Visualization Of Sensor Data Research Articles

Open source software for visualization and quality control of continuous hydrologic and water quality sensor data

It is common for in situ hydrologic and water quality data to be collected at high frequencies and for extended durations. These data streams, which may also be collected across many monitoring sites require infrastructure for data storage and management. The Observations Data Model (ODM), which is part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), was developed as a standard data model in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, an open source software application that allows users to query and export, visualize, and perform quality control post processing on time series of environmental observations data stored in an ODM database using automated Python scripting that records the corrections and adjustments made to data series in the quality control process and ensures data editing steps are traceable and reproducible.

New trends in biomedical engineering and bioinformatics applied to biomedicine - special issue of IWBBIO 2014.

New trends in biomedical engineering and bioinformatics applied to biomedicine - special issue of IWBBIO 2014.

Navigating a Robot through Big Visual Sensory Data

This paper describes a reinforcement learning architecture that is capable of incorporating deeply learned feature representation of a robot's unknown working environment. An autoencoder is used along with convolutional and pooling layers to deduce the reduced feature representation based on a set of images taken by the agent. This representation is used to discover and learn the best route to navigate to a goal. The features are fed to an actor layer that can learn from a value function calculated by a second output layer. The policy is ɛ-greedy and the effect is similar to actor-critic architecture where temporal difference error is back propagated from the critic to the actor. This compact architecture helps in reducing the overhead of setting up a desired fully fledged actor-critic architecture that typically needs extra processing time. Hence, the model is ideal for dealing with lots of data coming from visual sensor that needs speedy processing. The processing is accomplished off board due to the limitation of the used robot but latency was compensated by the speedy processing. Adaptability for the different data sizes, critical to big data processing, is realized by the ability to shrink or expand the whole architecture to fit different deeply learned feature dimensions. This added flexibility is crucial for setting up such model since the space dimensionality is not known prior to operating in the environment. Initial experimental results on real robot show that the agent accomplished good level of accuracy and efficacy in reaching the goal.

무구속 방식의 호흡 측정 시스템 구현

In this paper, we implemented a system to measure respiration rate with nonrestraint sensors comfortable for people to do their everyday life. The proposed system consists of a pad covered with a Piezoelectric sensor, a respiration measuring device able to send the signal data after amplifying and filtering the source signals to the viewer, a viewer providing sensor data visualization and implementing the respiration measuring algorithm. The algorithm is based on a breathing cycle with the local peak points extracted from threshold on sensor data. Respiration measurements on 3 subjects were performed by changing moving averages and thresholds. The proposed system showed less than 5% error rate when proper moving averages are N

A Comparative Effectiveness Evaluation of Map Construction Protocols in Wireless Sensor Networks

Multiple applications in wireless sensor networks (WSN) require node-level accuracy. One popular example of these applications is the map-based visualization of live spatial sensor data. The specific WSN properties such as energy imply that the data collection and/or the in-network map construction should carefully address the tradeoff between energy/network overhead and map accuracy. Accordingly, several map construction approaches have been proposed. The energy efficiency of reducing the network traffic overhead has been proved in the corresponding papers for a set of carefully selected parameters. To date, there exists no quantitative comparative study that considers a wide range of physical phenomena and network properties to provide a comprehensive evaluation of these map construction approaches. In this paper, we present a series of protocol efficiency evaluation criteria that are then used to evaluate the comparative effectiveness of the existing map construction approaches by analyzing their design and simulating their performance in a wide range of environment scenarios and network settings. Using extensive simulations, we identify which map construction approach outperforms the other for most common application scenarios. Our main achievements are detailed insights of the existing approaches and proposals for improving existing approaches.

