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Fall
2006
Boston
University
AM
520 Acoustics I
Prof. Robin Cleveland - Introductory
Graduate/Senior Level Undergraduate Course
Introduction to wave propagation and sound. General concepts
such as quantitative measures of sound, plane waves, and acoustic energy
density and intensity. Derivation of wave equation. Reflection, transmission
and refraction of sound. Normal modes: vibrating membranes, and sound
in a rectangular enclosures. Acoustic horns. Propagation in stratified
media - ray acoustics. Absorption and attenuation of sound waves. Acoustic
waves in spherical co-ordinate systems. Sound radiation from pistons. Text Book for Course: D.T. Blackstock, Fundamentals of Physical
Acoustics, (Wiley 2000)
Day(s) and Time(s): Monday and Wednesday, 2pm
- 4pm
Distance Learning: Available to Boston Area Students
Introduction
to Photonics (ENG SC 560)
Prof. Luca Dal Negro - Graduate Course
Introduction to ray optics, wave optics, Fourier optics
and holography, absorption, dispersion. Polarization, anisotropic media,
and crystal optics. Guided-wave and fiber optics. Elements of photon
optics. Laboratory experiments: interference; diffraction and spatial
filtering; polarizers, retarders, and liquid-crystal displays; fiber-optic
communication links. 4 cr.
Prerequisites: CAS PY 313
Day(s) and Time(s): Lectures - Tuesday and Thursday,
2:00pm to 4:00pm; Labs to be arranged Starting Tuesday, September 5th
Distance Learning: Available to BU and NU Students
Only
Biomedical
Optics and BioPhotonics (ENG SC 765 and BE 765)
Prof. Irving J. Bigio - Graduate
Course
This course surveys the applications of optical science
and engineering to a variety of biomedical problems, with emphasis on
optical and photonics technologies that enable real, minimally-invasive
clinical applications. The course teaches only those aspects of biology
itself that are necessary to understand the purpose of the application.
The first weeks introduce the optical properties of tissue, and following
lectures cover a range of topics in three general areas: 1) Optical
spectroscopy applied to diagnosis of cancer and other tissue diseases;
2) Photon migration and optical imaging of subsurface structures in
tissue; and 3) Laser-tissue interactions and other applications of light
for therapeutic purposes. In addition to formal lectures, recent publications
from the literature will be selected as illustrative of various topical
areas, and for each publication one student will be assigned to prepare
an informal presentation (with overhead slides or PowerPoint) reviewing
for the class the underlying principles of that paper and outlining
the research results.
Prerequisites: Prior course in optics/photonics
is highly recommended, and some cellular biology or physiology is also
useful.
Day(s) and Time(s): Tuesdays and Thursdays, 4:00
- 6:00 PM, Starting Tuesday, September 5, 2006
Distance Learning: Available to Boston Area Students
Rensselaer
Polytechnic Institute
Cell
and Tissue Image Analysis
Prof. Badri Roysam - Graduate
Course
Survey of image analysis applications in biology, biotechnology,
and medicine; Introduction to biological microscopy and selected medical
imaging systems; Image reconstruction and preprocessing; Grayscale and
geometric corrections; adaptive image segmentation; blob analysis, cell/colony
counting, and cell morphometry; vessel and neuron tracing algorithms,
with applications to neurobiology and medicine; feature extraction,
pattern analysis, cluster analysis and classification; image registration
algorithms with applications to mosaicing, spatial referencing, motion
estimation, and change detection.
Prerequisites: A course in programming. Exposure
to basic statistical concepts is desirable.
This class is open to a variety of students ranging
from Biology majors to Engineering and Computing majors. Each student
will be required to perform some image analysis programming using MATLAB,
so some programming background is necessary. Individuals, or teams of
two students each, can do the course projects. Cross-disciplinary teaming
is encouraged, for example a biologist teaming with a computer scientist.
Expectations on these projects will vary based on the student's background.
Day(s) and Time(s): Lectures -To Be Determined
Distance Learning: YES - Available to BU, NU,
RPI, and UPRM Students
Additional Information: To view course information
for the Fall 2005 course which will be similar in form and content to
the Fall 2006 course, go to http://www.ecse.rpi.edu/~roysam/CTIA/ where you will find files of voice-annotated PowerPoint lectures.
Spring
2006
Boston
University
Lasers
(ENG SC 570)
Prof. Selim Unlu - Graduate
Course
Review of wave optics. Gaussian and Hermite-Gaussian
optical beams. Planar-and spherical-mirror resonators. Photon streams.
Absorption,
spontaneous emission, and simulated emission. Laser amplification
and gain saturation. Laser oscillation; pulsed lasers. Photon interactions
in
semiconductors. LEDs and semiconductor injection lasers. Photon detectors.
Laboratory experiments: beams; divergence and collimation; electroluminescence;
semiconductor injection lasers.
Prerequisites: CAS PY313
Distance Learning: Available to Boston Area
Students
Day(s) and Time(s): Tuesdays and Thursdays
- 4:00pm to 6:00pm
Northeastern
University
Biomedical Signal Processing and Medical Imaging (ECE U664)
Prof. Dana Brooks
- Undergraduate
Course for upperlevel (junior/senior) undergrads
The first part of this course will cover some important
bioelectric signals, with particular emphasis on electroencephelograms
and electrocardiograms, and relevant signal processing techniques
for these signals. The second part ill be a survey of some medical
imaging modalities including CT, PET and SPECT, ultrasound, and MRI,
with emphasis on image formation from a signal processing point of
view. The exact coverage will depend partly on student background
and interest, and students will be expected to participate actively
in the class.
Prerequisites: A Probability or Stochastic
Processes course and a Linear Systems course
Distance Learning: Available to BU and NU students
only.
