Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks.

Science.gov (United States)

Ma, Junjie; Meng, Fansheng; Zhou, Yuexi; Wang, Yeyao; Shi, Ping

2018-02-16

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network

Directory of Open Access Journals (Sweden)

Kai Lin

2016-07-01

Full Text Available With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC. The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

Science.gov (United States)

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

Marker-Based Multi-Sensor Fusion Indoor Localization System for Micro Air Vehicles.

Science.gov (United States)

Xing, Boyang; Zhu, Quanmin; Pan, Feng; Feng, Xiaoxue

2018-05-25

A novel multi-sensor fusion indoor localization algorithm based on ArUco marker is designed in this paper. The proposed ArUco mapping algorithm can build and correct the map of markers online with Grubbs criterion and K-mean clustering, which avoids the map distortion due to lack of correction. Based on the conception of multi-sensor information fusion, the federated Kalman filter is utilized to synthesize the multi-source information from markers, optical flow, ultrasonic and the inertial sensor, which can obtain a continuous localization result and effectively reduce the position drift due to the long-term loss of markers in pure marker localization. The proposed algorithm can be easily implemented in a hardware of one Raspberry Pi Zero and two STM32 micro controllers produced by STMicroelectronics (Geneva, Switzerland). Thus, a small-size and low-cost marker-based localization system is presented. The experimental results show that the speed estimation result of the proposed system is better than Px4flow, and it has the centimeter accuracy of mapping and positioning. The presented system not only gives satisfying localization precision, but also has the potential to expand other sensors (such as visual odometry, ultra wideband (UWB) beacon and lidar) to further improve the localization performance. The proposed system can be reliably employed in Micro Aerial Vehicle (MAV) visual localization and robotics control.

Marker-Based Multi-Sensor Fusion Indoor Localization System for Micro Air Vehicles

Directory of Open Access Journals (Sweden)

Boyang Xing

2018-05-01

Full Text Available A novel multi-sensor fusion indoor localization algorithm based on ArUco marker is designed in this paper. The proposed ArUco mapping algorithm can build and correct the map of markers online with Grubbs criterion and K-mean clustering, which avoids the map distortion due to lack of correction. Based on the conception of multi-sensor information fusion, the federated Kalman filter is utilized to synthesize the multi-source information from markers, optical flow, ultrasonic and the inertial sensor, which can obtain a continuous localization result and effectively reduce the position drift due to the long-term loss of markers in pure marker localization. The proposed algorithm can be easily implemented in a hardware of one Raspberry Pi Zero and two STM32 micro controllers produced by STMicroelectronics (Geneva, Switzerland. Thus, a small-size and low-cost marker-based localization system is presented. The experimental results show that the speed estimation result of the proposed system is better than Px4flow, and it has the centimeter accuracy of mapping and positioning. The presented system not only gives satisfying localization precision, but also has the potential to expand other sensors (such as visual odometry, ultra wideband (UWB beacon and lidar to further improve the localization performance. The proposed system can be reliably employed in Micro Aerial Vehicle (MAV visual localization and robotics control.

An SOI CMOS-Based Multi-Sensor MEMS Chip for Fluidic Applications †

Science.gov (United States)

Mansoor, Mohtashim; Haneef, Ibraheem; Akhtar, Suhail; Rafiq, Muhammad Aftab; De Luca, Andrea; Ali, Syed Zeeshan; Udrea, Florin

2016-01-01

An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors), a piezoresistive-type pressure sensor and nine hot film-based flow rate sensors fabricated within the oxide layer of the SOI wafers. The silicon dioxide layers with embedded sensors are relieved from the substrate as membranes with the help of a single DRIE step after chip fabrication from a commercial CMOS foundry. Very dense sensor packing per unit area of the chip has been enabled by using technologies/processes like SOI, CMOS and DRIE. Independent apparatuses were used for the characterization of each sensor. With a drive current of 10 µA–0.1 µA, the thermodiodes exhibited sensitivities of 1.41 mV/°C–1.79 mV/°C in the range 20–300 °C. The sensitivity of the pressure sensor was 0.0686 mV/(Vexcit kPa) with a non-linearity of 0.25% between 0 and 69 kPa above ambient pressure. Packaged in a micro-channel, the flow rate sensor has a linearized sensitivity of 17.3 mV/(L/min)−0.1 in the tested range of 0–4.7 L/min. The multi-sensor chip can be used for simultaneous measurement of fluid pressure, temperature and flow rate in fluidic experiments and aerospace/automotive/biomedical/process industries. PMID:27827904

Sensor-based Human Activity Recognition in a Multi-user Scenario

DEFF Research Database (Denmark)

Wang, Liang; Gu, Tao; Tao, Xianping

2009-01-01

Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activiti...

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Junjie Ma

2018-02-01

Full Text Available Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

A GIS-based multi-source and multi-box modeling approach (GMSMB) for air pollution assessment--a North American case study.

Science.gov (United States)

Wang, Bao-Zhen; Chen, Zhi

2013-01-01

This article presents a GIS-based multi-source and multi-box modeling approach (GMSMB) to predict the spatial concentration distributions of airborne pollutant on local and regional scales. In this method, an extended multi-box model combined with a multi-source and multi-grid Gaussian model are developed within the GIS framework to examine the contributions from both point- and area-source emissions. By using GIS, a large amount of data including emission sources, air quality monitoring, meteorological data, and spatial location information required for air quality modeling are brought into an integrated modeling environment. It helps more details of spatial variation in source distribution and meteorological condition to be quantitatively analyzed. The developed modeling approach has been examined to predict the spatial concentration distribution of four air pollutants (CO, NO(2), SO(2) and PM(2.5)) for the State of California. The modeling results are compared with the monitoring data. Good agreement is acquired which demonstrated that the developed modeling approach could deliver an effective air pollution assessment on both regional and local scales to support air pollution control and management planning.

Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory

Directory of Open Access Journals (Sweden)

Qian Ding

2014-10-01

Full Text Available Multi-sensor and information fusion technology based on Dempster-Shafer evidence theory is applied in the system of a building fire alarm to realize early detecting and alarming. By using a multi-sensor to monitor the parameters of the fire process, such as light, smoke, temperature, gas and moisture, the range of fire monitoring in space and time is expanded compared with a single-sensor system. Then, the D-S evidence theory is applied to fuse the information from the multi-sensor with the specific fire model, and the fire alarm is more accurate and timely. The proposed method can avoid the failure of the monitoring data effectively, deal with the conflicting evidence from the multi-sensor robustly and improve the reliability of fire warning significantly.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

Science.gov (United States)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

A New Multi-Sensor Track Fusion Architecture for Multi-Sensor Information Integration

National Research Council Canada - National Science Library

Jean, Buddy H; Younker, John; Hung, Chih-Cheng

2004-01-01

.... This new technology will integrate multi-sensor information and extract integrated multi-sensor information to detect, track and identify multiple targets at any time, in any place under all weather conditions...

