Distortion in a Pulse Count Modulation System

Clavier, A.G.; Panter, Philip F.; Grieg, D. D.

doi:10.1109/t-aiee.1947.5059539

Cited by 33 publications

(22 citation statements)

References 3 publications

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“…The definition and some properties of frames are given next. The largest number A and smallest number B that satisfy (55) are called frame bounds. Some important remarks about frames are:…”

Section: A3 Framesmentioning

confidence: 99%

“…It is easy to show that, for a normalized tight frame, r = A, where A is the lower frame bound in (55). Another important property of the elements of a frame {ψ k } k∈K is that they are also a Bessel sequence, i.e., they satisfy where B is the upper frame bound in (55).…”

Section: Definition 4 (Framementioning

confidence: 99%

“…Another important property of the elements of a frame {ψ k } k∈K is that they are also a Bessel sequence, i.e., they satisfy where B is the upper frame bound in (55). From remark 2 and the above properties, orthogonal bases are a special type of Riesz basis, whilst Riesz bases are exact frames.…”

Section: Definition 4 (Framementioning

confidence: 99%

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Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

Goodwin

Derpich

Quevedo

2006

2006 Chinese Control Conference

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Abstract:In order that signals can be stored, transmitted or processed it is necessary that they first be converted into digital form. This, in turn, raises the problem of how to digitize data so as to achieve the best trade-off between data load and performance, i.e., "how to make the most out of a little". Two issues are involved in this problem, namely temporal quantization (i.e., sampling) and spatial quantization. These two problems have traditionally been addressed separately. Indeed, there exists substantial literature dealing with the temporal quantization problem, covering both band-limited and non-band-limited signals. The usual underlying paradigm is that of an analysis filter, followed by a sampler, followed by a reconstruction filter. Various parts of this architecture can be optimized once other parts have been specified. On the other hand, spatial quantization has been studied extensively for a given sampling strategy, particularly in the framework of sigma delta conversion. Finally, it is also possible to formulate the joint design problem for sampling and spatial quantization. This typically leads to enhanced performance compared to that achievable by considering the two aspects separately. This paper will survey the general area of sampling and quantization and analyze methods for achieving efficient data representations for signal processing and control applications. We will show how, on the one hand, contemporary control theory can contribute to the design of sampling and quantization systems and, on the other hand, how these systems impact on the performance of modern feedback control systems.

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“…The definition and some properties of frames are given next. The largest number A and smallest number B that satisfy (55) are called frame bounds. Some important remarks about frames are:…”

Section: A3 Framesmentioning

confidence: 99%

Section: Definition 4 (Framementioning

confidence: 99%

See 1 more Smart Citation

Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

Goodwin

Derpich

Quevedo

2006

2006 Chinese Control Conference

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“…A data change of the form indicates that there exists a point on the interval where goes through the quantization threshold which is between and . 3 The amplitude of at is thus known with infinite precision, , whereas is known with uncertainty . An alternative way to view this data change is to consider that it gives the value of at with an error proportional to the size of the sampling interval.…”

Section: Definitionmentioning

confidence: 99%

“…This comes about partly because the additive-noise model has a very limited scope and is hard to justify asymptotically, when, due to fine sampling, correlations between the error samples become more pronounced. An exact analysis of quantization error by Clavier, Panter, and Grieg [3], [4], for signals which are superpositions of two sinusoids, demonstrated the nonwhite nature of the quantization error. It has also been experimentally verified that the quantization error, when measured for linear reconstruction, does not tend to zero along with in the manner described by (1), but rather reaches a nonzero floor level for some finite .…”

Section: Introductionmentioning

confidence: 99%

On simple oversampled A/D conversion in L/sup 2/(R)

Cvetković

Vetterli

2001

IEEE Trans. Inform. Theory

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Abstract-Accuracy of oversampled analog-to-digital (A/D) conversion, the dependence of accuracy on the sampling interval and on the bit rate are characteristics fundamental to A/D conversion but not completely understood. These characteristics are studied in this paper for oversampled A/D conversion of band-limited signals in 2 ( ). We show that the digital sequence obtained in the process of oversampled A/D conversion describes the corresponding analog signal with an error which tends to zero as 2 in energy, provided that the quantization threshold crossings of the signal constitute a sequence of stable sampling in the respective space of band-limited functions. Further, we show that the sequence of quantized samples can be represented in a manner which requires only a logarithmic increase in the bit rate with the sampling frequency, = ( log ), and hence that the error of oversampled A/D conversion actually exhibits an exponential decay in the bit rate as the sampling interval tends to zero.

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A 3.3-V 12b 50-MS/s A/D Converter in 0.6-µm CMOS with over 80-dB SFDR

Pan

Segami

Choi

et al. 2000

Analog Circuit Design

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This paper discusses the impact of SFDR specification on the design of AID converter (ADC) in CMOS technology and describes the implementation of a prototype optimized for wide band SFDR performance for use in modem wireless base stations. The 6b-7b two-stage pipelined ADC using bootstrapping to linearize the sampling switch of on-chip track-hold achieves over 80 dB SFDR for signal frequencies up to 75 MHz at 50 MS/s without the need for trimming, calibration and dithering. INL is 1.3LSB, DNL is 0.8LSB. The 6b and 7b sub-ADC's are made efficient with averaging and folding. In 0.61J.m CMOS, the 16mm 2 ADC dissipates 850mW from 3.3V supply.

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Distortion in a Pulse Count Modulation System

Cited by 33 publications

References 3 publications

Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

On simple oversampled A/D conversion in L/sup 2/(R)

A 3.3-V 12b 50-MS/s A/D Converter in 0.6-µm CMOS with over 80-dB SFDR

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