Wireless Sensor Network (WSN) technology used to sense various types of physical and environmental conditions with the availability of small and low-cost sensor nodes. Main drawback in WSN is limited battery power in the sensor nodes. It is needed to distribute the energy dissipated through WSN and also needed to maximize the lifespan of sensor nodes. Energy efficiency can be accomplished through hierarchical routing protocols. One of the fundamental protocol in this class is Low Energy Adaptive Clustering Hierarchy (LEACH). This paper gives a survey of LEACH routing protocol for WSN and compared the performance in homogeneous and heterogeneous environment. Here, first analyzed the basic distributed clustering routing protocol LEACH, which is in a homogeneous environment, then analysed with the heterogeneity concept in nodes to increase the life of WSN. Simulation results were obtained using MATLAB that shows the LEACH heterogeneous environment significantly reduces energy consumption and increases the total lifetime of the WSN than LEACH homogeneous environment.

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The paper aims to classify the defects in a fabric material using deep learning and neural network methodologies. For this paper, 6 classes of defects are considered, namely, Rust, Grease, Hole, Slough, Oil Stain, and, Broken Filament. This paper has implemented both the YOLOv2 model and the YOLOv3 Tiny model separately using the same fabric data set which was collected for this research, which consists of six types of defects, and uses the convolutional weights which were pretrained on Imagenet dataset. Observed and documented the success rate of both the model in detecting the defects in the fabric material.

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