Chemical ripening and contaminations detection using neural networks-based image features and spectrometric signatures

In this pandemic-prone era, health is of utmost concern for everyone and hence eating good quality fruits is very much essential for sound health. Unfortunately, nowadays it is quite very difficult to obtain naturally ripened fruits, due to existence of chemically ripened fruits being ripened using hazardous chemicals such as calcium carbide. However, most of the state-of-the art techniques are primarily focusing on identification of chemically ripened fruits with the help of computer vision-based approaches, which are less effective towards quantification of chemical contaminations present in the sample fruits. To solve these issues, a new framework for chemical ripening and contamination detection is presented, which employs both visual and IR spectrometric signatures in two different stages. The experiments conducted on both the GUI tool as well as hardware-based setups, clearly demonstrate the efficiency of the proposed framework in terms of detection confidence levels followed by the percentage of presence of chemicals in the sample frui

SINK RELOCATION AND NODE RECOVERY FRAMEWORK FOR NETWORK LIFETIME ENHANCEMENT IN WSN

In recent years, wireless sensor network (WSN) has gained more importance mainly in the field of environmental monitoring, intrusion detection and battle field surveillance. However as the sensors batteries are too tiny in size,it leads to significant limitation in terms of energy and their life period. In the conventional WSN generally sensor nodes deliver sensed data back to the sink/ base station via multi-hopping. Since this delivery process consumes more battery energy, the sensor nodes near the sink quickly drain out of their battery energy and consequently shorten the network lifetime. To overcome this problem this paper proposes a sink relocation and node recovery framework which enhance the network lifetime of WSN. The proposed mechanism incorporates the following two stages. In the first stage it employs battery energy-aware sink relocation (BEASR) algorithm for sink relocation. In the second stage a dead node recovery technique using genetic algorithm (GA) is implemented which replaces the dead nodes and successfully enhances the network lifetime. The experimental result conducted on MATLAB demonstrates the improved performance of proposed framework in terms of reduction in average power consumption, number of hops and number of dead nodes

Rhizospheric metagenome of the terrestrial mangrove fern Acrostichum from Indian Sunderbans

This study reports the analyses of the rhizospheric microbiome of the terrestrial mangrove fern Acrostichum aureum Linn. from the Indian Sunderbans. Samples were collected using standard protocols and 16S rRNA gene V3–V4 region amplicon sequencing was performed to identify the microbial communities prevalent in the rhizosphere. A total of 1,931,252 quality checked reads were assembled into 204,818 contigs and were analysed using QIIME to reveal the abundance of Proteobacteria, Acidobacteria and Planctomycetes. The data is available at the NCBI - Sequence Read Archive with accession number: SRX2660456. This is the first report of the rhizospheric microbiome belonging to a fern species