T-L Plane Abstraction-Based Energy-Efficient Real-Time Scheduling for Multi-Core Wireless Sensors

Directory of Open Access Journals (Sweden)

Youngmin Kim

2016-07-01

Full Text Available Energy efficiency is considered as a critical requirement for wireless sensor networks. As more wireless sensor nodes are equipped with multi-cores, there are emerging needs for energy-efficient real-time scheduling algorithms. The T-L plane-based scheme is known to be an optimal global scheduling technique for periodic real-time tasks on multi-cores. Unfortunately, there has been a scarcity of studies on extending T-L plane-based scheduling algorithms to exploit energy-saving techniques. In this paper, we propose a new T-L plane-based algorithm enabling energy-efficient real-time scheduling on multi-core sensor nodes with dynamic power management (DPM. Our approach addresses the overhead of processor mode transitions and reduces fragmentations of the idle time, which are inherent in T-L plane-based algorithms. Our experimental results show the effectiveness of the proposed algorithm compared to other energy-aware scheduling methods on T-L plane abstraction.

Extending lifetime of wireless sensor networks using multi-sensor ...

Indian Academy of Sciences (India)

SOUMITRA DAS

In this paper a multi-sensor data fusion approach for wireless sensor network based on bayesian methods and ant colony ... niques for efficiently routing the data from source to the BS ... Literature review ... efficient scheduling and lot more to increase the lifetime of ... Nature-inspired algorithms such as ACO algorithms have.

On the feasibility of measuring urban air pollution by wireless distributed sensor networks.

Science.gov (United States)

Moltchanov, Sharon; Levy, Ilan; Etzion, Yael; Lerner, Uri; Broday, David M; Fishbain, Barak

2015-01-01

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the network's capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the system's performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution. Copyright © 2014 Elsevier B.V. All rights reserved.

Multi-rate sensor fusion-based adaptive discrete finite-time synergetic control for flexible-joint mechanical systems

International Nuclear Information System (INIS)

Xue Guang-Yue; Ren Xue-Mei; Xia Yuan-Qing

2013-01-01

This paper proposes an adaptive discrete finite-time synergetic control (ADFTSC) scheme based on a multi-rate sensor fusion estimator for flexible-joint mechanical systems in the presence of unmeasured states and dynamic uncertainties. Multi-rate sensors are employed to observe the system states which cannot be directly obtained by encoders due to the existence of joint flexibilities. By using an extended Kalman filter (EKF), the finite-time synergetic controller is designed based on a sensor fusion estimator which estimates states and parameters of the mechanical system with multi-rate measurements. The proposed controller can guarantee the finite-time convergence of tracking errors by the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach. (general)

PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter.

Science.gov (United States)

Li, Xiaohua; Li, Yaan; Yu, Jing; Chen, Xiao; Dai, Miao

2015-11-06

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.

Enhanced polymeric encapsulation for MEMS based multi sensors for fisheries research

DEFF Research Database (Denmark)

Birkelund, Karen; Nørgaard, Lars; Thomsen, Erik Vilain

2011-01-01

light intensity, temperature, pressure and conductivity. For precise and fast measurements a direct exposure of the sensor to the water is desirable. A potted tube encapsulation concept has shown to be promising for accurate and fast measurements in harsh environment, provided a tight sealing......This paper presents the challenges of a packaged MEMS-based multi sensor system that allow for direct exposure of the sensing part to sea water. The system is part of a data storage tag used on fish to provide the researcher with information on fish behaviour and migration. The sensor measures...... compared to low pressure chemical vapor deposited (LPCVD) silicon nitride and untreated silicon dioxide....

A multi-axis MEMS sensor with integrated carbon nanotube-based piezoresistors for nanonewton level force metrology

International Nuclear Information System (INIS)

Cullinan, Michael A; Panas, Robert M; Culpepper, Martin L

2012-01-01

This paper presents the design and fabrication of a multi-axis microelectromechanical system (MEMS) force sensor with integrated carbon nanotube (CNT)-based piezoresistive sensors. Through the use of proper CNT selection and sensor fabrication techniques, the performance of the CNT-based MEMS force sensor was increased by approximately two orders of magnitude as compared to current CNT-based sensor systems. The range and resolution of the force sensor were determined as 84 μN and 5.6 nN, respectively. The accuracy of the force sensor was measured to be better than 1% over the device’s full range. (paper)

Mercury Specie and Multi-Pollutant Control

Energy Technology Data Exchange (ETDEWEB)

Rob James; Virgil Joffrion; John McDermott; Steve Piche

2010-05-31

This project was awarded to demonstrate the ability to affect and optimize mercury speciation and multi-pollutant control using non-intrusive advanced sensor and optimization technologies. The intent was to demonstrate plant-wide optimization systems on a large coal fired steam electric power plant in order to minimize emissions, including mercury (Hg), while maximizing efficiency and maintaining saleable byproducts. Advanced solutions utilizing state-of-the-art sensors and neural network-based optimization and control technologies were proposed to maximize the removal of mercury vapor from the boiler flue gas thereby resulting in lower uncontrolled releases of mercury into the atmosphere. Budget Period 1 (Phase I) - Included the installation of sensors, software system design and establishment of the as-found baseline operating metrics for pre-project and post-project data comparison. Budget Period 2 (Phase II) - Software was installed, data communications links from the sensors were verified, and modifications required to integrate the software system to the DCS were performed. Budget Period 3 (Phase III) - Included the validation and demonstration of all control systems and software, and the comparison of the optimized test results with the targets established for the project site. This report represents the final technical report for the project, covering the entire award period and representing the final results compared to project goals. NeuCo shouldered 61% of the total project cost; while DOE shouldered the remaining 39%. The DOE requires repayment of its investment. This repayment will result from commercial sales of the products developed under the project. NRG's Limestone power plant (formerly owned by Texas Genco) contributed the host site, human resources, and engineering support to ensure the project's success.

MULTI-TEMPORAL AND MULTI-SENSOR IMAGE MATCHING BASED ON LOCAL FREQUENCY INFORMATION

Directory of Open Access Journals (Sweden)

X. Liu

2012-08-01

Full Text Available Image Matching is often one of the first tasks in many Photogrammetry and Remote Sensing applications. This paper presents an efficient approach to automated multi-temporal and multi-sensor image matching based on local frequency information. Two new independent image representations, Local Average Phase (LAP and Local Weighted Amplitude (LWA, are presented to emphasize the common scene information, while suppressing the non-common illumination and sensor-dependent information. In order to get the two representations, local frequency information is firstly obtained from Log-Gabor wavelet transformation, which is similar to that of the human visual system; then the outputs of odd and even symmetric filters are used to construct the LAP and LWA. The LAP and LWA emphasize on the phase and amplitude information respectively. As these two representations are both derivative-free and threshold-free, they are robust to noise and can keep as much of the image details as possible. A new Compositional Similarity Measure (CSM is also presented to combine the LAP and LWA with the same weight for measuring the similarity of multi-temporal and multi-sensor images. The CSM can make the LAP and LWA compensate for each other and can make full use of the amplitude and phase of local frequency information. In many image matching applications, the template is usually selected without consideration of its matching robustness and accuracy. In order to overcome this problem, a local best matching point detection is presented to detect the best matching template. In the detection method, we employ self-similarity analysis to identify the template with the highest matching robustness and accuracy. Experimental results using some real images and simulation images demonstrate that the presented approach is effective for matching image pairs with significant scene and illumination changes and that it has advantages over other state-of-the-art approaches, which include: the

Multi-Domain SDN Survivability for Agricultural Wireless Sensor Networks.

