1. Cancer Node Detection Using Ultrasonic MIMO Radar AKHILESH MISHRA

2. Outline  Introduction  System Model  Simulation  RADAR Detection Problem  Results  References

3. Introduction  Disadvantages of Existing techniques for cancer detection X ray Mammography -> Chances high for false negative Microwave Imaging -> Provides low spatial resolution MRI -> Painful and long  Alternative is to use Ultrasound Waves in MIMO(Multiple Input Multiple Output) RADAR. Why we use that ?? High Resolution, No radiation exposure, Safe

4. System Model [1]-Breast Cancer Nodes Detection Using Ultrasonic Microscale Subarrayed MIMO RADAR- A. Taparugssanagorn, S. Siwamaogasatham, C.Raez  Frequency used 10- 15MHz  N t transmit Antennas, N r receive Antennas both divided in N s subarrays  Matched Filtering for signal waveform extraction  RCS is now a random variable  Multiple independent measurements – Better detection performance and better spatial resolution  Relative permittivities of Normal tissue and Tumor tissue [2]  Statistical behavior of received signal [3] Phase is uniformly distributed Magnitude modelled as Nakagami-distribution

5. Simulations (Single Input Multiple Output) RADAR Parameters : Center frequency 15 MHz Pulse Duration 20  s Transmitted wave Chrip with 5 MHz BW Transmit window Hanning Sampling frequency 80MHz Receive Antenna Elements 6, Uniform Linear Array Speed 1500m/s Targets 3 point targets at 0 degrees, -60 degrees and 30 degrees

6. Fig 3. Frequency Spectrum of Transmitted ChirpFig 4. Radiation Pattern of Receive Antenna

7. Fig 5. Received Signal Echoes from 3 point targetsFig 6. Pulse Compressed Received Signal

8. Fig 7. MUSIC Periodogram to estimate Angle of Arrival Angle of Arrival Estimation using MUSIC Algorithm

9. Fig 8. MVDR Radiation Pattern for  = 0 degree Fig 9. Clutter removed from signal Case 1: Assuming signal at 0 degree is our signal of interest

10. Fig 10. MVDR Radiation Pattern for  = -60 degree Case 2: Assuming signal at -60 degree is our signal of interest Fig 11. Clutter Removed from the signal

11. Radar Detection  Use the MAP decision rule for detection of target.  Maximum Likelihood estimate of angle of target and its amplitude

12. Results Table 1,2 & 3- [1]-Breast Cancer Nodes Detection Using Ultrasonic Microscale Subarrayed MIMO RADAR- A. Taparugssanagorn, S. Siwamaogasatham, C.Raez

13. References 1. A. Taparugssanagorn, S. Siwamaogasatham, C.Raez -”Breast Cancer Nodes Detection Using Ultrasonic Microscale Subarrayed MIMO RADAR”, Advances in Bioinformatics Volume 2014, Article ID 797013, 8 pages 2. S. K. Davis, H. Tandradinata, S. C. Hagness, and B. D. van Veen, “Ultrawideband microwave breast cancer detection: a detection-theoretic approach using the generalized likelihood ratio test,” IEEE Transactions on Biomedical Engineering, vol. 52,no. 7, pp. 1237–1250, 2005. 3. N. Bahbah, H. Djelouah, and A. Bouakaz, “Use of Nakagami statisticalmodel in ultrasonic tissue mimicking phantoms characterization,”in Proceedings of the 24th International Conference on Microelectronics (ICM ’12), December 2012. 4. E. Brookner, “Phase arrays around the world progress and future trends,” in Proceedings of the IEEE International SymposiumPhased Array Systems and Technology, pp. 1–8, October 2008. 5. J. Y. Lee and E. A. Morris, “Breast MRI: historical overview,” in Breast MRI: diagnosis and intervention, E. A. Morris and L. Liberman, Eds., pp. 3–6, Springer, New York, NY, USA, 2005. 6. L. Galluccio, T. Melodia, S. Palazzo, and G. E. Santagati, “Challenges and implications of using ultrasonic communications in intra-body area networks,” in Proceedings of the 9 th Annual Conference on Wireless On-Demand Network