Floods and earthquakes are among the most frequently occurring natural disasters. They account for high mortality rates due to their rapidness and uncertainty of occurrence. Inundated lands require a quick response fo...
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ISBN:
(纸本)9781665439206
Floods and earthquakes are among the most frequently occurring natural disasters. They account for high mortality rates due to their rapidness and uncertainty of occurrence. Inundated lands require a quick response for rapid evacuation, arresting fatalities, and consequential economic losses. People tend to seek shelter at dry and open lands at times of calamity. The manual Search and Rescue (SAR) operations have their shortcomings due to the difficulties in identifying the human presence. It requires a longer time for evacuation and therefore increased mortalities. This paper proposes a quadcopter for real-time monitoring of isolated places and automatically detecting stranded humans during floods using imageprocessing techniques at affordable rates. Live video streaming is possible with a camera and a video transmission system attached to the quadcopter. The rescue centers automatically receive the location of humans in case of human detection. Our model integrates an Open-Source autopilot system model, APM 2.8 multicopter flight controller that efficiently stabilizes the flight, and a YOLOv5 object tracking convolutional neural network algorithm for faster detection of human beings. The model is trained using a dedicated dataset of more than 1000 images and attains 0.954 mAP. We have developed a drone using open-source hardware and software tools, conducted test flights to check its stability and the efficiency of the object detection algorithm. We also conducted a mini-survey of one of the most flood-prone areas of Thrissur district in Kerala, using Mission Planner open-source software to evaluate how quickly our drone can assess the entire area. The aim is to save more human lives by quick and efficient aerial assessment in the most cost-efficient manner.
Adaptive object selection technique based on the results of multi-threshold imageprocessing is considered. The keynote feature of the proposed approach is the use of information about the properties of the selected o...
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The Automatic Number Plate Recognition (ANPR) is a imageprocessing innovation that utilizes the vehicle number (permit) plate for vehicle identification. The goal is to utilize the vehicle number plate to plan a prod...
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Interferometric data processing (InSAR) for extraction of information about the Earth terrain heights is one of the general guidelines in development of contemporary space-based radar systems. The InSAR processing for...
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The problem of automatic reliability monitoring and reliability-centered maintenance is increasingly important today. In this paper, we compare the accuracy of four machine learning approaches for fault detection in a...
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Insects represent a large majority of biodiversity on Earth, yet so few species are described. Describing new species typically requires specific taxonomic expertise to identify morphological characters that distingui...
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image classification is one of the most important tasks in image analysis and computer vision. BP neural network is a successful classifier for the task. However, with regard to the low study efficiency and the slow c...
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ISBN:
(纸本)9781728140698
image classification is one of the most important tasks in image analysis and computer vision. BP neural network is a successful classifier for the task. However, with regard to the low study efficiency and the slow convergence speed in BP algorithm, some optimization algorithms have been proposed for achieving better results. Among all these methods, BP neural network improved by particle swarm optimization (PSO) and genetic algorithm (GA) may be the most successful and classical ones. Nevertheless, both GA and PSO are easy to fall into the local optimal solution, which has a great impact on the precision of classification. As a result, a novel optimization algorithm called sine cosine algorithm (SCA) is presented to improve the classification performance. The experimental results manifest that the proposed method has good performances, and the classification accuracy is better than BP neural network optimized by GA, PSO or other algorithms.
The report discusses the creation of software for pre-processing of images and audio signals based on a concrete model for clustering of signal elements. A uniform signal representation by means of a linearly ordered ...
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This article addresses the study of the anomaly and fraud detection problem in the data from social services. The problem of detecting anomalies is extremely relevant for data-driven processes in the digital economy. ...
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When a digital chaotic system is realized on hardware of finite computing precision, it will lead to short period orbits. Although many existing image encryption algorithms declared that the average cycle lengths of t...
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When a digital chaotic system is realized on hardware of finite computing precision, it will lead to short period orbits. Although many existing image encryption algorithms declared that the average cycle lengths of their digital chaotic systems are larger than the required cycle lengths in image encryption, there are still many period orbits whose lengths are far smaller than the average cycle length. To further improve security with finite precision, an improved cryptosystem is proposed based on a new two-dimensional chaotic map derived from the Sine map, the Chebyshev map and a linear function (2D-SCL). Performance estimation demonstrates that the 2D-SCL has good ergodicity, hyperchaotic behavior, large cycle lengths under low computing precisions, and its complexity is larger and more stable than that of several newly developed 2D chaotic maps. Thus we design an improved cryptosystem based on the 2DSCL map. In the scheme, we combine the confusion and diffusion processes in one stage to improve the running speed. Based on the SHA-1 hash values of plain image and the chaotic sequence, a pseudorandom sequence is designed and then an anti -degradation method is introduced to improve the dynamic degradation of the 2D-SCL map under finite computing precision. Meanwhile, this algorithm also updates the initial values of the 2D-SCL map in real-time, thus enhancing the ability to resist known-plaintext and chosen-plaintext attacks. The largest precision is set at 2(-16), and simulation results show that this algorithm has high security, low time complexity, and the ability to withstand common attacks. (C) 2019 Elsevier B.v. All rights reserved.
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