Low noise signal-to-noise ratio enhancing readout circuit for current-mediated active pixel sensors

2006 ◽  
Vol 24 (3) ◽  
pp. 770-773 ◽  
Author(s):  
Tony Ottaviani ◽  
Karim S. Karim ◽  
Arokia Nathan ◽  
John A. Rowlands
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Low Noise ◽  
Readout Circuit ◽  
Active Pixel Sensors ◽  
Signal To Noise ◽  
Active Pixel ◽  
Pixel Sensors ◽  
Noise Ratio

Magnetoelectric Sensors With Directly Integrated Charge Sensitive Readout Circuit—Improved Field Sensitivity and Signal-to-Noise Ratio

2011 ◽  
Vol 11 (10) ◽  
pp. 2260-2265 ◽  
Author(s):  
Zhao Fang ◽  
Ninad Mokhariwale ◽  
Feng Li ◽  
Suman Datta ◽  
Q. M. Zhang
Keyword(s):  
Room Temperature ◽  
Signal To Noise Ratio ◽  
Low Noise ◽  
Magnetic Sensors ◽  
Readout Circuit ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Field Sensitivity ◽  
Piezoelectric Layers ◽  
Noise Ratio

The large magnetoelectric (ME) coupling in the ME laminates makes them attractive for ultrasensitive room temperature magnetic sensors. Here ,we investigate the field sensitivity and signal-to-noise ratio (SNR) of ME laminates, consisting of magnetostrictive and piezoelectric layers (Metglas and piezopolymer PVDF were used as the model system), which are directly integrated with a low noise readout circuit. Both the theoretical analysis and experimental results show that increasing the number of piezoelectric layers can improve the SNR, especially at low frequencies. We also introduce a figure of merit to measure the overall influence of the piezolayer properties on the SNR and show that the newly developed piezoelectric single crystals of PMN-PT and PZN-PT have the promise to achieve a very high SNR and consequently ultra-high sensitivity room temperature magnetic sensors. The results show that the ME coefficients used in early ME composites development works may not be relevant to the SNR. The results also show that enhancing the magnetostrictive coefficient, for example, by employing the flux concentration effect, can lead to enhanced SNR.

A Low Noise Pixel Architecture for Scientific CMOS Monolithic Active Pixel Sensors

2010 ◽  
Vol 57 (5) ◽  
pp. 2490-2496 ◽  
Author(s):  
Rebecca E. Coath ◽  
Jamie P. Crooks ◽  
Adam Godbeer ◽  
Matthew D. Wilson ◽  
Zhige Zhang ◽  
...  
Keyword(s):  
Low Noise ◽  
Active Pixel Sensors ◽  
Active Pixel ◽  
Pixel Sensors

Speech in noise testing before and after grommet insertion

2012 ◽  
Vol 126 (10) ◽  
pp. 1010-1015 ◽  
Author(s):  
V Possamai ◽  
G Kirk ◽  
A Scott ◽  
D Skinner
Keyword(s):  
Small Group ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Sound Field ◽  
Noise Signal ◽  
Signal To Noise ◽  
Speech In Noise ◽  
Before And After ◽  
Noise Test ◽  
Noise Ratio

AbstractObjectives:To assess the feasibility of designing and implementing a speech in noise test in children before and after grommet insertion, and to analyse the results of such a test in a small group of children.Methods:Twelve children aged six to nine years who were scheduled to undergo grommet insertion were identified. They underwent speech in noise testing before and after grommet insertion. This testing used Arthur Boothroyd word lists read at 60 dB in four listening conditions presented in a sound field: firstly in quiet conditions, then in signal to noise ratios of +10 (50 dB background noise), 0 (60 dB) and −10 (70 dB).Results:Mean phoneme scores were: in quiet conditions, 28.1 pre- and 30 post-operatively (p = 0.04); in 50 dB background noise (signal to noise ratio +10), 24.2 pre- and 29 post-operatively (p < 0.01); in 60 dB background noise (signal to noise ratio 0), 22.6 pre- and 27.5 post-operatively (p = 0.06); and in 70 dB background noise (signal to noise ratio −10), 13.9 pre- and 21 post-operatively (p = 0.05).Conclusion:This small study suggests that speech in noise testing is feasible in this scenario. Our small group of children demonstrated a significant improvement in speech in noise scores following grommet insertion. This is likely to translate into a significant advantage in the educational environment.

scholarly journals Comparing current noise in biological and solid-state nanopores