Science.gov (United States)

Huang, Tao; Yan, Siyu; Yang, Fan; Liu, Jiang

2016-11-06

Wireless sensor networks (WSNs) have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs) have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely multi-domain software-defined wireless sensor networks (SDWSNs). However, when the SDNs controlling agriculture networks suddenly become unavailable, whether intra-domain or inter-domain, sensor network communication is abnormal because of the loss of control. Moreover, there are controller and switch info-updating problems even if the controller becomes available again. To resolve these problems, this paper proposes a new approach based on an Open vSwitch extension for multi-domain SDWSNs, which can enhance agriculture network survivability and stability. We achieved this by designing a connection-state mechanism, a communication mechanism on both L2 and L3, and an info-updating mechanism based on Open vSwitch. The experimental results show that, whether it is agricultural inter-domain or intra-domain during the controller failure period, the sensor switches can enter failure recovery mode as soon as possible so that the sensor network keeps a stable throughput, a short failure recovery time below 300 ms, and low packet loss. Further, the domain can smoothly control the domain network again once the controller becomes available. This approach based on an Open vSwitch extension can enhance the survivability and stability of multi-domain SDWSNs in precision agriculture.

Multi-Domain SDN Survivability for Agricultural Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Tao Huang

2016-11-01

Full Text Available Wireless sensor networks (WSNs have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely multi-domain software-defined wireless sensor networks (SDWSNs. However, when the SDNs controlling agriculture networks suddenly become unavailable, whether intra-domain or inter-domain, sensor network communication is abnormal because of the loss of control. Moreover, there are controller and switch info-updating problems even if the controller becomes available again. To resolve these problems, this paper proposes a new approach based on an Open vSwitch extension for multi-domain SDWSNs, which can enhance agriculture network survivability and stability. We achieved this by designing a connection-state mechanism, a communication mechanism on both L2 and L3, and an info-updating mechanism based on Open vSwitch. The experimental results show that, whether it is agricultural inter-domain or intra-domain during the controller failure period, the sensor switches can enter failure recovery mode as soon as possible so that the sensor network keeps a stable throughput, a short failure recovery time below 300 ms, and low packet loss. Further, the domain can smoothly control the domain network again once the controller becomes available. This approach based on an Open vSwitch extension can enhance the survivability and stability of multi-domain SDWSNs in precision agriculture.

Dense Clustered Multi-Channel Wireless Sensor Cloud

Directory of Open Access Journals (Sweden)

Sivaramakrishnan Sivakumar

2015-08-01

Full Text Available Dense Wireless Sensor Network Clouds have an inherent issue of latency and packet drops with regards to data collection. Though there is extensive literature that tries to address these issues through either scheduling, channel contention or a combination of the two, the problem still largely exists. In this paper, a Clustered Multi-Channel Scheduling Protocol (CMSP is designed that creates a Voronoi partition of a dense network. Each partition is assigned a channel, and a scheduling scheme is adopted to collect data within the Voronoi partitions. This scheme collects data from the partitions concurrently and then passes it to the base station. CMSP is compared using simulation with other multi-channel protocols like Tree-based Multi-Channel, Multi-Channel MAC and Multi-frequency Media Access Control for wireless sensor networks. Results indicate CMSP has higher throughput and data delivery ratio at a lower power consumption due to network partitioning and hierarchical scheduling that minimizes load on the network.

Multi-Directional Environmental Sensors

Science.gov (United States)

Manohara, Harish (Inventor); Del Castillo, Linda Y. (Inventor); Mojarradi, Mohammed M. (Inventor)

2016-01-01

Systems and methods in accordance with embodiments of the invention implement multi-directional environmental sensors. In one embodiment, a multi-directional environmental sensor includes: an inner conductive element that is substantially symmetrical about three orthogonal planes; an outer conductive element that is substantially symmetrical about three orthogonal planes; and a device that measures the electrical characteristics of the multi-directional environmental sensor, the device having a first terminal and a second terminal; where the inner conductive element is substantially enclosed within the outer conductive element; where the inner conductive element is electrically coupled to the first terminal of the device; and where the outer conductive element is electrically coupled to the second terminal of the device.

Smart City Environmental Pollution Prevention and Control Design Based on Internet of Things

Science.gov (United States)

Peng, He; Bohong, Zheng; Qinpei, Kuang

2017-11-01

Due to increasingly serious urban pollution, this paper proposes an environmental pollution prevention and control system in combination with Internet of things. The system transfers data through the Internet, which also utilizes sensor, pH sensor and smoke sensor to obtain environmental data. Besides, combined with the video data acquired through monitoring, the data are transferred to data center to analyze the haze pollution, water pollution and fire disaster in environment. According to the results, multi-purpose vehicles are mobilized to complete the tasks such as spraying water to relieve haze, water source purification and fire fighting in city environment. Experiments show that the environmental pollution prevention and control system designed in this paper can automatically complete the urban environmental pollution detection, prevention and control, which thus reduces human and material resources and improves the efficiency of pollution prevention and control. Therefore, it possesses greatly practical significance to the construction of smart city.

Transgenic Plants as Sensors of Environmental Pollution Genotoxicity

Directory of Open Access Journals (Sweden)

Olga Kovalchuk

2008-03-01

Full Text Available Rapid technological development is inevitably associated with manyenvironmental problems which primarily include pollution of soil, water and air. In manycases, the presence of contamination is difficult to assess. It is even more difficult toevaluate its potential danger to the environment and humans. Despite the existence ofseveral whole organism-based and cell-based models of sensing pollution and evaluationof toxicity and mutagenicity, there is no ideal system that allows one to make a quick andcheap assessment. In this respect, transgenic organisms that can be intentionally altered tobe more sensitive to particular pollutants are especially promising. Transgenic plantsrepresent an ideal system, since they can be grown at the site of pollution or potentiallydangerous sites. Plants are ethically more acceptable and esthetically more appealing thananimals as sensors of environmental pollution. In this review, we will discuss varioustransgenic plant-based models that have been successfully used for biomonitoringgenotoxic pollutants. We will also discuss the benefits and potential drawbacks of thesesystems and describe some novel ideas for the future generation of efficient transgenicphytosensors.

