Workshop in Computer Science: Learning and Signal Processing

סדנה במדעי המחשב: למידה ועיבוד אותות

0368-3500-46, Autumn 2010-11

Tuesday 13-15 Dan David 204

Prof. Nathan Intrator


Course Mailing List         Yedion



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 the 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 (real-time) data mining projects with multi-dimensional representations.   A typical project or a couple of projects have the following structure:






Most of the projects will acquire online data from the web and one or two will use offline-database. The main programming language will be Matlab™ and there will be a couple of data reading projects  in Java/Python to provide an interface between Matlab and the web for specific datasets. 

Datasets to be used:

Physionet    Earthquakes – Seismic   Financial data of various types


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.



1.       Maya Lieber, Orli Chen: Real time synchronized commodity prices (Eddie)

2.       Yair Spira Roy Russo: Analysis of financial data  (Eddie)

3.       Shir Goldstein  Kobi Aizer  Tzach Tzabag:  Real Time EEG Analysis  (Vera, Alex)

4.       Etgar Petrov  Lihi Mena: Real time acquiring of Seismic Data  (Vera)

5.       Amir Ayalon, Maya Levil: GUI Biomedical on Top of Guy's GUI l  (Alex)

6.       Yuval Sharmi: : Prediction comparison ML Biomedical Data (Shahar)

7.       Maya Levi : Puzzle building (Daniel)


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.






Student Advisor


Pres1   Users Man Code    Pres2






















Related links to relevant literature and code


Sound analysis


Introduction to Acoustics

Signification of High Dimensional Data

Auditory display of hyperspectral colon tissue images

Singing the Mind Listening

Sound features

Chris Raphael Rhythm changes


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

Heart Mechanical and Electrical System

Segmentation of EKG signals

Heart info and abnormalities (video)


Max/MSP Multimedia creation

TinyOS  operating sys for wireless applications



Cheap off-the shelf TinyOs operated robots

PicoRadio: Low power wireless node with sensors

Sensors Magazine

Xbow sensors


Machine learning and Statistics

Pattern recognition and neural networks B. Ripley

Neural networks for pattern recognition Bishop



Op amps for EKG, Piezo  EMG  BioMed Signals

AD620 Diff Amp







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

Description: 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.



1.    Transfer function and complimentary information from heart sounds in different chest locations

Dima Adler and Doron Ayad