Compressive Sensing system for recording of ECoG signals in-vivo

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Figure B.2.2.1. Block diagram for an analog implementation of a CS channel for neuralacquisition.The total power cost for the analog implementation of a multipath CS encoder, without takinginto consideration the random generation and the mixers cost, has been calculated in [3] as:(25)Figure B.2.2.2. Power consumption versus bandwidth for the analog implementation of a CSpath. Specifications from Table 3.1.1 have been taken into consideration.Tthe power consumption has been introduced versus the bandwidth of the input signal, (seeFig. B.2.2.2). In this case, regarding to the calculations have been presented above, the largestcontribution to the power consumption is the one due to each of the amplifiers which have beenconsidered as necessary for each of the paths of the matrix multiplication. In 3.1 this78
approximation is discussed by taking into consideration a different block diagram, which hasbeen considered as a better approximation to the analog implementation of a CS path in termsof power saving. In the same way, Chapter 6 a new path design has been considered byexploiting an area saving on chip.B.2.3. Charge ModeAn incremental Sigma-Delta ADC (ΣΔ-ADC), for CS applications has been submitted in [28]. Thenovelty of this implementation is the simultaneity of the acquisition and quantization of CSmeasurements, due to the fact that a Switched Capacitor (SC) integrator of has beenincorporated in the close loop of the converter. The converter occupies a small area of 0.047mm 2 on a target 0.5 μm CMOS process, and it can be suitable for CS applications. A sketch ofthe complete implementation has been depicted in Fig. B.2.3.1.Figure B.2.3.1. Switched Capacitor circuit implementation of the CS ADC.In [1] it has been proposed a binary-weighted SC Multiplying Digital-to-Analog Converter(MDAC) for the processing of ECG and EMG signals. The architecture that is included inFig.B.2.3.2. implements a multiplication between the actual random coefficient and the inputsignal sample during Φ 1 and the accumulation of this value during Φ 2, these two are settled innon-overlapping. For instead, the first random coefficient which corresponds to element row oneand column one from the random matrix, Φ 11 , and the first signal sample X 1 , are multiplied whenΦ 1 is closed. When Φ 2 is closed, the result is accumulated in the integration capacitance. Onceagain, when Φ 1 is closed, Φ 12 and X 2 are multiplied, and during the next high level Φ 2 , the resultof this operation is added to the previous value has been accumulated, and so on. The MostSignificant Bit (MSB) is used as sign bit.79
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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
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RiassuntoIl progetto “Compressive
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IndexAcknowledgments 7Abstract 9Gan
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E.2. Signal Digital Conversion 84Gl
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Figure 6.2.1.2. Input signal Spectr
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Index of TablesTable 2.1.1. Summary
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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 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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