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On neural networks for analog to digital conversion
IEEE Transactions on Neural Networks, 1995In this paper we compare analog to digital conversion (ADC) delay in Hopfield ADC and asymmetrical (lower triangular) neural network-based ADC due to Avitabile et al. (1991). It is shown that, although Hopfield ADC has extensive feedback, its behavior in asynchronous mode is similar to that of lower triangular ADC.
CHANDE, V, POONACHA, PG
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1996
In this chapter we will look at the properties of analog-to-digital converters (ADC) and the limitations they introduce into sampled data. We will discuss the relationship between the resolution and the dynamic range of ADCs and end this chapter by simulating techniques for improving the dynamic range of ADCs by gain ranging and oversampling.
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In this chapter we will look at the properties of analog-to-digital converters (ADC) and the limitations they introduce into sampled data. We will discuss the relationship between the resolution and the dynamic range of ADCs and end this chapter by simulating techniques for improving the dynamic range of ADCs by gain ranging and oversampling.
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Stochastic Flash Analog-to-Digital Conversion
IEEE Transactions on Circuits and Systems I: Regular Papers, 2010A stochastic flash analog-to-digital converter (ADC) is presented. A standard flash uses a resistor string to set individual comparator trip points. A stochastic flash ADC uses random comparator offset to set the trip points. Since the comparators are no longer sized for small offset, they can be shrunk down into digital cells.
Skyler Weaver +4 more
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Analog to digital conversion: technical aspects
Annales Des Télécommunications, 2002An ideal radio communication receiver places the analog to digital conversion just after the antenna. It is an objective in a “software radio” perspective. The available silicon technologies do not provide the performance required by this application.
Patrick Loumeau +4 more
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1994
Many of the communications systems addressed in this book begin with an analog signal or have an analog signal for the output. For example, a radio receiver accepts an analog signal from the antenna, processes it and outputs either an analog audio signal or digital data depending on the information being received. A radio transmitter, on the other hand,
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Many of the communications systems addressed in this book begin with an analog signal or have an analog signal for the output. For example, a radio receiver accepts an analog signal from the antenna, processes it and outputs either an analog audio signal or digital data depending on the information being received. A radio transmitter, on the other hand,
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Stochastic analog-to-digital conversion
48th Midwest Symposium on Circuits and Systems, 2005., 2005This paper suggests a stochastic approach to data conversion. It is applicable to serial, parallel, two-step as well as delta-sigma ADCs and DACs. In the serial implementation of this scheme, a sample-and-hold circuit, a noise source and a comparator are combined with an accumulate-and-dump digital stage to perform serial multibit A/D conversion.
J.L. Ceballos, I. Galton, G.C. Temes
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1991
Since naturally occurring physical phenomena such as temperature, pressure, displacement, and so on, are analog, and since most practical methods of data collection, manipulation, and analysis are digital, a conversion from the analog quantities to digital quantities must take place.
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Since naturally occurring physical phenomena such as temperature, pressure, displacement, and so on, are analog, and since most practical methods of data collection, manipulation, and analysis are digital, a conversion from the analog quantities to digital quantities must take place.
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Improved neural networks for analog to digital conversion
1990 IJCNN International Joint Conference on Neural Networks, 1990In this paper we study the problem of designing a neural network that gives the correct binary representation of a given real number. Previously this problem has been studied by Tank and Hopfield. The network proposed by them exhibits “hysteresis” in the sense that the current vector of the network sometimes converges towards a binary vector that isnot
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