Precision pointing and tracking through random media by exploitation of the enhanced backscatter phenomenon

The active illumination of a target through a turbulent medium with a monostatic transmitter-receiver results in a naturally occurring conjugate wave caused by reciprocal scattering paths that experience identical phase variations. This reciprocal path-scattering phenomenon produces an enhanced backscatter in the retroverse direction (precisely along the boresight of the pointing telescope). A dual aperture causes this intensity enhancement to take the form of Young's interference fringes. Interference fringes produced by the reciprocal path-scattering phenomenon are temporally stable even in the presence of time-varying turbulence. Choosing the width-to-separation ratio of the dual apertures appropriately and utilizing orthogonal polarizations to suppress the time-varying common-path scattered radiation allow one to achieve interferometric sensitivity in pointing accuracy through a random medium or turbulent atmosphere. Computer simulations are compared with laboratory experimental data. This new precision pointing and tracking technique has potential applications in ground-to-space laser communications, laser power beaming to satellites, and theater missile defense scenarios.     

Survey on Diagnosing CORONA VIRUS from Radiography Chest X-ray Images Using Convolutional Neural Networks

Wireless Personal Communications - Corona Virus continues to harms its effects on the people lives across the globe. The screening of infected persons has to be identified is a vital step because...     

Plant leaf disease classification and damage detection system using deep learning models

Agriculture is the primary source of livelihood for about 70% of the rural population in India. The crop variety cultivated in India is very diverse. There are more than 500 crop varieties grown in India. Despite the technological advances, the agricultural practices are still manual and involve less automation than western countries. Most of the diseases affecting a plant will reflect the damage in the leaves. The diseases affecting the plant can thus be identified from the leaf images. This paper presents an automatic plant leaf damage detection and disease identification system. The first stage of the proposed method identifies the type of the disease based on the plant leaf image using DenseNet. The DenseNet model is trained on images categorized according to their nature, i.e., healthy and the type of the disease. This model is then used for testing new leaf images. The proposed DenseNet model produced a classification accuracy of 100%, with fewer images used during the training stage. The second stage identifies the damage in the leaf using deep learning-based semantic segmentation. Each RGB pixel value combination in the image is extracted, and supervised training is performed on the pixel values using the 1D Convolutional Neural Network (CNN). The trained model can detect the damage present in the leaves at a pixel level. Evaluation of the proposed semantic segmentation resulted in an accuracy of 97%. The third stage suggests a remedy for the disease based on the disease type and the damage state. The proposed method detects various defects in different plants in the experimental analysis, namely apple, grape, potato, and strawberry. The proposed model is compared with the existing techniques and obtained better performance in comparison with those methods.

     

Lung Nodule Classification on Computed Tomography Images Using Deep Learning

Wireless Personal Communications - Lung Cancer is the most fast growing cancer around the world and is mostly diagnosed at an advanced stage. Due to enhancement in medical imaging modalities like...     

Energy-Efficient Load Balancing Strategy for Wireless Sensor Networks using Quasi-oppositional based Jaya Optimization

Wireless Personal Communications - The area of wireless sensor networks (WSNs) has gained significant attention from researchers due to its expansive range of applications, such as industrial...     

Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm-Based Cluster Head Selection Scheme for WSNs

The process of cluster head selection under the process of cluster formation in wireless sensor networks is determined to be essential for extending the lifetime of the network. In this paper, a Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm (SADSS-IABCA)-based Cluster Head Selection Scheme is proposed for prolonging the lifetime of the network with improved Quality of Service. The differential search strategies employed in the SADSS-IABCA-based Cluster Head Selection Scheme are reliable in updating the dependent variables in periodic intervals of time through the integration of mutation and crossover. This proposed SADSS-IABCA-based Cluster Head Selection Scheme incorporated diversified search strategies associated with differential evolution with the employee and onlooker bee phase in order to improve the local searching ability of ABC with the view to eliminate its limitations of delayed convergence. In addition, the appropriate selection of differential evolution strategies is computed through the probability-based self adapting process for effective selection of cluster heads. The simulation results of the proposed SADSS-IABCA-based Cluster Head Selection Scheme also confirmed a predominant improvement in percentage of alive nodes, throughput and mean residual energy compared to the benchmarked cluster head schemes used for investigation.

