2. Developing a DSP Core using an FPGA Prototype for Scintillation Detector Signals Submitted to: Communication & Electronics Dept., Al Azhar University Supervised by: Prof. Dr. Ahmed Safwat Prof. Dr. Mahmoud Ashour Dr. Ashraf Aboshosha Prepared by: Eng. Mahmoud Kamel

3. Outline This core gives us all important features of the scintillation detector signals such as shaping, counting, pulse height and multichannel analyzing. The main purpose of this research work is to de-noise, compress and reconstruct the scintillation signals by which the processing speed, storage and precision will be improved. 3

4. Outline This core is implemented to apply the forward wavelet transform and interpolation technique. A new contribution of this framework arises from employing the interpolation techniques to reconstruct the signals where the mother wavelet and details are not required. Building a Multi-Channel Analyzer of the scintillation detector signals 4

5. Index of Content Scintillation detectors Importance of scintillation detectors Data Acquisition System Proposed digital processing algorithm Wavelets – Interpolation Technique Comparative study with the previous techniques Single channel and multi channel analyzer Conclusions and future work 5

6. Scintillation Detector 6 Figure 1 : Schematic diagram of a scintillation detector

7. Scintillation Detector A scintillator is a material that emits light, scintillates, when absorbing radiation. The energy can be determined by measuring the pulse height spectrum. This is called spectroscopy. A scintillation detector is obtained when a scintillator is coupled to an electronic light sensor such as PMT or photodiode. 7

8. Importance of Scintillation Detectors Detection of mixed ionizing fluxes near nuclear objects. Radionuclide control of samples and radiation pollution. Determination of the type and energy of high-energy particles and products of their reactions with targets. Nuclear medicine (Gamma Camera, PET Tomography, …) 8

9. Data Acquisition System 9 Figure 2 : The practical data acquisition system of scintillation detector Signals. (1) Scope, (2) high voltage source, (3) scintillator, (4) power supply

10. Why FPGA ? FPGA incorporates thousands of logic cells linked by programmable switches Highly parallel configurable digital signal processor A many channel signal processing was required in these detector to obtain a precise signals Availability of high-level design entry method FPGA designs easily changed, recompiled and low cost 10

11. FPGA Design Flow of the Solution Synthesis Translate Design into Device Specific Primitives Optimization to Meet Required Area & Performance Constraints Design Specification Place & Route Map Primitives to Specific Locations inside Target Technology with Reference to Area & Performance Constraints Specify Routing Resources to Be Used Design Entry/RTL Coding Behavioral or Structural Description of Design LE MEM I/O RTL Simulation Functional Simulation Verify Logic Model & Data Flow (No Timing Delays) 11

12. FPGA Design Flow of the solution Timing Analysis - Verify Performance Specifications Were Met - Static Timing Analysis Gate Level Simulation - Timing Simulation - Verify Design Will Work in Target Technology Program & Test - Program & Test Device on Board t clk 12

13. 13 Pre-processing Phase 1-Wavelet based Decomposition 2- Interpolation based Reconstruction Pulse Shaping & Counting Multichannel analyzer Store & Show data Figure 3: The overall proposed solution

14. 14 Pre Amplifier Main Amplifier SCA MCA Counter A B Figure 4: The proposed solution

15. Hardware System 15 Figure 5: The FPGA XSC50k-Spartan II and the PC-based parallel interface

16. Pre-processing Phase De-noising Compression Reconstruction 16

17. Effect of Noise on Pulse Shaping & Counting 17 Figure 6: Effect of noise on pulse shaping

18. Wavelets The wavelet analysis procedure is to adopt a wavelet prototype function, called an analyzing wavelet or mother wavelet. Wavelet transform decompose the original signal into different scales of resolution; these called the approximation and detail coefficients. 18

19. Wavelet Decomposition Levels 19 H G 2 2 d1d1 X0X0 H G 2 2 d2d2 H G 2 2 d3d3 Figure 7: Three wavelet decomposition levels A3A3

