sensor network applications collect and use sensordata from distributed sensor networks and databases. These real-time and time series sensordata are very useful for many location-based systems that manage and brows...
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ISBN:
(纸本)9781424412310
sensor network applications collect and use sensordata from distributed sensor networks and databases. These real-time and time series sensordata are very useful for many location-based systems that manage and browse various kinds of personal spatial information such as digital photograph, travel diary and so on. Such location-based system for personal use requires a user-centric framework that can support intuitive query request and direct sensordata mapping. This paper proposes a user-centric approach for interactive visualization and mapping of geographically distributed sensordata. The overview of the prototype system that we are developing is briefly described. Our approach generates a query for distributed sensordatabases based on user's track log. This method produces understandable information from collected sensordata based on GIS (Geographic Information System) methods.
In this paper, we introduce an alternative solution to the many existing IoT data acquisition and storage systems. We present a self-designed and developed prototype electronic circuit extension for Raspberry Pi devel...
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ISBN:
(纸本)9781728111544
In this paper, we introduce an alternative solution to the many existing IoT data acquisition and storage systems. We present a self-designed and developed prototype electronic circuit extension for Raspberry Pi development board used for collecting sensordata. There is also presented a Pi4Java API based Java application used for sensordata collection and storage. We set up an Apache Cassandra database cluster, to stores large amounts of sensordata on lowcost servers, providing high availability. In addition, a web application is also presented, that allows different datavisualization operations to be performed on the stored data. The presented system is a full IoT data acquisition, storage and visualization solution
Wireless sensor networks (WSNs) have great potential to revolutionize many science and engineering domains. We present a novel environmental monitoring system with a focus on overall system architecture for seamless i...
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Wireless sensor networks (WSNs) have great potential to revolutionize many science and engineering domains. We present a novel environmental monitoring system with a focus on overall system architecture for seamless integration of wired and wireless sensors for long-term, remote, and near-real-time monitoring. We also present a unified framework for sensordata collection, management, visualization, dissemination, and exchange, conforming to the new sensor Web Enablement standard. Some initial field testing results are also presented. The monitoring system is being integrated into the Texas Environmental Observatory infrastructure for long-term operation. As part of the integrated system, a new WSN-based soil moisture monitoring system is developed and deployed to support hydrologic monitoring and modeling research. This work represents a significant contribution to the empirical study of the emerging WSN technology. We address many practical issues in real-world application scenarios that are often neglected in the existing WSN research.
We introduce a visualization system of micro-scale air quality monitoring system in the virtual reality environment. The system is targeting to provide everyday air quality by adopting VR-based visualization method. W...
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ISBN:
(纸本)9781424452446
We introduce a visualization system of micro-scale air quality monitoring system in the virtual reality environment. The system is targeting to provide everyday air quality by adopting VR-based visualization method. With the system casual users can get insight of air quality data intuitively. The system targets to provide interactive manipulation of actual sensors in ubiquitous environment, to retrieve specific data. Users can manipulate sensors in VR environment. To provide interactive manipulation of massive sensordata, an adaptive visualization method is proposed. For the adaptive visualization, the visualization and interaction data are separated from air quality data. With this layered approach, the visualization module can interactively decide appearances and detail levels for interactivity independently to original data quality and complexity. As an exemplar system an application with a display-wall is constructed. This system can provide accessibility for air quality data to people one public space.
In recent years, the application of Deep Neural Networks to gas recognition has been developing. The classification performance of the Deep Neural Network depends on the efficient representation of the input data samp...
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In recent years, the application of Deep Neural Networks to gas recognition has been developing. The classification performance of the Deep Neural Network depends on the efficient representation of the input data samples. Therefore, a variety of filtering methods are firstly adopted to smooth filter the gas sensing response data, which can remove redundant information and greatly improve the performance of the classifier. Additionally, the optimization experiment of the Savitzky-Golay filtering algorithm is carried out. After that, we used the Gramian Angular Summation Field (GASF) method to encode the gas sensing response data into two-dimensional sensing images. In addition, data augmentation technology is used to reduce the impact of small sample numbers on the classifier and improve the robustness and generalization ability of the model. Then, combined with fine-tuning of the GoogLeNet neural network, which owns the ability to automatically learn the characteristics of deep samples, the classification of four gases has finally been realized: methane, ethanol, ethylene, and carbon monoxide. Through setting a variety of different comparison experiments, it is known that the Savitzky-Golay smooth filtering pretreatment method effectively improves the recognition accuracy of the classifier, and the gas recognition network adopted is superior to the fine-tuned ResNet50, Alex-Net, and ResNet34 networks in both accuracy and sample processing times. Finally, the highest recognition accuracy of the classification results of our proposed route is 99.9%, which is better than other similar work.
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