2019 ◽  
Author(s):  
A. Fragasso ◽  
S. Schmid ◽  
C. Dekker
Keyword(s):  
Solid State ◽  
Single Molecule ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Ionic Current ◽  
Low Noise ◽  
Signal To Noise ◽  
Experimental Conditions ◽  
High Frequencies ◽  
Noise Ratio

AbstractNanopores bear great potential as single-molecule tools for bioanalytical sensing and sequencing, due to their exceptional sensing capabilities, high-throughput, and low cost. The detection principle relies on detecting small differences in the ionic current as biomolecules traverse the nanopore. A major bottleneck for the further progress of this technology is the noise that is present in the ionic current recordings, because it limits the signal-to-noise ratio and thereby the effective time resolution of the experiment. Here, we review the main types of noise at low and high frequencies and discuss the underlying physics. Moreover, we compare biological and solid-state nanopores in terms of the signal-to-noise ratio (SNR), the important figure of merit, by measuring free translocations of a short ssDNA through a selected set of nanopores under typical experimental conditions. We find that SiNx solid-state nanopores provide the highest SNR, due to the large currents at which they can be operated and the relatively low noise at high frequencies. However, the real game-changer for many applications is a controlled slowdown of the translocation speed, which for MspA was shown to increase the SNR >160-fold. Finally, we discuss practical approaches for lowering the noise for optimal experimental performance and further development of the nanopore technology.

A 99.95% linearity readout circuit with 72 dB dynamic range for active pixel sensors

2017 ◽  
Author(s):  
Leonardo Bruno de Sá ◽  
Mauricio Henrique Costa Dias ◽  
Antoine Dupret ◽  
Antonio Carneiro de Mesquita Filho
Keyword(s):  
Dynamic Range ◽  
Readout Circuit ◽  
Active Pixel Sensors ◽  
Active Pixel ◽  
Pixel Sensors

scholarly journals Improving the Signal to Noise Ratio of QTF Preamplifiers Dedicated for QEPAS Applications

2020 ◽  
Vol 10 (12) ◽  
pp. 4105
Author(s):  
Piotr Z. Wieczorek ◽  
Tomasz Starecki ◽  
Frank K. Tittel
Keyword(s):  
Operational Amplifier ◽  
Narrow Band ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Electrical Signal ◽  
Low Noise ◽  
Detection Sensitivity ◽  
Noise Amplitude ◽  
Signal To Noise ◽  
Noise Ratio

The signal-to-noise ratio (SNR) is a major factor that limits the detection sensitivity of quartz-enhanced photoacoustic spectroscopy (QEPAS) sensors. The higher the electrical signal level compared to the noise amplitude is the lower the concentration of gases that can be detected. For this reason the preamplifier circuits used in QEPAS should be optimized for low-frequency narrow-band applications. Moreover, special care should be taken when choosing a particular operational amplifier in either a transimpedance or voltage (differential) configuration. It turns out that depending on the preamp topology different operational amplifier parameters should be carefully considered when a high SNR of the whole QEPAS system is required. In this article we analyzed the influence of the crucial parameters of low-noise operational preamplifiers used in QEPAS applications and show the resulting limitations of transimpedance and voltage configurations.

scholarly journals The robustness of the differential quantizer in the case of the variable signal to noise ratio

2017 ◽  
Vol 14 (1) ◽  
pp. 149-160
Author(s):  
Lazar Cokic ◽  
Aleksandra Marjanovic ◽  
Sanja Vujnovic ◽  
Zeljko Djurovic
Keyword(s):  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Fixed Number ◽  
Signal To Noise ◽  
Input And Output ◽  
Noise Ratio ◽  
The Difference ◽  
Optimal Signal ◽  
Theoretical Overview

In this paper a short theoretical overview of differential quantizer and its implementations is given. Afterward, the effect of the order of prediction in differential quantizer and the effect of the difference in order of predictor in the input and output of differential quantizer is analyzed. Then it was proceeded with the examination of the robustness of the differential quantizer in the case in which a noise signal is brought to the input of the differential quantizer, instead of the clean speech signal. The analysis was conducted with a uniform distribution, as well as the noise with the gaussian distribution, and the obtained results were adequately commented on. Also, experimentally a limit was set which refers to the intensity of the noise and still enable results which are better that a regular uniform quantizer. The whole analysis is done by using the fixed number of bits in quantization, i.e. 12-bit quantizer is used in all the implementations of differential quantizer. In the conclusion of this paper there is a discussion about the possibility of implementing a differential quantizer which will be able to recognize which noise attacks the system, and in addition to that, in what form it adapts its coefficients so that it at any moment acquires the optimal signal to noise ratio.

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