Block Reservation Time Division Multiple Access (BRTDMA) Protocol for a High Capacity Wireless Network

Next generation high capacity wireless networks need to support various types of traffic, including voice, video and data, each of which have different Quality of Service (QoS) requirements for successful transmission. This paper presents an advanced reservation packet access protocol BRTDMA (Block Reservation Time Division Multiple Access) that can accommodate voice and data traffic with equal efficiency in a wireless network. The proposed BRTDMA protocol has been designed to operate in a dynamic fashion by allocating resources according to the QoS criteria of voice and data traffic. Most of the existing reservation protocols offers reservation to voice traffic while data packets are transmitted using contention mode. In this paper we propose a block reservation technique to reserve transmission slots for data traffic for a short duration, which minimizes the speech packet loss and reduce the end-to-end delay for wireless data traffic. The optimum block reservation length for data traffic has been studied in a cellular mobile radio environment using a simulation model. Simulation results show that the BRTDMA protocol offers higher traffic capacity than standard PRMA protocol for integrated voice and data traffic and offers flexibility in accommodating multimedia traffic.     

Progress in modification of sunflower oil to expand its industrial value

Increasing the sunflower seed oil content as well as improving its quality makes it compatible for industrial demands. This is an important breeding objective of sunflower which increases its market value and ensures high returns for the producers. The present review focuses on determining the progress of improving sunflower seed oil content and modifying its quality by empirical and advanced molecular breeding methods. It is known that the sunflower oil content and quality have been altered through empirical selection methods and mutation breeding programmes in various parts of the world. Further improvement in seed oil content and its components (such as phytosterols, tocopherols and modified fatty acid profile) has been slowed down due to low genetic variation in elite germplasm and complex of hereditary traits. Introgression from wild species can be carried out to modify the fatty acids profile and tocopherol contents with linkage drags. Different transgenes introduced through biotechnological methods may produce novel long‐chain fatty acids within sunflower oil. Bio‐engineering of sunflower oil could allow it to be used in diverse industrial products such as bio‐diesel or bio‐plastics. These results showed that past and current trends of modifying sunflower oil quality are essential for its further expansion as an oilseed crop. © 2017 Society of Chemical Industry     

RAM versus CAM

Fifty years ago John von Neumann proposed the concept of the stored-program computer in which both instructions and data are confined within the boundaries of a storage device called memory. Today a computer architect is faced with a bewildering variety of memory-types to choose from when implementing this concept in the hardware. The most commonly used storage device is called random-access memory (RAM). That is, the process of locating a word within the storage array involves giving its address. The time needed to retrieve the word remains the same irrespective of the physical location of the word in the array. However, many data processing applications require searching items in some data structure, such as a table, stored in the memory. The established procedure to search a table is: 1) to store all the items where they can be accessed in sequence; 2) choose a sequence of addresses; 3) read the contents of memory at each address, and 4) compare the information read with the item being searched until a match occurs. This is exorbitantly time-consuming if the table is very large and/or the search algorithm is relatively inefficient. This time can be significantly reduced if the stored data can be identified for access by the content of the data itself rather than by an address. A memory unit accessed by content is called an associative memory or content addressable memory (CAM). This type of memory is accessed simultaneously and in parallel on the basis of the data content. This paper addresses the question: Is content addressability better than random-accessibility?     

A study of some monoazo dyes containing the 1H-benzotriazole nucleus

Dyes containing the benzotriazole nucleus with various groups imparting a chelating property to the dye molecules have been synthesised. The dyes showed interesting properties for application to cotton and polyester.     

