On the sampling of three-dimensional polycrystalline microstructures for distribution determination

How to sample three-dimensional microstructure and effectively reduce experimental error is a challenging problem. Taking seven single-phase polycrystalline structures generated by 400×400×400 Potts Monte Carlo simulation as the study object, effects of sampling strategy on the determination of various characteristic parameters of the grain size distribution and grain topology distribution are studied. The mean voxel value (or volume) of individual grains in the three-dimensional simulated microstructure varies from 4.56×10(4) to 1.0×10(3) , and the number of grains contained in the structure varies from 63 901 to 1403. The results show that, a minimum of 200 sampled grains can ensure the relative error to be less than 5% for determination of the mean grain volume, the mean grain face number and the coefficient of variance of the distribution of grain size and the grain face number. Whereas for the coefficient of the skewness and the kurtosis of grain size distribution or grain face number distribution, a minimum of 800 sampled grains are required for the same error level. However, if some exceptional big grains appear, e.g. a grain larger than with eight multiples of mean grain volume and/or three multiples of mean grain face number, abnormal values of the two parameters would be resulted, even the number of examined grains is over 1000.     

An Energy-Efficient and Congestion Control Data-Driven Approach for Cluster-Based Sensor Network

Mobile Networks and Applications - In Wireless Sensor Networks (WSNs), a dense deployment of sensor nodes produce data that contain intra-temporal and inter-spatial correlation. To reduce the...     

MIMO iterative receiver SNR estimation strategies for link to system interfacing

This paper presents link to system (L2S) interfacing technique for multiple input and multiple output (MIMO) iterative receivers. In L2S interfacing, usually the post detection signal to noise ratio (SNR)‐based frame error rate lookup tables (LUT) are used to predict the link level performance of receivers. While L2S interfacing for linear MIMO receivers can be conveniently implemented, it is more challenging for MIMO iterative receivers due to unavailability of the closed form SNR expressions. In this paper, we propose three methods for post detection SNR estimation for MIMO iterative receivers. The first is based on the QR decomposition of the channel matrix, the second relies on the residual noise calculation based on the soft symbols, and the third exploits the closed form SNR expressions for linear receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. It is shown that linear and residual noise‐based SNR approximations result in a very good prediction performance whereas the performance of QR decomposition‐based method degrades for higher order modulations and coding schemes. This paper presents link to system interfacing technique for MIMO iterative receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. Three post detection SNR evaluation schemes have been proposed for link to system interfacing all of which give good prediction performance especially at lower order modulation.     

Recent progress in the modification of carbon materials and their application in composites for electromagnetic interference shielding

With the development in the modern technologies such as telecommunication instruments and scientific electronic devices, large amount of the electromagnetic radiations are produced, which lead to harmful effect on the highly sensitive electronic devices as well as on the health of human beings. To minimize the effect of electromagnetic radiations produced by different technologies, more efficient shielding materials are required which must be cost-effective, lightweight and good corrosion resistive. In this review, we focused on the shielding materials based on composites of carbon nanotubes and graphene. The typical surface modification of carbon nanotubes and graphene to optimize their interactions with polymers matrix has also summarized. It was found that the composites based on these carbon fillers were more efficient for electromagnetic interference shielding due to their unique properties (i.e., superior electrical, mechanical and thermal) together with lightweight, easy processing. Hence, the carbon nanotubes and graphene-based composites are excellent shielding materials against the electromagnetic radiations.     

Influence of oxygen vacancies on the structural,dielectric, and magnetic properties of (Mn,Co) co-doped ZnO nanostructures

