Image and video processing on mobile devices: a survey.

Image processing and computer vision on mobile devices have a wide range of applications such as digital image enhancement and augmented reality. While images acquired by cameras on mobile devices can be processed with generic image processing algorithms, there are numerous constraints and external issues that call for customized algorithms for such devices. In this paper, we survey mobile image processing and computer vision applications while highlighting these constraints and explaining how the algorithms have been modified/adapted to meet accuracy and performance demands. We hope that this paper will be a useful resource for researchers who intend to apply image processing and computer vision algorithms to real-world scenarios and applications that involve mobile devices.

Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations.

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

Development of multi-directional functional near-infrared spectroscopy system for human neuroimaging studies.

Multi-directional measurement using multi-directional light sources and multi-directional photodetectors drastically increases the amount of observation data without increasing the number of optical probes. In this study, we developed a novel multi-directional functional near-infrared spectroscopy (fNIRS) system for human neuroimaging studies. We tested our system by measuring the cortical hemodynamic changes of a single subject during a motor task and compared them with the same subject's functional magnetic resonance imaging (fMRI) data. We detected the direction-dependent fNIRS signals that originate from the cortical hemodynamic changes that are consistent with the fMRI data.

"On the Land-Ocean Contrast of Tropical Convection and Microphysics Statistics Derived from TRMM Satellite Signals and Global Storm-Resolving Models".

A 14-year climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multi-sensor signal statistics reveal a distinct land-ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM precipitation radar and Microwave Imager (TMI) show a large land-ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of more/larger frozen convective hydrometeors. This strong land-ocean contrast in deep convection is invariant over seasonal and multi-year time-scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land-ocean statistics using the TRMM Triple-sensor Three-step Evaluation via a satellite simulator. The models evaluated are the NASA Multi-scale Modeling Framework (MMF) and the Non-hydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land-ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moisture in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land-ocean contrast in in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling.

Madden-Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer.

Global cloud/cloud system-resolving models are perceived to perform well in the prediction of the Madden-Julian Oscillation (MJO), a huge eastward -propagating atmospheric pulse that dominates intraseasonal variation of the tropics and affects the entire globe. However, owing to model complexity, detailed analysis is limited by computational power. Here we carry out a simulation series using a recently developed supercomputer, which enables the statistical evaluation of the MJO prediction skill of a costly new-generation model in a manner similar to operational forecast models. We estimate the current MJO predictability of the model as 27 days by conducting simulations including all winter MJO cases identified during 2003-2012. The simulated precipitation patterns associated with different MJO phases compare well with observations. An MJO case captured in a recent intensive observation is also well reproduced. Our results reveal that the global cloud-resolving approach is effective in understanding the MJO and in providing month-long tropical forecasts.

A Madden-Julian oscillation event realistically simulated by a global cloud-resolving model.

A Madden-Julian Oscillation (MJO) is a massive weather event consisting of deep convection coupled with atmospheric circulation, moving slowly eastward over the Indian and Pacific Oceans. Despite its enormous influence on many weather and climate systems worldwide, it has proven very difficult to simulate an MJO because of assumptions about cumulus clouds in global meteorological models. Using a model that allows direct coupling of the atmospheric circulation and clouds, we successfully simulated the slow eastward migration of an MJO event. Topography, the zonal sea surface temperature gradient, and interplay between eastward- and westward-propagating signals controlled the timing of the eastward transition of the convective center. Our results demonstrate the potential making of month-long MJO predictions when global cloud-resolving models with realistic initial conditions are used.

[Clinical feature of 23 cases of combined influenza virus and bacterial pneumonia].

We investigated 23 patients with combined influenza virus and bacterial pneumonia of whom 10 patients were men, 13 were women and the mean age was 78.0 years old. The causative virus of 12 patients was type A, that of 10 patients was type B, and 1 patient was unclassified. S. pneumonia was isolated from 12 patients, S. aureus and H. influenza was isolated from 2 patients. B. Catarrhalis and Milleri spp was isolated from one patient each. The mean interval between onset of influenza and influenza pneumonia was 2.09 days. Fourteen cases developed pneumonia within 2 days after influenza infection. In particular, all nine cases with chronic respiratory disease developed pneumonia within 2 days. All cases except one were treated with oseltamivir and antibiotics, resulting in cure. These data suggest that patients with chronic respiratory disease might develop influenza pneumonia earlier than past reported cases.

Detection of genetically modified organisms in foreign-made processed foods containing corn and potato.

Investigations of the validity of labeling regarding genetically modified (GM) products were conducted using polymerase chain reaction (PCR) methods for foreign-made processed foods made from corn and potato purchased in the Tokyo area and in the USA. Several kinds of GM crops were detected in 12 of 32 samples of processed corn samples. More than two GM events for which safety reviews have been completed in Japan were simultaneously detected in 10 samples. GM events MON810 and Bt11 were most frequently detected in the samples by qualitative PCR methods. MON810 was detected in 11 of the 12 samples, and Bt11 was detected in 6 of the 12 samples. In addition, Roundup Ready soy was detected in one of the 12 samples. On the other hand, CBH351, for which the safety assessment was withdrawn in Japan, was not detected in any of the 12 samples. A trial quantitative analysis was performed on six of the GM maize qualitatively positive samples. The estimated amounts of GM maize in these samples ranged from 0.2 to 2.8%, except for one sample, which contained 24.1%. For this sample, the total amount found by event-specific quantitative analysis was 23.8%. Additionally, Roundup Ready soy was detected in one sample of 21 potato-processed foods, although GM potatoes were not detected in any sample.