Spectrally shaped DP-16QAM super-channel transmission with multi-channel digital back-propagation.
Ontology highlight
ABSTRACT:
SUBMITTER: Maher R
PROVIDER: S-EPMC4314642 | biostudies-other | 2015
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Superchannel transmission spaced at the symbol rate, known as Nyquist spacing, has been demonstrated for effectively maximizing the optical communication channel capacity and spectral efficiency. However, the achievable capacity and reach of transmission systems using advanced modulation formats are affected by fibre nonlinearities and equalization enhanced phase noise (EEPN). Fibre nonlinearities can be effectively compensated using digital back-propagation (DBP). However EEPN which arises from the interaction between laser phase noise and dispersion cannot be efficiently mitigated, and can significantly degrade the performance of transmission systems. Here we report the first investigation of the origin and the impact of EEPN in Nyquist-spaced superchannel system, employing electronic dispersion compensation (EDC) and multi-channel DBP (MC-DBP). Analysis was carried out in a Nyquist-spaced 9-channel 32-Gbaud DP-64QAM transmission system. Results confirm that EEPN significantly degrades the performance of all sub-channels of the superchannel system and that the distortions are more severe for the outer sub-channels, both using EDC and MC-DBP. It is also found that the origin of EEPN depends on the relative position between the carrier phase recovery module and the EDC (or MC-DBP) module. Considering EEPN, diverse coding techniques and modulation formats have to be applied for optimizing different sub-channels in superchannel systems.
| S-EPMC4568472 | biostudies-other
Project description:Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks-e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called "seven pillars of aging" combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.
| S-EPMC7261843 | biostudies-literature
Project description:Folding a polymer chain into a well-defined single-chain polymeric nanoparticle (SCPN) is a fascinating approach to obtaining structured and functional nanoparticles. Like all polymeric materials, SCPNs are heterogeneous in their nature due to the polydispersity of their synthesis: the stochastic synthesis of polymer backbone length and stochastic functionalization with hydrophobic and hydrophilic pendant groups make structural diversity inevitable. Therefore, in a single batch of SCPNs, nanoparticles with different physicochemical properties are present, posing a great challenge to their characterization at a single-particle level. The development of techniques that can elucidate differences between SCPNs at a single-particle level is imperative to capture their potential applications in different fields such as catalysis and drug delivery. Here, a Nile Red based spectral point accumulation for imaging in nanoscale topography (NR-sPAINT) super-resolution fluorescence technique was implemented for the study of SCPNs at a single-particle level. This innovative method allowed us to (i) map the small-molecule binding rates on individual SCPNs and (ii) map the polarity of individual SCPNs for the first time. The SCPN designs used here have the same polymeric backbone but differ in the number of hydrophobic groups. The experimental results show notable interparticle differences in the binding rates within the same polymer design. Moreover, a marked polarity shift between the different designs is observed. Interestingly, interparticle polarity heterogeneity was unveiled, as well as an intraparticle diversity, information which has thus far remained hidden by ensemble techniques. The results indicate that the addition of hydrophobic pendant groups is vital to determine binding properties and induces single-particle polarity diversity. Overall, NR-sPAINT represents a powerful approach to quantifying the single-particle polarity of SCPNs and paves the way to relate the structural heterogeneity to functionality at the single-particle level. This provides an important step toward the aim of rationally designing SCPNs for the desired application.
| S-EPMC9801428 | 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:Optical manipulation of small particles in the form of trapping, pushing, or sorting has developed into a vast field with applications in the life sciences, biophysics, and atomic physics. Recently, there has been increasing effort toward integration of particle manipulation techniques with integrated photonic structures on self-contained optofluidic chips. Here, we use the wavelength dependence of multi-spot pattern formation in multimode interference (MMI) waveguides to create a new type of reconfigurable, integrated optical particle trap. Interfering lateral MMI modes create multiple trapping spots in an intersecting fluidic channel. The number of trapping spots can be dynamically controlled by altering the trapping wavelength. This novel, spectral reconfigurability is utilized to deterministically move single and multiple particles between different trapping locations along the channel. This fully integrated multi-particle trap can form the basis of high throughput biophotonic assays on a chip.