Stochastic maximum power point tracking of photovoltaic energy system under partial shading conditions

A large portion of the available power generation of a photovoltaic (PV) array could be wasted due to partial shading, temperature and irradiance efects, which create current/voltage imbalance between the PV modules. Partial shading is a phenomenon which occurs when some modules in a PV array receive non-uniform irradiation due to dust, cloudy weather or shadows of nearby objects such as buildings, trees, mountains, birds etc. Maximum power point tracking (MPPT) techniques are designed in order to deal with this problem. In this research, a Markov Decision Process (MDP) based MPPT technique is proposed. MDP consists of a set of states, a set of actions in each state, state transition probabilities, reward function, and the discount factor. The PV system in terms of the MDP framework is modelled frst and once the states, actions, transition probabilities, and reward function, and the discount factor are defned, the resulting MDP is solved for the optimal policy using stochastic dynamic programming. The behavior of the resulting optimal policy is analyzed and characterized, and the results are compared to existing MPPT control methods.     

Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially. This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network (F-RCNN). The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions. Furthermore, image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation. The permanent changes in climate are of serious concern. The leading causes beyond these destructive variations are ozone layer depletion, greenhouse gas release, deforestation, pollution, water resources contamination, and UV radiation. This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues, e.g., skin cancer, damage to marine life, crops damage, and impacts on living being’s immune systems. We have tried to classify the ozone images dataset into two major classes, depleted and non-depleted regions, to extract the required persuading features through F-RCNN. Furthermore, CNN has been used for feature extraction in the existing literature, and those extricated diverse RoIs are passed on to the CNN for grouping purposes. It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results. The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91% to 93% in identifying climate variation through ozone concentration classification, whether the region in the image under consideration is depleted or non-depleted. Our proposed model presented 93% accuracy, and it outperforms the prevailing techniques.     

Electrical Tree Image Segmentation Using Hybrid Multi Scale Line TrackingAlgorithm

Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a high misclassification rate during segmentation. Therefore, this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm (MSLTA) by integrating batch processing method. The proposed method, h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy. The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation. The treeing images are then sampled and binarized in pre-processing. In the next phase, segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration. Finally, the comparative investigation has been conducted using standard performance assessment metrics, including accuracy, sensitivity, specificity, Dice coefficient and Matthew’s correlation coefficient (MCC). Based on segmentation performance evaluation against several established segmentation methods, h-MSLTA achieved better results of 95.43% accuracy, 97.28% specificity, 69.43% sensitivity rate with 23.38% and 24.16% average improvement in Dice coefficient and MCC score respectively over the original algorithm. In addition, h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image. These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.     

Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid

Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment, unlike non-renewable energy resources. However, they often fail to meet energy requirements in unfavorable weather conditions. The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load. In this paper, an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure. An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system. The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization (PSO) named recurrent PSO (rPSO). Firstly, rPSO algorithm minimizes the installation cost for nanogrid. Thereafter, the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources. State-of-the-art optimization models, including Genetic Algorithm (GA), bat and different Mathematical Programming Language (AMPL) solvers, are used to evaluate the model. The study's outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment.     

ZnO Nanowires,Nanotubes, and Complex Hierarchical Structures Obtained by Electrochemical Deposition

Increasing the aspect ratio of ZnO nanostructures is one possible strategy to improve their thermoelectric properties. ZnO nanostructures with one-dimensional (1D) and three-dimensional (3D) morphologies were obtained using electrochemical deposition. Adjusting various deposition parameters made it possible to obtain arrays of vertically aligned ZnO nanowires (NWs) with controlled dimensions, density, and electrical properties. The concentrations of zinc or chloride ions in the solution were found to be key parameters. ZnO NWs were transformed into ZnO nanotubes (NTs), with an increased aspect ratio compared with the NWs, by selectively dissolving the core of the ZnO NWs in a concentrated KCl solution. The aspect ratio was strongly increased when the ZnO NWs were hierarchically organized in a 3D morphology. The synthesis of thin films composed of ordered hollow urchin-like ZnO NW structures was performed by combining the electrochemical deposition and polystyrene sphere templating methods. The electronic properties of the urchin-like ZnO structures were investigated by means of photoluminescence and transmission measurements.