Journal articles on the topic 'Ecological sensors'
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1
Petrović, Davor, and Željko Barač. "Different Sensor Systems for the Application of Variable Rate Technology in Permanent Crops." Tehnički glasnik 12, no. 3 (September 25, 2018): 188–95. http://dx.doi.org/10.31803/tg-20180213125928.
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The paper presents a review of different sensory systems for trees’ characterization and detection in permanent crops and the detection of plant health status in crop conditions for the purpose of applying the variable application rate. The use of new technologies enables the use of variable inputs in production with the aim of increasing the economic profit and reducing the negative impact on the environment. World trends increasingly emphasize the use of various sensor systems to achieve precision agriculture and apply the following: ultrasonic sensors for the detection of permanent crops; LIDAR (optical) sensors for treetop detection and characterization; infrared sensors with similar characteristics of optical sensors, but with very low cost prices and N - sensors for variable nitric fertilization. The daily development of sensor systems applied in agricultural production improves the performance and quality of the machines they are installed on. With a more intensive use of sensors in agricultural mechanization, their price becomes more acceptable for widespread use by achieving high quality work with respect to the ecological principles of sustainable production.
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Jayant Gupchup, Andreas Terzis, Zhiliang Ma, and Carey Priebe. "Classification-based event detection in ecological monitoring networks." Electronic Journal of Structural Engineering, no. 01 (June 1, 2010): 36–44. http://dx.doi.org/10.56748/ejse.13001.
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Power-budgeting is a fundamental challenge in sensor networks today and the energy requirement of different sensing modalities is unevenly distributed. As a result, it is advisable to activate power-hungry sensors only during informative periods. Using low-power sensors, one can predict these informative periods due to strong correlations exhibited by environmental modalities. In this article, we consider an application of detecting “events” using classification based methods to increase the lifetime of the network. Specifically, we explore the problem of using low-power sensors to predict precipitation, which is one of the primary drivers of ecological activity. Such predictions can allow us to schedule the activation of expensive sensors (such as CO2) when they are most informative. In order to achieve this trade-off between power and collecting informative data, we focus our efforts on predicting/ classifying precipitation based on features extracted from inexpensive ambient temperature and barometric pressure modalities. Experimental results obtained from weather data collected over multiple years demonstrates that we can achieve accuracy towards 80% using these low-cost modalities and simple linear classifiers.
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Meng, Xiaoliang, Feng Wang, Yichun Xie, Guoqiang Song, Shifa Ma, Shiyuan Hu, Junming Bai, and Yiming Yang. "An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web." Sensors 18, no. 11 (October 25, 2018): 3619. http://dx.doi.org/10.3390/s18113619.
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Due to the rapid installation of a massive number of fixed and mobile sensors, monitoring machines are intentionally or unintentionally involved in the production of a large amount of geospatial data. Environmental sensors and related software applications are rapidly altering human lifestyles and even impacting ecological and human health. However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. In this paper, we propose an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the GSW. We design a Human and Ecological health Risks Ontology (HERO) based on a semantic sensor network ontology template. We also illustrate a web-based prototype, the Human and Ecological Health Risk Management System (HaEHMS), which helps health experts and decision makers to estimate human and ecological health risks. We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk.
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Gupta, Anju, and R. K. Bathla. "Energy Efficient Opportunistic Sensing Management in Fog Cloud Environment." International Journal of Computer Science and Mobile Computing 10, no. 10 (October 30, 2021): 20–26. http://dx.doi.org/10.47760/ijcsmc.2021.v10i10.004.
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With so many people now wearing mobile devices with sensors (such as smartphones), utilizing the immense capabilities of these business mobility goods has become a prospective skill to significant behavioural and ecological sensors. A potential challenge for pervasive context assessment is opportunistic sensing, has been effectively used to a wide range of applications. The sensor cloud combines cloud technology with a wireless sensor, resulting in a scalable and cost-effective computing platform for real-time applications. Because the sensor's battery power is limited and the data centre’s servers consume a significant amount of energy to supply storage, a sensor cloud must be energy efficient. This study provides a Fog-based semantic for enabling these kinds of technologies quickly and successfully. The suggested structure is comprised of fundamental algorithms to help set up and coordinate the fog sensing jobs. It creates effective multihop routes for coordinating relevant devices and transporting acquired sensory data to fog sinks. It was claimed that energy-efficient sensor cloud approaches were categorized into different groups and that each technology was examined using numerous characteristics. The outcomes of a series of thorough test simulation in NS3 to define the practicality of the created console, as well as the proportion of each parameter utilized for each technology, are computed.
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Mayton, Brian, Gershon Dublon, Spencer Russell, Evan F. Lynch, Don Derek Haddad, Vasant Ramasubramanian, Clement Duhart, Glorianna Davenport, and Joseph A. Paradiso. "The Networked Sensory Landscape: Capturing and Experiencing Ecological Change Across Scales." Presence: Teleoperators and Virtual Environments 26, no. 2 (May 1, 2017): 182–209. http://dx.doi.org/10.1162/pres_a_00292.
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What role will ubiquitous sensing play in our understanding and experience of ecology in the future? What opportunities are created by weaving a continuously sampling, geographically dense web of sensors into the natural environment, from the ground up? In this article, we explore these questions holistically, and present our work on an environmental sensor network designed to support a diverse array of applications, interpretations, and artistic expressions, from primary ecological research to musical composition. Over the past four years, we have been incorporating our ubiquitous sensing framework into the design and implementation of a large-scale wetland restoration, creating a broad canvas for creative exploration at the landscape scale. The projects we present here span the development and wide deployment of custom sensor node hardware, novel web services for providing real-time sensor data to end user applications, public-facing user interfaces for open-ended exploration of the data, as well as more radical UI modalities, through unmanned aerial vehicles, virtual and augmented reality, and wearable devices for sensory augmentation. From this work, we distill the Networked Sensory Landscape, a vision for the intersection of ubiquitous computing and environmental restoration. Sensor network technologies and novel approaches to interaction promise to reshape presence, opening up sensorial connections to ecological processes across spatial and temporal scales.
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Buters, Todd M., Philip W. Bateman, Todd Robinson, David Belton, Kingsley W. Dixon, and Adam T. Cross. "Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration." Remote Sensing 11, no. 10 (May 17, 2019): 1180. http://dx.doi.org/10.3390/rs11101180.
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The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future.
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Boublil, Brittney L., Clarice Anna Diebold, and Cynthia F. Moss. "Mechanosensory Hairs and Hair-like Structures in the Animal Kingdom: Specializations and Shared Functions Serve to Inspire Technology Applications." Sensors 21, no. 19 (September 24, 2021): 6375. http://dx.doi.org/10.3390/s21196375.