Advances in Multi-Pollutant Control

Energy Technology Data Exchange (ETDEWEB)

NONE

2013-11-01

Pollutants, such as nitrogen oxides (nitrogen dioxide (NO2) and nitric oxide (NO)), sulphur dioxide (SO2), sulphur trioxide (SO3), carbon dioxide (CO2), mercury (Hg) and particulate matter (PM), are formed when coal is combusted in a power plant boiler. With the concern over the environmental and health consequences of these pollutants, legislation and regulations have been implemented limiting the amounts that can be emitted to the atmosphere. Emission control systems on conventional coal-fired power plants typically employ technologies designed to remove one specific pollutant.These are then combined, in series, to remove several pollutants in order to meet the emission regulations. This report discusses multi-pollutant systems which remove two or more of the principal regulated pollutants (SO2, NOx, mercury, particulate matter and CO2) in a single reactor or a single system designed for the purpose. The emphasis is on commercial or near commercial processes, and those that are under active development. Ways to improve the co-benefit removal of oxidised mercury in conventional limestone wet scrubbers, spray dry scrubbers and circulating dry scrubbers are also included. Multi-pollutant systems can have lower capital and operating costs than a series of traditional systems to remove the s ame number of pollutants. Nevertheless, many of the multi-pollutant technologies rely on by-product sales to be economically competitive. Their footprint is often smaller than conventional single pollutant counterparts treating a similar volume of flue gas, making them easier to install in retrofit applications. Some of the systems use modular designs that ensures easy scalability for larger boilers.

An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

Science.gov (United States)

Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

2018-05-01

The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

Science.gov (United States)

Taboun, Mohammed S; Brennan, Robert W

2017-09-14

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

Multi-hop localization algorithm based on grid-scanning for wireless sensor networks.

Science.gov (United States)

Wan, Jiangwen; Guo, Xiaolei; Yu, Ning; Wu, Yinfeng; Feng, Renjian

2011-01-01

For large-scale wireless sensor networks (WSNs) with a minority of anchor nodes, multi-hop localization is a popular scheme for determining the geographical positions of the normal nodes. However, in practice existing multi-hop localization methods suffer from various kinds of problems, such as poor adaptability to irregular topology, high computational complexity, low positioning accuracy, etc. To address these issues in this paper, we propose a novel Multi-hop Localization algorithm based on Grid-Scanning (MLGS). First, the factors that influence the multi-hop distance estimation are studied and a more realistic multi-hop localization model is constructed. Then, the feasible regions of the normal nodes are determined according to the intersection of bounding square rings. Finally, a verifiably good approximation scheme based on grid-scanning is developed to estimate the coordinates of the normal nodes. Additionally, the positioning accuracy of the normal nodes can be improved through neighbors' collaboration. Extensive simulations are performed in isotropic and anisotropic networks. The comparisons with some typical algorithms of node localization confirm the effectiveness and efficiency of our algorithm.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

Science.gov (United States)

Brennan, Robert W.

2017-01-01

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network. PMID:28906452

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

Science.gov (United States)

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-08-04

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

A novel method of range measuring for a mobile robot based on multi-sensor information fusion

International Nuclear Information System (INIS)

Zhang Yi; Luo Yuan; Wang Jifeng

2005-01-01

The traditional measuring range for a mobile robot is based on a sonar sensor. Because of different working environments, it is very difficult to obtain high precision by using just one single method of range measurement. So, a hybrid sonar sensor and laser scanner method is put forward to overcome these shortcomings. A novel fusion model is proposed based on basic theory and a method of information fusion. An optimal measurement result has been obtained with information fusion from different sensors. After large numbers of experiments and performance analysis, a conclusion can be drawn that the laser scanner and sonar sensor method with multi-sensor information fusion have a higher precision than the single method of sonar. It can also be the same with different environments

Miniaturized multi-sensor for aquatic studies

International Nuclear Information System (INIS)

Birkelund, Karen; Hyldgård, Anders; Mortensen, Dennis; Thomsen, Erik V

2011-01-01

We have developed and fabricated a multi-sensor chip for fisheries' research and demonstrated the functionality under controlled conditions. The outer dimensions of the sensor chip are 3.0 × 7.4 × 0.8 mm 3 and both sides of the chip are utilized for sensors. Hereby a more compact chip is achieved that allows for direct exposure to the seawater and thereby more accurate measurements. The chip contains a piezo-resistive pressure sensor, a pn-junction photodiode sensitive to visible light, a four-terminal platinum resistor for temperature measurement and four conductivity electrodes for the determination of the salinity of saltwater. Pressure, light intensity, temperature and salinity are all essential parameters when mapping the migration route of fish. The pressure sensor has a sensitivity of S = 1.44 × 10 −7 Pa −1 and is optimized to 20 bar pressure; the light sensor has a quantum efficiency between 52% and 74% in the range of visible light. The temperature sensor responds linearly with temperature and has a temperature coefficient of resistance of 2.9 × 10 −3 K −1 . The conductivity sensor can measure the salinity with an accuracy of ±0.1 psu. This is all together the smallest and best functioning fully integrated MEMS-based multi-sensor made to date for this specific application. However, each single-sensor performance can be optimized by introducing a considerably more complicated process sequence. In this paper, a new simpler process for integrating the four sensors on one single chip is presented in details for the first time. Further, an optimized performance of the individual sensors is presented

DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS

Directory of Open Access Journals (Sweden)

Y. C. Lai

2015-05-01

Full Text Available This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS. There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system

Development of a Pedestrian Indoor Navigation System Based on Multi-Sensor Fusion and Fuzzy Logic Estimation Algorithms

Science.gov (United States)

Lai, Y. C.; Chang, C. C.; Tsai, C. M.; Lin, S. Y.; Huang, S. C.

2015-05-01

This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its

Humidity Sensor Based on Multi-Walled Carbon Nanotube Thin Films

International Nuclear Information System (INIS)

Cao, C.L.; Hu, C.G.; Fang, L.; Wang, S.X.; Cao, C.L.; Tian, Y.S.; Pan, C.Y.

2009-01-01

The properties of the humidity sensors made of chemically treated and untreated multi-walled carbon nano tube (MWCNT) thin films are investigated systematically. It shows that both the chemically treated and untreated MWCNT thin films demonstrate humidity sensitive properties, but the former have stronger sensitivity than the latter. In the range of 11%-98% relative humidity (RH), the resistances of the chemically treated and untreated MWCNT humidity sensors increase 120% and 28%, respectively. Moreover, the treated humidity sensors showed higher sensitivity and better stability. In addition, the response and recover properties, and stabilization of the humidity sensors are measured, and the humidity sensitive mechanisms of the sensors are analyzed. The humidity sensitivity of carbon nano tube thin films indicates it promise as a kind of humidity sensitive material

Chemical sensors and microsystems for pollution abatement. Brief study

International Nuclear Information System (INIS)

Drost, S.; Aberl, F.; Endres, H.E.

1994-01-01

The demand for chemical sensors and microsystems is assessed on the basis of the substances which the pollution regulations identify as air and water pollutants in accordance with defined immission standards. Microsystems technology can do away with the disadvantages of environmental analysis. Chemical sensors offer many advantages but must be improved as regards their measuring accuracy and service life. These sensors must be developed further (transducers and coatings) and be combined into multisensor systems. Special sensor signal processing methods (pattern recognition) must be developed for the latter as microsystems technology advances. (orig./EF) [de

Recognizing Multi-user Activities using Body Sensor Networks

DEFF Research Database (Denmark)

Gu, Tao; Wang, Liang; Chen, Hanhua

2011-01-01

The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of a single user. In this work, we address the fundamental problem...... activity classes of data—for building activity models and design a scalable, noise-resistant, Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single- and multi-user activities. We develop a multi-modal, wireless body sensor network for collecting real-world traces in a smart...... home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability and robustness....