Smart Blueprints: How Simple Sensors Can Collaboratively Map Out Their Own Locations in the Home

Off-the-shelf home automation technology is making it easier than ever for people to convert their own homes into smart homes. However, manual configuration is tedious and error-prone. In this article, we present and compare a family of solutions that automatically generate a map of the home and the devices within it using data from the smart home sensors themselves (e.g., light and motion sensors). These solutions can be used to automatically configure home automation systems or to automatically produce an intuitive map-like interface for visualizing sensor data and interacting with controllers. We call our approach Smart Blueprints because it automatically maps out the unique configuration of each smart home. We demonstrate the Smart Blueprints using a variety of sensor combinations, including light sensors, motion sensors, and magnetometers deployed on the doors and/or windows of the home. For evaluation of each combination on sensor-map generation, we deployed more than 200 sensors in seven different houses at different locations and compared the ability to use a variety of techniques to map out the configuration. We show that, in almost all houses, our system can automatically narrow the configuration down to 1--5 candidates per home using only one week of collected data.

Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.

Prototyping the visualization of geographic and sensor data for agriculture

The effectiveness of decision-making processes in the agricultural domain can be improved by integrating current local environmental and agromonitoring with the Geographic Information System (GIS) and wireless sensor networks (WSNs). The presented paper describes conceptual approaches to context-based cartographic visualization methods for agricultural and metrological data acquired by WSN and a portal prototype for integrated visualization.Each sensor used for agricultural applications has a location and can be placed within a broader spatial context. In our study, sensor characteristics (soil temperature and moisture, atmospheric temperature and moisture) were automatically monitored at frequent intervals and these readings were aggregated with geospatial data from both local and remote (Web Map Service) sources. An experimental portal for the integration and visualization of sensor data and geospatial data was designed and prototyped on the basis of an open source interoperable platform. Conceptual approaches were successfully implemented on four experimental small-plot fields planted with different crop species and operated using different soil tillage practices. Experimental fields were situated in the southeast of the Czech Republic. Very Long range Identification Tag (VLIT) technology was used for wireless communication. Sensor observations verified differences in agrometeorological variables for the conventional tillage of soil and the no-tillage variant. Contextual cartographic visualization was successfully deployed for map view, dynamic cartographic symbology, and the dynamic measurement chart.

Visualization of wearable sensor data during swimming for performance analysis

Sensor-based biomechanical monitoring of sporting activity requires the interpretation of large data-sets of time series data-sets. Visualization techniques are a powerful method for displaying these data in a meaningful way to assist in understanding the complex interrelationships of the data and biomechanics. In particular, repetitive actions such as seen in many sports, including swimming can benefit from such analysis where overlay and visual comparison of multiple strokes can be advantageous. Many other disciplines, such as medicine visualize repetitive data and are translational opportunities for the investigation of biomechanical data, such as swimming. This paper presents a case study in which inertial sensor time series data from an elite and sub-elite swimmer were compared using visualization techniques to highlight differences in their action and performance. In particular, the metrics of body roll velocity was captured from the gyroscope sensor and was used as the key time series data to be vi...

Augmented Reality-Based Embedded Sensor Location Recognition and Data Visualization Technique for Concrete Structural Health Monitoring

In recent years, numerous mega-sized and complex civil infrastructures are being constructed all over the world. Therefore, more precise construction and maintenance technologies are required for these complicated construction projects. So a variety of sensors-based structural health monitoring (SHM) techniques have been studied, but these techniques could not manage the sensors efficiently access the database obtained from the sensors. Recently, Quick response (QR) code and AR-based data access technologies have been developed. In this paper, an AR-based concrete curing strength monitoring technique for sensor management and efficient access of the measured data is introduced. It is confirmed that the AR-based concrete curing strength monitoring technique is useful for construction process. In addition, it is concluded that both efficient sensor location recognition and data visualization at anytime, anywhere, and any smart PC devices are promising.

