scholarly journals Spherical logarithmic quantization and its application for DPCM

2004 ◽  
Vol 17 (2) ◽  
pp. 165-184 ◽  
Author(s):  
Johannes Huber ◽  
Bernd Matschkal
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Rate Distortion ◽  
Differential Pulse ◽  
High Dynamic Range ◽  
Pulse Code Modulation ◽  
Signal To Noise ◽  
Differential Pulse Code Modulation ◽  
Source Signal ◽  
Noise Ratio

A new method for efficient digitizing analog signals while preserving the original waveform as close as possible with respect to the relative quantization error is presented. Logarithmic quantization is applied to short vectors of samples represented in sphere coordinates. The resulting advantages, i.e. a constant Signal-to-Noise Ratio over a very high dynamic range at a small loss with respect to rate-distortion theory are discussed. In order to increase the Signal-to-Noise Ratio (SNR) by exploitation of correlations within the source signal, a method of combining differential pulse code modulation (DPCM) with spherical logarithmic quantization is presented. The resulting technique achieves an efficient digital representation of waveforms with a high long term as well as segmental SNR at an extreme low delay of the signal.

A signal-to-noise ratio comparison of high dynamic range CMOS image sensors

2009 ◽  
Author(s):  
Leo H. C. Braga ◽  
Suzana Domingues ◽  
José G. Gomes ◽  
Antonio C. Mesquita
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
High Dynamic Range ◽  
Image Sensors ◽  
Signal To Noise ◽  
Cmos Image Sensors ◽  
High Dynamic ◽  
Noise Ratio

Performance study of multistage of 40 kb/s ADPCM systems

2015 ◽  
Vol 6 (2) ◽  
pp. 85-88
Author(s):  
M. Al-Rawi
Keyword(s):  
Signal To Noise Ratio ◽  
Differential Pulse ◽  
Data Rate ◽  
Performance Study ◽  
Pulse Code Modulation ◽  
Signal To Noise ◽  
Differential Pulse Code Modulation ◽  
Code Modulation ◽  
Simulation Results ◽  
Better Than

The contribution of this paper is that the measure of the performance of multistage of 40 kb/s Adaptive Differential Pulse Code Modulation (ADPCM) using signal-to-noise-ratio formula previously derived by AL-Rawi. The multistage performance is tested using QAM signal at data rate of 9.6 kb/s with four types of constellations, rectangular, and (5,11), (4,12), (8,8) circular. The simulation results show that the performance degrades with increasing the number of stages of ADPCM. Also, the performance with circular constellation is better than that with rectangular one.

scholarly journals Probing delayed-end reionization histories with the 21-cm LAE cross-power spectrum

2020 ◽  
Vol 494 (1) ◽  
pp. 703-718 ◽  
Author(s):  
Lewis H Weinberger ◽  
Girish Kulkarni ◽  
Martin G Haehnelt
Keyword(s):  
Power Spectrum ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
High Dynamic Range ◽  
Integration Time ◽  
Signal To Noise ◽  
Total Signal ◽  
The Cross ◽  
Noise Ratio ◽  
Cross Power Spectrum

ABSTRACT We model the 21-cm signal and Lyman-α emitter (LAE) population evolution during the epoch of reionization in order to predict the 21-cm LAE cross-power spectrum. We employ high-dynamic-range simulations of the intergalactic medium to create models that are consistent with constraints from the cosmic microwave background, Lyman-α forest, and LAE population statistics. Using these models we consider the evolution of the cross-power spectrum for a selection of realistic reionization histories and predict the sensitivity of current and upcoming surveys to measuring this signal. We find that the imprint of a delayed end to reionization can be observed by future surveys, and that strong constraints can be placed on the progression of reionization as late as z = 5.7 using a Subaru–SKA survey. We make predictions for the signal-to-noise ratios achievable by combinations of Subaru/PFS (Prime Focus Spectrograph) with the MWA, LOFAR, HERA, and SKA interferometers for an integration time of 1000 h. We find that a Subaru–SKA survey could measure the cross-power spectrum for a late reionization at z = 6.6 with a total signal-to-noise ratio greater than 5, making it possible to constrain both the timing and bubble size at the end of reionization. Furthermore, we find that expanding the current Subaru/PFS survey area and depth by a factor of three would double the total signal-to-noise ratio.

An Over 120dB Dynamic Range Linear Response Single Exposure CMOS Image Sensor with Two-stage Lateral Overflow Integration Trench Capacitors

2020 ◽  
Vol 2020 (7) ◽  
pp. 143-1-143-6 ◽  
Author(s):  
Yasuyuki Fujihara ◽  
Maasa Murata ◽  
Shota Nakayama ◽  
Rihito Kuroda ◽  
Shigetoshi Sugawa
Keyword(s):  
Linear Response ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Signal To Noise ◽  
Single Exposure ◽  
Two Stage ◽  
Noise Ratio ◽  
Maximum Signal

This paper presents a prototype linear response single exposure CMOS image sensor with two-stage lateral overflow integration trench capacitors (LOFITreCs) exhibiting over 120dB dynamic range with 11.4Me- full well capacity (FWC) and maximum signal-to-noise ratio (SNR) of 70dB. The measured SNR at all switching points were over 35dB thanks to the proposed two-stage LOFITreCs.

scholarly journals Estimation and Correction of Sampling Time Offsets in Tiadcs using Optimization Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1217-1222
Keyword(s):  
Optimization Algorithm ◽  
High Frequency ◽  
Performance Metrics ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Sampling Time ◽  
Signal To Noise ◽  
High Sampling ◽  
Noise Ratio ◽  
Estimation And Correction

In recent communication technologies, very high sampling rates are required for rf signals particularly for signals coming under ultra high frequency (UHF), super high frequency (SHF) and extremely high frequency (EHF) ranges. The applications include global positioning system (GPS), satellite communication, radar, radio astronomy, 5G mobile phones etc. Such high sampling rates can be accomplished with time-interleaved analog to digital converters (TIADCs). However, sampling time offsets existing in TIADCs produce non-uniform samples. This poses a drawback in the reconstruction of the signal. The current paper addresses this drawback and offers a solution for improved signal reconstruction by estimation and correction of the offsets. A modified differential evolution (MDE) algorithm, which is an optimization algorithm, is used for estimating the sampling time offsets and the estimated offsets are used for correction. The estimation algorithm is implemented on an FPGA board and correction is implemented using MATLAB. The power consumption of FPGA for implementation is 57mW. IO utilization is 27% for 4-channel TIADCs and 13% for 2-channel TIADCs. The algorithm estimated the sampling time offsets precisely. For estimation the algorithm uses a sinusoidal signal as a test signal. Correction is performed with sinusoidal and speech signals as inputs for TIADCs. Performance metrics used for evaluating the algorithm are SNR (signal to noise ratio), SNDR (signal to noise and distortion ratio), SFDR (spurious-free dynamic range) and PSNR (peak signal to noise ratio). A noteworthy improvement is observed in the above mentioned parameters. Results are compared with the existing state of the art algorithms and superiority of the proposed algorithm is verified.

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