Design of fractal patch antenna for size and radar cross-section reduction

This research study presents a novel design of star-shaped fractal patch antenna for miniaturisation and backscattering radar cross-section (RCS) reduction. The proposed fractal antenna gives 50% size reduction compared with a conventional circular microstrip patch (CCMP) antenna. The antenna is studied experimentally for return loss behaviour using vector network analyser R&S ZVA40. It can be useful for wireless application in 0.85-4 GHz frequency band. Further, the study focuses on backscattering RCS (both monostatic and bistatic) reduction by the proposed antenna compared with the CCMP antenna. It is found that increase in number of fractal iterations included in the conventional patch to design fractal antenna geometry reduces backscattering RCS at multiband compared to the conventional patch antenna. This reduction in backscattering RCS by the antenna is observed at multiband. The antenna can be tuned for low backscattering by variation in the substrate dielectric constant and thickness and the superstrate dielectric constant and thickness. For maximum RCS reduction by the antenna, optimisation of substrate thickness becomes necessary. The study also deals with effect of frequency and aspect angle variation on backscattering RCS reduction.     

Efficient localization of target in large scale farmland using generalized regression neural network

Although simple to implement, the traditional trilateration technique is generally associated with significant location estimation errors because of highly nonlinear relationship between Received Signal Strength Indicator (RSSI) and distance. In case of agricultural farmland, there is always noise uncertainty in the RSSI measurements because of signal propagation issues such as NLOS, multipath propagation, and reflection. In the context of such environmental dynamicity, the localization algorithm must be efficient in terms of Localization Accuracy and Execution Speed to provide real‐time performance. The Generalized Regression Neural Network (GRNN) is a noniterative highly parallel neural architecture with the capability to get trained quickly using very few training samples. This paper introduces a range free GRNN localization algorithm as an alternative to the traditional range‐based trilateration technique for a large scale wheat farmland. This paper also presents the modified Optimal Fitted Parametric Exponential Decay Model (OFPEDM)‐based signal path loss model to deal with the issue of environmental dynamicity. The evaluation of localization performance of the trilateration and the proposed GRNN‐based approaches is carried out with the help of Wireless Sensor Network (WSN) using three path loss models, namely, Log Normal Shadow Fading (LNSM), Original OFPEDM, and proposed Modified OFPEDM. For all these implementations, the proposed GRNN algorithm demonstrates superior localization performance (localization accuracy of the order of few centimeters) over traditional trilateration irrespective of nonlinear system dynamics, path loss model, and environmental dynamicity. The execution speed of the proposed algorithm is of the order of few milliseconds.     

SCT Based Adaptive Data Aggregation for Wireless Sensor Networks

Communication overhead is a major concern in wireless sensor networks because of inherent behavior of resource constrained sensors. To degrade the communication overhead, a technique called data aggregation is employed. The data aggregation results are used to make crucial decisions. Certain applications apply approximate data aggregation in order to reduce communication overhead and energy levels. Specifically, we propose a technique called semantic correlation tree, which divides a sensor network into ring-like structure. Each ring in sensor network is divided into sectors, and each sector consists of collection of sensor nodes. For each sector, there will be a sector head that is aggregator node, the aggregation will be performed at sector head and determines data association on each sector head to approximate data on sink node. We propose a doorway algorithm to approximate the sensor node readings in sector head instead of sending all sensed data. The main idea of doorway algorithm is to reduce the congestion and also the communication cost among sensor nodes and sector head. This novel approach will avoid congestion by controlling the size of the queue and marking packets. Specifically, we propose a local estimation model to generate a new sensor reading from historic data. The sensor node sends each one of its parameter to sector head, instead of raw data. The doorway algorithm is utilized to approximate data with minimum and maximum bound value. This novel approach, aggregate the data approximately and efficiently with limited energy. The results demonstrate accuracy and efficiency improvement in data aggregation.     

Clustering Algorithm for Deployment of Independent Heterogeneous Wireless Sensor Network

Wireless Personal Communications - Wireless Sensor Network (WSN) is the main element of modern Industrial Internet of Things system in event monitoring. However, WSN is unable to respond for longer...     

Z number improved reference ideal method-based decision-making process for enforcing cooperation during data dissemination in mobile ad hoc networks

In mobile ad hoc networks (MANETs), cooperative communication and resource constraint are the two important core characteristics essential to guarantee trusted data dissemination. The cooperative communication between mobile nodes depends on the trust rendered by them towards the process of reliable data routing. However, stringent resource constraints of mobile nodes such as energy, memory, communications, and computations result in the introduction of selfish and malicious node that completely degrades the network performance in different dimensions. In this paper, Z number improved reference ideal method (RIM)-based decision-making process (NIRIMDMP) is proposed with the merits of maximizing deviation method (MDM) and best–worst method (BWM) to ensure reliable data routing by modeling the cooperation degree in terms of Z number. This NIRIMDMP adopted Z number to represent the information reliability and handle the problem of inherent uncertainty during the process of evaluating each mobile node in the routing process. In specific, MDM and BWM are included into the proposed NIRIMDMP to determine comprehensive attribute weights based on the calculated objective and subjective weights that could be possible derived in routing. It extended the merits of classical RIM using Z numbers to confirm reliable ranking of mobile nodes, even when the optimal solution exists amid extreme values taken into consideration for assessing the mobile nodes during decision making. Simulation investigations of the proposed NIRIMDMP confirmed improved throughput and network lifetime with reduced control overhead, energy consumptions, and delay independent of the amount of malicious and non-cooperative nodes.     

Dropping False Packet to Increase the Network Lifetime of Wireless Sensor Network using EFDD Protocol

In Wireless Sensor Network (WSN) sensors are densely deployed where the intruders can compromise some sensor nodes and inject false data in order to raise false alarms, reduce network lifetime, utilize bandwidth resources and so on. False Data Injection can possibly occur in Data Aggregation (DA) and Data Forwarding (DF). This paper analyses EFDD Protocol-Early False Data Detection Protocol which addresses the two possibilities in a simple and secure way considering the constraints of sensor nodes. The main idea is the selection of the network structure; this protocol will work effectively in Spatial/Semantic Correlation Tree Structure (SCT). False Data Detection in DA is done using some monitoring nodes which will monitor the Data Aggregator. EFDD in SCT structure reduce the counterfeit data transmission when compared to other structure in a better way. The result shows that EFDD reduce data transmission by dropping false data earlier and it also reduces computation when compared to the existing schemes.     

A Novel Approach on Reusing Silicon Wafer Kerf Particle as Potential Filler Material in Polymer Composite

Silicon - In solar panel production stage, it is unavoidable to neglect the huge amount of solar grade silicon particles displaced during the slicing of silicon ingot operation. A subject of the...     

Frame selection for OCR from video stream of book flipping

Multimedia Tools and Applications - Optical Character Recognition (OCR) in video stream of flipping pages is a challenging task because flipping at random speed causes difficulties in identifying...     

Effect of omega fatty acid-rich oil blend supplementation on the growth,carcass, meat quality,and gene expression of Barbari goats

This study investigated the effect of supplementing omega fatty acids-rich oil blend, composed of sunflower oil (1.5% and 3.0%), linseed oil (1.5% and 3.0%), and FineXNV1810 (20 g) on the carcass, meat quality, fatty acid profile, and genes (peroxisome proliferator-activated receptor-α, stearoyl-CoA desaturase, acetyl-CoA carboxylase, hydroxy-3-methylglutaryl coenzyme A, and leptin) of Barbari goats. The goat kids (n = 18) were divided into three groups, namely, group A: basal diet; group B: basal diet + oil blend level 1; and group C: basal diet + oil blend level 2, and subjected to the feeding trial for 120 days followed by slaughter and meat quality studies. No treatment effect was recorded in carcass characteristics, pH, water holding capacity, and proximate composition of meat. However, a significant (p < 0.05) treatment effect was observed in cooking loss, lightness, yellowness, and shear force values of meat. There were significant differences (p < 0.05) in linoleic acid, α-linolenic acids, conjugated linoleic acid (CLA), polyunsaturated fatty acids (PUFA), n − 3 and n − 6 PUFA, PUFA/saturated fatty acids and n − 6/n − 3 ratios, and thrombogenic index among groups. An upregulation of the studied genes in the supplemented groups was observed. There were upregulations in the studied genes in the supplemented groups. Practical applications: Goat meat is in great demand the world over, especially in tropical countries, including India, and does not carry any social or religious prohibition. Although goat meat has relatively less fat, consumers express their concern over the presence of undesirable fatty acids. The present study shows that the fatty acid configuration of goat meat can be improved by a dietary supplementation of an oil blend rich in omega fatty acids. The amount of n − 3 PUFA, n − 6 PUFA, and CLA in goat meat was significantly increased due to the dietary oil blend making it healthy for the consumers. Moreover, the dietary oil blend at the studied levels did not significantly affect the growth and meat quality parameters of the goats. Thus, the studied approach can be successfully followed to produce healthier goat meat.     

Electrochemical fabrication of Rh–Pd particles and electrocatalytic applications

Electrochemical deposition method was employed for the fabrication of rhodium–palladium (Rh–Pd) particles on the glassy carbon electrode (GCE) and indium tin oxide (ITO) electrode surface. Surface morphological analysis of Rh–Pd film has been carried out using scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis. Here, the electrodeposited Rh–Pd particles were found in the average size range of 30–200 nm. The electrochemical activities of the Rh–Pd film have been investigated using cyclic voltammetry (CV) and electrochemical impedance spectroscopic (EIS) analysis. The Rh–Pd particles-modified GCE successfully detects the hydrogen peroxide (H₂O₂) (in pH 7.0 phosphate buffer solution (PBS)) in the linear range in the lab (10–460 μM) and real samples (10–340 μM). The Rh–Pd particles-modified GCE possesses the good sensitivity and selectivity for the detection of H₂O₂ in lab and real samples.