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Biological mechanosensation has been a source of inspiration for advancements in artificial sensory systems. Animals rely on sensory feedback to guide and adapt their behaviors and are equipped with a wide variety of sensors that carry stimulus information from the environment. Hair and hair-like sensors have evolved to support survival behaviors in different ecological niches. Here, we review the diversity of biological hair and hair-like sensors across the animal kingdom and their roles in behaviors, such as locomotion, exploration, navigation, and feeding, which point to shared functional properties of hair and hair-like structures among invertebrates and vertebrates. By reviewing research on the role of biological hair and hair-like sensors in diverse species, we aim to highlight biological sensors that could inspire the engineering community and contribute to the advancement of mechanosensing in artificial systems, such as robotics.
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Sun, Ling, and Ze Sheng Zhu. "Optimal Ecological Restoration of Degraded Wetland Ecosystem by Using Satellite Sensors." Applied Mechanics and Materials 511-512 (February 2014): 138–41. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.138.
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Ecological restoration of degraded wetland ecosystem can be achieved by planning experts according to ground investigation, but with low efficiencies. We report that satellite sensors, maximizing ecosystem service values and minimizing ecological restoration cost improve ecological restoration efficiency. In particular, multi-objective linear programming (MOLIP), an optimal programming, improves ecological restoration efficiency by more than 250% in the return on investment of ecological restoration, using TM satellite as area sensors. MOLIP also enables efficient introduction of ecological restoration management without introduction of planning experts and ground investigation.
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Guandong, Gao, Jia Yuchen, and Xiao Ke. "An IOT-based Multi-sensor Ecological Shared Farmland Management System." International Journal of Online Engineering (iJOE) 14, no. 03 (March 30, 2018): 81. http://dx.doi.org/10.3991/ijoe.v14i03.8199.
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With the wide adoption of a shared economy, the term “share” has become closely linked with people’s daily life. To improve the economic output per unit area of farmland and allow urban residents to experience the fun of cultivating, an IOT-based multi-sensor ecological shared farmland management system is presented and designed in this paper. And an agricultural economy model that remote planting and gains sharing was also presented. This system consists of multiple sensors for data collection, automatically controlled equipment, a transport module (with a ZigBee module and a WiFi module), an app-based smart monitoring and control module and a WeChat public platform. Various information, including air temperature and humidity, soil humidity, illuminance, CO2 concentration, pH, real-time video and images, is collected for users to ensure an optimal crop growing environment via remote control and by sharing agricultural product information in harvesting seasons. Functions such as picking, leasing and transferring are implemented via the WeChat public platform. Not only is detailed information on vegetables growing in rented farmland available remotely via RFID electronic tags and multiple sensors but the real-time growing state of the planted crops is also available via camera. Control of functions such as irrigating, fertilizing and shading is also supported. The test shows that in this system, the data collected via multiple sensors are accurate; real-time information is transmitted and managed smoothly.
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Myland, Paul, Sebastian Babilon, and Tran Quoc Khanh. "Tackling Heterogeneous Color Registration: Binning Color Sensors." Sensors 21, no. 9 (April 22, 2021): 2950. http://dx.doi.org/10.3390/s21092950.
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Intelligent systems for interior lighting strive to balance economical, ecological, and health-related needs. For this purpose, they rely on sensors to assess and respond to the current room conditions. With an augmented demand for more dedicated control, the number of sensors used in parallel increases considerably. In this context, the present work focuses on optical sensors with three spectral channels used to capture color-related information of the illumination conditions such as their chromaticities and correlated color temperatures. One major drawback of these devices, in particular with regard to intelligent lighting control, is that even same-type color sensors show production related differences in their color registration. Standard methods for color correction are either impractical for large-scale production or they result in large colorimetric errors. Therefore, this article shows the feasibility of a novel sensor binning approach using the sensor responses to a single white light source for cluster assignment. A cluster specific color correction is shown to significantly reduce the registered color differences for a selection of test stimuli to values in the range of 0.003–0.008 Δu′v′, which enables the wide use of such sensors in practice and, at the same time, requires minimal additional effort in sensor commissioning.
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Ivanov, V. P., and V. L. Filippov. "A set of sensors for solving ecological monitoring problems." Journal of Optical Technology 69, no. 4 (April 1, 2002): 223. http://dx.doi.org/10.1364/jot.69.000223.
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Birge, Wesley J., David J. Price, Joseph R. Shaw, Julann A. Spromberg, Andrew J. Wigginton, and Christer Hogstrand. "Metal body burden and biological sensors as ecological indicators." Environmental Toxicology and Chemistry 19, no. 4 (April 2000): 1199–212. http://dx.doi.org/10.1002/etc.5620190454.
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Li, Linsen. "Node Location Method of Ecological Environment Monitoring Network Based on Zigbee." Discrete Dynamics in Nature and Society 2022 (October 10, 2022): 1–13. http://dx.doi.org/10.1155/2022/5854569.
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In view of the poor initialization performance of ecological environment monitoring network node location, a method of ecological environment monitoring network node location based on Zigbee is proposed. The node data collection model of ecological environment monitoring network is built based on Zigbee, and the performance is stable, which is more suitable for the node location of the ecological environment monitoring network; it is hoped that this study can provide reliable value reference and help for the future ecological studies. Through the installation of different types of sensors, the data of the ecological environment monitoring network nodes are automatically collected and sent to the server. The static weight coefficient of the collected data of the ecological environment monitoring network nodes is modified. According to the modified results, the ecological environment monitoring network is modified by DV-HOP positioning algorithm. The nodes of the ecological environment monitoring network are located by the three-way positioning method. The experimental results show that the initialization performance of this method is better, the accuracy is about 98%, and it is stable. It is more suitable for the node location of ecological environment monitoring network, which mainly includes ZigBee wireless sensor network module, embedded ARM, and Linux.
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Rossi, Mattia, Georg Niedrist, Sarah Asam, Giustino Tonon, Enrico Tomelleri, and Marc Zebisch. "A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics." Remote Sensing 11, no. 3 (February 1, 2019): 296. http://dx.doi.org/10.3390/rs11030296.
