Improving spatial resolution of confocal Raman microscopy by super-resolution image restoration

A new super-resolution image restoration confocal Raman microscopy method (SRIR-RAMAN) is proposed for improving the spatial resolution of confocal Raman microscopy. This method can recover the lost high spatial frequency of the confocal Raman microscopy by using Poisson-MAP super-resolution imaging restoration, thereby improving the spatial resolution of confocal Raman microscopy and realizing its super-resolution imaging. Simulation analyses and experimental results indicate that the spatial resolution of SRIR-RAMAN can be improved by 65% to achieve 200 nm with the same confocal Raman microscopy system. This method can provide a new tool for high spatial resolution micro-probe structure detection in physical chemistry, materials science, biomedical science and other areas

Transport of the Saharan dust air plumes over the tropical North Atlantic from FORMOSAT–3/COSMIC observation

AbstractLong–range transport of the Saharan dust plumes during May–August of each year is a prominent feature over the tropical North Atlantic. Observations of temperature profiles are highly desirable in clarifying the impacts of the Saharan dust plumes over the tropical North Atlantic. In this work we evaluate positive temperature anomalies (inversions in the lower troposphere) in the North Atlantic low troposphere originating from Western Africa, and to examine the correspondence of these events to Saharan dust plumes, using several temperature sources and satellite–detected measurements of Aerosol Index. We combine profiles of temperature observations from FORMOSAT–3/COSMIC (F3C) with aerosol observations from Ozone Monitoring Instrument (OMI) to provide direct evidence of the Saharan dust plumes modifying environmental stability. The F3C observations show good profile measurements compared with the radiosondes in the lower troposphere, with the average temperature differences less than 0.5K. The F3C results were also compared with the Aqua Advanced Infrared Radiation Sounder (AIRS) and meteorological analyses from the National Centers for Environmental Predictions (NCEP), the United Kingdom Met Office (UKMO), and the European Centre for Medium Range Weather Forecast (ECMWF). Our results show that hot air plumes are associated with the Saharan dust plumes during their transport across the tropical North Atlantic. There were eleven distinctive hot air plumes during May–August 2007 and 2008, respectively. These hot air plumes increase environmental temperatures below 5–km altitudes, with the maximum increase of 1–2K around 2–km. This leads to increase of environmental stability below 2–km altitudes and decrease of environmental stability between 2– and 5–km altitudes. By changing the vertical distribution of environmental stability, the Saharan dust plumes act to stabilize environmental air below 2–km while destabilize environmental air from 2– to 5–km altitudes. These results are distinctively presented in the F3C and AIRS observations but less obvious in the meteorological analyses

Angular-Distance Based Channel Estimation for Holographic MIMO

This paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO's electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.Comment: This paper has been accepted for publication in IEEE JSA

Product-based Neural Networks for User Response Prediction

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201