scholarly journals Fire Recognition based on Image Processing using Raspberry pi

Fire is a procedure of ignition that brings calamity. It becomes unsafe when fire loses control and spreads out. The fire detection becomes more and more important with the rapid development of image and video processing, the fire detection technology based on video processing is becoming the focal point of some research due to its advantages of high intuitive, speed and anti-jamming capability. This method uses colour and motion information extracted from video sequences to detect fire. It can work both indoor and outdoor environments. Moreover, it detects fire at the beginning of the burning process. The method performs the region growing segmentation to identify colour pixels in the scene and then identify moving pixels based on the ratio of height and width of suspected fire region. This method can get low false alarm rate by eliminating the fire-like colours because it just needs a fire pixel as the seed pixel.

Author(s):  
Mukul Singh ◽  
Md Nawaj Khan ◽  
Mohammad Makki ◽  
Md Irshad ◽  
Manohar Hussain ◽  
...  

This paper is the survey of Smart camera based surveillance monitoring system using Raspberry pi. Camera based surveillance is important in all sectors, they can be colleges and hospitals, shopping malls and other challenging indoor and outdoor environments require high end cameras. This paper focus on low-cost project on single board computer Raspberry Pi. This is new technology and far less expensive and, it is being used as a main platform for video detection and acquisition. It can be used with involvement of mobile network (internet) to provide essential security and surveillance to our properties and for other control applications. The security system records information and transmits it via network to a Smart Phone using web application Raspberry pi.


Author(s):  
Brandon K Hopkins ◽  
Priyadarshini Chakrabarti ◽  
Hannah M Lucas ◽  
Ramesh R Sagili ◽  
Walter S Sheppard

Abstract Global decline in insect pollinators, especially bees, have resulted in extensive research into understanding the various causative factors and formulating mitigative strategies. For commercial beekeepers in the United States, overwintering honey bee colony losses are significant, requiring tactics to overwinter bees in conditions designed to minimize such losses. This is especially important as overwintered honey bees are responsible for colony expansion each spring, and overwintered bees must survive in sufficient numbers to nurse the spring brood and forage until the new ‘replacement’ workers become fully functional. In this study, we examined the physiology of overwintered (diutinus) bees following various overwintering storage conditions. Important physiological markers, i.e., head proteins and abdominal lipid contents were higher in honey bees that overwintered in controlled indoor storage facilities, compared with bees held outdoors through the winter months. Our findings provide new insights into the physiology of honey bees overwintered in indoor and outdoor environments and have implications for improved beekeeping management.


2021 ◽  
Vol 11 (4) ◽  
pp. 1902
Author(s):  
Liqiang Zhang ◽  
Yu Liu ◽  
Jinglin Sun

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.


Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 359
Author(s):  
Ewa Brągoszewska

The Atmosphere Special Issue entitled “Health Effects and Exposure Assessment to Bioaerosols in Indoor and Outdoor Environments” comprises five original papers [...]


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