scholarly journals Locating and Imaging through Scattering Medium in a Large Depth

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 90
Author(s):  
Shuo Zhu ◽  
Enlai Guo ◽  
Qianying Cui ◽  
Lianfa Bai ◽  
Jing Han ◽  
...  
Keyword(s):  
Phase Space ◽  
Signal To Noise Ratio ◽  
Speckle Pattern ◽  
Heuristic Method ◽  
Scattering Medium ◽  
Scattering Media ◽  
Signal To Noise ◽  
Practical Applications ◽  
Large Depth ◽  
Strong Scattering

Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media.

Estimating the image spectrum signal-to-noise ratio for imaging through scattering media

2015 ◽  
Vol 54 (1) ◽  
pp. 013102 ◽  
Author(s):  
Mohamed E. Hanafy ◽  
Michael C. Roggemann ◽  
Durdu O. Guney
Keyword(s):  
Signal To Noise Ratio ◽  
Scattering Media ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Dual-polarized multiplexed meta-holograms utilizing coding metasurface

Nanophotonics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 3605-3613 ◽  
Author(s):  
Chunsheng Guan ◽  
Jian Liu ◽  
Xumin Ding ◽  
Zhuochao Wang ◽  
Kuang Zhang ◽  
...  
Keyword(s):  
Data Storage ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Split Ring ◽  
Practical Applications ◽  
Good Imaging ◽  
Linearly Polarized ◽  
Novel Method ◽  
Noise Ratio ◽  
Total Transmittance

AbstractIn this paper, a novel method is proposed to achieve two distinct information channels by simultaneously manipulating both the transmitted cross- and co-polarized components of a 1-bit coding metasurface under linearly polarized incidence. Compared to previously demonstrated incidence-switchable or position multiplexed holograms, our proposed coding meta-hologram can simultaneously project two independent holographic images without inevitable change of the incidence state and can at the same time also avoid crosstalk between different channels. Moreover, the orientation of the double-layered split ring (SR) apertures is specially designed to be 45° or 135° to achieve identical multiplexed functionality for both x-polarized and y-polarized incidences. The proof-of-concept experimental demonstrations present total transmittance efficiency above 30% for the dual linearly polarized incidences at 15 GHz, and good imaging performances with 53.98%/48.18% imaging efficiency, 1.55%/1.46% RMSE, and 29.9/28.72 peak signal-to-noise ratio for the cross-/co-polarized channels under y-polarized incidence, and 47.27%/45.75% imaging efficiency, 1.55%/1.43% RMSE, and 18.74/25.93 peak signal-to-noise ratio under x-polarized incidence, demonstrating great potential of the proposed multiplexed coding meta-hologram in practical applications such as data storage and information processing.

scholarly journals Binaural Speech Intelligibility and Interaural Cross-Correlation Under Disturbing Noise and Reverberation

2012 ◽  
Vol 10 (3) ◽  
Author(s):  
A. L. Padilla-Ortíz ◽  
F. Orduña‐Bustamante
Keyword(s):  
Acoustic Communication ◽  
Speech Intelligibility ◽  
Communication Systems ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Practical Applications ◽  
Subjective Tests ◽  
Noise Ratio

Subjective tests were carried out in order to investigate speech intelligibility, and the possible relative improvements that can be obtained in practical applications to acoustic communication systems, for different forms of presentation through headphones: monaural, monophonic, binaural at 0º (in front of the listener) and binaural at ±30º (right or left, relative to the listener), played back undisturbed, and also with the addition of extreme levels of disturbing noise and reverberation, with a signal to noise ratio of

PERIODIC AND APERIODIC STOCHASTIC RESONANCE WITH OUTPUT SIGNAL-TO-NOISE RATIO EXCEEDING THAT AT THE INPUT

1999 ◽  
Vol 09 (01) ◽  
pp. 267-272 ◽  
Author(s):  
FRANÇOIS CHAPEAU-BLONDEAU
Keyword(s):  
Nonlinear System ◽  
Stochastic Resonance ◽  
Nonlinear Effect ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Practical Applications ◽  
Different Types ◽  
Signal Detectability ◽  
Noise Ratio

Stochastic resonance (SR) is a nonlinear effect whereby a system is able to improve, via noise addition, the detectability of a signal in noise. SR has been demonstrated with different types of systems and signals where in each case, an appropriate detectability measure is shown improvable at the output of the stochastic resonator when noise is added at its input. A complementary issue, important for practical applications of SR, is the possibility of making the signal detectability at the ouput exceed that at the input when noise is added. We demonstrate this possibility, for both periodic and aperiodic SR, with a simple nonlinear system that we show exactly tractable analytically.

scholarly journals Estimation of regression-based model with bulk noisy data

2019 ◽  
Vol 9 (5) ◽  
pp. 3649
Author(s):  
Chanintorn Jittawiriyanukoon
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Noisy Data ◽  
Absolute Error ◽  
Average Error ◽  
Practical Case ◽  
Mean Square ◽  
Learning Tools ◽  
Signal To Noise ◽  
Practical Applications

<span>The bulk noise has been provoking a contributed data due to a communication network with a tremendously low signal to noise ratio. An appreciated method for revising massive noise of individuals through information theory is widely discussed. One of the practical applications of this approach for bulk noise estimation is analyzed using intelligent automation and machine learning tools, dealing the case of bulk noise existence or nonexistence. A regression-based model is employed for the investigation and experiment. Estimation for the practical case with bulk noisy datasets is proposed. The proposed method applies slice-and-dice technique to partition a body of datasets down into slighter portions so that it can be carried out. The average error, correlation, absolute error and mean square error are computed to validate the estimation. Results from massive online analysis will be verified with data collected in the following period. In many cases, the prediction with bulk noisy data through MOA simulation reveals Random Imputation minimizes the average error.</span>

scholarly journals Artificial intelligence-assisted light control and computational imaging through scattering media

