Real-Time System Design for Sensing, Recording and Analyzing Elephant Seismic Waves Through Ground Vibration Algorithm

Author(s):  
R. Ramkumar ◽  
Sanjoy Deb

In this paper, a real-time low-cost geophone-based Elephant Footstep Vibration Detection and Identification (EFVDI) system is proposed. The system design started with a real-time low-cost generalized Footstep Vibration Recording and Analyzing (FVRA) system. A series of field experiments to record elephant footstep vibration (target) signals and other possible interfering ground vibration (noise) sources are conducted using the FVRA system. System’s actual field performance was evaluated in terms of maximum detection range, signal amplitude, detection ratio, signal frequency, signal time span, etc. Variations of system’s performance with several input parameters are also investigated. The recorded signals from target as well as noise sources are analyzed to extract different Signal Parameters (SPs). All SPs are saved in a Ground Vibration Signal Pattern Library (GVSPL) which is then used to frame accurate indigenous Elephant Identification Algorithm (EIA). The EIA is embedded in FVRA system to reshape it as specific Elephant Footstep Vibration Detection and Identification (EFVDI) system. The EFVDI system has successfully segregated elephant footsteps from other noise vibrations with high accuracy under simulated field experiment. The results from the proposed system will provide important data to the ongoing research of developing the much needed highly accurate Elephant Early Warning System (EEWS) in future.

Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).


Author(s):  
Husam Kareem

<p>A major issue that happens in kitchens of houses and/or restaurants is the leakage of gas used as a fuel for cooker stove, which is commonly referred to as LPG (liquefied petroleum gas). LPG leakage may lead to a serious fire or even a deadly explosion that might affect the surrounding people. A substantial solution to avoid such disasters is by stopping its main cause. Therefore designing a device capable of monitoring and detecting such gases can minimize the dangerous and unwanted incidents by LPG leakage. This paper introduces a low cost and energy efficient real-time monitoring system that able to sense different dangerous gases, specifically those used for stove cooker. This system considers the pros of the previously introduced systems and fixes the cons available in those systems. In addition, the manufacturing cost has been taken into consideration. If the system senses any type of LPG gas (there is a gas leakage), it will react by making three different actions. It will make an alert sound to notify the people around the leakage place, send an SMS to two cell phones, and show, on an LCD screen, the leakage location.</p>


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