Handheld multi-channel LAPS device as a transducer platform for possible biological and chemical multi-sensor applications

International Nuclear Information System (INIS)

Wagner, Torsten; Molina, Roberto; Yoshinobu, Tatsuo; Kloock, Joachim P.; Biselli, Manfred; Canzoneri, Michelangelo; Schnitzler, Thomas; Schoening, Michael J.

2007-01-01

The light-addressable potentiometric sensor is a promising technology platform for multi-sensor applications and lab-on-chip devices. However, many prior LAPS developments suffer from their lack in terms of non-portability, insufficient robustness, complicate handling, etc. Hence, portable and robust LAPS-based measurement devices have been investigated by the authors recently. In this work, a 'chip card'-based light-addressable potentiometric sensor system is presented. The utilisation of ordinary 'chip cards' allows an easy handling of different sensor chips for a wide range of possible applications. The integration of the electronic and the mechanical set-up into a single reader unit results in a compact design with the benefits of portability and low required space. In addition, the presented work includes a new multi-frequency measurement procedure, based on an FFT algorithm, which enables the simultaneous real-time measurement of up to 16 sensor spots. The comparison between the former batch-LAPS and the new FFT-based LAPS set-up will be presented. The immobilisation of biological cells (CHO: Chinese hamster ovary) demonstrates the possibility to record their metabolic activity with 16 measurement spots on the same chip. Furthermore, a Cd 2+ -selective chalcogenide-glass layer together with a pH-sensitive Ta 2 O 5 layer validates the use of the LAPS for chemical multi-sensor applications

End-user perspective of low-cost sensors for outdoor air pollution monitoring.

Science.gov (United States)

Rai, Aakash C; Kumar, Prashant; Pilla, Francesco; Skouloudis, Andreas N; Di Sabatino, Silvana; Ratti, Carlo; Yasar, Ansar; Rickerby, David

2017-12-31

Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network. Copyright © 2017 Elsevier B.V. All rights reserved.

A risk-based multi-objective model for optimal placement of sensors in water distribution system

Science.gov (United States)

Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein

2018-02-01

In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value

AN INTEROPERABLE ARCHITECTURE FOR AIR POLLUTION EARLY WARNING SYSTEM BASED ON SENSOR WEB

Directory of Open Access Journals (Sweden)

F. Samadzadegan

2013-09-01

proposed an architecture to represent how integrate air quality sensor data stream into geospatial data infrastructure to present an interoperable air quality monitoring system for supporting disaster management systems by real time information. Developed system tested on Tehran air pollution sensors for calculating Air Quality Index (AQI for CO pollutant and subsequently notifying registered users in emergency cases by sending warning E-mails. Air quality monitoring portal used to retrieving and visualize sensor observation through interoperable framework. This system provides capabilities to retrieve SOS observation using WPS in a cascaded service chaining pattern for monitoring trend of timely sensor observation.

An Interoperable Architecture for Air Pollution Early Warning System Based on Sensor Web

Science.gov (United States)

Samadzadegan, F.; Zahmatkesh, H.; Saber, M.; Ghazi khanlou, H. J.

2013-09-01

architecture to represent how integrate air quality sensor data stream into geospatial data infrastructure to present an interoperable air quality monitoring system for supporting disaster management systems by real time information. Developed system tested on Tehran air pollution sensors for calculating Air Quality Index (AQI) for CO pollutant and subsequently notifying registered users in emergency cases by sending warning E-mails. Air quality monitoring portal used to retrieving and visualize sensor observation through interoperable framework. This system provides capabilities to retrieve SOS observation using WPS in a cascaded service chaining pattern for monitoring trend of timely sensor observation.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

Science.gov (United States)

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

An electrostatic sensor for the continuous monitoring of particulate air pollution

Energy Technology Data Exchange (ETDEWEB)

Intra, Panich; Yawootti, Artit [Rajamangala University of Technology Lanna, Chiang Mai (Thailand); Tippayawong, Nakorn [Chiang Mai University, Chiang Mai (Thailand)

2013-12-15

We developed and evaluated a particulate air pollution sensor for continuous monitoring of size resolved particle number, based on unipolar corona charging and electrostatic detection of charged aerosol particles. The sensor was evaluated experimentally using combustion aerosol with particle sizes in the range between approximately 50 nm and several microns, and particle number concentrations larger than 10{sup 10} particles/m{sup 3}. Test results were very promising. It was demonstrated that the sensor can be used in detecting particle number concentrations in the range of about 2.02x10{sup 11} and 1.03x10{sup 12} particles/m{sup 3} with a response of approximately 100 ms. Good agreement was found between the developed sensor and a commercially available laser particle counter in measuring ambient PM along a roadside with heavy traffic for about 2 h. The developed sensor proved particularly useful for measuring and detecting particulate air pollution, for number concentration of particles in the range of 10{sup 8} to 10{sup 12} particles/m{sup 3}.

Optical chemical sensors for atmospheric pollutants based on nano porous materials: application to the formaldehyde and the other carbonyl compounds

International Nuclear Information System (INIS)

Paolacci, H.

2006-12-01

Formaldehyde, a well-identified indoor pollutant, was recently classified as carcinogenic. New regulations for the air quality are expected and therefore there is a need for low-cost sensors, sensitive and selective with a fast response time for the detection of formaldehyde at ppb level. In the present work, we had developed a chemical sensor based on nano-porous matrices doped with Fluoral-P and optical methods of detection. The nano-porous matrices, elaborated via the Sol-Gel process, display nano-pores whose cavity is tailored for the trapping of the targeted pollutant. They provide a first selectivity with the discrimination of the pollutants by their size. A second selectivity is obtained with a molecular probe, Fluoral-P, which reacts specifically with formaldehyde leading to the 3,5- di-acetyl-1,4-dihydro-lutidine (DDL). The kinetics of formation of DDL was studied as function of many parameters such as the concentration of Fluoral-P in the matrix, the pollutant content in gas mixture, the flow rate, the relative humidity of the gas mixtures and interference with other carbonylated compounds. The present chemical sensor can detect, via absorbance measurements, 2 ppb of formaldehyde within 30 min over a O to 60% relative humidity range. Moreover, to detect the total carbonylated compounds, we also explored the potentiality of a chemical sensor using, as a probe molecule, the 2'4-dinitro-phenyl-hydrazine which forms with these compounds the corresponding hydrazones derivatives. A patent was deposited for these two sensors. We have also developed a semi-miniaturized prototype for demonstration, using a flow cell, a miniaturized spectrophotometer, a light source and a lap-top. (author)

Calibrating a novel multi-sensor physical activity measurement system

International Nuclear Information System (INIS)

John, D; Sasaki, J E; Howe, C A; Freedson, P S; Liu, S; Gao, R X; Staudenmayer, J

