Compressive Sensing system for recording of ECoG signals in-vivo

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Figure 6.2.2.3. Compressed signals comparison.In the same way, in Fig.6.2.2.4 and Fig.6.2.2.5 LASSO-based ( ) and BPDN-based( ) signal recovery have been respectively sketched and compared for 6.2.1 and 6.2.2cases.Figure 6.2.2.4. LASSO reconstruction comparison.60
Figure 6.2.2.5. BPDN method reconstruction comparison.6.2.3. DC-Offset Controlled Active IntegrationAt zero frequency, the capacitor in Fig.6.2.2.1 is an open circuit and the amplifier in theintegrator loses feedback. For non-ideal amplifiers this can cause undesirable DC offset at theoutput. To provide a DC feedback at DC, a resistor can be used in parallel with C as shown inFig.6.2.3.1.Figure 6.2.3.1. Modified active integrator.At low frequencies, where C is an open circuit, the magnitude of the voltage gain is limited bythe value, and so the transfer function for the voltage gain of the integrator is given by:(19)61
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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 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 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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