We analysed the variation and effect of oxygen vacancies on the structural, dielectric and magnetic properties in case of Mn (4%) and Co (1, 2 and 4%) co-doped ZnO nanoparticles (NPs), synthesized by chemical precipitation route and annealed at 750 °C for 2 h. From the XRD, the calculated average crystallite size increased from15.30?±?0.73 nm to 16.71?±?012 nm, when Co content is increased from 1 to 4%. Enhancement of dopants (Mn, Co) introduced more and more oxygen vacancies to ZnO lattice confirmed from EDX and XPS. The high-temperature annealing leads to reduction of the dielectric properties due to enhancement in grain growth (large grain volume and lesser number of grain boundaries) with the incorporation of Co and Mn ions into the ZnO lattice. The electrical conductivity of the Mn doped and (Mn, Co) co-doped ZnO samples were enhanced due to increase in the volume of conducting grains and charge density (liberation of trapped charge carriers in oxygen vacancies and free charge carriers at higher frequencies). The Mn-doped and (Mn, Co) co-doped ZnO NPs show ferromagnetic (FM) behaviour. The saturation and remnant magnetizations (M_s and M_r) elevates from (0.235 to 1.489)?×?10^?2 and (0.12 to 0.27)?×?10^?2 emu/g while Coercivity (H_c) reduced from 97 to 36 Oe with enhancement in the concentration of dopants in ZnO matrix. Oxygen vacancies were found to be the main reason for room-temperature ferromagnetism (RTFM) in the doped and co-doped ZnO NPs. The results show that the enhanced dielectric and magnetic properties of Mn doped and (Mn, Co) co-doped ZnO is strongly correlated with the concentration of oxygen vacancies. The observed enhanced RTFM, dielectric properties and electrical conductivity makes TM doped ZnO nanoparticles suitable for spintronics, microelectronics and optoelectronics based applications.     

Creatinine-based non-phospholipid vesicular carrier for improved oral bioavailability of Azithromycin

Context: Novel, safe, efficient and cost effective nano-carriers from renewable resources have got greater interest for enhancing solubility and bioavailability of hydrophobic dugs.

Objectives: This study reports the synthesis of a novel biocompatible non-phospholipid human metabolite "Creatinine" based niosomal delivery system for Azithromycin improved oral bioavailability.

Methods: Synthesized surfactant was characterized through spectroscopic and spectrometric techniques and then the potential for niosomal vesicle formation was evaluated using Azithromycin as model drug. Drug loaded vesicles were characterized for size, polydispersity index (PDI), shape, drug encapsulation efficiency (EE), in vitro release and drug–excipient interaction using zetasizer, atomic force microscope (AFM), LC–MS/MS and FTIR. The biocompatibility of surfactant was investigated through cells cytotoxicity, blood hemolysis and acute toxicity. Azithromycin encapsulated in niosomes was investigated for in vivo bioavailability in rabbits.

Results: The vesicles were spherical with 247?±?4.67?nm diameter hosting 73.29?±?3.51% of the drug. Surfactant was nontoxic against cell cultures and caused 5.80?±?0.51% hemolysis at 1000?µg/mL. It was also found safe in mice up to 2.5?g/kg body weight. Synthesized surfactant based niosomal vesicles revealed enhanced oral bioavailability of Azithromycin in rabbits.

Conclusions: The results of the present study confirm that the novel surfactant is highly biocompatible and the niosomal vesicles can be efficiently used for improving the oral bioavailability of poor water soluble drugs.     

A Secure NDN Framework for Internet of Things Enabled Healthcare

Healthcare is a binding domain for the Internet of Things (IoT) to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet. The current IP-based Internet architecture suffers from latency, mobility, location dependency, and security. The Named Data Networking (NDN) has been projected as a future internet architecture to cope with the limitations of IP-based Internet. However, the NDN infrastructure does not have a secure framework for IoT healthcare information. In this paper, we proposed a secure NDN framework for IoT-enabled Healthcare (IoTEH). In the proposed work, we adopt the services of Identity-Based Signcryption (IBS) cryptography under the security hardness Hyperelliptic Curve Cryptosystem (HCC) to secure the IoTEH information in NDN. The HCC provides the corresponding level of security using minimal computational and communicational resources as compared to bilinear pairing and Elliptic Curve Cryptosystem (ECC). For the efficiency of the proposed scheme, we simulated the security of the proposed solution using Automated Validation of Internet Security Protocols and Applications (AVISPA). Besides, we deployed the proposed scheme on the IoTEH in NDN infrastructure and compared it with the recent IBS schemes in terms of computation and communication overheads. The simulation results showed the superiority and improvement of the proposed framework against contemporary related works.     

A Framework for Systematic Classification of Assets for Security Testing

Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper, we propose an asset security classification technique to classify the System Under Test (SUT) based on various factors such as system exposure, data criticality and security requirements. We perform an extensive evaluation of our technique on a sample of 451 information systems. Further, we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities. Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.     

Race Classification Using Deep Learning

Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a race-classification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used to create segmentation results. The probabilistic classification method is used, and probability maps (PMs) are created for each semantic class. We investigated five salient facial features from among seven that help in race classification. Features are extracted from the PMs of five classes, and a new model is trained based on the DCNN. We assessed the performance of the proposed race classification method on four standard face datasets, reporting superior results compared with previous studies.     

Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAM-ResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research.