2011-01-01

Advancing the field of physical activity (PA) monitoring requires the development of innovative multi-sensor measurement systems that are feasible in the free-living environment. The use of novel analytical techniques to combine and process these multiple sensor signals is equally important. This paper describes a novel multi-sensor 'integrated PA measurement system' (IMS), the lab-based methodology used to calibrate the IMS, techniques used to predict multiple variables from the sensor signals, and proposes design changes to improve the feasibility of deploying the IMS in the free-living environment. The IMS consists of hip and wrist acceleration sensors, two piezoelectric respiration sensors on the torso, and an ultraviolet radiation sensor to obtain contextual information (indoors versus outdoors) of PA. During lab-based calibration of the IMS, data were collected on participants performing a PA routine consisting of seven different ambulatory and free-living activities while wearing a portable metabolic unit (criterion measure) and the IMS. Data analyses on the first 50 adult participants are presented. These analyses were used to determine if the IMS can be used to predict the variables of interest. Finally, physical modifications for the IMS that could enhance the feasibility of free-living use are proposed and refinement of the prediction techniques is discussed

Refractive index sensors based on the fused tapered special multi-mode fiber

Science.gov (United States)

Fu, Xing-hu; Xiu, Yan-li; Liu, Qin; Xie, Hai-yang; Yang, Chuan-qing; Zhang, Shun-yang; Fu, Guang-wei; Bi, Wei-hong

2016-01-01

In this paper, a novel refractive index (RI) sensor is proposed based on the fused tapered special multi-mode fiber (SMMF). Firstly, a section of SMMF is spliced between two single-mode fibers (SMFs). Then, the SMMF is processed by a fused tapering machine, and a tapered fiber structure is fabricated. Finally, a fused tapered SMMF sensor is obtained for measuring external RI. The RI sensing mechanism of tapered SMMF sensor is analyzed in detail. For different fused tapering lengths, the experimental results show that the RI sensitivity can be up to 444.517 81 nm/RIU in the RI range of 1.334 9—1.347 0. The RI sensitivity is increased with the increase of fused tapering length. Moreover, it has many advantages, including high sensitivity, compact structure, fast response and wide application range. So it can be used to measure the solution concentration in the fields of biochemistry, health care and food processing.

A Multi-Phase Based Fluid-Structure-Microfluidic interaction sensor for Aerodynamic Shear Stress

Science.gov (United States)

Hughes, Christopher; Dutta, Diganta; Bashirzadeh, Yashar; Ahmed, Kareem; Qian, Shizhi

2014-11-01

A novel innovative microfluidic shear stress sensor is developed for measuring shear stress through multi-phase fluid-structure-microfluidic interaction. The device is composed of a microfluidic cavity filled with an electrolyte liquid. Inside the cavity, two electrodes make electrochemical velocimetry measurements of the induced convection. The cavity is sealed with a flexible superhydrophobic membrane. The membrane will dynamically stretch and flex as a result of direct shear cross-flow interaction with the seal structure, forming instability wave modes and inducing fluid motion within the microfluidic cavity. The shear stress on the membrane is measured by sensing the induced convection generated by membrane deflections. The advantages of the sensor over current MEMS based shear stress sensor technology are: a simplified design with no moving parts, optimum relationship between size and sensitivity, no gaps such as those created by micromachining sensors in MEMS processes. We present the findings of a feasibility study of the proposed sensor including wind-tunnel tests, microPIV measurements, electrochemical velocimetry, and simulation data results. The study investigates the sensor in the supersonic and subsonic flow regimes. Supported by a NASA SBIR phase 1 contract.

A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

Directory of Open Access Journals (Sweden)

Detong Kong

2012-02-01

Full Text Available Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

Directory of Open Access Journals (Sweden)

Huile Xu

2016-12-01

Full Text Available Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT or wavelet transform (WT. However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA and instantaneous frequency (IF by means of empirical mode decomposition (EMD, as well as instantaneous energy density (IE and marginal spectrum (MS derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Water quality assessment by an integrated multi-sensor based on semiconductor RuO2 nanostructures

International Nuclear Information System (INIS)

Zhuiykov, Serge; O'Brien, David; Best, Michael

2009-01-01

A multi-sensor based on a nanostructured semiconductor ruthenium oxide (RuO 2 ) sensing electrode (RuO 2 -SE) deposited on an alumina substrate and capable of being coupled with a simple turbidity sensor has been evaluated for long-term pH stability during a 12-month non-stop trial. The multi-sensor is designed to detect the main parameters of water quality: pH, dissolved oxygen (DO), temperature, conductivity and turbidity over a temperature range of 9–30 °C. The morphology of the film SE used in the sensor structure was investigated by scanning electron microscopy and energy dispersive x-ray-analysis at the beginning of the trial and after 12 months of service. It was found that both morphology and surface compositions of nanostructured RuO 2 -SEs did not change significantly. They keep their high sensitivity to adsorption of superoxide ions (O 2 − ) despite heavy depositions of bio-fouling. The sensors with a RuO 2 -SE have demonstrated a stable Nernstian response to pH from 2.0 to 13.0 and were also capable of measuring DO in the range of 0.6–8.0 ppm. The measurement results show very good linearity, and excellent reproducibility was obtained during the trial. The Nernstian slope was approximately 58 mV pH −1 at a temperature of 23 °C. Although RuO 2 -SEs have been shown to exhibit very good response time for pH changes, within a few seconds at a temperature of 23 °C, as the water temperature cooled down, the sensor response time increased significantly and was about 8–10 min or longer at a temperature of 9 °C. The influence of hydrogen ion (H + ) diffusion in nanostructured RuO 2 films on the output emf drift during pH measurements was also investigated. Additional turbidity and conductivity measurements revealed that the multi-sensor is capable of measuring both high and low ranges at different temperatures, exhibiting a high linearity of characteristics

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion

Science.gov (United States)

Liu, Bailing; Zhang, Fumin; Qu, Xinghua

2015-01-01

An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%∼78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results. PMID:25850067

End-user perspective of low-cost sensors for outdoor air pollution monitoring

OpenAIRE

Rai, Aakash C.; Kumar, Prashant; Pilla, Francesco; Skouloudis, Andreas N.; Di Sabatino, Silvana; Ratti, Carlo; Yasar, Ansar; Rickerby, David

2017-01-01

Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to ...

Advances in multi-sensor data fusion: algorithms and applications.

Science.gov (United States)

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

Multi-channel fiber optic dew and humidity sensor

Science.gov (United States)

Limodehi, Hamid E.; Mozafari, Morteza; Amiri, Hesam; Légaré, François

2018-03-01

In this article, we introduce a multi-channel fiber optic dew and humidity sensor which works using a novel method based on relation between surface plasmon resonance (SPR) and water vapor condensation. The proposed sensor can instantly detect moisture or dew formation through its fiber optic channels, separately situated in different places. It enables to simultaneously measure the ambient Relative Humidity (RH) and dew point temperature of several environments with accuracy of 5%.

