Compressive Sensing system for recording of ECoG signals in-vivo

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Figure 5.2.2. Original and reconstructed signal by applying SCS.5.3. Reconstruction Method ApplicationAs it is included in 2.4, nowadays there is an intense research in achieving the fastest and moreefficient algorithms to solve the undetermined systems of equations which derive from CSoperations. The study and comparison of this large literature has slightly been under the scopeof this project, and as it has been presented previously, two of these methods have beenchosen to carry out the recovery: a) BPDM and LASSO [30]. They are described in 5.3.1.5.3.1. Basis Pursuit Denoising Method (BPDM)The Matlab code developed in [30] was designed to solve the convergence problem myminimizing the Eq.14.(14)Where A is the M x N measurements matrix, y is the compressed vector and σ is a nonnegativescalar which represents the noise margin. If σ is zero, then the Basis Pursuit Method(BPM) is solved, being the only constraint Ax = y.52
5.3.2. Least Absolute Shrinkage and Selection Operator (LASSO)The Matlab code [30] solves the convergence problem my minimizing the Eq.15.(15)Where A is the M x N measurements matrix, y is the compressed vector and τ is a non-negativescalar which represents the input signal margins.53
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 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

Compressive Sensing system for recording of ECoG signals in-vivo

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