Chromospheric Velocities of a C-class Flare

We use high spatial and temporal resolution observations from the Swedish Solar Telescope to study the chromospheric velocities of a C-class flare originating from active region NOAA 10969. A time-distance analysis is employed to estimate directional velocity components in H-alpha and Ca II K image sequences. Also, imaging spectroscopy has allowed us to determine flare-induced line-of-sight velocities. A wavelet analysis is used to analyse the periodic nature of associated flare bursts. Time-distance analysis reveals velocities as high as 64 km/s along the flare ribbon and 15 km/s perpendicular to it. The velocities are very similar in both the H-alpha and Ca II K time series. Line-of-sight H-alpha velocities are red-shifted with values up to 17 km/s. The high spatial and temporal resolution of the observations have allowed us to detect velocities significantly higher than those found in earlier studies. Flare bursts with a periodicity of approximately 60 s are also detected. These bursts are similar to the quasi-periodic oscillations observed at hard X-ray and radio wavelength data. Some of the highest velocities detected in the solar atmosphere are presented. Line-of-sight velocity maps show considerable mixing of both the magnitude and direction of velocities along the flare path. A change in direction of the velocities at the flare kernel has also been detected which may be a signature of chromospheric evaporation.Comment: Accepted for publication in Astronomy and Astrophysics, 5 figure

Impact of land readjustment project on farmland use and structural adjustment: The case of Niigata, Japan

This paper examines the impact of farmland readjustment projects on farmland use and structural adjustment in Niigata, Japan. We use census data of rural communities for 1990 and 2000 to conduct pooled regression, first-difference, difference-in-differences, and propensity score matching estimations. We find positive impact of farmland readjustment projects in alleviating farmland abandonment and in facilitating outsourcing of agricultural works and farmland rental. The results suggest that farmland consolidation may be an effective option to resolve farmland fragmentation and to promote structural adjustment in concentrating farmland to efficient farmers which is a premise for strengthening the agricultural sector under economies of scale.land readjustment, consolidation, fragmentation, farmland rental, farmland abandonment, Japan, Land Economics/Use,

Combining wireless and visual tracking for an indoor environment

There has been a lot of research done towards both camera and Wi-Fi tracking respectively, both these techniques have their benefits and drawbacks. By combining these technologies it is possible to eliminate their respective weaknesses, to increase the possibilities of the system as a whole. This is accomplished by fusing the sensor data from Wi-Fi and camera before inserting it in a particle filter. This will result in a more accurate and robust localization system

Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust \emph{time series cluster kernel} (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.Comment: 23 pages, 6 figure

Weighted k-Nearest-Neighbor Techniques and Ordinal Classification

In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into account. In this sense there is a close connection to LOESS, a local regression technique. In addition we show possibilities to use nearest neighbor for classification in the case of an ordinal class structure. Empirical studies show the advantages of the new techniques

Automatic facial analysis for objective assessment of facial paralysis

Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance