Design and analysis of a fault tolerant hybrid mobile scheme

Mobile computing systems provide users with access to information regardless of their geographical location. In these systems, Mobile Support Stations (MSSs) play the role of providing reliable and uninterrupted communication and computing facilities to mobile hosts. The failure of a MSS can cause interruption of services provided by the mobile system. Two basic schemes for tolerating the failure of MSSs exist in the literature. The first scheme is based on the principle of checkpointing used in distributed systems. The second scheme is based on state information replication of mobile hosts in a number of secondary support stations. Depending on the replication scheme used, the second approach is further classified as a pessimistic or an optimistic technique. In this paper, we propose a hybrid scheme which combines the pessimistic and the optimistic replication schemes. In the proposed scheme, an attempt is made to strike a balance between the long delay caused by the pessimistic and the high memory requirements of the optimistic schemes. In order to find the best ratio between the number of pessimistic to the number of optimistic secondary stations in the proposed scheme, we used fuzzy logic. We also used simulation to compare the performance of the proposed scheme with those of the optimistic and the pessimistic schemes. Simulation results showed that the proposed scheme performs better than either schemes in terms of delay and memory requirements.     

In situ synthesis of hydroxyapatite-grafted titanium nanotube composite

ABSTRACT

The present study is an investigation to demonstrate the effectiveness of in situ approach in the synthesis of hydroxyapatite-grafted titanium nanotube composite (HA-TNT). This method involves combining the process of HA sol–gel and rapid breakdown anodisation of titanium in a novel solution consisting of NaCl and N₃PO₄. This new synthesis approach produced a uniform dispersion of Anatase and Rutile phases of TiO₂ nanotubes with minimal agglomeration in the matrix of crystalline HA. The characterisation of homogenised HA-TNT composite was investigated via field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), transmission electron microscope (TEM) and X-ray diffraction (XRD). FESEM and TEM images indicated the nanostructure of composite with TiO₂ nanotube diameter of approximately 10 nm. XRD and EDS analyses confirmed the formation of HA crystalline with the Ca/P ratio of 1.58 and formation of Anatase and Rutile phase of TiO₂ nanotubes.     

Sorption of Mo(VI) on zirconium molybdosilicate gel and potential application as a <Superscript>99</Superscript>Mo/<Superscript>99<Emphasis Type="Italic">m</Emphasis> </Superscript>Tc generator

Sorption of Mo(VI) from aqueous nitrate solutions onto zirconium molybdosilicate (ZrMoSi) gel was studied using ⁹⁹Mo radiotracer. The acid–base titration curve showed that the prepared ZrMoSi gel was an amphoteric material with the point of zero charge (PZC) at pH 2.5. The highest distribution coefficient (600 mL g^–1) of ⁹⁹Mo(VI) on ZrMoSi from nitrate media was achieved at pH 2.1. The sorption kinetics of ⁹⁹Mo(VI) onto ZrMoSi obeyed the pseudo-second-order model. The breakthrough sorption capacity of ZrMoSi gel was found to be 4.25 × 10^–2 [mmol Mo(VI)] g^–1. The prepared ⁹⁹Mo/^99mTc chromatographic column generator showed a good performance: The ^99mTc elution yield was 87.7%, pH of the eluate ranged from 5.6 to 7.2, and the radionuclidic purity of the eluted ^99mTc was >99.99% with a radiochemical purity of 98.31% (as ^99mTcO₄^–).     

Experimental and numerical results of dynamics behavior of a nonlinear piezoelectric beam

This article presents a numerical formulation and experimental implementation for the dynamics behavior verification of the nonlinear piezoelectric beam through harmonic excitation. The nonlinear piezoelectric beam dynamic analysis program is developed with MATLAB software. To verify the nonlinear piezoelectric beam dynamic analysis results, the experimental results are used for the vibration analysis of a piezoelectric beam to the harmonic excitation of the base of the beam. Then, the piezoelectric effect on the output voltage, velocity, acceleration values, and the time response are obtained. Afterwards, the effects of the excitation velocity and the position of concentrated mass on the output voltage are verified.     

SENTIMENT ASSESSMENT OF TEXT BY ANALYZING LINGUISTIC FEATURES AND CONTEXTUAL VALENCE ASSIGNMENT

Text is not only an important medium to describe facts and events, but also to effectively communicate information about the writer's positive or negative sentiment underlying an opinion, or to express an affective or emotional state, such as happiness, fearfulness, surpriseness, and so on. We consider sentiment assessment and emotion sensing from text as two different problems, whereby sentiment assessment is the task that we want to solve first. Thus, this article presents an approach to sentiment assessment, i.e., the recognition of negative or positive valence of a sentence. For the purpose of sentiment recognition from text, we perform semantic dependency analysis on the semantic verb frames of each sentence, and then apply a set of rules to each dependency relation to calculate the contextual valence of the whole sentence. By employing a domain-independent, rule-based approach our system is able to automatically identify sentence-level sentiment. A linguistic tool called “SenseNet” has been developed to recognize sentiments in text, and to visualize the detected sentiments. We conducted several experiments with a variety of datasets containing data from different domains. The obtained results indicate significant performance gains over existing state-of-the-art approaches.     