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Grasslands cover up to 40% of the mountain areas globally and 23% of the European Alps and affect numerous key ecological processes. An increasing number of optical sensors offer a great opportunity to monitor and address dynamic changes in the growth and status of grassland vegetation due to climatic and anthropogenic influences. Vegetation indices (VI) calculated from optical sensor data are a powerful tool in analyzing vegetation dynamics. However, different sensors have their own characteristics, advantages, and challenges in monitoring vegetation over space and time that require special attention when compared to or combined with each other. We used the Normalized Difference Vegetation Index (NDVI) derived from handheld spectrometers, station-based Spectral Reflectance Sensors (SRS), and Phenocams as well as the spaceborne Sentinel-2 Multispectral Instrument (MSI) for assessing growth and dynamic changes in four alpine meadows. We analyzed the similarity of the NDVI on diverse spatial scales and to what extent grassland dynamics of alpine meadows can be detected. We found that NDVI across all sensors traces the growing phases of the vegetation although we experienced a notable variability in NDVI signals among sensors and differences among the sites and plots. We noticed differences in signal saturation, sensor specific offsets, and in the detectability of short-term events. These NDVI inconsistencies depended on sensor-specific spatial and spectral resolutions and acquisition geometries, as well as on grassland management activities and vegetation growth during the year. We demonstrated that the combination of multiple-sensors enhanced the possibility for detecting short-term dynamic changes throughout the year for each of the stations. The presented findings are relevant for building and evaluating a combined sensor approach for consistent vegetation monitoring.
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Pavlov, S. V., Waldemar Wójcik, R. L. Holyaka, O. D. Azarov, S. V. Bohomolov, and Yang Longyin. "Analysis of development state of the thermal flow sensors of general, biomedical and ecological designation." Optoelectronic Information-Power Technologies 43, no. 1 (December 28, 2022): 82–93. http://dx.doi.org/10.31649/1681-7893-2022-43-1-82-93.
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The paper analyzed the characteristics of microelectronic flow sensors, which made it possible to draw a number of important conclusions, namely: modern microelectronic thermal flow sensors, and in particular biomedical sensors, are characterized by a significant variety of principles of signal formation - from elementary linear converters based on one sensitive element to non-linear ones ( generation, time-dependent) converters based on matrices of functionally integrated elements. The problem of energy consumption of thermal flow sensors remains relevant. This is especially characteristic when powering destination sensors from autonomous, i.e., small-sized, low-power, low-voltage electrochemical cells. A decrease in energy consumption (power and heating temperature) leads to the parasitic effect of signal line resistances and, as a result, to the deterioration of functional characteristics, in particular, to a decrease in the accuracy of flow rate measurement.
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Xiang, Hongyong, Qinghua Cai, Yuan Li, Zhenxing Zhang, Lina Cao, Kun Li, and Haijun Yang. "Sensors Applied for the Detection of Pesticides and Heavy Metals in Freshwaters." Journal of Sensors 2020 (February 11, 2020): 1–22. http://dx.doi.org/10.1155/2020/8503491.
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Water is essential for every life living on the planet. However, we are facing a more serious situation such as water pollution since the industrial revolution. Fortunately, many efforts have been done to alleviate/restore water quality in freshwaters. Numerous sensors have been developed to monitor the dynamic change of water quality for ecological, early warning, and protection reasons. In the present review, we briefly introduced the pollution status of two major pollutants, i.e., pesticides and heavy metals, in freshwaters worldwide. Then, we collected data on the sensors applied to detect the two categories of pollutants in freshwaters. Special focuses were given on the sensitivity of sensors indicated by the limit of detection (LOD), sensor types, and applied waterbodies. Our results showed that most of the sensors can be applied for stream and river water. The average LOD was 72.53±12.69 ng/ml (n=180) for all pesticides, which is significantly higher than that for heavy metals (65.36±47.51 ng/ml, n=117). However, the LODs of a considerable part of pesticides and heavy metal sensors were higher than the criterion maximum concentration for aquatic life or the maximum contaminant limit concentration for drinking water. For pesticide sensors, the average LODs did not differ among insecticides (63.83±17.42 ng/ml, n=87), herbicides (98.06±23.39 ng/ml, n=71), and fungicides (24.60±14.41 ng/ml, n=22). The LODs that differed among sensor types with biosensors had the highest sensitivity, while electrochemical optical and biooptical sensors showed the lowest sensitivity. The sensitivity of heavy metal sensors varied among heavy metals and sensor types. Most of the sensors were targeted on lead, cadmium, mercury, and copper using electrochemical methods. These results imply that future development of pesticides and heavy metal sensors should (1) enhance the sensitivity to meet the requirements for the protection of aquatic ecosystems and human health and (2) cover more diverse pesticides and heavy metals especially those toxic pollutants that are widely used and frequently been detected in freshwaters (e.g., glyphosate, fungicides, zinc, chromium, and arsenic).
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Hall, Emma C., and Mark J. Lara. "Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands." Remote Sensing 14, no. 14 (July 18, 2022): 3453. http://dx.doi.org/10.3390/rs14143453.
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Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers.
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Branda, Eric, and Tobias Wurzbacher. "Motion Sensors in Automatic Steering of Hearing Aids." Seminars in Hearing 42, no. 03 (August 2021): 237–47. http://dx.doi.org/10.1055/s-0041-1735132.
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AbstractA requirement for modern hearing aids is to evaluate a listening environment for the user and automatically apply appropriate gain and feature settings for optimal hearing in that listening environment. This has been predominantly achieved by the hearing aids' acoustic sensors, which measure acoustic characteristics such as the amplitude and modulation of the incoming sound sources. However, acoustic information alone is not always sufficient for providing a clear indication of the soundscape and user's listening needs. User activity such as being stationary or being in motion can drastically change these listening needs. Recently, hearing aids have begun utilizing integrated motion sensors to provide further information to the hearing aid's decision-making process when determining the listening environment. Specifically, accelerometer technology has proven to be an appropriate solution for motion sensor integration in hearing aids. Recent investigations have shown benefits with integrated motion sensors for both laboratory and real-world ecological momentary assessment measurements. The combination of acoustic and motion sensors provides the hearing aids with data to better optimize the hearing aid features in anticipation of the hearing aid user's listening needs.
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White, Laura A., and Rebecca K. Gibson. "Comparing Fire Extent and Severity Mapping between Sentinel 2 and Landsat 8 Satellite Sensors." Remote Sensing 14, no. 7 (March 30, 2022): 1661. http://dx.doi.org/10.3390/rs14071661.
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Mapping of fire extent and severity across broad landscapes and timeframes using remote sensing approaches is valuable to inform ecological research, biodiversity conservation and fire management. Compiling imagery from various satellite sensors can assist in long-term fire history mapping; however, inherent sensor differences need to be considered. The New South Wales Fire Extent and Severity Mapping (FESM) program uses imagery from Sentinel and Landsat satellites, along with supervised classification algorithms, to produce state-wide fire maps over recent decades. In this study, we compared FESM outputs from Sentinel 2 and Landsat 8 sensors, which have different spatial and spectral resolutions. We undertook independent accuracy assessments of both Sentinel 2 and Landsat 8 sensor algorithms using high-resolution aerial imagery from eight training fires. We also compared the FESM outputs from both sensors across 27 case study fires. We compared the mapped areas of fire severity classes between outputs and assessed the classification agreement at random sampling points. Our independent accuracy assessment demonstrated very similar levels of accuracy for both sensor algorithms. We also found that there was substantial agreement between the outputs from the two sensors. Agreement on the extent of burnt versus unburnt areas was very high, and the severity classification of burnt areas was typically either in agreement between the sensors or in disagreement by only one severity class (e.g., low and moderate severity or high and extreme severity). Differences between outputs are likely partly due to differences in sensor resolution (10 m and 30 m pixel sizes for Sentinel 2 and Landsat 8, respectively) and may be influenced by landscape complexity, such as terrain roughness and foliage cover. Overall, this study supports the combined use of both sensors in remote sensing applications for fire extent and severity mapping.
