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Cardiac Monitoring

Brain Monitoring

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Sonar Methods

Computer Systems and Networks of Biomedical sensors

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Sonar, Seismic and Ultrasound Computation Methods

Related work: see presentation by Ido Yariv and Talmor at my Advanced Seminar.

A recent project was started on the infrasound properties of the mole rat and the way it acquires information about the underground environment from infrasound.

Mathematical Properties of the Cross-correlation Function

This is a topic requires some knowledge in large deviation bounds, such as the Barankin bound, and the Ziv Lemple bound. Some overview of the topic appears in Judah’s thesis. The goal is to analyze the properties of the cross correlation function from multiple sonar returns with the purpose of devising an optimal fusion of the information that can be extracted from each of the cross-correlations. A simple paper on this topic is Robust statistics from multiple pings improves noise tolerance in sonar.

Ultrasound Image Enhancement using Multiple Pings

This work relies on the work that we did with enhancement of Sonar images using multiple pings and attempts to apply the same concept for medical ultrasound. It relies on the multiple pings idea (Robust statistics from multiple pings improves noise tolerance in sonar) and robust motion estimation of the ultrasound sensor (Multiple ping sonar accuracy improvement using robust motion estimation and ping fusion). The goal is to achieve a far more accurate ultrasound with less energy for lower damage to the fetus.

Neuronal Optimal Coding

Related work: Neuronal Goals: Efficient Coding and Coincidence Detection.

A fundamental question in neural computation and computer vision is concerned with the nature of object representations and the nature of representations of relationship between objects. In particular, we depend on the ability to adjust our expectations according to the past context. This suggests that neurons should in addition to detecting features in their input representation, transmit some information about the a-priori probability of occurrence of these features.

High Dimensional Data Representation via Sound

Related work: J. Berger and R. Coifman.

This project is done in collaboration with Miri Segal (PhD in Math and Visual & Audio Artist) and Assaf Talmudi (PhD in Acoustics, and Musician) from the center for Digital Art in Holon.

The idea is to provide acoustic information as an additional aid to visual information and thus extending the number of free dimensions which can be ‘observed’ concurrently. This is important when a lot of information has to be analyzed together, for example a radiologist that has to decide about a malignant tumor, can get additional about a wider spectrum of the target via sound.