scholarly journals Landslide Victories

2021 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Deep Learning ◽  
Event Detection ◽  
Tensor Decomposition ◽  
Pollution Monitoring ◽  
Warning Systems ◽  
Learning Networks ◽  
Data Logging ◽  
Air Pollution Monitoring ◽  
Early Bird ◽  
Word Detection

Tensor decompositions are defined for deep learning networks and active filter designs for the class of problems of event detection and wake word detection filters, for wildlife and demographic vocalization and footstep census and for landslide detection. The problems are proven Z complete from previous work, in published literature. An estimate on the minimal number of samples required to predict demographic and wildlife census and reasonably predict landslides within given confidence intervals is presented using clustered, stratified sampling.The Shaktiman(™) is introduced as a USB form factor IP 68 system for integration into computing for IoT applications using SoC RF solutions similar to BOMU and TOMU using the Shakti Risc V processor developed at IIT Madras. The Thunderboard Sense 2 module is directly integrated to 3D printed mathematical art to create solar lanterns for hymnology and early bird warning systems, for data logging, bioluminosity, early bird warning systems for natural disasters like landslides and other weather disturbances, using integrated temperature, humidity and hall sensors.Cloud integration with Google Firebase is used for a FaaS framework to the use of tensor decomposition in defining the architecture of deep learning and procedural A.I for event detection from multi sensor fusion. A commercial product already available through Shapeways.com, designed by the author is to be enhanced to add event detection and wake word detection functions for PID systems and natural disaster monitoring and prediction infrastructure to add to the existing pioneering efforts by IIT Mandi.Keywords: Disaster Prediction, Landslide, Footstep detection, Air Pollution Monitoring, Solar Garden Lamps, Hymnology, Early Bird Warning Systems, Indigenous Whistle Languages

scholarly journals Air pollution monitoring By sensors embedded on mobile phone

2013 ◽  
Vol 11 (5) ◽  
pp. 2628-2633
Author(s):  
Nasim Khozouie ◽  
Faranak Fotouhi-Ghazvini
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Mobile Technology ◽  
Pollution Monitoring ◽  
Data Logging ◽  
Air Pollution Monitoring ◽  
Phone Calls ◽  
Mobile Phone Network ◽  
Global Brain ◽  
Sheer Number

Mobile technology has been available for at least a decade and is increasingly being used in developing countries as away of contacting and connecting citizens and helping them to organize for a better life.Mobile phones are not just for phone calls, but they can also be used to collect data in several different formats and send them to a central server. There the data can be aggregated and analyzed, with tables and visualizations automatically generated. What is new is the sheer number of observation points that are potentially available by using mobile phones. With over 4 billion phones in use worldwide, the mobile phone network is emerging as a form of “global brain” with sensors everywhere. In addition, there are companies such as Fourier Systems that provide purpose-built mobile devices that are specifically designed for science experiments in school sand for data logging in any science project.

scholarly journals Air Pollution Monitoring and Prediction System

2019 ◽  
Vol 8 (2S3) ◽  
pp. 648-651
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
Pollution Monitoring ◽  
Warning Systems ◽  
Major Objective ◽  
Air Pollution Monitoring ◽  
The Public ◽  
The People ◽  
Environmental Air

Environmental Air Pollution Monitoring System is used for monitoring the concentrations of major air pollutants using gas sensors. The main target of this project is to monitor the air quality using sensors and analyze the existing trends in air pollution and make prediction about future. The major objective is to inform the public about the quality of air, raise the awareness and also to develop warning systems for the prevention of undesired air pollution episodes and to create awareness in order to reduce the amount of air pollution caused due to various sources. The system is also used to get the approximate quantity of pollutants present in air thereby giving awareness to the people of that specific region. Thus, the amount of pollution caused due to various sources can be reduced, leading a healthier and safer environment

scholarly journals Using deep learning to extend the range of air pollution monitoring and forecasting

2020 ◽  
Vol 408 ◽  
pp. 109278 ◽  
Author(s):  
Philipp Hähnel ◽  
Jakub Mareček ◽  
Julien Monteil ◽  
Fearghal O'Donncha
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Monitoring And Forecasting

A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

2016 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
HASAN MOHD. TAHSEENUL ◽  
CHOURASIA VIJAY S. ◽  
ASUTKAR SANJAY M. ◽  
◽  
◽  
...  
Keyword(s):  
Air Pollution ◽  
Comparative Analysis ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring
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