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Oguchi, Kimio, and Ryoya Ozawa. "Human Presence Recognition in a Closed Space by using Cost-effective CO2 Sensor and the Information Gain Processing Method." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (March 1, 2017): 549. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp549-555.
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<p>The recent rapid progress in ICT technologies such as smart/intelligent sensor devices, broadband/ubiquitous networks, and Internet of everything (IoT) has advanced the penetration of sensor networks and their applications. The requirements of human daily life, security, energy efficiency, safety, comfort, and ecological, can be achieved with the help of these networks and applications. Traditionally, if we want some information on, for example, environment status, a variety of dedicated sensors is needed. This will increase the number of sensors installed and thus system cost, sensor data traffic loads, and installation difficulty. Therefore, we need to find redundancies in the captured information or interpret the semantics captured by non-dedicated sensors to reduce sensor network overheads. This paper clarifies the feasibility of recognizing human presence in a space by processing information captured by other than dedicated sensors. It proposes a method and implements it as a cost-effective prototype sensor network for a university library. This method processes CO2 concentration, originally designed to check environment status. In the experiment, training data is captured with none, one, or two subjects. The information gain (IG) method is applied to the resulting data, to set thresholds and thus judge the number of people. Human presence (none, one or two people) is accurately recognized from the CO2 concentration data. The experiments clarify that a CO2 sensor in set in a small room to check environment status can recognize the number of humans in the room with more than 70 % accuracy. This eliminates the need for an extra sensor, which reduces sensor network cost.</p>
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Giuliani, Cristina, Claudio Franceschi, Donata Luiselli, Paolo Garagnani, and Stanley Ulijaszek. "Ecological Sensing Through Taste and Chemosensation Mediates Inflammation: A Biological Anthropological Approach." Advances in Nutrition 11, no. 6 (July 10, 2020): 1671–85. http://dx.doi.org/10.1093/advances/nmaa078.
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ABSTRACT Ecological sensing and inflammation have evolved to ensure optima between organism survival and reproductive success in different and changing environments. At the molecular level, ecological sensing consists of many types of receptors located in different tissues that orchestrate integrated responses (immune, neuroendocrine systems) to external and internal stimuli. This review describes emerging data on taste and chemosensory receptors, proposing them as broad ecological sensors and providing evidence that taste perception is shaped not only according to sense epitopes from nutrients but also in response to highly diverse external and internal stimuli. We apply a biological anthropological approach to examine how ecological sensing has been shaped by these stimuli through human evolution for complex interkingdom communication between a host and pathological and symbiotic bacteria, focusing on population-specific genetic diversity. We then focus on how these sensory receptors play a major role in inflammatory processes that form the basis of many modern common metabolic diseases such as obesity, type 2 diabetes, and aging. The impacts of human niche construction and cultural evolution in shaping environments are described with emphasis on consequent biological responsiveness.
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Garcia-Moreno, Francisco M., Maria Bermudez-Edo, José Luis Garrido, Estefanía Rodríguez-García, José Manuel Pérez-Mármol, and María José Rodríguez-Fórtiz. "A Microservices e-Health System for Ecological Frailty Assessment Using Wearables." Sensors 20, no. 12 (June 17, 2020): 3427. http://dx.doi.org/10.3390/s20123427.
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The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.
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Li, Cunli, Cuiling Jiang, Guangwei Zhu, Wei Zou, Mengyuan Zhu, Hai Xu, Pengcheng Shi, and Wenyi Da. "Estimation of Water Quality Parameters with High-Frequency Sensors Data in a Large and Deep Reservoir." Water 12, no. 9 (September 21, 2020): 2632. http://dx.doi.org/10.3390/w12092632.
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High-frequency sensors can monitor water quality with high temporal resolution and without environmental influence. However, sensors for detecting key water quality parameters, such as total nitrogen(TN), total phosphorus(TP), and other water environmental parameters, are either not yet available or have attracted limited usage. By using a large number of high-frequency sensor and manual monitoring data, this study establishes regression equations that measure high-frequency sensor and key water quality parameters through multiple regression analysis. Results show that a high-frequency sensor can quickly and accurately estimate dynamic key water quality parameters by evaluating seven water quality parameters. An evaluation of the flux of four chemical parameters further proves that the multi-parameter sensor can efficiently estimate the key water quality parameters. However, due to the different optical properties and ecological bases of these parameters, the high-frequency sensor shows a better prediction performance for chemical parameters than for physical and biological parameters. Nevertheless, these results indicate that combining high-frequency sensor monitoring with regression equations can provide real-time and accurate water quality information that can meet the needs in water environment management and realize early warning functions.
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Eingrüber, Nils, Wolfgang Korres, and Karl Schneider. "Microclimatic field measurements to support microclimatological modelling with ENVI-met for an urban study area in Cologne." Advances in Science and Research 19 (August 18, 2022): 81–90. http://dx.doi.org/10.5194/asr-19-81-2022.
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Abstract. Cities are particularly affected by climate change impacts. Due to global warming, the frequency and intensity of summer heat events increases for many cities around the globe. Urban climatological studies have shown significant positive trends in the number of hot days and tropical nights. Heat stress is an important health as well as economic risk. Thus, urban planning needs to adapt to climate change. This requires a sound scientific analysis of different adaptation measures and management options, which must be based on appropriate data, models and scenarios to assess their suitability and efficacy. The goal of this paper is to assess the suitability of a low cost weather station network consisting of 33 NETATMO weather sensors and ultrasonic anemometers to measure air temperature, relative air humidity, wind speed and wind direction of a 16 ha study area in Cologne with high temporal and spatial resolution to support microclimatological modelling. The temperature and humidity sensors were calibrated against a research grade reference sensor under laboratory conditions. In addition, a research grade meteorological station (Campbell Sci.) was set up in the study area as a reference. The NETATMO sensors were placed to identify local microclimatic effects due to different surface types, vegetation and building structures. Using descriptive statistical analyses and pairwise comparisons, significant differences in the microclimatic conditions of the various sites were found, which can clearly be attributed to specific small scale microclimate factors. Significant differences were particularly identified comparing an avenue and a narrow street as well as a backyard and an urban park area. The sensors proved to provide data reliably and with suitable quality to measure microclimatic effects. The choice of sensors lends itself well for citizen participation, needed to facilitate climate change adaptation. In our further research, the data will be used as reference data for microclimatological modelling with ENVI-met investigating particularly options of mitigating climate change effects.