Sensor-based demand controlled ventilation

Energy Technology Data Exchange (ETDEWEB)

De Almeida, A.T. [Universidade de Coimbra (Portugal). Dep. Eng. Electrotecnica; Fisk, W.J. [Lawrence Berkeley National Lab., CA (United States)

1997-07-01

In most buildings, occupancy and indoor pollutant emission rates vary with time. With sensor-based demand-controlled ventilation (SBDCV), the rate of ventilation (i.e., rate of outside air supply) also varies with time to compensate for the changes in pollutant generation. In other words, SBDCV involves the application of sensing, feedback and control to modulate ventilation. Compared to ventilation without feedback, SBDCV offers two potential advantages: (1) better control of indoor pollutant concentrations; and (2) lower energy use and peak energy demand. SBDCV has the potential to improve indoor air quality by increasing the rate of ventilation when indoor pollutant generation rates are high and occupants are present. SBDCV can also save energy by decreasing the rate of ventilation when indoor pollutant generation rates are low or occupants are absent. After providing background information on indoor air quality and ventilation, this report provides a relatively comprehensive discussion of SBDCV. Topics covered in the report include basic principles of SBDCV, sensor technologies, technologies for controlling air flow rates, case studies of SBDCV, application of SBDCV to laboratory buildings, and research needs. SBDCV appears to be an increasingly attractive technology option. Based on the review of literature and theoretical considerations, the application of SBDCV has the potential to be cost-effective in applications with the following characteristics: (a) a single or small number of dominant pollutants, so that ventilation sufficient to control the concentration of the dominant pollutants provides effective control of all other pollutants; (b) large buildings or rooms with unpredictable temporally variable occupancy or pollutant emission; and (c) climates with high heating or cooling loads or locations with expensive energy.

Multi-Channel Capacitive Sensor Arrays

Directory of Open Access Journals (Sweden)

Bingnan Wang

2016-01-01

Full Text Available In this paper, multi-channel capacitive sensor arrays based on microstrip band-stop filters are studied. The sensor arrays can be used to detect the proximity of objects at different positions and directions. Each capacitive sensing structure in the array is connected to an inductive element to form resonance at different frequencies. The resonances are designed to be isolated in the frequency spectrum, such that the change in one channel does not affect resonances at other channels. The inductive element associated with each capacitive sensor can be surface-mounted inductors, integrated microstrip inductors or metamaterial-inspired structures. We show that by using metamaterial split-ring structures coupled to a microstrip line, the quality factor of each resonance can be greatly improved compared to conventional surface-mounted or microstrip meander inductors. With such a microstrip-coupled split-ring design, more sensing elements can be integrated in the same frequency spectrum, and the sensitivity can be greatly improved.

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals.

Science.gov (United States)

Xiong, Jiping; Cai, Lisang; Wang, Fei; He, Xiaowei

2017-03-03

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects' hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

Development of novel EMAT-ECT multi-sensor and verification of its feasibility

International Nuclear Information System (INIS)

Suzuki, Kenichiro; Uchimoto, Tetsuya; Takagi, Toshiyuki; Sato, Takeshi; Guy, Philippe; Casse, Amelie

2006-01-01

In this study, we propose a novel EMAT-ECT multi sensor aiming at advanced structural health monitoring. For the purpose, proto-type EMAT-ECT multi-sensor was developed and their functions both as ECT and EMAT prove were evaluated. Experimental results of pulse ECT using the EMAT-ECT multi-sensor showed that the proposed sensor has a capability of detection and sizing of flaws. Experimental results of EMAT evaluation using the EMAT-ECT multi-sensor showed that ultrasonic wave was transmitted by EMAT-ECT multi sensor and flaw echo was observed. These results imply that EMAT-ECT multi sensor is available for pulse ECT and EMAT. (author)

Design of multi-function sensor detection system in coal mine based on ARM

Science.gov (United States)

Ge, Yan-Xiang; Zhang, Quan-Zhu; Deng, Yong-Hong

2017-06-01

The traditional coal mine sensor in the specific measurement points, the number and type of channel will be greater than or less than the number of monitoring points, resulting in a waste of resources or cannot meet the application requirements, in order to enable the sensor to adapt to the needs of different occasions and reduce the cost, a kind of multi-functional intelligent sensor multiple sensors and ARM11 the S3C6410 processor is used to design and realize the dust, gas, temperature and humidity sensor functions together, and has storage, display, voice, pictures, data query, alarm and other new functions.

Stretchable Dual-Capacitor Multi-Sensor for Touch-Curvature-Pressure-Strain Sensing.

Science.gov (United States)

Jin, Hanbyul; Jung, Sungchul; Kim, Junhyung; Heo, Sanghyun; Lim, Jaeik; Park, Wonsang; Chu, Hye Yong; Bien, Franklin; Park, Kibog

2017-09-07

We introduce a new type of multi-functional capacitive sensor that can sense several different external stimuli. It is fabricated only with polydimethylsiloxane (PDMS) films and silver nanowire electrodes by using selective oxygen plasma treatment method without photolithography and etching processes. Differently from the conventional single-capacitor multi-functional sensors, our new multi-functional sensor is composed of two vertically-stacked capacitors (dual-capacitor). The unique dual-capacitor structure can detect the type and strength of external stimuli including curvature, pressure, strain, and touch with clear distinction, and it can also detect the surface-normal directionality of curvature, pressure, and touch. Meanwhile, the conventional single-capacitor sensor has ambiguity in distinguishing curvature and pressure and it can detect only the strength of external stimulus. The type, directionality, and strength of external stimulus can be determined based on the relative capacitance changes of the two stacked capacitors. Additionally, the logical flow reflected on a tree structure with its branches reaching the direction and strength of the corresponding external stimulus unambiguously is devised. This logical flow can be readily implemented in the sensor driving circuit if the dual-capacitor sensor is commercialized actually in the future.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

Science.gov (United States)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was

Evaluating the performance of low cost chemical sensors for air pollution research.

Science.gov (United States)

Lewis, Alastair C; Lee, James D; Edwards, Peter M; Shaw, Marvin D; Evans, Mat J; Moller, Sarah J; Smith, Katie R; Buckley, Jack W; Ellis, Matthew; Gillot, Stefan R; White, Andrew

2016-07-18

Low cost pollution sensors have been widely publicized, in principle offering increased information on the distribution of air pollution and a democratization of air quality measurements to amateur users. We report a laboratory study of commonly-used electrochemical sensors and quantify a number of cross-interferences with other atmospheric chemicals, some of which become significant at typical suburban air pollution concentrations. We highlight that artefact signals from co-sampled pollutants such as CO2 can be greater than the electrochemical sensor signal generated by the measurand. We subsequently tested in ambient air, over a period of three weeks, twenty identical commercial sensor packages alongside standard measurements and report on the degree of agreement between references and sensors. We then explore potential experimental approaches to improve sensor performance, enhancing outputs from qualitative to quantitative, focusing on low cost VOC photoionization sensors. Careful signal handling, for example, was seen to improve limits of detection by one order of magnitude. The quantity, magnitude and complexity of analytical interferences that must be characterised to convert a signal into a quantitative observation, with known uncertainties, make standard individual parameter regression inappropriate. We show that one potential solution to this problem is the application of supervised machine learning approaches such as boosted regression trees and Gaussian processes emulation.

Assessment of space sensors for ocean pollution monitoring

Science.gov (United States)

Alvarado, U. R.; Tomiyasu, K.; Gulatsi, R. L.