Localization Services for Online Common Operational Picture and Situation Awareness

Many operations, be they military, police, rescue, or other field operations, require localization services and online situation awareness to make them effective. Questions such as how many people are inside a building and their locations are essential. In this paper, an online localization and situation awareness system is presented, called Mobile Urban Situation Awareness System (MUSAS), for gathering and maintaining localization information, to form a common operational picture. The MUSAS provides multiple localization services, as well as visualization of other sensor data, in a common frame of reference. The information and common operational picture of the system is conveyed to all parties involved in the operation, the field team, and people in the command post. In this paper, a general system architecture for enabling localization based situation awareness is designed and the MUSAS system solution is presented. The developed subsystem components and forming of the common operational picture are summarized, and the future potential of the system for various scenarios is discussed. In the demonstration, the MUSAS is deployed to an unknown building, in an ad hoc fashion, to provide situation awareness in an urban indoor military operation.

Can cellular automata be a representative model for visual perception dynamics?

Can cellular automata be a representative model for visual perception dynamics?

Activity Density Map Visualization and Dissimilarity Comparison for Eldercare Monitoring

In this paper, we present a methodology for analyzing passive infrared motion sensor data logged in the homes of seniors. The objective is to capture activity patterns that represent different health conditions. Recognizing changes in the activity patterns can then be used to provide early detection of health changes. A visualization of motion sensor data is introduced in the form of a density map that uses different colors to show varying levels of activity. For evaluating the activity density level accurately, time away from home is determined first using a system of fuzzy rules. In addition, a dissimilarity between two density maps is computed using texture features for automatically determining changes in activity patterns, which may indicate a health problem. The activity density maps are being used in an aging in place senior housing community to aid clinicians in early illness detection. Three case studies of elderly residents are included to illustrate how the density map and dissimilarity measure can be used to track general activity level and daily patterns over time, showing changes in physical, cognitive, and mental health.

Self‐supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

AbstractAutonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self‐supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. © 2012 Wiley Periodicals, Inc.

Sensing and responding to the changes of geometric surfaces in flexible manufacturing and assembly

A flexible manufacturing system is capable of dealing with changes and uncertainties occurring in a manufacturing environment. A rigid automation system adapts changes through reconfiguration or manual modification of its control programmes. In contrast, an advanced system responds to the changes autonomously or with a minimal manual intervention. One critical technology for a flexible manufacturing system is to detect or predict the changes and uncertainties and take them into account for the control of the manufacturing system. In this paper, an intelligent manufacturing system is proposed to accommodate product geometric variants autonomously by integrating a 3D vision system into manufacturing processes. The proposed system is capable of acquiring vision sensor data, detecting the geometric changes, and modifying the control programmes of the machine in response to the changes. In this innovative work, a new method for converting the raw vision sensor data into a surface model of the product is presented. A new surface reconstruction algorithm is proposed to generate the surface model directly from the raw 3D point-cloud data of a product. In addition, an advanced programming algorithm that uses acquired surface models to automatically update robot programmes for a new task with minimal manual intervention is presented.

Generation of Tactile Maps for Artificial Skin

Prior research has shown that representations of retinal surfaces can be learned from the intrinsic structure of visual sensory data in neural simulations, in robots, as well as by animals. Furthermore, representations of cochlear (frequency) surfaces can be learned from auditory data in neural simulations. Advances in hardware technology have allowed the development of artificial skin for robots, realising a new sensory modality which differs in important respects from vision and audition in its sensorimotor characteristics. This provides an opportunity to further investigate ordered sensory map formation using computational tools. We show that it is possible to learn representations of non-trivial tactile surfaces, which require topologically and geometrically involved three-dimensional embeddings. Our method automatically constructs a somatotopic map corresponding to the configuration of tactile sensors on a rigid body, using only intrinsic properties of the tactile data. The additional complexities involved in processing the tactile modality require the development of a novel multi-dimensional scaling algorithm. This algorithm, ANISOMAP, extends previous methods and outperforms them, producing high-quality reconstructions of tactile surfaces in both simulation and hardware tests. In addition, the reconstruction turns out to be robust to unanticipated hardware failure.

