Workshop
in Computer Science: Learning and Signal Processing
סדנה במדעי המחשב:
למידה ועיבוד אותות
Tuesday 13-15 Shenkar 105
Prof. Nathan Intrator
The workshop provides a window to the Neural Computation and
Signal Processing (NCSP) lab at TAU. Usually, projects will be related to work
of some of th e graduate students in the lab. In general, the lab applies
machine learning techniques and signal processing methods to real-world
applications.
This year, we shall concentrate on developing an interface of some
novel human sensors to the PC and an Android phone with some signal processing
and data mining.
A typical project or a couple of projects have the following
structure:
Projects this year will be of different types and therefore fit a
wide variety of students, each with their own background and interest.
Here is a list of potential project, as you can see they relate to
different backgrounds
1.
EEG Data Analysis
a. Bluetooth and other wireless interface of human
sensors to the PC and
Android
b. Data analysis on the PC
c. Data Analysis on the Android
d. GUI on the Android with feedback
e. Students with hardware background can participate in some hardware
project about human sensors.
2. Pulse Oximeter PPG operation and Electronic
Scheme, parts list
3. Bluetooth 24bit DAQ module
a. Driver to connect to Matlab (on a pc)
b. Driver to connect to Android and GUI
4. Seismic project: Real Time data download
from a set of stations.
You would need to check if the stations that we are interested
(in the Japan area) can be accessed by SeedLink and write an
interface
to download real time data from a pre-set of stations into Matlab.
5. Twitter data mining:
a. Suppose you want to retwit a message. Use the Twitter API to write
an application which helps you decide if you want to re twit a message to your
followers. You want to check each of your followers, if he/she received the
twit, namely if he follows any of the people who retwitted the twit. If the
fraction of your followers who received the twit is smaller than a threshold,
you retwit.
b. Remove duplicate twits
c. Check how many of those you follow simply retwit vs. produce
original material
I am especially looking for students who can translate code from
matlab to the Android, or can develop communication with the Android and a GUI.
There may be some
projects analyzing existing (EEG or other biomedical data) from Physionet.
Submission and Grading Reports and Presentations
Each project will have to submit the following:
1. (20%) Review and plan of work: power point presentation – this
presentation will be done in the middle of the semester and it will serve to
evaluate whether the students are going in the right direction and will be able
to finish the project on time. This will be the first students’ presentation.
2. (40%) Source code and Users manual at the end of the project. User’s manual will be comprehensive,
providing information of what each function is doing and how it is doing it,
what are the inputs, outputs, and examples of how to use the code. The code will be submitted in one ZIP file.
Please create as few files as possible, with as many functions that you like in
a file. Documentation inside the code is critical.
3. (40%) Final word document and power point presentation: In the
second and final student presentation, they will describe what they have done,
what problem they tried to address and how was it addressed before. They will be
demoing the code, how to use it and its capability – this presentation and
report will be given at the final project presentations.
If there is no presentation, then the word document report should
include the presentation items as well.
The projects will be based on the individual background of each
participant and will thus, vary in nature. Projects will have an emphasis
either on programming (Matlab, Java, C etc) or will be more emphasizing the
data mining aspect.
The projects will be done in couples or triples, but each of the
participants will be assigned a specific part and in the first presentation,
each will present their expected part in the project.
There is room to combine a project on building a presentation that
is optimized for a cell phone – iphone, android, blackberry etc.
Projects
1. TBD
Preliminary and Partial List of Projects
1. Real time seismic data analysis for earthquake prediction
a. Real time acquiring of seismic data from a given set of stations
(GUI defined, or otherwise). Also labels such as magnitude of seismic activity
at specific locations will be added.
b. Real time processing of large amounts of data, pre-processing into
Time/Frequency representation.
c. Real time creation of Best Basis (from wavelet packets
representation) of the data (background will be provided)
d. Real Time clustering + GUI
e. Real time machine learning modeling and prediction
2. Same type of project on financial data analysis
a. Real-time acquiring of financial data (multiple sources, extensive)
b. Prep-processing and analysis
c. Machine learning for prediction training
3. EEG Data analysis
4. Cardiac data analysis
5. fMRI data analysis and modeling
No bio background or signal processing is necessary for some of
the projects.
Those with bio or signal processing background will be able to
utilize their background.
For machine learning projects, a machine learning, pattern
recognition or neural computation course or some background is a prerequisite.
For a signal processing project, some course in signal processing,
or a course that mentioned Fourier transform is needed. Preference is given to
wavelets and best basis background.
Students |
Student Advisor |
Title |
Pres1 Users Man
Code Pres2 |
|
Shahar |
|
|
|
Yehudit |
|
|
|
Alex |
|
|
|
Yaron |
|
|
|
Elad |
|
|
Related links to relevant literature and code
Sound analysis Ambionics Introduction to Acoustics Signification
of High Dimensional Data Auditory
display of hyperspectral colon tissue images Biomedical signals and sensors Biomedical
Signal Analysis R. M. Rangayyan Breath Sounds
Methodology N. Gavriely Digital
Signal Analysis: A Computer Science Perspective J. Stein. Robust
measurement of Carotid Heart sound delay |
Software TinyOS
operating sys for wireless applications Weka – Java Machine
Learning Code (Open Source) Sensors Cheap
off-the shelf TinyOs operated robots PicoRadio:
Low power wireless node with sensors Machine learning and Statistics Pattern
recognition and neural networks B. Ripley Neural
networks for pattern recognition Bishop Hardware |
Some past project titles (extensive use of eeglab)
An important reference paper to many projects: Mirowski,
LeCun et al
Signal Processing and Math Intensive Projects
Examples of past projects:
1.