20. Wavelet Families Haar Daubechies Biorthogonal Coifelt Symelet Myer 20

21. Haar Wavelet Design Pro: Allows good approximation with a subset of coefficients. It can be computed quickly and easily. Implemented easily by FPGA. 21

22. Design Block Diagram 22 Figure 8: Design block diagram

23. Selecting the best Decomposition Level The quality of the compressed signals is the main criterion to select the best decomposition level in terms of Peak Signal to Noise Ratio (PSNR). The other similarity measure are Euclidean Distance (ED), Cross Correlation coefficient (CC) and Mean Square Error(MSE). 23

24. Decomposition Levels 24 Figure 9: Four approximation coefficients of Haar wavelet transform

25. Statistics of Four Decomposition Levels 25 CCEDMSEPSNRLevel 0.968120.47780.169327.8567 One 0.983014.73950.157530.7084 Two 0.986612.79900.144331.9258 Three 0.702157.06253.256118.9554 Four Table 1: Statistics of four levels Haar transform

26. 26 CCEDMSEPSNR Mother Wavelet 0.986612.79900.164331.926Haar 0.989011.81390.141832.5656Daubechies 0.990011.38340.132432.8635Coiflet 0.0148106.87811.423013.5046Meyer 0.988612.27760.153332.227Biorthogonal Table 2 : Similarity measure of constructed and original signals of the different mother wavelets Comparison of Different Mother Wavelets

27. Interpolation The Interpolation is a method of constructing new data points within the range of a discrete set of known data points. Interpolation is performed by fitting the supplied data with polynomial functions between data points and evaluating the appropriate function at the desired points. 27

28. Reconstruct Signals Using Interpolation 28 Figure 10: a) Original signals. b) Transformed signal. c) Reconstructed signals

29. Interpolation Algorithms Nearest neighbor interpolation Linear interpolation Cubic Hermit Interpolation Cubic spline interpolation 29

30. 30 CCEDMSEPSNR Method 0.978216.46690.273129.7192 Linear 0.986612.79900.164331.9258 Cubic Spline 0.930728.84620.838027.8501 Nearst 0.981814.89840.222630.6066 Cubic Hermit Table 3: Statistics of different interpolation techniques Comparison of Applying Different Interpolation Techniques

31. Previous Pre-processing Techniques 1.Accumulation Technique 2.Median filter 31

32. Accumulation Technique 32 Figure 11: Digital processing algorithm of scintillation detector signals

33. Median Filter The value of an output sample is determined by the median of the neighborhood signals. 33 Figure 12: Reconstructed signals using Median filter

34. 34 CCEDMSEPSNR Method 0.968021.34330.455527.4972 Accumulation Tech 0.983114.78560.218630.6856 Median filter 0.986612.79900.164331.9258 Proposed Solution. Table 4: Statistics of the preprocessing techniques Comparison of the Preprocessing Results

35. 35 Figure 13: Pulse shaping after denoising

36. 36 Figure 14: Pulse counting

37. Multi Channel Analyzer The MCA system is used to measure the height of each output pulse and the number of each output pulses simultaneously. By performing this operation for all detector events in a given interval the MCA generates a spectrum of the distribution of energy for a measured events with the y axis representing counts and the x axis representing channel value. 37

38. Multi Channel Analyzer 38 Figure 15: Divided original signals into 16 channels

39. Multi Channel Analyzer 39 Figure 16: Energy spectrum with 16 channels

40. Channel Calibration Energy channel values are converted into kilo electron volts with a channel-to-kilo electron volt conversion factor which is determined from a comparison of photo peak energies and channel location close to the energy of interest. 40

41. Conclusions One of the most important advantages of this system is the high compression rate (12.5%) using the interpolated wavelets Compared with the accumulation technique and median filtering, the proposed design achieved the best precision Capability of constructing MCA from SCA Coiflet is the best mother wavelet and Cubic spline is the best interpolation technique. Combining both of them for down and up sapling in wavelets is a new theoretical contribution of this framework 41

42. Future work Applying more complex wavelet filters. Modifying the proposed architecture to process more scintillator detectors. Employing the presented results as a base to identify radiation type and isotopes. 42