Performed by: Oleg Golan Mentor: Yoav Kimchy, Ph.D Instructor: Mony Orbach Bi-Semesterial, Spring 2014, part A Adaptive filter For noise cancellation of.

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Presentation transcript:

Performed by: Oleg Golan Mentor: Yoav Kimchy, Ph.D Instructor: Mony Orbach Bi-Semesterial, Spring 2014, part A Adaptive filter For noise cancellation of ELS

Agenda 2 1.Introduction 2.Problem description 3.Project goal 4.Solution presentation 5.Project development environment 6.Gant chart 7.Q&A

First imaging capsule for Colorectal Cancer screening No bowel cleansing required Designed for increased compliance 3 Check-Cap - creating a new standard of colon 3D imagery

Compton backscattered flux of photons detected by the capsule are attenuated by the colon contents in direct proportion to their distance traveled in the colon contents, as some of the photons are absorbed by the contrast agent The x-ray Florescence flux detected by the capsule's detectors depends monotonically on the distance traveled in the colon contents mixed with the contrast agent 4 Back-Scattering X-ray Fluorescence Check-Cap Imaging Technology

5 Reconstruction with dimensions ELS Tracking Capture ELS – Electromagnetic Localization System Movement/Position Tracking VS Reconstruction

Capsule and receiver communication 6 3D-Accelerometer 3D-Magnetometer Magnetic, solid freq. burst RF link Air coil Relative orientation Distance and direction

7 Noise on amplitudes The problem ELS magnetic burst structure Noise on capsule position Noise on capsule velocity False-positive scan activations Capsule battery drained too fast

– Signal sampling window 2 – Noise sampling window The goal Improve amplitudes SNR using adaptive filter Assumption – similar quasi-static noise in both signal and noise sampling windows, simple correlation.

9 Adaptive filter Principle Adaptive filter will be implemented using ANN – Artificial Neural Network.

10 Adaptive filter Implementation - design options High level design 1.Adaptive filtering of raw data? 2.Adaptive filtering of 1st stage IIR/FIR filter? Network type 1. ADALINE? 2. MADALINE? 3. Elman backpropagation 4. Feed-forward backpropagation? Transfer function 1. tansig? 3. purelin? Number of inputs, layers (performance) Training method 1.Levenberg-Marquardt backpropagation? 2.Gradient descent (with momentum?) backpropagation? 3.Sequential order training? SIMULATOR!

11 Development environment GUI, processing and testability MATLAB – useful for processing signals, utilize filters and neural networks. LABVIEW – convenient for data presentation, simulations, analyzing.

12 Gant chart

Q&A 13

THANK YOU 14