforces important concepts.
- ECE 438 - Digital Signal Processing with Applications - The course is presented in five units. Foundations: the review of continuous-time and discrete-time signals and spectral analysis; design of finite impulse response and infinite impulse response digital filters; processing of random signals. Speech processing; vocal tract models and characteristics of the speech waveform; short-time spectral analysis and synthesis; linear predictive coding. Image processing: two-dimensional signals, systems and spectral analysis; image enhancement; image coding; and image reconstruction. The laboratory experiments are closely coordinated with each unit. Throughout the course, the integration of digital signal processing concepts in a design environment is emphasized. Prof. Mimi teaches this course sometimes.
- ECE 52100 - Acoustics In Engineering And Medicine - An introduction to the uses of acoustics in medical imaging, flaw detection, blood flow measurement, and signal processing. Topics include physical acoustics, bulk, surface and plate waves, transducer design, ultrasonic lenses and mirrors, pulse echo and ultrasonic doppler systems, bulk and surface wave signal processing devices, holographic imaging, clinical applications of ultrasonic imaging, and acoustic flaw detection.
- ECE 51300 - Diffraction, Fourier Optics, And Imaging - Modern theories of diffraction and Fourier optics for imaging, optical communications, and networking. Imaging techniques involving diffraction and/or Fourier analysis with application to tomography, magnetic resonance imaging, synthetic aperture radar, and confocal microscopy. Additional topics in optical communications and networking, including wave propagation in free space, fiber, integrated optics, and related design issues. Simulation studies, using Matlab and other software packages for analysis and design.
- ECE 52200 - Problems In The Measurement Of Physiological Events - Lectures devoted to the methods used to measure physiological events with demonstrations and laboratory exercises to emphasize the practical aspects of quantitative measurements on living subjects. The systems covered are cardiovascular, respiratory, central and peripheral nervous, gastrointestinal, and renal.
- BME 495/420 - Control for Biomedical and Healthcare Engineering (Fall) - This course will present modern control theory fundamentals from the biomedical engineering perspective. The concepts of feedback control and open loop control will be presented with an emphasis on biological and healthcare systems. Theory for linear state space models and feedback controller design will be taught. Examples will be drawn from physiological regulation of cardiac output and ventilation, pacemeaker design, automated insulin delivery, and patient scheduling.
- BME 560 - Modeling and Analysis of Physiological and Healthcare Systems - Introduces students to healthcare engineering research through a variety of delivery decision problems that can be formulated and analyzed with engineering techniques such as simulation and linear programming.
- BME 528/ ECE 528 - Measurement and Stimulation of the Nervous System (Spring) - Engineering principles addressing questions of clinical significance in the nervous system: neuroanatomy, fundamental properties of excitable tissues, hearing, vision, motor function, electrical and magnetic stimulation, functional neuroimaging, disorders of the nervous system, development and refinement of sensory prostheses.
- ECE 511/ PSY 511 - Psychophysics (Fall) - An examination of the relationship between physical stimuli and perception (visual, auditory, haptics, etc.). Includes a review of various methods for studying this relationship and of the mathematical and computational tools used in modeling perceptual mechanisms.
- ECE 53800 - Digital Signal Processing I - Theory and algorithms for processing of deterministic and stochastic signals. Topics include discrete signals, systems, and transforms, linear filtering, fast Fourier transform, nonlinear filtering, spectrum estimation, linear prediction, adaptive filtering, and array signal processing.
- ECE 38200 - Feedback System Analysis And Design - In this course, classical concepts of feedback system analysis and associated compensation techniques are presented. In particular, the root locus, Bode diagram, and Nyquist criterion are used as determinants of stability.
- BME 595/510 - Analog Integrated-Circuit Design (Fall) - This course will be specially offered next fall, taught by Professor Pedro Irazoqui. In this course, students will learn to make the circuits used to record biological signals.
- ECE 473 - Intro to Artificial Intelligence (Spring) - The course introduces fundamental areas of artificial intelligence: knowledge representation and reasoning; machine learning; planning; game playing; natural language processing; and vision.
- ECE 48300 - Digital Control Systems Analysis And Design - The course introduces feedback computer controlled systems, the components of digital control systems, and system models on the z-domain (z-transfer functions) and on the time domain (state variable representations.) The objectives for system design and evaluation of system performance are considered. Various discrete-time controllers are designed including PID-controllers, state and output feedback controllers, and reconstruction of states using observers. The systems with the designated controllers are tested by simulations.
- ECE 52600 - Fundamentals Of MEMS And Micro-Integrated Systems (BME 58100) - Key topics in micro-electro-mechanical systems (MEMS) and biological micro-integrated systems; properties of materials for MEMS; microelectronic process modules for design and fabrication. Students will prepare a project report on the design of a biomedical MEMS-based micro-integrated system.
- ECE 57000 - Artificial Intelligence - Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how, in some cases, it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamental issues in all aspects of artificial intelligence