| S-EPMC4833011 | biostudies-literature
Project description:Artificial neural networks (ANNs) are important building blocks in technical applications. They rely on noiseless continuous signals in stark contrast to the discrete action potentials stochastically exchanged among the neurons in real brains. We propose to bridge this gap with Spike-by-Spike (SbS) networks which represent a compromise between non-spiking and spiking versions of generative models. What is missing, however, are algorithms for finding weight sets that would optimize the output performances of deep SbS networks with many layers. Here, a learning rule for feed-forward SbS networks is derived. The properties of this approach are investigated and its functionality is demonstrated by simulations. In particular, a Deep Convolutional SbS network for classifying handwritten digits achieves a classification performance of roughly 99.3% on the MNIST test data when the learning rule is applied together with an optimizer. Thereby it approaches the benchmark results of ANNs without extensive parameter optimization. We envision this learning rule for SBS networks to provide a new basis for research in neuroscience and for technical applications, especially when they become implemented on specialized computational hardware.
| S-EPMC6700320 | biostudies-literature
Project description:Digital breast tomosynthesis (DBT) is a 3D x-ray imaging modality in which tomographic sections of the breast are generated from a limited range of tube angles. Because oblique x-ray incidence shifts the image of an object in subpixel detector element increments with each increasing projection angle, it is demonstrated that DBT is capable of super-resolution (i.e., subpixel resolution).By convention, DBT reconstructions are performed on planes parallel to the breast support at various depths of the breast volume. In order for resolution in each reconstructed slice to be comparable to the detector, the pixel size should match that of the detector elements; hence, the highest frequency that can be resolved in the plane of reconstruction is the alias frequency of the detector. This study considers reconstruction grids with much smaller pixelation to visualize higher frequencies. For analytical proof of super-resolution, a theoretical framework is developed in which the reconstruction of a high frequency sinusoidal input is calculated using both simple backprojection (SBP) and filtered backprojection. To study the frequency spectrum of the reconstruction, its Fourier transform is also determined. The experimental feasibility of super-resolution was investigated by acquiring images of a bar pattern phantom with frequencies higher than the detector alias frequency.Using analytical modeling, it is shown that the central projection cannot resolve frequencies exceeding the detector alias frequency. The Fourier transform of the central projection is maximized at a lower frequency than the input as evidence of aliasing. By contrast, SBP reconstruction can resolve the input, and its Fourier transform is correctly maximized at the input frequency. Incorporating filters into the reconstruction smoothens pixelation artifacts in the spatial domain and reduces spectral leakage in the Fourier domain. It is also demonstrated that the existence of super-resolution is dependent on position in the reconstruction and on the directionality of the input frequency. Consistent with the analytical results, experimental reconstructions of bar patterns showed visibility of frequencies greater than the detector alias frequency. Super-resolution was present at positions predicted from analytical modeling.This work demonstrates the existence of super-resolution in DBT. Super-resolution has the potential to impact the visualization of fine structural details in the breast, such as microcalcifications and other subtle signs of cancer.
| S-EPMC3523583 | biostudies-other
Project description:We designed an end-to-end dual-branch residual network architecture that inputs a low-resolution (LR) depth map and a corresponding high-resolution (HR) color image separately into the two branches, and outputs an HR depth map through a multi-scale, channel-wise feature extraction, interaction, and upsampling. Each branch of this network contains several residual levels at different scales, and each level comprises multiple residual groups composed of several residual blocks. A short-skip connection in every residual block and a long-skip connection in each residual group or level allow for low-frequency information to be bypassed while the main network focuses on learning high-frequency information. High-frequency information learned by each residual block in the color image branch is input into the corresponding residual block in the depth map branch, and this kind of channel-wise feature supplement and fusion can not only help the depth map branch to alleviate blur in details like edges, but also introduce some depth artifacts to feature maps. To avoid the above introduced artifacts, the channel interaction fuses the feature maps using weights referring to the channel attention mechanism. The parallel multi-scale network architecture with channel interaction for feature guidance is the main contribution of our work and experiments show that our proposed method had a better performance in terms of accuracy compared with other methods.
| S-EPMC7146598 | biostudies-literature
Project description:In this paper, an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame processing. The samples are illuminated by a nearly coherent illumination system, where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging sensor. To increase the resolution, a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene, with small planar displacements. Displacements are resolved by a hybrid approach: (i) alignment of the LR images by a fast feature-based registration method, and (ii) fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum solution. Numerical method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed sample. The presented approach was evaluated with various biological samples including sperm and platelets, whose dimensions are in the order of a few microns. The obtained results demonstrate a spatial resolution of 1.55 µm on a field-of-view of ?30 mm2.
| S-EPMC5898403 | biostudies-literature