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Published on Mahmoud Kamel, Developing a DSP Core using an FPGA Prototype for Scintillation Detector Signals, supervised by Dr. Ashraf Aboshosha, ICGST Mahmoud Kamel, Developing a DSP Core using an FPGA Prototype for Scintillation Detector Signals, supervised by Dr. Ashraf Aboshosha, ICGST

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  • A scintillator is a material that converts energy lost by ionizing radiation into pulses of light
  • The compression performance is the basis for the choice of these wavelets among the different wavelet families in terms of PSNR. Signals in this case are the scintillation detector signals, and the noise is the error introduced by compression Therefore, in some cases one reconstruction may appear to be closer to the original than another, even though it has a lower PSNR (a higher PSNR would normally indicate that the reconstruction is of higher quality)
  • (a) Approximation coefficients at level one (b) Approximation coefficients at level two (c) Approximation coefficients at level three (d) Approximation coefficients at level four
  • There are a number of factors affect energy resolution, the size thickness and state of the transducer, the photon energy, and the characteristics of the electronic stage all determine the limits of energy resolution
  • Transcript

    • 1.  
    • 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.
    • 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
    • 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
    • 6. Scintillation Detector 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.
    • 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, …)
    • 9. Data Acquisition System 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
    • 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)
    • 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
    • 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. Pre Amplifier Main Amplifier SCA MCA Counter A B Figure 4: The proposed solution
    • 15. Hardware System Figure 5: The FPGA XSC50k-Spartan II and the PC-based parallel interface
    • 16. Pre-processing Phase
      • De-noising
      • Compression
      • Reconstruction
    • 17. Effect of Noise on Pulse Shaping & Counting 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 .
    • 19. Wavelet Decomposition Levels H G d 1 X 0 d 2 d 3 Figure 7: Three wavelet decomposition levels A 3 H G
    • 20. Wavelet Families
      • Haar
      • Daubechies
      • Biorthogonal
      • Coifelt
      • Symelet
      • Myer
    • 21. Haar Wavelet Design
      • Pro:
      • Allows good approximation with a subset of coefficients.
      • It can be computed quickly and easily.
      • Implemented easily by FPGA.
    • 22. Design Block Diagram 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).
    • 24. Decomposition Levels Figure 9: Four approximation coefficients of Haar wavelet transform
    • 25. Statistics of Four Decomposition Levels Table 1: Statistics of four levels Haar transform CC ED MSE PSNR Level 0.9681 20.4778 0.1693 27.8567 One 0.9830 14.7395 0.1575 30.7084 Two 0.9866 12.7990 0.1443 31.9258 Three 0.7021 57.0625 3.2561 18.9554 Four
    • 26. Table 2 : Similarity measure of constructed and original signals of the different mother wavelets Comparison of Different Mother Wavelets CC ED MSE PSNR Mother Wavelet 0.9866 12.7990 0.1643 31.926 Haar 0.9890 11.8139 0.1418 32.5656 Daubechies 0.9900 11.3834 0.1324 32.8635 Coiflet 0.0148 106.878 11.4230 13.5046 Meyer 0.9886 12.2776 0.1533 32.227 Biorthogonal
    • 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.
    • 28. Reconstruct Signals Using Interpolation 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
    • 30. Table 3: Statistics of different interpolation techniques Comparison of Applying Different Interpolation Techniques CC ED MSE PSNR Method 0.9782 16.4669 0.2731 29.7192 Linear 0.9866 12.7990 0.1643 31.9258 Cubic Spline 0.9307 28.8462 0.8380 27.8501 Nearst 0.9818 14.8984 0.2226 30.6066 Cubic Hermit
    • 31. Previous Pre-processing Techniques
      • Accumulation Technique
      • Median filter
    • 32. Accumulation Technique 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.
      Figure 12: Reconstructed signals using Median filter
    • 34. Comparison of the Preprocessing Results Table 4: Statistics of the preprocessing techniques CC ED MSE PSNR Method 0.9680 21.3433 0.4555 27.4972 Accumulation Tech 0.9831 14.7856 0.2186 30.6856 Median filter 0.9866 12.7990 0.1643 31.9258 Proposed Solution.
    • 35. Figure 13: Pulse shaping after denoising
    • 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.
    • 38. Multi Channel Analyzer Figure 15: Divided original signals into 16 channels
    • 39. Multi Channel Analyzer 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.
    • 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
    • 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.
    • 43.