Compressive Sensing system for recording of ECoG signals in-vivo

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5.3.1. Basis Pursuit Denoising Method (BPDM) 525.3.2. Least Absolute Shrinkage and Selection Operator (LASSO) 536. Analog Path Design 546.1. Design Discussion 546.2. Mixing and Integration 556.2.1. Passive Integration 556.2.2. Ideal Active Inverting Integration 586.2.3. DC-Offset Controlled Active Integration 616.2.4. Switched-Capacitor Integrator with parasitic effects 636.2.5. Non inverting SC Integrator without parasitic effects 666.2.6. SNR Calculations 687. Conclusions 717.1. Remarks 717.2. Next steps 72Appendix 73Appendix A 73Appendix B 75B.1. Digital Implementation 75B.2. Analog Implementation 76B.2.1. Current Mode 76B.2.2. Voltage Mode 77B.2.3. Charge Mode 79Appendix C 81C.1. Analog Implementation 81C.1.1. Direct Amplification of Noise 81C.1.2. High-Frequency Oscillator Sampling 81Appendix D 83Appendix E 84E.1. Amplification 8418
E.2. Signal Digital Conversion 84Glossary 87References 8919
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: IndexAcknowledgments 7Abstract 9Gan
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 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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