Kazunori Ogohara

Equatorial jet in the lower to middle cloud layer of Venus revealed by Akatsuki

The Venusian atmosphere is in a state of superrotation where prevailing westward winds move much faster than the planet’s rotation. Venus is covered with thick clouds that extend from about 45 to 70 km altitude, but thermal radiation emitted from the lower atmosphere and the surface on the planet’s night-side escapes to space at narrow spectral windows of near-infrared. The radiation can be used to estimate winds by tracking the silhouettes of clouds in the lower and middle cloud regions below about 57 km in altitude. Estimates of wind speeds have ranged from 50 to 70 m/s at low- to mid-latitudes, either nearly constant across latitudes or with winds peaking at mid-latitudes. Here we report the detection of winds at low latitude exceeding 80 m/s using IR2 camera images from the Akatsuki orbiter taken during July and August 2016. The angular speed around the planetary rotation axis peaks near the equator, which we suggest is consistent with an equatorial jet, a feature that has not been observed previously in the Venusian atmosphere. The mechanism producing the jet remains unclear. Our observations reveal variability in the zonal flow in the lower and middle cloud region that may provide new challenges and clues to the dynamics of Venus’s atmospheric superrotation.

Improved Automated Optic Cup Segmentation Based on Detection of Blood Vessel Bends in Retinal Fundus Images

Glaucoma is a leading cause of permanent blindness. Retinal imaging is useful for early detection of glaucoma. In order to evaluate the presence of glaucoma, ophthalmologists may determine the cup and disc areas and diagnose glaucoma using a vertical optic cup-to-disc (C/D) ratio and a rim-to-disc (R/D) ratio. Previously we proposed a method to determine cup edge by analyzing a vertical profile of pixel values, but this method provided a cup edge smaller than that of an ophthalmologist. This paper describes an improved method using the locations of the blood vessel bends. The blood vessels were detected by a concentration feature determined from the density gradient. The blood vessel bends were detected by tracking the blood vessels from the disc edge to the primary cup edge, which was determined by our previous method. Lastly, the vertical C/D ratio and the R/D ratio were calculated. Using forty-four images, including 32 glaucoma images, the AUCs of both the vertical C/D ratio and R/D ratio by this proposed method were 0.966 and 0.936, respectively.

Venus's winds and temperatures during the MESSENGER's flyby: An approximation to a three‐dimensional instantaneous state of the atmosphere

Even though many missions have explored the Venus atmospheric circulation, its instantaneous state is poorly characterized. In situ measurements vertically sampling the atmosphere exist for limited locations and dates, while remote sensing observations provide only global averages of winds at altitudes of the clouds: 47, 60, and 70 km. We present a three-dimensional global view of Venuss atmospheric circulation from data obtained in June 2007 by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) and Venus Express spacecrafts, together with ground-based observations. Winds and temperatures were measured for heights 47–110 km from multiwavelength images and spectra covering 40°N–80°S and local times 12 h–21 h. Dayside westward winds exhibit day-to-day changes, with maximum speeds ranging 97–143 m/s and peaking at variable altitudes within 75–90 km, while on the nightside these peak below cloud tops at ∼60 km. Our results support past reports of strong variability of the westward zonal superrotation in the transition region, and good agreement is found above the clouds with results from the Laboratoire de Meteorologie Dynamique (LMD) Venus general circulation model.

Overview of Akatsuki data products: definition of data levels, method and accuracy of geometric correction

We provide an overview of data products from observations by the Japanese Venus Climate Orbiter, Akatsuki, and describe the definition and content of each data-processing level. Levels 1 and 2 consist of non-calibrated and calibrated radiance (or brightness temperature), respectively, as well as geometry information (e.g., illumination angles). Level 3 data are global-grid data in the regular longitude–latitude coordinate system, produced from the contents of Level 2. Non-negligible errors in navigational data and instrumental alignment can result in serious errors in the geometry calculations. Such errors cause mismapping of the data and lead to inconsistencies between radiances and illumination angles, along with errors in cloud-motion vectors. Thus, we carefully correct the boresight pointing of each camera by fitting an ellipse to the observed Venusian limb to provide improved longitude–latitude maps for Level 3 products, if possible. The accuracy of the pointing correction is also estimated statistically by simulating observed limb distributions. The results show that our algorithm successfully corrects instrumental pointing and will enable a variety of studies on the Venusian atmosphere using Akatsuki data.