Self-Adjustable Neural Network for speech recognition

Neural networks with fixed input length are not able to train and test data with variable lengths in one network size. This issue is very crucial when the neural networks need to deal with signals of variable lengths, such as speech. Though various methods have been proposed in segmentation and feature extraction to deal with variable lengths of the data, the size of the input data to the neural networks still has to be fixed. A novel Self-Adjustable Neural Network (SANN) is presented in this paper, to enable the network to adjust itself according to different data input sizes. The proposed method is applied to the speech recognition of Malay vowels and TIMIT isolated words. SANN is benchmarked against the standard and state-of-the-art recogniser, Hidden Markov Model (HMM). The results showed that SANN was better than HMM in recognizing the Malay vowels. However, HMM outperformed SANN in recognising the TIMIT isolated words.     

Coupled hydroelastic vibrations of an elliptical cylindrical tank with an elastic bottom

An exact three-dimensional analysis based on the linear potential theory and the elaborated method of eigenfunction expansion in elliptic coordinates are presented to study the free coupled elasto-hyrodynamic characteristics of an upright non-deformable cylindrical container of elliptical planform with a flexible bottom plate, filled to an arbitrary depth with an inviscid incompressible liquid. Extensive numerical data are presented in an orderly fashion for the first few symmetric/anti-symmetric coupled hydroelastic natural frequencies as a function of fluid depth parameter for two plate aspect ratios. Also, selected hydrodynamic and structural deformation modes shapes are presented in graphical form. The effects of liquid level, bottom plate elasticity, and cross sectional aspect ratio on the sloshing frequencies and hydrodynamic pressure modes are examined. The validity of the results is examined through computations using a commercial finite element package as well as by comparison with the data available in literature.     

Trust region methods for solving the optimal output feedback design problem

We consider the problem of designing feedback control laws when a complete set of state variables is not available. For linear autonomous control systems with quadratic performance criterion, the design problem consists of choosing an appropriate static output feedback (SOF) gain matrix according to a certain objective function. The corresponding non-linear matrix optimization problem can be interpreted as an equality constrained minimization problem. For solving this problem, we propose a constrained trust region (CTR) algorithm, which presents a new and efficient numerical approach for this problem class. On the other hand, based on the formulation used in the past, the SOF problem can be also interpreted as an unconstrained programming problem. Thus, based on this interpretation, we also develop an unconstrained trust region (UTR) method. Finally, several numerical examples for optimal SOF problems demonstrate the applicability of the considered algorithm.     

Verification of two machine learning approaches for cloud masking based on reflectance of channel IR3.9 using Meteosat Second Generation over Middle East maritime

ABSTRACT

Most cold channels of Meteosat Second Generation (MSG) satellites can distinguish between the sea and ice cloud tops, except for the IR3.9 channel because of the close reflectance and radiance values of the IR3.9 channel for maritime, low-level cloud and ice cloud tops. In this article, we introduce and evaluate two machine learning methods for cloud masking of Spinning Enhanced Visible and Infrared Imager (SEVIRI) images in the day and night that use the reflectance value of the IR3.9 channel. We reached a good correlation by comparing the results of the modelled cloud masking of Meteosat satellite images with MODIS (Moderate Resolution Imaging Spectroradiometer) and CLM (Cloud Mask product of EUMETSAT) images in a way that the coefficient of determination (R²) value was 92.34%, 89.91% and 83.69%, 78.23% in the cold season and 90.17%, 87.09% and 80.37%, 76.48% in the warm season, respectively, using the CHAID (chi-squared automatic interaction detection) decision tree and RBF (radial basis function) neural network approaches.     

Corresponding regions for shadow restoration in satellite high-resolution images

ABSTRACT

High spatial resolution images available by satellites such as Ikonos, Quickbird, and WorldView-2 provide more information for remote sensing applications, such as object detection, classification, change detection, and object mapping. The presence of shadow reduces the amount of information that can be extracted and consequently makes these applications more difficult or even impossible. In this article, a shadow restoration approach for high-resolution satellite images is proposed. The approach detects the shadow area and segments the image into regions according to the land surface type. Then, shadow restoration is carried out for each region based on the degree of correspondence between shadow and neighbouring non-shadow regions. The proposed approach is applied to study areas from Ikonos and WorldView-2 satellite images. A comparison to the standard approaches for shadow restoration is performed, and an accuracy assessment is carried out by visual inspection and land-cover classification. The results show that the enhanced shadow regions using the proposed approach have better appearances and are highly compatible with their surrounding non-shadow regions. In addition, the overall accuracy is higher than those of the standard approaches.