3D Wave Propagation and an Ultra–Low-Frequency Instability in Magnetopause

A model set of equations for the low-frequency electromagnetic perturbations in a magnetized nonuniform plasma is presented. A more convenient and systematic procedure is suggested to treat the fluid equations in order to deduce electrostatic and electromagnetic limits. A general dispersion relation is derived for the waves propagating in 3D under local approximation in nonuniform plasmas, which includes almost all the known modes of cold ion magnetized plasmas in the limit < _i (where _i is the ion cyclotron frequency). Both the limits 1 and O(1) have been discussed briefly. The shear Alfvén waves and electromagnetic ion acoustic waves near ultra low frequency (ULF) range are found unstable in the high plasma of magnetopause. The results are in complete agreement with the satellite observations.     

Parameter identification of deep-bar induction motors using genetic algorithm

In recent years, the use of three-phase deep-bar induction motors in power systems has increased. Proper modeling and precise parameter identification of the model are essential for motors’ operating analysis. In this paper, among the proposed models of deep-bar induction motors, a model based on two-axis theory is discussed and developed to improve precision. A real coded genetic algorithm estimates the parameters of the model. The accuracy and validity of the model and its identified parameters are verified with the help of a 5.5 kW, 380 V, 50 Hz, 1,450 rpm deep-bar induction motor.     

Study of Half-Metallic Ferromagnetism in Be0.75Ti0.25Y (Y = S,Se, and Te) Using Ab Initio Calculations: Potential Candidate for Spintronic Devices

The structural, elastic, electronic, and magnetic properties of Be_0.75Ti_0.25Y (Y = S, Se, and Te) have been investigated to understand their potential applications in spintonic devices. Crystals of BeS, BeSe, and BeTe, individually doped with Ti with a dopant concentration of x = 0.25, have been evaluated by using full-potential linearized augmented plane-wave plus local orbital method within the framework of density functional theory. We employed the Wu–Cohen generalized gradient approximation for optimizing the crystal structure and evaluating elastic properties. In order improve bandgap values and optical parameters, the modified Becke and Johnson (mBJ) potential has been employed. The theoretical investigation of band structure and density of states confirms the half-metallic ferromagnetic nature of these compounds. The elastic constants are calculated by the charpin method which shows that the compounds under consideration are brittle and anisotropic. Moreover, it is noted that tetrahedral crystal field splits the 3d state of Ti into triple degenerate t_2g and double degenerate e_g states. The exchange splitting energies Δ _x (d) and Δ _x (pd) and exchange constants (N ₀ α) and (N ₀ β) are predicted from triple degenerate t_2g states, and negative values of N ₀ β justify that the nature of effective potential is more attractive in spin down case rather than that in the spin up case. We also find the crystal field splitting (ΔE _crystal = E _t2g?E _eg) energy and reduction of the local magnetic moment of Ti from its free space charge value and creation of small local magnetic moments on the non-magnetic Be, S, Se, and Te sites by p–d hybridization.     

Genetic programming based blind image deconvolution for surveillancesystems

Image acquisition, segmentation, object detection and tracking are essential parts of surveillance systems. Usually, image filtering approaches are employed as preprocessing step to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose genetic programming (GP) based blind-image deconvolution filter. A GP based numerical expression is developed for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded images. The performance of filter function is estimated using various degraded image sequences. Our comparative analysis highlight the effectiveness of GP based proposed filter.     

Adaptive Automated Teller Machines

Nowadays, Automated Teller Machines (ATMs) provide significant online support to bank customers. A limitation of ATM usage is that customers often have to wait in a queue, especially at ATMs installed at busy locations. Also, old people tend to consume more ATM usage time, possibly frustrating customers in the queue. In these situations, ATMs should “adapt” to the behavior of the customers to minimize the usage time. To this end, we apply data mining techniques to an ATM transaction dataset obtained from an international bank based in Kuwait. We pre-process this dataset, and convert it into a specific XML format to mine it through the ProM (process mining) tool. Our results reveal that customers withdraw money most frequently, followed by purchases (through an ATM card) and balance inquiry transactions. Customers re-do these transactions frequently, and also employ them one after the other. We acquire the distributions of the withdrawn amount, based on individual customers, the location (ATM terminal) and time of the withdrawl. Based on these results, we have proposed a set of five adaptive ATM interfaces, which show only frequent transactions and frequently-withdrawn amounts, display the current balance autonomously, and query explicitly for viewing purchase history, or for performing another withdrawl. An online survey on 216 ATM customers reveals that a majority of customers are willing to use these interfaces for minimizing their usage time. Our work has been approved by the banking authority of Pakistan, and we are currently implementing our interfaces for a Pakistani bank.     

Effect of bank slope on the flow patterns in river intakes

In this work, we studied the dimensions of stream tube in the vertical as well as inclined bank conditions. Data were collected from both a physical model and a 3-D numerical model（SSIIM 2）. Equations for predicting stream tube dimensions were presented and compared with existing formulae. In comparison with vertical bank, it is found that inclining bank causes the bottom stream tube width to be greater than at the surface. The strength of secondary current formed at the entrance of branch channel is reduced. These changes in flow pattern can reduce the amount of sediment delivery into the intake.     

Addendum to ‘(Singular) state models and (singular) LTID systems’

In this note, a construction of state representations of singular linear time-invariant differential systems is described directly in terms of trajectories.     

Fault buffers

Voltage scaling can be applied to cache memories to reduce their energy consumptions. However, reduced supply voltage to the cache memories increases the number of defective SRAM cells due to process variations, which will decrease their yields and nullify the benefits of voltage scaling. To mitigate this problem, we propose a fault buffer-based scheme for L1 caches. Faults are identified and isolated at the granularity of individual words in the L1 caches. Actively used faulty cache words are dynamically allocated in the fault buffers. The fault buffers are organized as multiple banks for low cost implementation and can be dynamically reconfigured to reflect varying performance demands of programs. This dynamic scheme is shown to be more energy- and area-efficient than, and to be performing comparably to, the previously proposed static schemes.     

An assessment of k-ε turbulence models for gas distribution analysis

This paper presents the gas distribution analysis by injecting air fountain into the containment and simulations with the HYDRAGON code. Turbulence models of standard k-ε(SKE), re-normalization group k-ε(RNG) and a realizable k-ε(RLZ) are used to assess the effects on the gas distribution analysis during a severe accident in a nuclear power plant. By comparing with experimental data,the simulation results of the RNG and SKE turbulence models agree well with the experimental data on the prediction of dimensionless density distributions. The results illustrate that the turbulence model choice had a small effect on the simulation results, particularly the region near to the air fountain source.     

Linear principal transformation: toward locating features in N-dimensional image space

Projection Functions have been widely used for facial feature extraction and optical/handwritten character recognition due to their simplicity and efficiency. Because these transformations are not one-to-one, they may result in mapping distinct points into one point, and consequently losing detailed information. Here, we solve this problem by defining an N-dimensional space to represent a single image. Then, we propose a one-to-one transformation in this new image space. The proposed method, which we referred to as Linear Principal Transformation (LPT), utilizes Eigen analysis to extract the vector with the highest Eigenvalue. Afterwards, extrema in this vector were analyzed to extract the features of interest. In order to evaluate the proposed method, we performed two sets of experiments on facial feature extraction and optical character recognition in three different data sets. The results show that the proposed algorithm outperforms the observed algorithms in the paper and achieves accuracy from 1.4 % up to 14 %, while it has a comparable time complexity and efficiency.