Compressive Sensing system for recording of ECoG signals in-vivo

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A can be floating when the mixing switch and the Φ 1 -controlled switch are closed. Its value canbe chosen really small by considering the relation below:(20)Figure 6.2.4.1. Switched-Capacitor Integrator with parasitic effect.By considering Eq.22, the integration operation is modified as shown in Eq.21:(21)Similarly, the fundamental frequency relation is modified to:(22)By taking into consideration Eq.20 and Eq.22, C 1 and C 2 can be defined as is shown in Table6.2.4.1.ParameterSampling frequency (f s )Switching frequency (f switch )Integration period (Δt)Signal BandwidthC 1C 2CRSpecification33 kHz200 kHz32μs12 kHz1 pF960 fF1 fF5 MΩTable 6.2.4.1. Main parameters for the simulation of the SC Integrator-based multipath channel.In practice, the SC inverting amplifier of Fig.6.2.4.1 is influenced by the parasitic capacitors dueto the bottom and top plate of the desired capacitor. The bottom plate is shorted out but the topplate parasitic adds directly to the value of C 1 . The parasitic of C 2 does not affect it, this isbecause one of the capacitors (i.e. the bottom plate) is in shunt with the amplifier input, which is64
a virtual ground. The top plate capacitor is in shunt with the output of the amplifier and onlyserves as a capacitive load for it. In 6.2.5 it is introduced SC circuits which have beendeveloped to be insensitive to the capacitor parasitic. A complete multipath acquisition arrayhas been implemented in Cadence by considering N = 128 and M = 64. The resultingcompressed signal has been compared in Fig.6.2.4.2. In Fig.6.2.4.3 and Fig.6.2.4.4 theresulting LASSO and BPDN reconstructions are respectively included. In this case the scalefactor has been considered as in 6.2.2, but taking into account the equivalent resistor shown inEq.20.Figure 6.2.4.2. Compressed signal comparison.Figure 6.2.4.3. LASSO method reconstruction comparison.65
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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 62 and 63: In this way, the pole frequency is
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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