Source localization for EEG
2.
Spectral Cross Correlation (few methods, 2
projects)
3.
Wavelet Denoising (Using Donoho’s method)
4.
Best basis formation for single electrodes (based on Coifman &
Wickerhauser)
5.
Nonlinear Independence (LeCun)
Programming intensive
1. Fast Adaptive
Principal Components with Visualization (Single electrode)
2.
Fast Adaptive Principal Components with Visualization (Multiple
electrodes)
3. Fast Adaptive ICA with
Visualization
4.
Adaptive Lyapunov Exponents estimation (more math intensive)
_______________________________________________________________________________________________________________________________
Past Projects Feb 7, 2008:
1.
Eugene Jorov: Design of a
recording and analysis microcontroller MSP430FG461x
a.
A/D for Heart Sound sensor, A/D for EKG sensor;
b.
Peak detection analysis of QRS complex and S1 sound
c.
Extraction of peak values and systolic and diastolic durations
Paramters: Window size 500-1200 samples at 400Hz, two channels. The
micro controller provides 4 channels of 16 bit sampling.
Constraints: no dynamic memory
allocation. Code will be written in C.
2.
Yuval Meir, Amit Ziv: Zigbee
communication module to MSP430FG461x
with connection to a windows mobile device
a.
Microcontroller part of the communication
b.
Windows mobile part of the communication
c.
A GUI presenting results and enabling the request of data from the
microcontroller (several forms of data presentations will be discussed later)
3.
Miki Avitan: Real time
code for principal components of the S1 heart sound
4.
Shy Zimmerman, Raphi Agiv: Real time
code for clustering of S1 and S2 heart sounds, about 6 clusters, with a
threshold changing to provide such number of cluseters.
5.
Liron Machluf, Nir Charny: Real time
code for detection (segmentation) of S2 and features extraction
6.
Dionis Teshler, Igor Dofman: Production
of Shimmer Mote device for EKG with Bluetooth
a.
Production of the Mote
b.
Bluetooth communication with a PC with PC GUI, Code for
Peak detection of QRS with duration information.
7.
Uri Korenstein, Nir Bitanski: EEG
Lab Utilizing Andrey’s Fisher LDA algorithm for MEG on EEG data
8.
Ron Kimchi, Tsachi Greemald: ICALab
project with Andrey
9.
Nadav Bendek, Uri Peltz: Clustering of
coherence measures of EEG signals for prediction of Seizure. Use graph rep of
the cluster structure
a.
Review of coherence methods used in EEG analysis; Review of clustering
methods
b.
Methodology: Implementation of different coherence methods,
implementation of different clustering methods, details about the data to be
used and the measures of comparison
c.
Results, clear graphs demonstrating which methods are best
d.
Using Shos Data files
10.
Orphan project: Producing
LPC-like coefficients from EEG data and analysis of their clustering
properties.
If you are interested in this project, please email me.
_______________________________________________________________________________________________________________________________
Intended participants are encouraged to email me and describe
their background and their interest in projects.
Updated (Jan 31, 08) specific list
of projects
I’d like each group to choose one
and email back to me, on a first comes basis
1.
Code writing on a micro-controller for real-time analysis of
biosignals
1.
Real Time detection of the S2 heart sound, Code in C or Java and
integration with the microcontroller
2.
Real time clustering of the S1/S2 heart sounds
3.
Real time principal components of the S1/S2 heart sounds
2.
Bluetooth 2 way communication with windows mobile device
3.
Gui on a windows mobile device
4.
Zigbee communication with a windows pc
5.
Participation The Google Online Marketing Challenge. Two groups of two
students each can join this for the workshop. Please read the instructions and
get back to me for confirmation. I expect people jumping on this fast, so you
need to respond fast to be chosen. See me tomorrow regarding this.
General
list of some potential projects:
Analysis of fMRI/EEG data
This work is done in collaboration with Dr. Talma Hendler at the
Souraski Medical Center (Ichilov) and Prof. Eshel Ben Yaakov in Physics. See
presentation of Itai Baruchi at my Advanced
Seminar.
TinyOS and Wireless Body Sensors Network
TinyOS is an open-source operating system
designed for wireless embedded sensor networks. It features a component-based
architecture which enables rapid development. This OS has become a standard
in the recent development of a Wireless Body
Sensors Network and tmote sky
platform. We shall develop algorithms for real time analysis of ECG using Pluto
that is based on tmote sky. This platform can handle up to six different
body sensors at a wireless range of 125m.
We shall also develop software and algorithms to embed acoustic
sensors into this platform.
A good place to start is a recent PhD
Thesis about Tele-Cardiology Sensor Network. Currently such networks are
heavily researched at Harvard University and Hospitals in the Boston area under
the CodeBlue
initiative.
There are also open projects to BioMedical Engineering students
in development of some sensors to this platform.
Analysis of Heart Sounds Via HMM
Related work by Ray
Watrous. See also presentation by Daniel Gil at my Advanced
Seminar, as well as background and publications of Guy Amit also in the advanced
seminar page. See also, Alex Weibel TDNN. Outline: Follow speech recognition
approach of extracting automatic features, vector quantization, HMM,
Segmentation and Clustering.
Seismic Data Analysis
Related work: see presentation by Ido Yariv and Talmor at my Advanced
Seminar.
Projects
1.
Transfer function and complimentary information from heart sounds
in different chest locations
Dima Adler and Doron Ayad