2019 ◽  
Vol 12 (04) ◽  
pp. 1930006 ◽  
Author(s):  
Shengfu Cheng ◽  
Huanhao Li ◽  
Yunqi Luo ◽  
Yuanjin Zheng ◽  
Puxiang Lai
Keyword(s):  
Artificial Intelligence ◽  
Phase Conjugation ◽  
Computational Imaging ◽  
Scattering Medium ◽  
Scattering Media ◽  
Neuron Network ◽  
Modal Dispersion ◽  
Speckle Patterns ◽  
Wavefront Shaping ◽  
Strong Scattering

Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007. Wavefront shaping is aimed at overcoming the strong scattering, featured by random interference, namely speckle patterns. This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber, yet this randomness is actually deterministic and potentially can be time reversal or precompensated. Various wavefront shaping approaches, such as optical phase conjugation, iterative optimization, and transmission matrix measurement, have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium. The performance of these modulations, however, is far from satisfaction. Most recently, artificial intelligence has brought new inspirations to this field, providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them. In this paper, we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning (deep learning in particular) for further advancements in the field.

scholarly journals Non-labeled lensless micro-endoscopic approach for cellular imaging through highly scattering media

2018 ◽  
Vol 38 (1) ◽  
Author(s):  
Omer Wagner ◽  
Aditya Pandya ◽  
Yoav Chemla ◽  
Hadar Pinhas ◽  
Irina Schelkanova ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Imaging Techniques ◽  
Post Processing ◽  
Scattering Medium ◽  
Scattering Media ◽  
Noise Levels ◽  
Signal To Noise ◽  
Blood Capillaries ◽  
In Vitro Investigation ◽  
Highly Scattering Medium

We describe an imaging approach based on an optical setup made up of a miniature, lensless, minimally invasive endoscope scanning a sample and matching post processing techniques that enable enhanced imaging capabilities. The two main scopes of this article are that this approach enables imaging beyond highly scattering medium and increases the resolution and signal to noise levels reaching single cell imaging. Our approach has more advantages over ordinary endoscope setups and other imaging techniques. It is not mechanically limited by a lens, the stable but flexible fiber can acquire images over long time periods (unlike current imaging methods such as OCT etc.), and the imaging can be obtained at a certain working distance above the surface, without interference to the imaged object. Fast overlapping scans enlarge the region of interest, enhance signal to noise levels and can also accommodate post-processing, super-resolution algorithms. Here we present that due to the setup properties, the overlapping scans also lead to dramatic enhancement of non-scattered signal to scattered noise. This enables imaging through highly scattering medium. We discuss results obtained from in vitro investigation of weak signals of ARPE cells, rat retina, and scattered signals from polydimethylsiloxane (PDMS) microchannels filled with hemoglobin and covered by intralipids consequently mimicking blood capillaries and the epidermis of human skin. The development of minimally invasive procedures and methodologies for imaging through scattering medium such as tissues can vastly enhance biomedical diagnostic capabilities for imaging internal organs. We thereby propose that our method may be used for such tasks in vivo.

Amplitude and Frequency Dependence of the Signal-to-Noise Ratio in LDV Measurements

2011 ◽  
Author(s):  
Patrick F. O’Malley ◽  
Joseph F. Vignola ◽  
John A. Judge
Keyword(s):  
Vibration Amplitude ◽  
Signal To Noise Ratio ◽  
Speckle Pattern ◽  
Laser Doppler Vibrometer ◽  
Signal To Noise ◽  
Velocity Response ◽  
Order Of Magnitude ◽  
Noise Ratio ◽  
Ldv Measurement ◽  
The Relationship

When making measurements using many sensors, it is expected that, within normal operating ranges, the signal-to-noise ratio is approximately linear (i.e. 20 dB/decade). This generality does not hold, however, when making measurements using a laser Doppler vibrometer (LDV). If the velocity of the target of an LDV measurement increases by an order of magnitude, changes in the speckle pattern will introduce noise into the measurand. An experiment was conducted using an LDV system to measure the velocity response of a speaker excited over several orders of magnitude in both frequency and amplitude. Results are presented showing the relationship between signal-to-noise ratio and vibration amplitude.

scholarly journals Asymptotic Performances of a Signal-To-Noise Ratio Moment-Based Estimator for Real Sinusoids in Additive Noise

2019 ◽  
Vol 9 (24) ◽  
pp. 5556 ◽  
Author(s):  
Gianmarco Romano
Keyword(s):  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Noise Power ◽  
Signal To Noise ◽  
Practical Applications ◽  
Unknown Variance ◽  
Noise Ratio ◽  
Asymptotic Variances ◽  
Frequency Phase ◽  
General Method

We considered the problem of the estimation of signal-to-noise ratio (SNR) with a real deterministic sinusoid with unknown frequency, phase and amplitude in additive Gaussian noise of unknown variance. A blind SNR estimator that does not require the knowledge of the instantaneous frequency of the sinusoid, through separate estimation of signal and noise power, was derived using the method of moments, a general method to derive estimators based on high-order moments. Statistical performances of the proposed estimators were studied theoretically through derivation of Cramer–Rao lower bounds (CRLBs) and asymptotic variances. Furthermore, results from Monte-Carlo simulations that confirm the validity of the theoretical analysis are presented along with some comments on the use of proposed estimators in practical applications.

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