Compressive Sensing system for recording of ECoG signals in-vivo

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In this way, the pole frequency is at and the frequency at 0dB-gain is .By taking into consideration that the bandwidth of the input signal is about 12 kHz, the pole hasbeen accounted at 100 Hz, as in 6.2.2, and the 0-dB frequency has been considered at 20 kHz,far enough from the bandwidth limit in order not to filter out any frequency component ofinterest. The other simulation parameters have been included in Table 6.2.1.1 and inFig.6.2.2.2. In order to estimate the scale factor which is applied over the final integration valuesimilarly as it is introduced in Eq.16, Eq.19 can be expressed in time domain as:(20)Figure 6.2.3.2. Compressed signal comparison.In this way, the scale has to be considered in each integration time, depends on –R 2 C and thegain which is applied in the integration frequency range, which has been calculated as in 6.2.1.A complete multipath acquisition array has been implemented in Cadence by considering N =128 and M = 64. The resulting compressed signal has been compared in Fig.6.2.3.2 with theones presented in 6.2.1. In Fig. 6.2.3.3 and Fig.6.2.3.4, reconstructions which have beenaccomplished are shown for all the compared topologies seen above for and .62
Figure 6.2.3.3. LASSO method reconstruction comparison.Figure 6.2.3.4. BPDN method reconstruction comparison.6.2.4. Switched-Capacitor Integrator with parasitic effectsIn order to reduce the area derived of the resistor, this is replaced by a Switched-Capacitorresistor as the one which is delighted in Fig.6.2.4.1. The capacitor, C, which has been includedin parallel after the mixing switch, has been considered during the simulations in order to avoidthat node63
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August, 31 th , 2012Compressive Sen
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The present project has been submit
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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 64 and 65: A can be floating when the mixing s
Page 66 and 67: Figure 6.2.4.4. BPDN method reconst
Page 68 and 69: Figure 6.2.5.3. 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
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Compressive Sensing system for recording of ECoG signals in-vivo

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