A 2-GHz analog-to-digital delta-sigma modulator for CDMA receivers with 79-dB signal-to-noise ratio in 1.23-MHz bandwidth

2004 ◽  
Vol 39 (11) ◽  
pp. 1819-1828 ◽  
Author(s):  
E.H. Dagher ◽  
P.A. Stu ◽  
W.K. Masenten ◽  
M. Conta ◽  
T.V. Dinh
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Analog To Digital ◽  
Delta Sigma Modulator ◽  
Delta Sigma ◽  
Noise Ratio

scholarly journals Uniform Quantization of Signals with Clipped and Truncated Laplace Distributions

2020 ◽  
Author(s):  
Jerald Bauck
Keyword(s):  
Signal To Noise Ratio ◽  
Laplace Distribution ◽  
Analog To Digital Converter ◽  
Digital Converter ◽  
Signal To Noise ◽  
Analog To Digital ◽  
Traditional Test ◽  
Uniform Quantization ◽  
Long Tails ◽  
Noise Ratio

Many natural and man-made signals including much of speech and music are well-modeled by the Laplace distribution. Methods of synthesizing such signals are available and sometimes preferred over traditional test signals. However, sometimes those Laplace-like signals are clipped or do not exhibit infinitely-long tails. These situations are analyzed to determine their variances with an application of estimating signal-to-noise ratio as they are quantized by an analog-to-digital converter.

scholarly journals Influence of Analog-To-Digital Conversion and Decimation Parameters on the Signal-To-Noise Ratio in the Radar Path

2021 ◽  
Vol 24 (6) ◽  
pp. 38-50
Author(s):  
S. R. Heister ◽  
V. V. Kirichenko
Keyword(s):  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Quantization Noise ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Analog To Digital ◽  
Noise Interference ◽  
Analog To Digital Conversion ◽  
Wide Range ◽  
Noise Ratio

Introduction. The digital representation of received radar signals has provided a wide range of opportunities for their processing. However, the used hardware and software impose some limits on the number of bits and sampling rate of the signal at all conversion and processing stages. These limitations lead to a decrease in the signal-to-interference ratio due to quantization noise introduced by powerful components comprising the received signal (interfering reflections; active noise interference), as well as the attenuation of a low-power reflected signal represented by a limited number of bits. In practice, the amplitude of interfering reflections can exceed that of the signal reflected from the target by a factor of thousands.Aim. In this connection, it is essential to take into account the effect of quantization noise on the signal-tointerference ratio.Materials and methods. The article presents expressions for calculating the power and power spectral density (PSD) of quantization noise, which take into account the value of the least significant bit of an analog-to-digital converter (ADC) and the signal sampling rate. These expressions are verified by simulating 4-, 8- and 16-bit ADCs in the Mathcad environment.Results. Expressions are derived for calculating the quantization noise PSD of interfering reflections, which allows the PSD to be taken into account in the signal-to-interference ratio at the output of the processing chain. In addition, a comparison of decimation options (by discarding and averaging samples) is performed drawing on the estimates of the noise PSD and the signal-to-noise ratio.Conclusion. Recommendations regarding the ADC bit depth and sampling rate for the radar receiver are presented.

scholarly journals Design of a 1-Bit MEMS Gyroscope Using the Model Predictive Control Approach

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 730 ◽  
Author(s):  
Xiaofeng Wu ◽  
Zhicheng Xie ◽  
Xueliang Bai ◽  
Trevor Kwan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Mems Gyroscope ◽  
Control Approach ◽  
Delta Sigma Modulator ◽  
Hard Constraints ◽  
Delta Sigma ◽  
Computational Requirement

In this paper, a bi-level Delta-Sigma modulator-based MEMS gyroscope design is presented based on a Model Predictive Control (MPC) approach. The MPC is popular because of its capability of handling hard constraints. In this work, we propose to combine the 1-bit nature of the bi-level Delta-Sigma modulator output with the MPC to develop a 1-bit processing-based MPC (OBMPC). This paper will focus on the affine relationship between the 1-bit feedback and the in-loop MPC controller, as this can potentially remove the multipliers from the controller. In doing so, the computational requirement of the MPC control is significantly alleviated, which makes the 1-bit MEMS Gyroscope feasible for implementation. In addition, a stable constrained MPC is designed, so that the input will not overload the quantizer while maintaining a higher Signal-to-Noise Ratio (SNR).

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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