hardware implementation of data compression ... - INFN Bologna

More documents

Recommendations

Info

92 2D compression algorithm and implementation ter reconstruction in all circumstances. Indeed in order to obtain a good compression factor using the 1D algorithm a threshold of about three times the RMS of the noise has to be used. Such threshold often determines a rather sharp cut of the tails of the anode signals containing high samples and, more important, it can completely suppress the anodic signals with small values which are on the sides of the cluster. Both these sharp cuts, particularly the latter, can significantly affect the spatial resolution. Though samples below a 3 RMS threshold have small information contents, it is conceivable that, in the more accurate off-line analysis, they can help to improve the pattern recognition and the fitting of the cluster features. In order to read out small-amplitude samples without increasing too much the collection of the noise, a twothreshold algorithm can be used, so that small samples that satisfy a low threshold are collected only when, along the drift direction, they are near to samples satisfying a high threshold. Since the charge cloud diffuses in two orthogonal directions for symmetry reasons and due the previous considerations, the two-threshold method should be applied along the anode axis too. We want that such a two-threshold twodimensional data compression and zero suppression algorithm satisfy the following criteria: – the values of the samples, in the neighbourhood of a cluster, be available both for an accurate measurement of the characteristics of the clusters and for a good monitoring and understanding of the characteristics of the background; – the statistical nature of the suppressed samples be available to monitor the noise level of the anodes and to obtain their baseline values, which have to be subtracted from the cluster samples in order to obtain a correct measurement of the related charge. Here follows a description of the studied algorithm: the data reduction algorithm is applied to the resulting matrix of 256 rows by 256 columns like the one shown in the upper part of Fig. 4.1. Each matrix element expresses an 8-bit quantized amplitude. A row represents a
4.1 — 2D compression algorithm Figure 4.1: Example of the digitized data produced by a half SDD time sequence of the samples from a single SDD anode and a column represents a spatial snapshot of the simultaneous anode outputs for an instant of time. For each charge cloud we expect several high values in one or more columns and rows. This extension in both time and space thus requires that correlations in both dimensions be preserved for future analysis. We refer to correlations within a column as space-like and correlations within a row as time-like. Therefore, in the proposed two-threshold two-dimensional algorithm, the high threshold TH must be satisfied by a pixel value in order that it be part of a cluster, and the 93
Page 1:
UNIVERSITÀ DEGLI STUDI DI BOLOGNA
Page 5 and 6:
Contents Introduction ix 1 The ALIC
Page 7 and 8:
CONTENTS 4 2D compression algorithm
Page 9 and 10:
Introduction This thesis work has b
Page 13 and 14:
Chapter 1 The ALICE experiment ALIC
Page 15 and 16:
1.1 — The Inner Tracking System 1
Page 17 and 18:
1.1 — The Inner Tracking System t
Page 19 and 20:
1.1 — The Inner Tracking System P
Page 21 and 22:
1.2 — Design of the drift layers
Page 23 and 24:
1.3 — The SDDs (Silicon Drift Det
Page 25 and 26:
PASCAL AMBRA 1.4 — SDD readout sy
Page 27 and 28:
data_in[0] data_in[1] data_in[2] da
Page 29 and 30:
1.4 — SDD readout system 1.4.2 Ev
Page 31 and 32:
1.4 — SDD readout system with a c
Page 33 and 34:
Chapter 2 Data compression techniqu
Page 35 and 36:
2.2 — Remarks on information theo
Page 37 and 38:
2.3 — Compression techniques 2.3.
Page 39 and 40:
2.4 — Lossless compression techni
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Original sequence Sequence after di
Page 47 and 48:
2.5 — Lossy compression technique
Page 49 and 50:
Page 51 and 52:
Page 53 and 54: 2.5 — Lossy compression technique
Page 63 and 64: 2.6 — Implementation of compressi
Page 65 and 66: Symlets sym2 sym3 sym4 sym8 Wavelet
Page 67 and 68: Chapter 3 1D compression algorithm
Page 69 and 70: 3.2 — 1D compression algorithm Fi
Page 71 and 72: 3.3 — 1D algorithm performances s
Page 73 and 74: 3.3 — 1D algorithm performances F
Page 75 and 76: 3.4 — CARLOS v1 Figure 3.6: CARLO
Page 77 and 78: 3.4 — CARLOS v1 Figure 3.7: Pictu
Page 79 and 80: 3.4 — CARLOS v1 3.4.3 Functions p
Page 81 and 82: 3.5 — CARLOS v2 have a 100% compa
Page 83 and 84: 3.5 — CARLOS v2 3.5.1 The firstch
Page 85 and 86: 3.5 — CARLOS v2 data and when loa
Page 87 and 88: 3.5 — CARLOS v2 which of the 8 qu
Page 89 and 90: - indat6 : input 32-bit bus; - inda
Page 91 and 92: 3.5 — CARLOS v2 - femux is a mult
Page 93 and 94: 3.5 — CARLOS v2 total optical fib
Page 95 and 96: 3.5 — CARLOS v2 Figure 3.11: Desi
Page 97 and 98: 3.5 — CARLOS v2 that CARLOS will
Page 99 and 100: 3.6 — CARLOS v2 design flow Figur
Page 101 and 102: 3.7 — Tests performed on CARLOS v
Page 103: Chapter 4 2D compression algorithm
Page 111 and 112: 4.2 — CARLOS v3 vs. the previous
Page 113 and 114: 4.3 — The final readout architect
Page 115 and 116: 4.5 — CARLOS v3 building blocks -
Page 117 and 118: 4.5 — CARLOS v3 building blocks T
Page 119 and 120: 4.5 — CARLOS v3 building blocks F
Page 121 and 122: 4.5 — CARLOS v3 building blocks c
Page 125 and 126: 4.5 — CARLOS v3 building blocks o
Page 129 and 130: 4.5 — CARLOS v3 building blocks u
Page 131 and 132: 4.5 — CARLOS v3 building blocks a
Page 133 and 134: 4.5 — CARLOS v3 building blocks p
Page 135 and 136: 4.7 — CARLOS layout features post
Page 137 and 138: Chapter 5 Wavelet based compression
Page 139 and 140: levels used: 5.1 — Wavelet based
Page 141 and 142: 5.2 — Multiresolution algorithm o
Page 153 and 154: 5.3 — Choice of the architecture
Page 155 and 156:
5.3 — Choice of the architecture
Page 157 and 158:
Dettaglio 1 1 D1 2 Downsample Hi_De
Page 159 and 160:
2 Upsample Dettaglio 1 1 D1 Hi_Rec
Page 161 and 162:
5.4 — Multiresolution algorithm p
Page 163 and 164:
5.5 — Hardware implementation ter
Page 165 and 166:
5.5 — Hardware implementation Fig
Page 167 and 168:
5.5 — Hardware implementation com
Page 169 and 170:
5.5 — Hardware implementation Haa
Page 171 and 172:
Conclusions The main goal of this t
Page 173 and 174:
Bibliography [1] ALICE Collaboratio
Page 175:
BIBLIOGRAPHY [19] D. Cavagnino, P.
show all

hardware implementation of data compression ... - INFN Bologna

Create successful ePaper yourself

Delete template?

Save as template?