Data Analysis Support by Combining Data Mining and Text Mining

In recent years, data mining and text mining techniques have been frequently used for analyzing questionnaire and review data. Data mining techniques such as association analysis and cluster analysis are used for marketing analysis, because those can discover relationships and rules hiding in enormous numerical data. On the other hand, text mining techniques such as keywords extraction and opinion extraction are used for questionnaire or review text analysis, because those can support us to investigate consumers opinion in text data. However, data mining tools and text mining tools cannot be used in a single environment. Therefore, a data which has both numerical and text data is not well analyzed because the numerical part and text part cannot be connected for interpretation.In this paper, a mining framework that can treat both numerical and text data is proposed. We can iterate data shrink and data analysis with both numerical and text analysis tools in the unique framework. Based on experimental results, the proposed system was effectively used to data analysis for review texts.

Retinal biometrics based on Iterative Closest Point algorithm

The pattern of blood vessels in the eye is unique to each person because it rarely changes over time. Therefore, it is well known that retinal blood vessels are useful for biometrics. This paper describes a biometrics method using the Jaccard similarity coefficient (JSC) based on blood vessel regions in retinal image pairs. The retinal image pairs were rough matched by the center of their optic discs. Moreover, the image pairs were aligned using the Iterative Closest Point algorithm based on detailed blood vessel skeletons. For registration, perspective transform was applied to the retinal images. Finally, the pairs were classified as either correct or incorrect using the JSC of the blood vessel region in the image pairs. The proposed method was applied to temporal retinal images, which were obtained in 2009 (695 images) and 2013 (87 images). The 87 images acquired in 2013 were all from persons already examined in 2009. The accuracy of the proposed method reached 100%.

Automated Blood Vessel Extraction Based on High-Order Local Autocorrelation Features on Retinal Images

Automated blood vessels detection on retinal images is an important process in the development of pathologies analysis systems. This paper describes about an automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images. Although HLAC features are shift-invariant, HLAC features are weak to turned image. Therefore, a method was improved by the addition of HLAC features to a polar transformed image. We have proposed a method using HLAC, pixel-based-features and three filters. However, we have not investigated about feature selection and machine learning method. Therefore, this paper discusses about effective features and machine learning method. We tested eight methods by extension of HLAC features, addition of 4 kinds of pixel-based features, difference of preprocessing techniques, and 3 kinds of machine learning methods. Machine learning methods are general artificial neural network (ANN), a network using two ANNs, and Boosting algorithm. As a result, our already proposed method was the best. When the method was tested by using “Digital Retinal Images for Vessel Extraction” (DRIVE) database, the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis was reached to 0.960.

Artery and vein diameter ratio measurement based on improvement of arteries and veins segmentation on retinal images

Retinal arteriolar narrowing is decided based on the artery and vein diameter ratio (AVR). Previous methods segmented blood vessels and classified arteries and veins by color pixels in the centerlines of blood vessels. AVR was definitively determined through measurement of artery and vein diameters. However, this approach was not sufficient for cases with close contact between the artery of interest and an imposing vein. Here, an algorithm for AVR measurement via new classification of arteries and veins is proposed. In this algorithm, additional steps for an accurate segmentation of arteries and veins, which were not identified using the previous method, have been added to better identify major veins in the red channel of a color image. To identify major arteries, a decision tree with three features was used. As a result, all major veins and 90.9% of major arteries were correctly identified, and the absolute mean error in AVRs was 0.12. The proposed method will require further testing with a greater number of images of arteriolar narrowing before clinical application.