Compressive Sensing system for recording of ECoG signals in-vivo

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80Figure B.2.3.2. Binary-weighted SC MDAC/summer for CS.
Appendix CC.1. Analog ImplementationThere is a large range of applications in which random generation is necessary in order to beused in signal processing blocks. Radiofrequency communications signal mixing, test systemsand more recently biosignal compression based on CS are the most relevant ones. By means ofan analog implementation of such randomness, a true random generation is achieved at theexpense of a larger circuit complexity of the integration of more exotic circuitry. The maintechniques have been developed are: direct amplification of noise, high-frequency oscillatorsampling and randomness generation based on chaotic systems, and they are described below.C.1.1. Direct Amplification of NoiseThis method uses a device noise to make a decision of a bit 1 or 0 depending if the signal isgreater than certain threshold or vice versa. This can be done by amplifying the white noise of aresistor and then using a comparator to obtain a random bit. These schemes need a high-gainbandwidthlow-offset amplifier due to the fact that if a low-bandwidth amplifier is used, theoutput noise spectrum can be concentrated around DC and so, noise samples are correlated.Thus, they are not suitable for Radio Frequency Identification, RFID, systems. Animplementation has been presented in [29], and the main architecture is shown in Fig.C.1.1.1.This approach is not feasible to an on-chip application, due to area constraints.Figure C.1.1.1. Random generator based on direct amplification of noise [29].C.1.2. High-Frequency Oscillator Sampling [29]A TRNG can be implemented by combining a low-frequency jitter clock and using it to sample ahigh-frequency oscillator. The timing jitter is a stochastic phenomenon caused y thermal noisepresent in the transistors of a ring oscillator. The drawback of this procedure is that themegahertz generation is power hungry, so it is not an option for an on-chip CS design.81
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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 78 and 79: Figure B.2.2.1. Block diagram for a
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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