Investigations on the Three Commercial Species of Rosaceae

Physico-chemical properties of the seed oils of three species of Rosaceae family, namely Prunus armeniaca (apricot), Prunus cerasifera (prune), and Prunus persica (peach) have been determined. Fatty acid composition of the oils and amino acids composition of seed cake proteins have also been determined and their utility in every day life has been discussed.     

Oxidation of Branched Chain Fatty Acids by Chromic Acid

The kinetics of oxidation of isobutyric and isovaleric acids by chromic acid in sulphuric acid medium have been studied. The end product acetic acid is supposed to be derived from the ketones which are the initial oxidation products of the corresponding branched chain fatty acids. The influence of acetic acid on the oxidation velocity has been examined and a marked effect on the rate constants with increasing acetic acid concentration has been observed. A mechanism involving the electrophilic attack of the oxidant on the tertiary carbon atom of the fatty acid to yield a carbonium ion has been postulated.     

Lithium/V6O13 cells using silica nanoparticle-based composite electrolyte

The electrochemical behavior of Li/V₆O₁₃ cells is investigated at room temperature (22 °C) both in liquid electrolyte consisting of oligomeric poly(ethyleneglycol)dimethylether+lithium bis(trifluoromethylsulfonylimide) and composite electrolytes formed by blending the liquid electrolyte with silica nanoparticles (fumed silica). The addition of fumed silica yields a gel-like electrolyte that demonstrates the desirable property of suppressing lithium dendrite growth due to the rigidity and immobility of the electrolyte structure. The lithium/electrolyte interfacial resistance for composite gel electrolytes is less than that for the corresponding base-liquid electrolyte, and the charge-discharge cycle performance and electrochemical efficiency for the Li/V₆O₁₃ cell is significantly improved. The effect of fumed silica surface group on the electrochemical performance is discussed; the native hydrophilic silanol surface group appears better than fumed silica that is modified with a hydrophobic octyl surface moiety.     

Molecular weight distributions and size fractionations of H-coal liquids

This Paper deals with a comparative study on the use of gel permeation chromatography (g.p.c.) and vapour pressure osmometry (v.p.o.) to obtain molecular weight data for the hexane-soluble fractions of three H-coal liquids. The use of two types of column packing materials, polyvinylacetate and styrene-divinylbenzene copolymer gels, is described. A successful, preparative use of the polyvinylacetate gel to fractionate the hexane-soluble fraction of H-coal liquid, atmospheric still overhead (ASO), has been established. Molecular weight data obtained by v.p.o. for the benzene-soluble fraction and the pyridine-soluble fraction of the three H-coal liquids are reported. Solvent extraction has been utilized also to find the amount of oil, asphaltenes and asphaltols in the three H-coal liquids.     

PCA-DTT 2D-DHCES based attack resilient imperceptible image watermarking

Most schemes exhibit low robustness due to LSB’s (Least Significant Bit) and MSB’s (Most Significant Bit) based information hiding in the cover image. However, most of these IW schemes have low imperceptibility as the cover image distortion reveals to the attacker due to information hiding in MSB’s. In this paper, a hybrid image watermarking scheme is proposed based on integrating Robust Principal Component Analysis (R-PCA), Discrete Tchebichef Transform (DTT), and Singular Value Decomposition (SVD). A grayscale watermark image is twisted/scrambled using a 2D Discrete Hyper-chaotic Encryption System (2D-DHCES) to boost up the robustness/heftiness and security. The original cover image is crumbled into sparse components using R-PCA and using DTT the substantial component is additionally decomposed and the watermark will be embedded in the cover image using SVD processing. In DTT, scarcer coefficients hold the utmost energy, also provide an optimum sparse depiction of the substantial image edges and features that supports proficient retrieval of the watermark image even after unadorned image distortion based channel attacks. The imperceptibility and robustness of the proposed method are corroborated against a variety of signal processing channel attacks (salt and pepper noise, multi-directional shearing, cropping, and frequency filtering, etc.). The visual and quantifiable outcomes reveal that the proposed image watermarking scheme is much effective and delivers high forbearance against several image processing and geometric attacks.

     

Sensor Fault Diagnosis and Unknown Disturbances Estimation of High Switching Frequency Single-phase PWM Rectifier

International Journal of Control, Automation and Systems - The fault and disturbances estimation has an important role in the modern traction railway system. This paper proposes a unique method for...     

