Metamaterial loads used in microstrip antenna for circular polarization: Review

The metamaterial elements and structures have been noticed for obtaining circular polarization (CP) while developing various procedures. In this paper, we have mentioned some of these cases and compared the metamaterial loads effect on antenna current distribution. We have classified them into four categories. The first case covers patch antenna based on composite right/left‐handed method, where the metamaterial has been used for changing the current distribution in the loop form. The second case has been achieved by radome and metasurface. In the third model, the interaction between feed and metamaterial load has been considered and the last case has been made by the metamaterial load with truncated structure. The metamaterial loading has been modeled based on Nicolson‐Ross or transmission/reflection techniques for extracting the permittivity and permeability. While the microstrip slot antennas are attractive for wider bandwidth, the truncated structure can change the current for achieving CP. Here, we have studied the antenna for wireless and WiMAX applications.     

Field Enhancement in Metamaterial Split Ring Resonator Aperture Nano-Antenna with Spherical Nano-Particle Arrangement

Silicon - In this paper, we present a split ring resonator (SRR) for a nano-aperture antenna for biomedical and spectroscopy applications. We have shown that while the graphene coat layer is...     

A new prediction model of electricity load based on hybrid forecast engine

In this paper, a new prediction model is introduced based on hybrid forecast engine and new feature selection. In this model, the load signal is filtered by feature selection to filter out the best candidates. Then, the proposed forecast engine is predicted the output of feature selection. In this model, the weights of proposed forecast engine are optimised by an intelligent algorithm to increase its accuracy. Effectiveness of the proposed method is applied over real-world engineering test case and compared with other different well-known methods. Obtained results proof the validity of the proposed method.     

Big Data Based Self-optimization Networking: A Novel Approach Beyond Cognition

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation wireless networks. A self-optimization technique is introduced as the only viable solution for controlling and managing this type of huge data networks. This technique allows control of resources and key performance indicators without human intervention, based solely on the network intelligence. The present study proposes a big data based self optimization networking (BD-SON) model for wireless networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate the required resources to each part of the network based on made decision in SON engine, which can satisfy QoS constraints of a multicast session in which satisfying interference constraints is the main challenge. A load-balanced gradient power allocation (L-GPA) scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. Experimental results confirm that the proposed power allocation techniques considerably increase the chances of finding an optimal solution. Also, results confirm that proposed model achieves significant gain in terms of quality of service and capacity along with low complexity and load balancing optimality in the network.     

Limit moment for a smooth pipe bend under in-plane bending

A lower bound for the limit moment of a smooth circular pipe bend of uniform thickness is calculated with no restrictions on the geometry except those of thin shell theory, though the bend angle and the effect of any straight length attachments are not considered. Results of the analyses suggest that the (bend radius)/(pipe radius) ratio is of importance as well as the pipe factor (bend radius x thickness)/(pipe radius)², which is usually assumed to be the only important geometric parameter.     

BlessMark: a blind diagnostically-lossless watermarking framework for medical applications based on deep neural networks

Nowadays, with the development of public network usage, medical information is transmitted throughout the hospitals. A watermarking system can help for the confidentiality of medical information distributed over the internet. In medical images, regions-of-interest (ROI) contain diagnostic information. The watermark should be embedded only into non-regions-of-interest (NROI) regions to keep diagnostically important details without distortion. Recently, ROI based watermarking has attracted the attention of the medical research community. The ROI map can be used as an embedding key for improving confidentiality protection purposes. However, in most existing works, the ROI map that is used for the embedding process must be sent as side-information along with the watermarked image. This side information is a disadvantage and makes the extraction process non-blind. Also, most existing algorithms do not recover NROI of the original cover image after the extraction of the watermark. In this paper, we propose a framework for blind diagnostically-lossless watermarking, which iteratively embeds only into NROI. The significance of the proposed framework is in satisfying the confidentiality of the patient information through a blind watermarking system, while it preserves diagnostic/medical information of the image throughout the watermarking process. A deep neural network is used to recognize the ROI map in the embedding, extraction, and recovery processes. In the extraction process, the same ROI map of the embedding process is recognized without requiring any additional information. Hence, the watermark is blindly extracted from the NROI. Furthermore, a three-layer fully connected neural network is used for the detection of distorted NROI blocks in the recovery process to recover the distorted NROI blocks to their original form. The proposed framework is compared with one lossless watermarking algorithm. Experimental results demonstrate the superiority of the proposed framework in terms of side information.

     

Semi-supervised and Unsupervised Privacy-Preserving Distributed Transfer Learning Approach in HAR Systems

Wireless Personal Communications - One of the challenges faced by machine learning in human activity recognition systems is the different distributions of the training and test samples. Transfer...     

Creep and plastic collapse lives of scarred boiler tubes

A method for life and failure thickness assessment of boiler tubes with localised scars or pits is described. The method is coded into a computer program coined ‘AUSI-TL1’. To the authors’ knowledge, the ‘AUSI-TL1’ program is the only program for life assessments of boiler tubes with localised scars. Other programs that are currently available assume a uniform loss of tube thickness occurring along the entire tube outside and inside surfaces. The application of the ‘AUSI-TL1’ to a typical reheater and a water-wall boiler tube shows that for short scars, the critical tube thickness at which the failure occurs can be a fraction of a millimetre. This demonstrates the reinforcement that locally exists for a short scar as compared with a long and wide eroded/corroded tube area.     

Topologies and performance of intelligent algorithms: a comprehensive review

Recently, optimization makes an important role in our day-to-day life. Evolutionary and population-based optimization algorithms are widely employed in several of engineering areas. The design of an optimization algorithm is a challenging endeavor caused of physical phenomena in order to obtain appropriate local and global search operators. Generally, local operators are fast. In contrast, global operators are used to find best solution in the search space; therefore they are slower compare to the local ones. The best review-knowledge of papers show that there are many optimization algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and etc in the engineering as a powerful tools. However, there is not a comprehensive review for theirs topologies and performance; therefore, the main goal of this paper is filling of this scientific gap. Moreover, several aspects of optimization heuristic designs and analysis are discussed in this paper. As a result, detailed explanation, comparison, and discussion on AI are achieved. Furthermore, some future research fields on AI are well summarized.