New EUV Fe IX emission line identifications from Hinode/EIS

Four Fe IX transitions in the wavelength range 188--198 A are identified for the first time in spectra from the EUV Imaging Spectrometer on board the Hinode satellite. In particular the emission line at 197.86 A is unblended and close to the peak of the EIS sensitivity curve, making it a valuable diagnostic of plasma at around 800,000 K - a critical temperature for studying the interface between the corona and transition region. Theoretical ratios amongst the four lines predicted from the CHIANTI database reveal weak sensitivity to density and temperature with observed values consistent with theory. The ratio of 197.86 relative to the 171.07 resonance line of Fe IX is found to be an excellent temperature diagnostic, independent of density, and the derived temperature in the analysed data set is log T=5.95, close to the predicted temperature of maximum ionization of Fe IX.Comment: 10 pages, 3 figures, 2 tables, submitted to ApJ Letter

Are you a researcher as well as a medical illustrator?

When we list the areas of practice for medical illustrators we always include research, but how involved in research are we? The aim of this activity is to encourage your professional development not just as a medical illustrator but your involvement with research whether that is undertaking your own research, undertaking evidence based practice (1) , working as part of a research team, advising researchers on the value of medical illustration or supporting a student undertaking a research project for their degree or post-graduate qualification

Velocity measurements for a solar active region fan loop from Hinode/EIS observations

The velocity pattern of a fan loop structure within a solar active region over the temperature range 0.15-1.5 MK is derived using data from the EUV Imaging Spectrometer (EIS) on board the Hinode satellite. The loop is aligned towards the observer's line-of-sight and shows downflows (redshifts) of around 15 km/s up to a temperature of 0.8 MK, but for temperatures of 1.0 MK and above the measured velocity shifts are consistent with no net flow. This velocity result applies over a projected spatial distance of 9 Mm and demonstrates that the cooler, redshifted plasma is physically disconnected from the hotter, stationary plasma. A scenario in which the fan loops consist of at least two groups of "strands" - one cooler and downflowing, the other hotter and stationary -- is suggested. The cooler strands may represent a later evolutionary stage of the hotter strands. A density diagnostic of Mg VII was used to show that the electron density at around 0.8 MK falls from 3.2 x 10^9 cm^-3 at the loop base, to 5.0 x 10^8 cm^-3 at a projected height of 15 Mm. A filling factor of 0.2 is found at temperatures close to the formation temperature of Mg VII (0.8 MK), confirming that the cooler, downflowing plasma occupies only a fraction of the apparent loop volume. The fan loop is rooted within a so-called "outflow region" that displays low intensity and blueshifts of up to 25 km/s in Fe XII 195.12 A (formed at 1.5 MK), in contrast to the loop's redshifts of 15 km/s at 0.8 MK. A new technique for obtaining an absolute wavelength calibration for the EIS instrument is presented and an instrumental effect, possibly related to a distorted point spread function, that affects velocity measurements is identified.Comment: 42 pages, 15 figures, submitted to Ap

Optimization for L1-Norm Error Fitting via Data Aggregation

We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the fitting function. The proposed algorithm generalizes the recent algorithm in the literature, aggregate and iterative disaggregate (AID), which selectively solves three specific L1-norm error fitting problems. With the proposed algorithm, any L1-norm error fitting model can be solved optimally if it follows the form of the L1-norm error fitting problem and if the fitting function satisfies the assumption. The proposed algorithm can also solve multi-dimensional fitting problems with arbitrary constraints on the fitting coefficients matrix. The generalized problem includes popular models such as regression and the orthogonal Procrustes problem. The results of the computational experiment show that the proposed algorithms are faster than the state-of-the-art benchmarks for L1-norm regression subset selection and L1-norm regression over a sphere. Further, the relative performance of the proposed algorithm improves as data size increases

Structure analysis of single- and multi-frequency subspace migrations in inverse scattering problems

In this literature, we carefully investigate the structure of single- and multi-frequency imaging functions, that are usually employed in inverse scattering problems. Based on patterns of the singular vectors of the Multi-Static Response (MSR) matrix, we establish a relationship between imaging functions and the Bessel function. This relationship indicates certain properties of imaging functions and the reason behind enhancement in the imaging performance by multiple frequencies. Several numerical simulations with a large amount of noisy data are performed in order to support our investigation.Comment: 11 pages, 10 figure

Frequency based Classification of Activities using Accelerometer Data

This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.Comment: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 200

High Accuracy Human Activity Monitoring using Neural network

This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.Comment: 6 pages, 4 figures, 4 Tables, International Conference on Convergence Information Technology, pp. 430-435, 2008 Third International Conference on Convergence and Hybrid Information Technology, 200