ENERGY AWARE FUZZY COLOR SEGMENTATION ALGORITHM — AN APPLICATION TO CRIMINAL IDENTIFICATION USING MOBILE DEVICES

Since its advent, the use of digital camera in mobile phones is getting more popular, where information retrieval based on visual appearance of an object is very useful when specific parameters for the object are not known. Though it is well-liked, it needs energy aware algorithms to carry out the various tasks such as segmentation and feature extraction. In this paper, a new energy aware fuzzy color segmentation algorithm is proposed and which has been applied for face segmentation in criminal identification using mobile devices. The criminals in the application are in three classes. They are New Criminal (NC), Suspected Criminal (SC) and Confirmed Criminal (CC). It is basically a mobile image-based content search engine that takes photographs of criminals as image queries and finds their relevant contents by matching them to the similar contents in the criminal databases. The energy aware fuzzy color segmentation is used to obtain the most significant parts of an image — facial regions of the persons and which are used in building image-based queries to the databases. Content search methodology in the application is also improved through the fuzzy modeling to make the application more flexible and simpler. Through the experiment conducted, it has been found that the proposed color segmentation algorithm is more robust and it reduces the computational time in searching process by minimizing the number of false cases. It could detect the faces in the images where the other known algorithms have failed to detect.

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A new Energy Aware Cluster Based Multi-hop Energy Efficient routing protocol for Wireless Sensor Networks

2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE) ◽

10.1109/sege.2018.8499464 ◽

2018 ◽

Cited By ~ 1

Author(s):

Amanjot Singh Toor ◽

A. K. Jain

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Routing Protocol ◽

Energy Efficient ◽

Wireless Sensor ◽

Energy Aware ◽

Energy Efficient Routing ◽

New Energy

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Pine wilt disease detection in high-resolution UAV images using object-oriented classification

Journal of Forestry Research ◽

10.1007/s11676-021-01420-x ◽

2021 ◽

Author(s):

Zhao Sun ◽

Yifu Wang ◽

Lei Pan ◽

Yunhong Xie ◽

Bo Zhang ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Digital Camera ◽

Object Oriented ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Segmentation Algorithm ◽

Tree Crown ◽

Multi Scale ◽

Pine Wilt

AbstractPine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

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An Improved Metaheuristic based Node Localization Technique for Wireless Sensor Networks

10.54216/jisiot.050204 ◽

2021 ◽

pp. 88-96

Author(s):

Mohamed Elsharkawy ◽

◽

I.S. Farahat

Keyword(s):

Energy Utilization ◽

Support Vector ◽

Energy Aware ◽

Node Localization ◽

Vm Migration ◽

Initial Stage ◽

Svm Model ◽

New Energy ◽

Unlimited Access ◽

Task Processing

Cloud computing (CC) becomes a familiar topic in offering unlimited access to services as well as resources via the Internet. A comprehensive CC management system is needed to collect details of the task processing and ensure proper resource allocation with the accomplishment of Quality of Service (QoS). At the same time, virtual machine (VM) migration is a crucial problem in the CC platform which contributes to energy utilization and resource usage. Therefore, this paper presents a new energy-aware elephant herd optimization-based VM migration (EAEHO-VMM) scheme. The EAEHO-VMM algorithm aims to migrate the VMs and prediction failure VMs. At the initial stage, the EHO algorithm is executed to minimize the energy utilization of the VM migration process in the CC environment. In addition, a support vector machine (SVM) model is applied to identify the failure VMs and allows relocation in an effective way. In order to make sure the better performance of the EAEHO-VMM algorithm, a series of simulations take place, and the results are investigated in terms of different aspects. The experimental outcomes ensured the enhanced VM migration performance of the EAEHO-VMM algorithm over the other techniques.

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