Equalization enhanced phase noise in Nyquist-spaced superchannel transmission systems using multi-channel digital back-propagation.
Ontology highlight
ABSTRACT:
SUBMITTER: Xu T
PROVIDER: S-EPMC4568472 | biostudies-other | 2015
REPOSITORIES: biostudies-other
Similar Datasets
Project description:The achievable transmission capacity of conventional optical fibre communication systems is limited by nonlinear distortions due to the Kerr effect and the difficulty in modulating the optical field to effectively use the available fibre bandwidth. In order to achieve a high information spectral density (ISD), while simultaneously maintaining transmission reach, multi-channel fibre nonlinearity compensation and spectrally efficient data encoding must be utilised. In this work, we use a single coherent super-receiver to simultaneously receive a DP-16QAM super-channel, consisting of seven spectrally shaped 10GBd sub-carriers spaced at the Nyquist frequency. Effective nonlinearity mitigation is achieved using multi-channel digital back-propagation (MC-DBP) and this technique is combined with an optimised forward error correction implementation to demonstrate a record gain in transmission reach of 85%; increasing the maximum transmission distance from 3190 km to 5890 km, with an ISD of 6.60 b/s/Hz. In addition, this report outlines for the first time, the sensitivity of MC-DBP gain to linear transmission line impairments and defines a trade-off between performance and complexity.
| S-EPMC4314642 | biostudies-other
Project description:Sensitivity in BOLD fMRI is characterized by the signal to noise ratio (SNR) of the time-series (tSNR), which contains fluctuations from thermal and physiological noise sources. Alteration of an acquisition parameter can affect the tSNR differently depending on the relative magnitude of the physiological and thermal noise, therefore knowledge of this ratio is essential for optimizing fMRI acquisitions. In this study, we compare image and time-series SNR from array coils at 3T with and without parallel imaging (GRAPPA) as a function of image resolution and acceleration. We use the "absolute unit" SNR method of Kellman and McVeigh to calculate the image SNR (SNR(0)) in a way that renders it comparable to tSNR, allowing determination of the thermal to physiological noise ratio, and the pseudo-multiple replica method to quantify the image noise alterations due to the GRAPPA reconstruction. The Kruger and Glover noise model, in which the physiological noise standard deviation is proportional to signal strength, was found to hold for the accelerated and non-accelerated array coil data. Thermal noise dominated the EPI time-series for medium to large voxel sizes for single-channel and 12-channel head coil configurations, but physiological noise dominated the 32-channel array acquisition even at 1 mm × 1mm × 3 mm resolution. At higher acceleration factors, image SNR is reduced and the time-series becomes increasingly thermal noise dominant. However, the tSNR reduction is smaller than the reduction in image SNR due to the presence of physiological noise.
| S-EPMC3039683 | biostudies-literature
Project description:ObjectiveTo reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated.MethodAccording to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system.ResultsThrough the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study.ConclusionThe research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest remaining fire, the results are applicable to the forests of various countries, with a wide range of applications.
| S-EPMC7380588 | biostudies-literature
Project description:BackgroundThe transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors.Methodology/principal findingsWe aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected.Conclusion/significanceBoth BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (?15) in higher-risk areas was the main target to be intervened for the disease transmission control.
| S-EPMC3605232 | biostudies-literature
Project description:The scan equalization digital mammography (SEDM) technique combines slot scanning and exposure equalization to improve low-contrast performance of digital mammography in dense tissue areas. In this study, full-field digital mammography (FFDM) images of an anthropomorphic breast phantom acquired with an anti-scatter grid at various exposure levels were superimposed to simulate SEDM images and investigate the improvement of low-contrast performance as quantified by primary signal-to-noise ratios (PSNRs).We imaged an anthropomorphic breast phantom (Gammex 169 "Rachel," Gammex RMI, Middleton, WI) at various exposure levels using a FFDM system (Senographe 2000D, GE Medical Systems, Milwaukee, WI). The exposure equalization factors were computed based on a standard FFDM image acquired in the automatic exposure control (AEC) mode. The equalized image was simulated and constructed by superimposing a selected set of FFDM images acquired at 2, 1, 1/2, 1/4, 1/8, 1/16, and 1/32 times of exposure levels to the standard AEC timed technique (125 mAs) using the equalization factors computed for each region. Finally, the equalized image was renormalized regionally with the exposure equalization factors to result in an appearance similar to that with standard digital mammography. Two sets of FFDM images were acquired to allow for two identically, but independently, formed equalized images to be subtracted from each other to estimate the noise levels. Similarly, two identically but independently acquired standard FFDM images were subtracted to estimate the noise levels. Corrections were applied to remove the excess system noise accumulated during image superimposition in forming the equalized image. PSNRs over the compressed area of breast phantom were computed and used to quantitatively study the effects of exposure equalization on low-contrast performance in digital mammography.We found that the highest achievable PSNR improvement factor was 1.89 for the anthropomorphic breast phantom used in this study. The overall PSNRs were measured to be 79.6 for the FFDM imaging and 107.6 for the simulated SEDM imaging on average in the compressed area of breast phantom, resulting in an average improvement of PSNR by ∼35% with exposure equalization. We also found that the PSNRs appeared to be largely uniform with exposure equalization, and the standard deviations of PSNRs were estimated to be 10.3 and 7.9 for the FFDM imaging and the simulated SEDM imaging, respectively. The average glandular dose for SEDM was estimated to be 212.5 mrad, ∼34% lower than that of standard AEC-timed FFDM (323.8 mrad) as a result of exposure equalization for the entire breast phantom.Exposure equalization was found to substantially improve image PSNRs in dense tissue regions and result in more uniform image PSNRs. This improvement may lead to better low-contrast performance in detecting and visualizing soft tissue masses and micro-calcifications in dense tissue areas for breast imaging tasks.
