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.

A dual-channel signal averager for spontaneous signals

1982 ◽  
Vol 242 (2) ◽  
pp. H291-H296
Author(s):  
A. Fabiato ◽  
A. O. Wist
Keyword(s):  
Data Collection ◽  
Threshold Voltage ◽  
Graphical Representation ◽  
Signal To Noise Ratio ◽  
Assembly Language ◽  
Cardiac Cells ◽  
Signal To Noise ◽  
Dual Channel ◽  
Direct Recording ◽  
Noise Ratio

This signal averager has been developed for spontaneous transients of tension and of aequorin bioluminescence in skinned cardiac cells. The signal with the largest signal-to-noise ratio (generally the tension transient) is entered in Channel 1 and triggers the system. Then the averager will unmask the signal entered in Channel 2 if it was buried in the noise during the direct recording. This signal averager uses an inexpensive microcomputer and a program written in Assembly Language. To facilitate its use, the averager is remotely controlled by a potentiometer for the adjustment of the threshold voltage above which the signal in Channel 1 will be collected and by three push buttons for starting the data collection, for averaging and displaying the results, and for resetting the system. The display is purely analog on the recorder, including averaged signals and interval for the signals in both channels together with a graphical representation of the standard deviations and of the number of observations. This signal averager could be used for any preparations developing spontaneous signals with variable intervals, provided that at least one of the synchronous signals has a large enough signal-to-noise ratio to trigger the system.

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.

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

A Least-Squares Strain Estimator for Elastography

1997 ◽  
Vol 19 (3) ◽  
pp. 195-208 ◽  
Author(s):  
Faouzi Kallel ◽  
Jonathan Ophir
Keyword(s):  
Least Squares ◽  
Spatial Resolution ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Gel Phantom

A least-squares strain estimator (LSQSE) for elastography is proposed. It is shown that with such an estimator, the signal-to-noise ratio in an elastogram ( SNRe) is significantly improved. This improvement is illustrated theoretically using a modified strain filter and experimentally using a homogeneous gel phantom. It is demonstrated that the LSQSE results in an increase of the elastographic sensitivity (smallest strain that could be detected), thereby increasing the strain dynamic range. Using simulated data, it is shown that a tradeoff exists between the improvement in SNRe and the reduction of strain contrast and spatial resolution.

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