1. Status of the compression/transmission electronics for the SDD. Cern, march 1999. Torino group, Bologna group.

2. Compression requirements. Reasonable goal for the data from two SDD layers on acquisition tape per event: 1.5Mbyte. Total amount of data produced by the wholes SDD layers per event: 50Mbyte. Desired compression coefficient for two SDD layers due to storage memory reason:  30  10 -3.

3. Time domain compression. The data comes out, from ADC, as a stream of samples that represents an anode time sequence; the difference between consecutive samples are calculated assuming the first reference as zero. The difference are codified by Huffman method (it means that a short symbol is associated to a frequent value). Sequence of zero (run), due to sequence of equal samples, are coded by the zero symbol followed by the run length value. When difference between samples is less than the tolerance parameter, they are considered as belonging to the same run. To improve the compression coefficient, the samples less than the threshold parameter are forced to zero.

4. Compression algorithm. symbol xsymbol zerorun length threshold tolerance x time amplitude

5. Schematic block. Differential encoder Zero packerHuffman encoderThreshold filterFIFO sample threshold tolerance Huffman table code

6. Compression performance. Test beam: 09.98.Sample average: 18. Sample standard deviation: 8.2.

7. Detector image before and after data compression. Test beam: 09.98.Threshold: 40 (average+2.68  standard deviation)

8. Anode image before and after data compression.

9. Data comparison before and after the compression.

10. Output architecture.

11. Board layout. 152  71mm 2

12. Amplitude vs distance.

13. Two domain compression. Most of the signals coming from the drift detector have signal/noise ratio not very good. To not lose information it is required to keep low the threshold, but that implies a poor compression coefficient. A new compression algorithm is under development that applies a two domain analysis: along the space and time dimensions. It considers two threshold: high threshold to select the clusters and a low threshold to collect peripheral information around the selected cluster.

14. Compression algorithm. High threshold:70 Low threshold:40

15. Logic architecture.

16. Behavioral description. central  low threshold or all signals  high threshold zero counter  0 anode completely scanned code(zero counter, central); length(zero counter, central); reset zero counter code(central); length(central) increment zero counter signal(sample) y n y y n n

17. Schematic block.

18. Consideration on the threshold. Statistically couples of noise samples pass the double threshold filter. Since the Gaussian noise distribution these isolated pairs represents elements of the normal distribution tail. With this assumption it is possible to recover information on average and standard deviation of the noise distribution.

19. Compression performance. Test beam: 09.98.Sample average: 18. Sample standard deviation: 8.2.

20. Ratio of the probabilities vs threshold above the average.