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Yi, Shuhua, Go Iwahana, Yu Qin, and Yi Sun. "Applications of UAVs in Cold Region Ecological and Environmental Studies." Remote Sensing 13, no. 13 (June 24, 2021): 2472. http://dx.doi.org/10.3390/rs13132472.
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Lightweight unmanned aerial vehicles (UAVs) have been developed rapidly due to the miniaturization of aircraft components and the low cost of various sensors, as well as embedded computers, and have become a commonly used platform for ecological and environmental studies [...]
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McKenna, Phillip B., Alex M. Lechner, Stuart Phinn, and Peter D. Erskine. "Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review." Remote Sensing 12, no. 21 (October 28, 2020): 3535. http://dx.doi.org/10.3390/rs12213535.
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The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010–2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone-based sensors across a diverse range of study locations contributed to our increased understanding of vegetation development post-mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers; while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object-Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic); while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field-based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological monitoring tool recommended by SER, to ensure that future remote sensing approaches are conducted with a greater focus on ecological perspectives, i.e., in terms of final targets and end land-use goals. In particular, given the key rehabilitation requirement of self-sustainability, the demonstration of ecosystem resilience to disturbance and climate change should be a key area for future research.
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Xiao, Shiyu, Mingliang Zhang, Changhua Liu, Chongwen Jiang, Xiaodong Wang, and Fuhua Yang. "CTD Sensors for Ocean Investigation Including State of Art and Commercially Available." Sensors 23, no. 2 (January 4, 2023): 586. http://dx.doi.org/10.3390/s23020586.
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Over 70% of the earth’s surface is covered by oceans; globally, oceans provides a huge source of wealth to humans. In the literature, several sensors have been developed to investigate oceans. Electrical conductivity temperature depth (CTD) sensors were used frequently and extensively. Long-term accurate CTD data is important for the study and utilization of oceans, e.g., for weather forecasting, ecological evolution, fishery, and shipping. Several kinds of CTD sensors based on electrics, optical, acoustic wave and radio waves have been developed. CTD sensors are often utilized by measuring electrical signals. The latest progress of CTD sensors will be presented in order of performance. The principles, structure, materials and properties of many CTD sensors were discussed in detail. The commercially available CTD sensors were involved and their respective performances were compared. Some possible development directions of CTD sensors for ocean investigation are proposed.
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Shakhnovich, O. A. "Ecological sensors and systems. instruments for effective air and gas monitoring." Analytics, no. 5 (2017): 61–63. http://dx.doi.org/10.22184/2227-572x.2017.36.5.61.63.
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Lesser, Dan, Itzhak Katra, Michael Dorman, Homero Harari, and Itai Kloog. "Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment." Sensors 21, no. 1 (December 24, 2020): 63. http://dx.doi.org/10.3390/s21010063.
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Particulate matter is a common health hazard, and under certain conditions, an ecological threat. While many studies were conducted in regard to air pollution and potential effects, this paper serves as a pilot scale investigation into the spatial and temporal variability of particulate matter (PM) pollution in arid urban environments in general, and Beer-Sheva, Israel as a case study. We explore the use of commercially off the shelf (COTS) sensors, which provide an economical solution for spatio-temporal measurements. We started with a comparison process against an A-grade meteorological station, where it was shown that under specific climatic conditions, a number of COTS sensors were able to produce robust agreement (mean R2=0.93, average SD=17.5). The second stage examined the COTS sensors that were proven accurate in a mobile measurement campaign. Finally, data collected was compared to a validated satellite prediction model. We present how these tests and COTS sensor-kits could then be used to further explain the continuity and dispersion of particulate matter in similar areas.
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Germanotta, Marco, Ilaria Mileti, Ilaria Conforti, Zaccaria Del Prete, Irene Aprile, and Eduardo Palermo. "Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation." Sensors 21, no. 2 (January 16, 2021): 601. http://dx.doi.org/10.3390/s21020601.
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The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.
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Čimo, Ján, and Beáta Novotná. "Global Solar Radiation Analysis of the Modified Savin-Angström Method by using Pyranometers CMP 6 and CMP 11." Acta Horticulturae et Regiotecturae 21, no. 1 (May 1, 2018): 20–24. http://dx.doi.org/10.2478/ahr-2018-0006.
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Abstract Solar energy is one of the most available energy sources and the most ecological one. Currently, the firm Kipp & Zonen belongs to prominent producer of sensors for measuring global radiation. These sensors are the most used ones in our country and also in network of meteorological measurements of WMO. Therefore, the two types of measuring sensors for global radiation (pyranometer PMP 11, CMP 6) in comparison with the calculation method Savin-Angstrom are analysed. By processing the experimental measurements of global radiation in locality Nitra, there can be observed differences between standard CMP 11 and CMP 6. The measured values by CMP 6 pyranometer in comparison with secondary standard CMP 11 are lower by about 21% to what corresponds to the accuracy level (First Class) of sensor CMP 6. Differences may have been caused by higher aberrance of non-linearity, aberrance at sunrise and sunset i.e. directional errors and also by the fact that sensor CMP 6 is not equipped with integrated temperature compensation. The similar situation was also in the comparison of global radiation, which was calculated according to the modified Savin-Angstrom method and CMP 11. Notional differences were 7% compared with the measured values of CMP 11 standard.
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Tomkiewicz, Stanley M., Mark R. Fuller, John G. Kie, and Kirk K. Bates. "Global positioning system and associated technologies in animal behaviour and ecological research." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1550 (July 27, 2010): 2163–76. http://dx.doi.org/10.1098/rstb.2010.0090.
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Biologists can equip animals with global positioning system (GPS) technology to obtain accurate (less than or equal to 30 m) locations that can be combined with sensor data to study animal behaviour and ecology. We provide the background of GPS techniques that have been used to gather data for wildlife studies. We review how GPS has been integrated into functional systems with data storage, data transfer, power supplies, packaging and sensor technologies to collect temperature, activity, proximity and mortality data from terrestrial species and birds. GPS ‘rapid fixing’ technologies combined with sensors provide location, dive frequency and duration profiles, and underwater acoustic information for the study of marine species. We examine how these rapid fixing technologies may be applied to terrestrial and avian applications. We discuss positional data quality and the capability for high-frequency sampling associated with GPS locations. We present alternatives for storing and retrieving data by using dataloggers (biologging), radio-frequency download systems (e.g. very high frequency, spread spectrum), integration of GPS with other satellite systems (e.g. Argos, Globalstar) and potential new data recovery technologies (e.g. network nodes). GPS is one component among many rapidly evolving technologies. Therefore, we recommend that users and suppliers interact to ensure the availability of appropriate equipment to meet animal research objectives.