Using ambient assisted living infrastructures to monitor emotional change: Applications in supporting dementia interventions

Background: This project aims to create a new method of preventative care based on early prediction of changes in emotional state and, based on this early detection, use novel “human-in-the-loop” interventionmethods specific to the needs of the individual to improve the quality of life of the older person before severe problems arise. The project is forged from two independent research and development activities currently on-going: Ambient Assisted Living (AAL) at the Netwell Centre and the study of human emotion at Queen’s University.Methods: On-going covariance and sensitivity analysis between quantitative sensor data and “ground truth” validated measures of emotional change inform the predictive model. Results: Visualization of sensor data has begun to reveal specific patterns of behavior over hours, days, weeks, and months. Collection of “ground truth” affect and health data has now commenced. Initial multivariate analysis is ongoing and will be discussed. Conclusions: To aid in the establishment and application of home-based interventions for cognitive decline and dementia, AAL environments can be used to observe, understand, and eventually model the needs of the older person. This work has created the infrastructure and is now beginning to analyze the data necessary to begin to instantiate this approach.

Using ambient assisted living infrastructures to monitor emotional change: Applications in supporting dementia interventions

Background: This project aims to create a new method of preventative care based on early prediction of changes in emotional state and, based on this early detection, use novel “human-in-the-loop” interventionmethods specific to the needs of the individual to improve the quality of life of the older person before severe problems arise. The project is forged from two independent research and development activities currently on-going: Ambient Assisted Living (AAL) at the Netwell Centre and the study of human emotion at Queen’s University.Methods: On-going covariance and sensitivity analysis between quantitative sensor data and “ground truth” validated measures of emotional change inform the predictive model. Results: Visualization of sensor data has begun to reveal specific patterns of behavior over hours, days, weeks, and months. Collection of “ground truth” affect and health data has now commenced. Initial multivariate analysis is ongoing and will be discussed. Conclusions: To aid in the establishment and application of home-based interventions for cognitive decline and dementia, AAL environments can be used to observe, understand, and eventually model the needs of the older person. This work has created the infrastructure and is now beginning to analyze the data necessary to begin to instantiate this approach.

A Psychovisual Quality Metric in Free-Energy Principle

In this paper, we propose a new psychovisual quality metric of images based on recent developments in brain theory and neuroscience, particularly the free-energy principle. The perception and understanding of an image is modeled as an active inference process, in which the brain tries to explain the scene using an internal generative model. The psychovisual quality is thus closely related to how accurately visual sensory data can be explained by the generative model, and the upper bound of the discrepancy between the image signal and its best internal description is given by the free energy of the cognition process. Therefore, the perceptual quality of an image can be quantified using the free energy. Constructively, we develop a reduced-reference free-energy-based distortion metric (FEDM) and a no-reference free-energy-based quality metric (NFEQM). The FEDM and the NFEQM are nearly invariant to many global systematic deviations in geometry and illumination that hardly affect visual quality, for which existing image quality metrics wrongly predict severe quality degradation. Although with very limited or even without information on the reference image, the FEDM and the NFEQM are highly competitive compared with the full-reference SSIM image quality metric on images in the popular LIVE database. Moreover, FEDM and NFEQM can measure correctly the visual quality of some model-based image processing algorithms, for which the competing metrics often contradict with viewers' opinions.

A Simulator for Generating and Visualizing Sensor Data for Ambient Intelligence Environments

The development of ambient intelligence environments has become a complex task and faces several challenges due to improved sensing possibilities and new fields of application, like detecting activities of daily living or classifying situations. Without having representative test data for experimentation during the development phase, the choice of suitable sensors and classification mechanisms becomes a critical issue. From an economical point of view, hardware and test labs are expensive and require lead time before test data are generated. To compensate the temporal gap, data needs to be simulated until real data are available for testing. In this paper we present an adaptive and extensible simulator which can be used during the design phase to generate data from various sensors within a simulated ambient intelligence environment. Our approach allows generating and visualizing different sensor data as well as simulating faulty or unexpected sensor behaviour.