Compressive Sensing system for recording of ECoG signals in-vivo

More documents

Recommendations

Info

Figure 6.2.5.3. BPDN method reconstruction comparison.In Fig.6.2.5.2 and Fig.6.2.5.3 input reconstruction is shown for all the ideal cases have beentested. It is clearly observable that the best recovery for the full energy range has beenachieved by the non-inverting SC Integrator without parasitic effects (in yellow), closely followedby the SC Integrator with parasitic effects. The continuous time configurations, in passive oractive topologies have demonstrated to carry out a worst charge and discharge of the capacitorduring the integration phases, and so the final accumulated values for each of the compressedsamples in each of the paths of the channels move away from the theoretical solution calculatedin Matlab, whereupon the reconstructed signal is more noisy than in discrete time-basedintegrators. This discussion is summarized in 6.2.6 with the SNR analysis.6.2.6. SNR Calculations [32]Signal to Noise Ratio has been calculated by considering Eq. 23:(23)Where is the original input signal and is the reconstructed one. In Table 6.2.6.1 SNRcalculations for each of the ideal topologies which have been presented in 6.5 are summarizedfor both of the recovery methods have been considered, LASSO-based and BPDN-based one.According to the results obtained below, the best topology to be improved is the one presentedin 6.2.5, based on switched capacitors and enhanced not to be sensitive to the parasitic whichare introduced by C 1 . Henceforward, a mixing-integration real topology based on switchedcapacitors is considered.68
Topology LASSO (dB) BPDN (dB)Matlab Code 36.3 36.9Passive Integrator (6.5.1) 9.8 10.4Active Integrator (6.5.2) 7.8 5.6Modified Active Int. (6.5.3) 8.2 3.6SC Int. Parasitic Sensitive (6.5.4) 12.7 14.7SC Int. Parasitic Insensitive(6.5.5)14.1 14.9Table 6.2.6.1. SNR comparison between topologies and reconstruction methods.The primary advantages of SC circuits include: a) compatibility with CMOS technology; b) goodaccuracy of time constants; c) good voltage linearity; d) good temperature characteristics; ande) less area needs. By the other hand, the main disadvantages are a) clock feedthrough; b) therequirement of nonoverlapping clock; and c) the necessity of the bandwidth of the signal beingless than the clock frequency. Beneath, these considerations are taken into account.69
Page 1:
August, 31 th , 2012Compressive Sen
Page 5:
The present project has been submit
Page 9:
AbstractThe project “Compressive
Page 13:
RiassuntoIl progetto “Compressive
Page 17 and 18: IndexAcknowledgments 7Abstract 9Gan
Page 19 and 20: E.2. Signal Digital Conversion 84Gl
Page 21 and 22: Figure 6.2.1.2. Input signal Spectr
Page 23: Index of TablesTable 2.1.1. Summary
Page 26 and 27: Figure 1.1. Energy a power costs fo
Page 29 and 30: 2. State of the art2.1 Neural Signa
Page 31 and 32: patterns in response to visual targ
Page 33 and 34: 2.4. Compressive Sensing2.4.1. Comp
Page 35 and 36: 2.4.2. Sparsifying basesSparsifying
Page 37 and 38: It is worth mentioning that compres
Page 39 and 40: y specially considering the integra
Page 41 and 42: Figure 4.1.1.1. LSFR parallel (top)
Page 43 and 44: 4.1.3. Serial Implementation with t
Page 45: 4.1.4. Randomness CheckingIn order
Page 48 and 49: een designed independently as it is
Page 50 and 51: time, which has been approximated a
Page 52 and 53: Figure 5.2.2. Original and reconstr
Page 54 and 55: 6. Analog Path Design6.1. Design Di
Page 56 and 57: The spectra of the input signal hav
Page 58 and 59: Figure 6.2.1.4. LASSO reconstructio
Page 60 and 61: Figure 6.2.2.3. Compressed signals
Page 62 and 63: In this way, the pole frequency is
Page 64 and 65: A can be floating when the mixing s
Page 66 and 67: Figure 6.2.4.4. BPDN method reconst
Page 71 and 72: 7. Conclusions7.1. RemarksAs it is
Page 73: AppendixAppendix AConvexOptimizatio
Page 76 and 77: The total power cost for the digita
Page 78 and 79: Figure B.2.2.1. Block diagram for a
Page 80 and 81: 80Figure B.2.3.2. Binary-weighted S
Page 82 and 83: The main model is shown in Fig.C.1.
Page 84 and 85: Appendix EE.1. AmplificationThe sma
Page 87 and 88: GlossaryADC: Analog-to-Digital Conv
Page 89 and 90: References[1] D. Gangopadhyay et al
Page 91: [35] M.F. Duarte et. al., Recovery
show all

Compressive Sensing system for recording of ECoG signals in-vivo

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?