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Röcke, Christina, Minxia Luo, and Hans-Werner Wahl. "UNDERSTANDING MOBILITY, HEALTH, AND WELL-BEING OF OLDER ADULTS USING SENSING TECHNOLOGIES." Innovation in Aging 6, Supplement_1 (November 1, 2022): 122. http://dx.doi.org/10.1093/geroni/igac059.488.
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Abstract Mobility has been identified as one important ingredient to older adults’ health and well-being and is considered a high priority in the global agenda of healthy and active aging. However, mobility is still a relatively understudied concept in aging research. This symposium, including three empirical studies and one concept paper, presents how different sensing technologies can be utilized to examine mobility, health and well-being in older adults. Using infrared motion sensors and contact sensors, Wu and colleagues examine indoor mobility and show its associations with physical, cognitive, and mental health in community-dwelling older adults living alone. Luo and colleagues use a custom-built mobile GPS sensor and a smartphone-based ambulatory assessment to examine daily mobility and well-being in community-dwelling older adults. They find that a day with larger life space area, more time spent in passive transport modes, and higher number of different locations is associated with higher daily life satisfaction. Similarly, using a GPS sensor combined with a smartphone-based ecological momentary assessment, Kamalyan and colleagues examine life-space mobility, social interactions, and well-being in older adults with and without HIV. They show that prior day’s at-home time is negatively associated with current day’s happiness and that prior day’s social interactions diminishes this association. Jansen presents a project combining sensor-based movement data, GPS-based geolocation data, and experience sampling to investigate relations between life-space mobility and social participation and the role of cultural and climatic differences across several European countries. Hans-Werner Wahl will discuss all papers from an ecological and contextualized aging perspective.
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Ventura, Bartolomeo, Andrea Vianello, Daniel Frisinghelli, Mattia Rossi, Roberto Monsorno, and Armin Costa. "A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards." ISPRS International Journal of Geo-Information 8, no. 4 (April 2, 2019): 167. http://dx.doi.org/10.3390/ijgi8040167.
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Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.
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Veloz Martínez, Irvin, Jackeline Iturbe Ek, Ethan C. Ahn, and Alan O. Sustaita. "Molecularly imprinted polymers via reversible addition–fragmentation chain-transfer synthesis in sensing and environmental applications." RSC Advances 12, no. 15 (2022): 9186–201. http://dx.doi.org/10.1039/d2ra00232a.
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Environmental contaminants constitute an ecological and health hazard, which requires green sensing. The RAFT-MIP approach for tailor-made selective receptors enhances them via binding affinities for use in environmental contaminant sensors.
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Chen, Limin, and Lei Zhang. "Design of Ecological Environment Monitoring System Based on Internet of Things Technology." Wireless Communications and Mobile Computing 2022 (July 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/2531425.
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Because of its high availability, Internet of Things technology is widely used in multipoint ecological environment monitoring and other related fields, which greatly improves the scientificity and accuracy of ecological environment monitoring results. The Internet of Things technology can realize multilevel and omnidirectional environmental monitoring functions by combining GPS systems and GIS systems and, at the same time, can significantly increase the monitoring range of environmental monitoring. On the basis of the Internet of Things monitoring technology, the use of electronic sensors can track and monitor the content and composition of various elements in the environment in real time, thereby reducing the work intensity of environmental monitoring personnel and effectively improving the environment reliability of monitoring results. In this study, we introduce the actual application and direction of the Internet of Things technology in multisite environmental monitoring systems. A water quality pH and temperature sensor monitoring system based on wireless data transmission is designed. Experimental analysis shows that the communication stability of the designed sensor system is relatively good. The average packet loss rate of the network is only 0.87%. The value measured by the designed sensor is very close to the reference value measured by the standard instrument. The average relative error of pH and temperature is 1.56% and 0.77%, respectively, which meets the water quality environmental monitoring standards. It is believed that the use of Internet of Things technology in ecological environment monitoring can effectively solve the problems of low efficiency and low rationality in traditional environmental monitoring methods, thereby fully improving the quality and level of monitoring in ecological environment monitoring.
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Napolitano, Ilda. "A method of using Living Cells as Signal Transducers in Environmental Engineering." Pollution Engineering 52, no. 2 (October 26, 2020): 01–02. http://dx.doi.org/10.17762/pe.v52i2.41.
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Use of the shrewd portable lab as a phone based measure for use in ecological checking is appeared. The secluded equipment engineering, the product structure of the framework and the sign preparing from ecological boundaries by means of cell digestion, EC-Sensors and hardware to a worker submission are portrayed. An analysis are expending glucose is appeared. The outcomes are summed up and the further advancements are introduced.
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Fanelli, Emanuela, Jacopo Aguzzi, Simone Marini, Joaquin del Rio, Marc Nogueras, Simonepietro Canese, Sergio Stefanni, Roberto Danovaro, and Fabio Conversano. "Towards Naples Ecological REsearch for Augmented Observatories (NEREA): The NEREA-Fix Module, a Stand-Alone Platform for Long-Term Deep-Sea Ecosystem Monitoring." Sensors 20, no. 10 (May 21, 2020): 2911. http://dx.doi.org/10.3390/s20102911.
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Deep-sea ecological monitoring is increasingly recognized as indispensable for the comprehension of the largest biome on Earth, but at the same time it is subjected to growing human impacts for the exploitation of biotic and abiotic resources. Here, we present the Naples Ecological REsearch (NEREA) stand-alone observatory concept (NEREA-fix), an integrated observatory with a modular, adaptive structure, characterized by a multiparametric video-platform to be deployed in the Dohrn canyon (Gulf of Naples, Tyrrhenian Sea) at ca. 650 m depth. The observatory integrates a seabed platform with optoacoustic and oceanographic/geochemical sensors connected to a surface transmission buoy, plus a mooring line (also equipped with depth-staged environmental sensors). This reinforced high-frequency and long-lasting ecological monitoring will integrate the historical data conducted over 40 years for the Long-Term Ecological Research (LTER) at the station “Mare Chiara”, and ongoing vessel-assisted plankton (and future environmental DNA-eDNA) sampling. NEREA aims at expanding the observational capacity in a key area of the Mediterranean Sea, representing a first step towards the establishment of a bentho-pelagic network to enforce an end-to-end transdisciplinary approach for the monitoring of marine ecosystems across a wide range of animal sizes (from bacteria to megafauna).
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Perrotin, Noé, Nicolas Gardan, Arnaud Lesprillier, Clément Le Goff, Jean-Marc Seigneur, Ellie Abdi, Borja Sanudo, and Redha Taiar. "Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions." Applied Sciences 11, no. 5 (February 26, 2021): 2093. http://dx.doi.org/10.3390/app11052093.