     

Real-Time Flow Forecasting in a Watershed Using Rainfall Forecasting Model and Updating Model

Water Resources Management - Watershed is the basic unit for studying different hydrologic processes. Flow forecasting in a watershed is dependent upon the rainfall. The effect of erroneous...     

ICT4D research: a call for a strong critical approach

ABSTRACT

ICT for development ( ICT4D) research seeks to examine the social and economic changes in developing countries brought about by the deployment and use of ICT. This intent of ICT4D research parallels that of the critical research paradigm in IS, since both focus on transformation and change. The overall goals of this paper are to( 1)understand the extent of critical research in ICT4Dand ( 2)propose an approach, the“strong critical” approach, to conduct critical research in ICT4D. The proposed approach is based on the writings of two social theorists, Arturo Escobar and Gayatri Spivak, and consists of four concepts– the nature of the post-colonial state, provenience or local history of the ICT phenomena, the influence of the Washington Consensusand the issues of representation and subjectivity of subaltern subjects. A review of ICT4D papers showed that only about 20% follow the critical research approach. In-depth reviews ofeight papers that follow the critical approach showed that the“strong critical” lens can enable a deeper and richer analysis. The main contribution of this paper is in addressing a gap in the ICT4D literature about theorizing in the context of developing countries. The paper also reveals, through in-depth reviews, the value of the strong critical approach.     

Towards ontology-based multilingual URL filtering: a big data problem

Web content filtering is one among many techniques to limit the exposure of selective content on the Internet. It has gotten trivial with time, yet filtering of multilingual web content is still a difficult task, especially while considering big data landscape. The enormity of data increases the challenge of developing an effective content filtering system that can work in real time. There are several systems which can filter the URLs based on artificial intelligence techniques to identify the site with objectionable content. Most of these systems classify the URLs only in the English language. These systems either fail to respond when multilingual URLs are processed, or over-blocking is experienced. This paper introduces a filtering system that can classify multilingual URLs based on predefined criteria for URL, title, and metadata of a web page. Ontological approaches along with local multilingual dictionaries are used as the knowledge base to facilitate the challenging task of blocking URLs not meeting the filtering criteria. The proposed work shows high accuracy in classifying multilingual URLs into two categories, white and black. Evaluation results conducted on a large dataset show that the proposed system achieves promising accuracy, which is on a par with those achieved in state-of-the-art literature on semantic-based URL filtering.     

Tri‐band and quad‐band dual L‐slot coupled circularly polarized dielectric resonator antennas

In this paper, two Dielectric Resonator Antenna (DRA) models fed through a pair of diagonally coupled asymmetric L‐slots are incorporated on the ground plane of size 44 X 44 mm² with a strip line feed underneath the substrate are presented. The proposed DRA‐1 is a triband antenna, resonates at 5.2GHz, 6.7GHz and 9.85GHz with a gain of 5.6dBi, 5.66dBi and 9.8dBi respectively. The bandwidth offered at Circularly Polarized (CP) band by DRA‐1 is 1.95 GHz (6‐7.95 GHz). The proposed second model DRA‐2 operates at 5 GHz, 6.4 GHz, 7.8 GHz and 10.3 GHz with a peak gain of 5.5dBi, 5dBi, 6.1dBi and 7.8dBi respectively. The quad‐band DRA‐2 offers two CP bands with bandwidths of 1.3GHz (7‐8.3 GHz) and 1.2 GHz (9.8‐11 GHz). The multiple operating bands of the proposed DRAs are appropriate for different wireless applications such as WLAN, C‐Band and X‐Band range of frequencies.