Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

The exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.     

Magneto-resistance and Excess Conductivity Studies of Tl(Ba2−y Mg y )Ca 2 Cu 3 O 10−δ Samples

The flux pinning characteristics of T l(B a _2?y M g _y )C a ₂ C u ₃ O _10?δ(0.0≤y≤ 1.5) samples have been studied under the influence of various dc magnetic fields in the light of thermally activated flux flow model. It was observed form the magneto resistivity measurements that the activation energy U has been increased in the sample with Mg incorporation of y=0.5, however, it is decreased in the samples with higher Mg doping. This shows that lower Mg doping concentration promotes enhancement, whereas its higher concentration suppresses the flux pinning characteristics of Tl(Ba _2?yMg _y )Ca ₂Cu ₃ O _10?δ (0.0 ≤y≤1.5) samples. The excess conductivity analysis of Tl(Ba _2?yMg _y )Ca ₂Cu ₃ O _10?δ (0.0 ≤ y ≤ 1.5) samples has shown that the coherence length along the c-axis ξ _c(0), the interlayer coupling J, the phase relaxation time of the carriers τ _? , and the Fermi velocity v _F of the carriers are suppressed with Mg doping. However, the values of B _c0(T), B _c1(T), and J _c(0) are enhanced which complement the magnetoresistivity analysis. These results showed that the flux pinning characteristics are enhanced for lower doping concentration of Mg at the charge reservoir layer sites.     

Development of Al356/SiCp cast composites by injection of SiCp containing composite powders

One of the great challenges of producing cast metal matrix composites is the agglomeration tendency of the reinforcements. This would normally result in poor distribution of the particles, high porosity content, and low mechanical properties. In the present work, a new method for uniform distribution of very fine SiC particles with average size of less than 3 μm was employed. The key idea was to allow for gradual in situ release of properly wetted SiC particles in the liquid metal. For this purpose, SiC particles were injected into the melt in three different forms, i.e., untreated SiC_p, milled particulate Al–SiC_p composite powder, and milled particulate Al–SiC_p–Mg composite powder. The resultant composite slurries were then cast from either fully liquid (stir casting) or semisolid (compocasting) state. Consequently, the effects of the casting method and the type of the injected powder on the microstructural characteristics as well as the mechanical properties of the cast composites were investigated. The results showed that the distribution of SiC particles in the matrix and the porosity content of the composites were greatly improved by injecting milled composite powders instead of untreated-SiC particles into the melt. Casting from semisolid state instead of fully liquid state had similar effects. The average size of SiC particles incorporated into the matrix was also significantly reduced from about 8 to 3 μm by injecting milled composite powders. The ultimate tensile strength, yield strength and elongation of Al356/5 vol.%SiC_p composite manufactured by compocasting of the (Al–SiC_p–Mg)_cp injected melt were increased by 90%, 103% and 135%, respectively, compared to those of the composite manufactured by stir casting of the untreated-SiC_p injected melt.     

Modeling the free convection heat transfer in a partitioned cavity using ANFIS

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used to predict the free convection in a partitioned cavity consisting of an adiabatic partition. The main focus of the present paper is to consider the effects of partition angle and Rayleigh number variation on average heat transfer in the partitioned cavity. The training data for optimizing the ANFIS structure is obtained experimentally. For the best ANFIS structure obtained in this study, the mean relative errors of the train and test data were found to be 0.055% and 1.735% respectively, which shows that ANFIS can predict the experimental results precisely.     

Prevalence of virulent Yersinia enterocolitica in bulk raw milk and retail cheese in northern-west of Iran

To determine the prevalence of virulent Yersinia enterocolitica, 554 samples consisting of 354 bulk raw milks and 200 traditional cheeses were collected from different parts of Eastern-Azerbaijan province, during a 23-month period from 2008 to 2010. The occurrence of virulent strains of Y. enterocolitica in samples enriched in peptone sorbitol bile broth (PSBB) was evaluated via the detection of attachment invasion locus (ail) gene by PCR. The viability of virulent Y. enterocolitica in the PCR-positive samples was tested using conventional culture method and the isolates were confirmed by the second-phase ail-PCR. According to the results, 8.66% of total samples including 7.62% of bulk raw milks and 10.5% of raw milk cheeses were found ail-positive by PCR method; subsequently Y. enterocolitica was isolated by the culture method and confirmed by the second phase ail-PCR in 2.88% of total samples including 2.26% of raw milks and 4% of cheese samples. It was concluded that, a sample enrichment followed by ail-PCR was more sensitive and robust to detect and distinguish the virulent strains of Y. enterocolitica compared to the conventional culture method.     

Multi spectral image fusion by deep convolutional neural network and new spectral loss function

In this paper we propose a new multispectral image fusion architecture. The proposed method includes two steps related to two neural networks. First the extracted spatial information, from panchromatic (Pan) image, is injected to upsampled multi-spectral (MS) image. In this step, the method employed a deep convolution neural network (DCNN) to estimate the spatial information of the MS image, according to multi-resolution analysis (MRA) scheme. This DCNN is trained by the low-spatial resolution version of Pan as an input, and by the spatial information as the target. This trained DCNN is called ‘Fusion network (FN)’. The FN, adaptively, estimates the spatial information of the MS images, and operates as an injection gain in the MRA scheme. In the second step, the spectral compensation is performed on the fused MS image. For this purpose, we used a novel loss function for this DCNN, to reduce the spectral distortion in the fused images, and simultaneously maintain the spatial information. This network is called ‘Spectral compensation network (SCN)’. Finally, the proposed method is compared to the several state-of-the-art methods on three datasets, using both full-reference and reduced reference criterion. The experimental results show that the proposed method can achieve competitive performance in both spatial and spectral information.     

Autonomous Eyewitness Identification by Employing Linguistic Rules for Disaster Events

Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users. Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness account. However, this approach suffers some limitation. Firstly, automatically extracting the feature-words remains a perplexing task against each feature identified by the approach. Secondly, all identified features were not incorporated in the implementation. This paper has utilized the language structure, linguistics, and word relation to achieve automatic extraction of feature-words by creating grammar rules. Additionally, all identified features were implemented which were left out by the state-of-the-art model. A generic approach is taken to cover different types of disaster such as earthquakes, floods, hurricanes, and wildfires. The proposed approach was then evaluated for all disaster-types, including earthquakes, floods, hurricanes, and fire. Based on the static dictionary, the Zahra et al. approach was able to produce an F-Score value of 0.92 for Eyewitness identification in the earthquake category. The proposed approach secured F-Score values of 0.81 in the same category. This score can be considered as a significant score without using a static dictionary.     

Assessing the dependency of selection function parameters with batch mill design

The scale-up of laboratory grinding data to industrial milling operations generally relies on tests carried out in cylindrical ball mills run in batch mode. This approach imposes no restriction on the diameter and length of the laboratory mill.In this work, the breakage characteristics of a copper ore were measured using two batch mills of different designs. For each mill, a number of feed samples of similar size distributions were prepared for testing under various conditions. Product size distributions were then measured after predefined milling time intervals. Finally, the selection function and breakage function parameters of the copper ore were back-calculated from the milling data.Results showed that the breakage function parameters from the two mills are statistically similar indicative of a normalisable copper ore. It was also found that the scale-up equations for batch grinding data described well the effect of mill diameter on the selection function parameters.     

An Improved Chaotic Cryptosystem for Image Encryption and Digital Watermarking

Wireless Personal Communications - In this article, enhanced chaotic range map is used for data hiding and multimedia security. By using complex properties of improved chaotic logistic map that...