Day(s) and Time(s): Mondays & Thursdays
11:45 AM-1:25 PM
Intro
to Subsurface Sensing and Imaging (ECE U1467)
Prof.
Charles DiMarzio and Prof. Eric Miller - Undergraduate
Course
This course is an introductory unified look at the
emerging field of subsurface sensing and imaging (SSI). Major themes
include the interrelatedness of the three technological levels of
sensing, modeling and signal processing, and computational technology,
the similarity of SSI across diverse problem domains and size scales,
and the variety of information extraction strategies such as localized
imaging and the use of multiple views in space, wavelength, etc. The
course will be organized around hands-on experience with a particular
SSI modality which will include experimental measurement and subsequent
processing and visualization of the measured data using Matlab. The
format and content will be similar to last time the course was offered,
for more details - http://www.ece.neu.edu/courses/eceu692/2004sp.
Prerequisites: ECE 1333 (Discrete Linear Systems),
ECE 1360 (Electromagnetic Fields and Waves), and MTH 1230 (Linear
Algebra for Engineers), or by permission of instructor. Note: Interested CE majors who are concerned about the ECE 1360 and MTH
1230 prerequisites are urged to talk to Prof. Miller or Prof. DiMarzio
about their background.
Distance Learning: Available to BU and NU students
only.
Day(s) and Time(s): Tuesdays & Fridays
from 1:35 - 3:15 PM, beginning January 10th.
Pattern
Recognition (ECE G313)
Prof. Jennifer Dy - Graduate
Course
Discusses introductory concepts, statistical classification
problem, and the Bayes classifier. Covers parametric estimation and
supervised learning, ML and Bayes approaches, and Bayes learning.
Topics include nonparametric techniques, Parzen windows, nearest neighbor
rules, convergence properties, and error bounds. Examines linear discriminant
functions, linear separability, perceptrons and their training, and
relaxation techniques. Discusses unsupervised learning and clustering,
unsupervised Bayes learning, ML estimates, k-means algorithm, and
learning vector quantization. Introduces neural network structures,
feed-forward nets, ADALINE, Widrow-Hopf approach, the back propagation
training algorithm, Kolmogorov's theorem, and capacity of feed-forward
nets. Focuses on Hopfield model and learning, associative memory,
bidirectional associative memory, stable states and convergence, and
capacity of the Hopfield model. Also covers unsupervised learning,
adaptive resonance theory, and self-organizing feature maps.
Prerequisites: ECE G204
Distance Learning: Available to BU and NU Students
Only
Day(s) and Time(s): Monday, Wednesday, Thursday
1:30pm to 3:10pm
Quantum
Optics (ECE
G398)
Prof. Charles DiMarzio and Prof. Anthony Devaney - Graduate
Course
This year (2005) Professor Roy J. Glauber, of Harvard
University, shared the Nobel Prize in physics "for his contribution
to the quantum theory of optical coherence," making this a particularly
appropriate semester to present our first-ever course in Quantum Optics.
Most of optics can be treated classically by the use of Maxwell's
equations, supplemented by the occasional mention of the photon when
absolutely necessary. This pedagogical approach creates the unfortunate
impression of a dichotomy between "wave theory" and "particle
theory." This course, taught by Profs. Devaney and DiMarzio,
will show how these two concepts are reconciled through quantization
of the field, and will provide the student with the fundamental principles
underlying the operation of lasers and detectors, the theory of signals
and noise in optics, and the principles of such inherently quantum-mechanical
phenomena as squeezed states, entangled-photon interferometry, and
quantum cryptography. We will use the recent book (2004) Introductory
Quantum Mechanics, by Gerry and Knight but, recognizing that most
ECE students lack a strong background in quantum mechanics, we will
begin with a review of the basic principles, and then cover about
the first seven chapters. We also intend to supplement the reading
with some of Prof. Glauber's original papers. Our goal is for the
students to gain
- An ability to read and understand the literature on new developments
in quantum optics,
- An ability to use absorption and emission of light by materials
in modern spectroscopic instrumentation, including multi-photon microscopes,
- An understanding of the fundamental limits on noise in optical and
other systems.
For more information on this course, go to http://www.ece.neu.edu/faculty/dimarzio/2share/eceg398.html.
Prerequisites: TBA
Distance Learning: Available to BU and NU Students
Only
Day(s) and Time(s): Mondays and Wednesdays
from 2:30pm to 3:30pm, beginning on Monday, January 9, 2005.
Rensselaer
Polytechnic Institute
Intro
to Subsurface Sensing and Imaging Systems (ECSE-4962-01; CRN54506)
Dr. Kai E. Thomenius, Chief Technologist, Imaging Technologies, GE
Corporate R&D Center - Undergraduate
Course
Engineers are often faced with the problem of sensing
and imaging objects that are hidden under a surface. For example,
detecting tumors using laser scanning microscopy, locating underground
mines using radio waves, imaging infants using ultrasound, detecting
cracks in machine parts and bridges, and medical imaging by x-ray
radiography, magnetic resonance imaging (MRI), and computer assisted
tomography (CAT). This course will introduce the student to the basics
of subsurface sensing and imaging:
Properties of probes such as optical beams, x-rays,
ultrasonic waves, and electromagnetic waves. .How the probes interact
with media-transmission, reflection, scattering, diffusion.Sensors
for detecting subsurface signals .Extracting information from subsurface
signals using multi-view tomography (MVT), Localized probing and mosaicing
(LPM), and multi-spectral discrimination (MSD).
Prerequisites: ECSE-2100 (Fields and Waves I),
ECSE 2410 (Signals and Systems)
Day(s) and Time(s): Tuesdays & Fridays:
12:00 - 12:50 PM
Distance Learning: NO
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