Implementation and performance evaluation of a scheduling algorithm for divisible load parallel applications in a cloud computing environment

Cloud computing is an emerging technology in which information technology resources are virtualized to users in a set of computing resources on a pay‐per‐use basis. It is seen as an effective infrastructure for high performance applications. Divisible load applications occur in many scientific and engineering applications. However, dividing an application and deploying it in a cloud computing environment face challenges to obtain an optimal performance due to the overheads introduced by the cloud virtualization and the supporting cloud middleware. Therefore, we provide results of series of extensive experiments in scheduling divisible load application in a Cloud environment to decrease the overall application execution time considering the cloud networking and computing capacities presented to the application's user. We experiment with real applications within the Amazon cloud computing environment. Our extensive experiments analyze the reasons of the discrepancies between a theoretical model and the reality and propose adequate solutions. These discrepancies are due to three factors: the network behavior, the application behavior and the cloud computing virtualization. Our results show that applying the algorithm result in a maximum ratio of 1.41 of the measured normalized makespan versus the ideal makespan for application in which the communication to computation ratio is big. They show that the algorithm is effective for those applications in a heterogeneous setting reaching a ratio of 1.28 for large data sets. For application following the ensemble clustering model in which the computation to communication ratio is big and variable, we obtained a maximum ratio of 4.7 for large data set and a ratio of 2.11 for small data set. Applying the algorithm also results in an important speedup. These results are revealing for the type of applications we consider under experiments. The experiments also reveal the impact of the choice of the platforms provided by Amazon on the performance of the applications under study. Considering the emergence of cloud computing for high performance applications, the results in this paper can be widely adopted by cloud computing developers. Copyright © 2014 John Wiley & Sons, Ltd.     

Using model transformation to refactor use case models based on antipatterns

Use Case modeling is a popular technique for documenting functional requirements of software systems. Refactoring is the process of enhancing the structure of a software artifact without changing its intended behavior. Refactoring, which was first introduced for source code, has been extended for use case models. Antipatterns are low quality solutions to commonly occurring design problems. The presence of antipatterns in a use case model is likely to propagate defects to other software artifacts. Therefore, detection and refactoring of antipatterns in use case models is crucial for ensuring the overall quality of a software system. Model transformation can greatly ease several software development activities including model refactoring. In this paper, a model transformation approach is proposed for improving the quality of use case models. Model transformations which can detect antipattern instances in a given use case model, and refactor them appropriately are defined and implemented. The practicability of the approach is demonstrated by applying it on a case study that pertains to biodiversity database system. The results show that model transformations can efficiently improve quality of use case models by saving time and effort.     

Modeling of microstrip antennas using neural networks techniques: A review

Artificial neural networks modeling have recently acquired enormous importance in microwave community especially in analyzing and synthesizing of microstrip antennas (MSAs) due to their generalization and adaptability features. A trained neural model estimates response very fast, which is nearly equal to its measured and/or simulated counterpart. Thus, it completely bypasses the repetitive use of conventional models as these models need rediscretization for every minor changes in the geometry, which itself is a time‐consuming exercise. The purpose of this article is to review this emerging area comprehensively for both analyzing and synthesizing of the MSAs. During reviewing process, some untouched cases are also observed, which are essentially required to be resolved for antenna designers. Unique and efficient neural networks‐based solutions are suggested for these cases. The proposed neural approaches are validated by fabricating and characterizing of the prototypes too. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:747–757, 2015.     

Wavelet based sub-band parameters for classification of unaspirated Hindi stop consonants in initial position of CV syllables

This paper proposes a new feature extraction technique using wavelet based sub-band parameters (WBSP) for classification of unaspirated Hindi stop consonants. The extracted acoustic parameters show marked deviation from the values reported for English and other languages, Hindi having distinguishing manner based features. Since acoustic parameters are difficult to be extracted automatically for speech recognition. Mel Frequency Cepstral Coefficient (MFCC) based features are usually used. MFCC are based on short time Fourier transform (STFT) which assumes the speech signal to be stationary over a short period. This assumption is specifically violated in case of stop consonants. In WBSP, from acoustic study, the features derived from CV syllables have different weighting factors with the middle segment having the maximum. The wavelet transform has been applied to splitting of signal into 8 sub-bands of different bandwidths and the variation of energy in different sub-bands is also taken into account. WBSP gives improved classification scores. The number of filters used (8) for feature extraction in WBSP is less compared to the number (24) used for MFCC. Its classification performance has been compared with four other techniques using linear classifier. Further, Principal components analysis (PCA) has also been applied to reduce dimensionality.