| S-EPMC3247925 | biostudies-other
Project description:All optical method is considered as a promising technique for high symbol rate Nyquist signal generation, which has attracted a lot of research interests for high spectral-efficiency and high-capacity optical communication system. In this paper, we extend our previous work and report the fully experimental demonstration of polarization-division multiplexed (PDM) all-optical Nyquist signal generation based on Sinc-shaped Nyquist pulse with advanced modulation formats, fiber-transmission and single-receiver full-band coherent detection. Using this scheme, we have successfully demonstrated the generation, fiber transmission and single-receiver full-band coherent detection of all-optical Nyquist PDM-QPSK and PDM-16QAM signals up to 125-GBaud. 1-Tb/s single-carrier PDM-16QAM signal generation and full-band coherent detection is realized, which shows the advantage and feasibility of the single-carrier all-optical Nyquist signals.
| S-EPMC4555172 | biostudies-other
Project description:One of the main drawbacks of Digital Holography (DH) is the coherent nature of the light source, which severely corrupts the quality of holographic reconstructions. Although numerous techniques to reduce noise in DH have provided good results, holographic noise suppression remains a challenging task. We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms, block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions. The optimized joint action of these different image-denoising methods permits the removal of up to 98% of the noise while preserving the image contrast. The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH. Experimental validation is provided for both single-wavelength and multi-wavelength DH, and a comparison with the most used holographic denoising methods is performed.
| S-EPMC6059929 | biostudies-literature
Project description:Series MAPK enzymatic cascades, ubiquitously found in signaling networks, act as signal amplifiers and play a key role in processing information during signal transduction in cells. In activated cascades, cell-to-cell variability or noise is bound to occur and thereby strongly affects the cellular response. Commonly used linearization method (LM) applied to Langevin type stochastic model of the MAPK cascade fails to accurately predict intrinsic noise propagation in the cascade. We prove this by using extensive stochastic simulations for various ranges of biochemical parameters. This failure is due to the fact that the LM ignores the nonlinear effects on the noise. However, LM provides a good estimate of the extrinsic noise propagation. We show that the correct estimate of intrinsic noise propagation in signaling networks that contain at least one enzymatic step can be obtained only through stochastic simulations. Noise propagation in the cascade depends on the underlying biochemical parameters which are often unavailable. Based on a combination of global sensitivity analysis (GSA) and stochastic simulations, we developed a systematic methodology to characterize noise propagation in the cascade. GSA predicts that noise propagation in MAPK cascade is sensitive to the total number of upstream enzyme molecules and the total number of molecules of the two substrates involved in the cascade. We argue that the general systematic approach proposed and demonstrated on MAPK cascade must accompany noise propagation studies in biological networks.
| S-EPMC3341401 | biostudies-literature
Project description:Reproductive physiology depends on the control of biosynthesis in the pituitary gonadotrope by hypothalamic gonadotropin-releasing hormone (GnRH). The responses to GnRH include activation of extracellular signal-regulated kinase (ERK) and induction of Egr1. Using population and single cell signaling assays, we investigated the signal and noise transmission through this signaling and gene circuit, analyzing data obtained from 43,775 individual cells in 40 experiments. After exposure to GnRH, phosphorylated ERK (pERK) is elevated in 50% of the cells at 1.7 (SD = 0.3) min. Studies of the cell-to-cell response showed that for both pERK and for Egr1 protein production the mean response (mu) and standard deviation (sigma) within individual cells were linearly related (sigma = kmu) and had similar values of k. To understand the basis for the scaling observed for noise propagation through this system, we determined the relationship between pERK and egr1 mRNA levels induced at varying concentration of GnRH. While both pERK and egr1 mRNA show a saturating sigmoidal relationship to the concentration of GnRH exposure, egr1 mRNA is linearly related to the levels of pERK. These results explain the basis for variation in cellular responses in an important mammalian signaling pathway leading to gene induction.
| S-EPMC2098712 | biostudies-literature
Project description:The precise nature of information flow through a biological network, which is governed by factors such as response sensitivities and noise propagation, greatly affects the operation of biological systems. Quantitative analysis of these properties is often difficult in naturally occurring systems but can be greatly facilitated by studying simple synthetic networks. Here, we report the construction of synthetic transcriptional cascades comprising one, two, and three repression stages. These model systems enable us to analyze sensitivity and noise propagation as a function of network complexity. We demonstrate experimentally steady-state switching behavior that becomes sharper with longer cascades. The regulatory mechanisms that confer this ultrasensitive response both attenuate and amplify phenotypical variations depending on the system's input conditions. Although noise attenuation allows the cascade to act as a low-pass filter by rejecting short-lived perturbations in input conditions, noise amplification results in loss of synchrony among a cell population. The experimental results demonstrating the above network properties correlate well with simulations of a simple mathematical model of the system.
| S-EPMC552778 | biostudies-literature