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The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10 km. The three sensors used were (i) a Stryd sensor (Stryd Inc., Boulder, CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.
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García-Moreno, Francisco M., Estefanía Rodríguez-García, María José Rodríguez-Fórtiz, José Luis Garrido, María Bermúdez-Edo, Carmen Villaverde-Gutiérrez, and José Manuel Pérez-Mármol. "Designing a Smart Mobile Health System for Ecological Frailty Assessment in Elderly." Proceedings 31, no. 1 (November 20, 2019): 41. http://dx.doi.org/10.3390/proceedings2019031041.
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The increasing adoption of mobile computing technology in the health and social domains offers new possibilities, for instance, promoting active aging. Health deterioration in elderly people could be successfully assessed by monitoring activities of daily living (ADLs) through mobile technology. In particular, frailty affects several dimensions (physical, psychological, and social) of human functioning, which are required to perform instrumental ADLs (IADLs). Starting from the definition of a model, this paper proposes the design of an intelligent mobile health system to assess frailty in an ecological way: to automatize the frailty assessment through wearable sensors, unobtrusively in free-living environments, and using machine learning in order to reduce the traditional efforts of clinicians assessing frailty. It supports automatic data collection from sensors and artificial intelligence analysis during the performance of real IADLs by elderly. The proposed system uses mobile/wearable devices, follows a microservices software architecture, and implements machine learning algorithms. A technical validation of the proposal is shown.
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Hu, Jiameng, Baoying Ye, Zhongke Bai, and Jiawei Hui. "Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data." Land 11, no. 3 (February 23, 2022): 325. http://dx.doi.org/10.3390/land11030325.
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Following vegetation reclamation in mining areas, secondary damage may occur at any time, especially in locations that have been mined for decades or even hundreds of years. Effective monitoring strategies are required to accurately assess plant growth and to detect the ecological effects of reclamation. Single satellite monitoring is often difficult to ensure vegetation monitoring needs, therefore multi-source remote sensing is preferred. Different sensor parameters and variation in spectral bands can lead to differences in the type of data obtained, and subsequently, methods for evaluating these differences are required for simultaneous sensor/band use. In this study, NDVI was selected to characterize the vegetation growth of the Antaibao Open-pit Coal Mine Dump by analyzing the correlation between different types of sensors (Landsat 8, HJ, Sentinel-2) and vegetation greenness in order to facilitate satellites’ replacement and supplement. Results show that: (1) Landsat 8 and Sentinel-2 satellite have a high relevance for monitoring the vegetation, but the correlation between these two sensors and HJ is relatively low, (2) the correlation between NDVI values varied by vegetation type, tree (R = 0.8698) > combined grass, shrub and tree (R = 0.7788) > grass (R = 0.7619) > shrub (R = 0.7282), and (3) the phenomenon of “Low value is high, high value is low” in the NDVI value with HJ satellite monitoring may have been caused by a weak signal strength and low sensitivity of the HJ sensor. Comparing the correlation of multi-source sensors to monitor the vegetation in the mining areas can be helpful to determine the alternative supplement of sensors through conversion formulas, which are helpful in realizing the long-term monitoring of dumps and detecting reclamation response in mining areas.
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Zheng, Yili, Yandong Zhao, Weiping Liu, Shengbo Liu, and Ruting Yao. "An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning." Sensors 18, no. 12 (December 16, 2018): 4457. http://dx.doi.org/10.3390/s18124457.
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Based on Internet-of-Things and multi-sensor technology, an intelligent wireless monitoring system was developed to obtain field ecological parameters and provide forest fire warning in real-time. The GPRS and China’s Beidou satellite communication were selectively used for date transmission in the field with weak cell phone signals. This monitoring system is mainly composed of several field ecological monitoring stations, a cloud server, and online system software. Atmosphere, soil, sunlight and plant parameters of different regions are obtained real-time by sensors stably and reliably. This system has functions such as field ecological data storage, dynamic query, report generation, and data analysis. As an example of typical application, the forest fire weather grade, which was supplemented with the litter layer soil humidity, was calculated to realize the early warning of the local forest fire in this system through continuous experiments at Beijing Jiufeng National Forest Park from March to May 2017 and Inner Mongolia from March to June 2018. The success ratios of data transmission through Beidou satellite were 98.57%, 99.43%, 99.59%, and 98.85%, respectively, in Beijing, and through GPRS were 99.89% and 99.90% in Inner Mongolia. Long-term real-time field ecological monitoring and forest fire warning were successfully realized. This system can be widely used for big data field acquisition and analysis in forest and agriculture regions.
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Schimak, Gerald, Andrea E. Rizzoli, and Kym Watson. "Sensors and the environment – Modelling & ICT challenges." Environmental Modelling & Software 25, no. 9 (September 2010): 975–76. http://dx.doi.org/10.1016/j.envsoft.2010.03.022.
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Zhao, Bintao, Meng Sun, Zhixiang Cai, Ziwen Su, Jiyao Li, Zhijun Shen, Ruijuan Ma, Juan Yan, and Mingliang Yu. "Aroma Profiling Analysis of Peach Flowers Based on Electronic Nose Detection." Horticulturae 8, no. 10 (September 23, 2022): 875. http://dx.doi.org/10.3390/horticulturae8100875.
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Aroma profiling peach flowers can improve the landscape and ecological value of the surrounding environment. In order to preliminarily explore and screen the flower aromas of peach germplasms, the floral aroma profiles of 50 peach germplasms were analyzed by electronic nose detection. The clustering results from ten sensors showed that it was possible to identify each peach blossom aroma type. ‘Gansutao2’ was separately classified as cluster I; ‘Yuntaishanshantao’, ‘Dazhuanggansutao’, and ‘Zhouxingshantao’ as cluster II; ‘Xinjiangpantao’ and ‘Xinjianghuangrou’ as cluster III; and the remaining germplasms as cluster IV. The flower aromas of clusters I, II, and III were different from that of cluster IV and were mainly highlighted by the significant differences in the response values of the three main sensors, which were W1S (methane), W1W (hydrogen sulfide), and W5S (nitrogen oxides). The principal component analysis and significance analysis of the sensor response values showed that ‘Hua3’, ‘Hongfenjiaren’, and ‘Yuntaishanshantao’ had special flower aromas. The response values of these three varieties produced by the W1S, W1W, and W5S sensors and two aromatic sensors, W2W (aromatic components and organic sulfides) and W3S (methane-aliph), were significantly different from most of the other germplasms. The ‘Yuntaishanshantao’ response values produced by the five sensors’ were low, which showed that it had a light aroma, while the ‘Hongfenjiaren’ and ‘Hua3’ values were high, which showed that they had strong aromas. The results from this study provided basic data that could be used to screen peach germplasms with obvious floral aromas, cultivate new varieties with strong aromas, and aid the development and utilization of peach floral aroma substances.
