Post on 20-Jan-2016
transcript
Status of the compression/transmission electronics for the SDD.
Cern, march 1999.
Torino group,
Bologna group.
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:
3010-3.
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.
Compression algorithm.
symbol x symbol zero run length
thre
shol
d
tole
ranc
e
x
time
ampl
itude
Schematic block.
Differential encoder
Zero packer
Huffman encoder
Threshold filter
FIFO
sample
threshold
tolerance
Huffmantable
code
Compression performance.
Compression coefficient evaluated on real data (test beam of 09.98).
0
100
200
300
400
500
600
20 25 30 35 40
threshold (ADC count)
co
mp
res
sio
n c
oe
ffic
ien
t (x
10-3
)
0
10
tolerance
Test beam: 09.98. Sample average: 18.Sample standard deviation: 8.2.
Detector image before and after data compression.
Test beam: 09.98. Threshold: 40 (average+2.68standard deviation)
Test beam: 09.98. Threshold: 40 (average+2.68standard deviation)
Anode image before and after data compression.
Data comparison before and after the compression.
Output architecture.
Board layout.
15271mm2
Amplitude vs distance.
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.
Compression algorithm.
High threshold: 70Low threshold: 40
27 26 37 17 42 3038 40 51 65 39 3645 42 86 95 54 2836 36 35 43 35 4578 39 37 25 36 44
Logic architecture.
Behavioral description.
central low thresholdor
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
Schematic block.
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.
Compression performance.
Compression coefficient on real data (test beam of 09.98).
0
100
200
300
400
500
600
20 25 30 35 40
high threshold (ADC count)
Co
mp
res
sio
n c
oe
ffic
ien
t (x
10-3
)
-6
-2
delta threshold
Test beam: 09.98. Sample average: 18.Sample standard deviation: 8.2.
Ratio of the probabilities vs threshold above the average.