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Schmidt, Marcus, Tobias Alt, Kevin Nolte, and Thomas Jaitner. "Comment on “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor”." Sensors 20, no. 10 (May 25, 2020): 2995. http://dx.doi.org/10.3390/s20102995.
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The recent paper “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor” (Sensors 2020, 20, 354) proposes a wearable system based on a foot-worn miniature inertial measurement unit (MIMU) and different methods to detect hurdle clearance and to identify the leading leg during 400-m hurdle races. Furthermore, the presented system identifies changes in contact time, flight time, running speed, and step frequency throughout the race. In this comment, we discuss the original paper with a focus on the ecological validity and the applicability of MIMU systems for field-based settings, such as training or competition for elite athletes.
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Valchanov, H., and V. Aleksieva. "Blockchain and IoT integration for smart transportation." Journal of Physics: Conference Series 2339, no. 1 (September 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2339/1/012012.
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Abstract The safe transport of special and dangerous cargos is essential for the ecological environment and human health. Modern solutions are based on monitoring of the parameters of their transport with sensors in real time, which allows rapid response to unexpected events. Despite the information available from the sensors, the process of proving an insurance event and paying compensation is the same as for other insurances. This paper proposes a blockchain and IoT based model that chronologically records the data from the sensors located at the vehicle with goods, and the smart contract which sends timely signals to stakeholders (including the insurer) when the parameters of the sensors exceed the set thresholds. An experimental implementation on HyperLedger Fabric is presented, which proves the applicability of the proposed model.
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Wang, Zhigang, Liqin Tian, Wenxing Wu, Lianhai Lin, Zongjin Li, and Yinghua Tong. "A Metaheuristic Algorithm for Coverage Enhancement of Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (May 12, 2022): 1–23. http://dx.doi.org/10.1155/2022/7732989.
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When wireless sensors are randomly deployed in natural environments such as ecological monitoring, military monitoring, and disaster monitoring, the initial position of sensors is generally formed through deployment methods such as air-drop, and then, the second deployment is carried out through the existing optimization methods, but these methods will still lead to serious coverage holes. In order to solve this problem, this paper proposes an algorithm to improve the coverage rate for wireless sensor networks based on an improved metaheuristic algorithm. The sensor deployment coverage model was firstly established, and the sensor network coverage problem was transformed into a high-dimensional multimodal function optimization problem. Secondly, the global searching ability and searching range of the algorithm are enhanced by the reverse expansion of the initial populations. Finally, the firefly principle is introduced to reduce the local binding force of sparrows and avoid the local optimization problem of the population in the search process. The experimental results showed that compared with ALO, GWO, BES, RK, and SSA algorithms, the EFSSA algorithm is better than other algorithms in benchmark function tests, especially in the test of high-dimensional multimodal function. In the tests of different monitoring ranges and number of nodes, the coverage of EFSSA algorithm is higher than other algorithms. The result can tell that EFSSA algorithm can effectively enhance the coverage of sensor deployment.
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Rawtaer, Iris, Rathi Mahendran, Ee Heok Kua, Hwee Pink Tan, Hwee Xian Tan, Tih-Shih Lee, and Tze Pin Ng. "Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study." Journal of Medical Internet Research 22, no. 5 (May 5, 2020): e16854. http://dx.doi.org/10.2196/16854.
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Background Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection. Objective The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively. Methods We recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered. Results Of the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system. Conclusions We demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information.
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Zhang, Bin, and YuFeng Liu. "Research on Sustainable Development of Olympic Games Based on Ecological Carrying Capacity Analysis." Journal of Sensors 2022 (May 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/4907366.
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The Olympic Games is a comprehensive social and cultural activity with the most complicated system and the largest scale. With the development of the Olympic Games, the ecological problems brought by the Olympic Games have attracted increasing attention, and the sustainable development of the Olympic Games has been put on the agenda. Based on the comprehensive index of Ecological Carrying Capacity (ECC), Environmental Kuznets Curve, and carbon footprint analysis of the Olympic Games host city competition cycle, this study analyzes three modes of ECC of the host city: light urban ecological burden mode, heavy urban ecological burden mode, and overload urban ecological burden mode. Based on the temperature sensor and GPS positioning, the land surface temperature change map of Tokyo, Japan, from 1990 to 2015 is obtained, and the heat island effect of Tokyo is obtained. This paper analyzes the case of using sensors for intelligent event management such as venue detection in the sustainable development plan of the 2020 Tokyo Olympics; the idea and practice of thrifty hosting of 2022 Beijing Winter Olympics and 2022 Hangzhou Asian Games holds that in most cases, the ECC of the host city of the Olympic Games is under great pressure, so it is necessary to pay attention to the ECC of the host city. The sustainable development of the Olympic Games is an important issue in the development of the Olympics, but the sustainable development plan of the Olympics is still being explored. It is suggested that the sustainable development of the Olympic Games needs to evaluate the ECC of the host city in the whole cycle, establish the principle of ecological priority to avoid the overload mode of ecological burden, strive to achieve carbon neutrality in the competition, and practice the idea of scientific frugality in running the competition.
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Burman, Scott G., Jingya Gao, Gregory B. Pasternack, Nann A. Fangue, Paul Cadrett, Elizabeth Campbell, and Dipak Ghosal. "TempMesh – A Flexible Wireless Sensor Network for Monitoring River Temperatures." ACM Transactions on Sensor Networks 19, no. 1 (December 8, 2022): 1–28. http://dx.doi.org/10.1145/3542697.
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For a Chinook salmon restoration project in the lower Yuba River in California, we designed and deployed a wireless sensor network to monitor river temperatures at micro-habitat scales. The study required that temperatures be measured along a 3 km study reach, across the channel, and into off-channel areas. To capture diel and seasonal fluctuations, sensors were sampled quarter-hourly for the full duration of the six-month juvenile salmon winter residency. This sampling duration required that nodes minimize power-use. We adopted event-based software on MSP430 micro-controllers with 433 MHz radio and minimized the networking duty-cycle. To address link failures, we included network storage. As the network lacked real-time clocks, data were timestamped at the destination. This, coupled with the storage, yielded timestamp inaccuracies, which we re-aligned using a novel algorithm. We collected over six months of temperature data from 35 sensors across seven nodes. Of the packets collected, we identified 21% as being incorrectly timestamped and were able to re-align 41% of these incorrectly timestamped packets. We collected temperature data through major floods, and the network uploaded data until the flood destroyed the sensors. The network met an important need in ecological sampling with ultra-low power (multi-year battery life) and low-throughput.