1980-01-01

Several passive and active microwave, as well as passive optical remote sensors, applicable to the monitoring of oil spills and waste discharges at sea, are considered. The discussed types of measurements relate to: (1) spatial distribution and properties of the pollutant, and (2) oceanic parameters needed to predict the movement of the pollutants and their impact upon land. The sensors, operating from satellite platforms at 700-900 km altitudes, are found to be useful in mapping the spread of oil in major oil spills and in addition, can be effective in producing wind and ocean parameters as inputs to oil trajectory and dispersion models. These capabilities can be used in countermeasures.

Deployment Considerations for Low-cost Air Quality Sensor Networks; Examining Spatial Variability of Gas-Phase Pollutants Around a Building in Los Angeles

Science.gov (United States)

Collier-Oxandale, A. M.; Hannigan, M.; Casey, J. G.; Johnston, J.; Coffey, E.; Thorson, J.

2017-12-01

The field of low-cost air quality sensing technologies is growing rapidly through the continual development of new sensors, increased research into sensor performance, and more and more community groups utilizing sensors to investigate local issues. However, as this technology is still in an exploratory phase, there are few `best-practices' available to serve as guidelines for these projects and the standardization of some procedures could benefit the research community as a whole. For example, deployment considerations such as where and how to place a monitor at a given location are often determined by accessibility and safety, power-requirements, and what is an ideal for sampling the target pollutant. Using data from multiple gas-phase sensors, we will examine the importance of siting considerations for low-cost monitoring systems. During a sampling campaign in Los Angeles, a subset of monitors was deployed at one field site to explore the variability in air quality sensor data around a single building. The site is a three story, multi-family housing unit in a primarily residential neighborhood that is near two major roadways and other potential sources of pollution. Five low-cost monitors were co-located prior to and following the field deployment. During the approximately 2.5-month deployment, these monitors were placed at various heights above street level, on different sides of the building, and on the roof. In our analysis, we will examine how monitor placement affects a sensor's ability to detect local verses more regional trends and how this building-scale spatial variability changes over time. Additionally, examining data from VOC sensors (quantified for methane and total non-methane hydrocarbon signals) and O3 sensors will allow us to compare the variability of primary and secondary pollutants. An outcome of this analysis may include guidelines or `best practices' for siting sensors that could aid in ensuring the collection of high quality field data

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

Science.gov (United States)

Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

Low cost sensors: Field evaluations and multi-sensor approaches for emissions factors

Science.gov (United States)

The development, and application of low cost sensors to measure both particulate and gas-phase air pollutants is poised to explode over the next several years. The need for the sensors is driven by poor air quality experienced in inhabited regions throughout the world, in both de...

Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores

Directory of Open Access Journals (Sweden)

Youngmin Kim

2017-12-01

Full Text Available In wireless sensor networks (WSNs, sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods.

Multi-sensor image fusion and its applications

CERN Document Server

Blum, Rick S

2005-01-01

Taking another lesson from nature, the latest advances in image processing technology seek to combine image data from several diverse types of sensors in order to obtain a more accurate view of the scene: very much the same as we rely on our five senses. Multi-Sensor Image Fusion and Its Applications is the first text dedicated to the theory and practice of the registration and fusion of image data, covering such approaches as statistical methods, color-related techniques, model-based methods, and visual information display strategies.After a review of state-of-the-art image fusion techniques,

Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

Science.gov (United States)

Qin, Liguo; He, Xiao; Zhou, D. H.

2017-10-01

This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.

Performance evaluation of multi-sensor data fusion technique for ...

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

Multi-sensor data fusion; Test Range application; trajectory .... Kalman filtering technique utilizes the noise statistics of the underlying system under con- ..... Hall D L 1992 Mathematical techniques in multi-sensor data fusion (Boston, MA: ...

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

Directory of Open Access Journals (Sweden)

Jiping Xiong

2017-03-01

Full Text Available Although wrist-type photoplethysmographic (hereafter referred to as WPPG sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

Esopo: sEnsors and SOcial POllution measurements

OpenAIRE

Cozza, Vittoria; Guagliardi, Ilaria; Rubino, Michelangelo; Cozza, Raffaele; Martello, Alessandra; Picelli, Marco; Zhupa, Eustrat

2015-01-01

In the following we present the idea of a smart sensor distributed platform where users collect pollution measurements by simply placing a small smart device out of their office or home window, a device that interacts with their smartphones. They provide time-geolocalized information that, trough an app, will be made available to the community that will have the chance to control the pollution level and eventually share it on the most popular social networks along with the related user?s opin...

SnO2 Nanostructure as Pollutant Gas Sensors: Synthesis, Sensing Performances, and Mechanism

Directory of Open Access Journals (Sweden)

Brian Yuliarto

2015-01-01

Full Text Available A significant amount of pollutants is produced from factories and motor vehicles in the form of gas. Their negative impact on the environment is well known; therefore detection with effective gas sensors is important as part of pollution prevention efforts. Gas sensors use a metal oxide semiconductor, specifically SnO2 nanostructures. This semiconductor is interesting and worthy of further investigation because of its many uses, for example, as lithium battery electrode, energy storage, catalyst, and transistor, and has potential as a gas sensor. In addition, there has to be a discussion of the use of SnO2 as a pollutant gas sensor especially for waste products such as CO, CO2, SO2, and NOx. In this paper, the development of the fabrication of SnO2 nanostructures synthesis will be described as it relates to the performances as pollutant gas sensors. In addition, the functionalization of SnO2 as a gas sensor is extensively discussed with respect to the theory of gas adsorption, the surface features of SnO2, the band gap theory, and electron transfer.

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association

Directory of Open Access Journals (Sweden)

Yu Liu

2017-11-01

Full Text Available This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA, and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF is utilized to estimate the targets’ state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems.

Integration of Fiber-Optic Sensor Arrays into a Multi-Modal Tactile Sensor Processing System for Robotic End-Effectors

Directory of Open Access Journals (Sweden)

Peter Kampmann

2014-04-01

Full Text Available With the increasing complexity of robotic missions and the development towards long-term autonomous systems, the need for multi-modal sensing of the environment increases. Until now, the use of tactile sensor systems has been mostly based on sensing one modality of forces in the robotic end-effector. The use of a multi-modal tactile sensory system is motivated, which combines static and dynamic force sensor arrays together with an absolute force measurement system. This publication is focused on the development of a compact sensor interface for a fiber-optic sensor array, as optic measurement principles tend to have a bulky interface. Mechanical, electrical and software approaches are combined to realize an integrated structure that provides decentralized data pre-processing of the tactile measurements. Local behaviors are implemented using this setup to show the effectiveness of this approach.

Security of nuclear materials using fusion multi sensor wavelett

International Nuclear Information System (INIS)

Djoko Hari Nugroho

2010-01-01

Security of a nuclear material in an installation is determined by how far the installation is to assure that nuclear material remains at a predetermined location. This paper observed a preliminary design on nuclear material tracking system in the installation for decision making support based on multi sensor fusion that is reliable and accurate to ensure that the nuclear material remains inside the control area. Capability on decision making in the Management Information System is represented by an understanding of perception in the third level of abstraction. The second level will be achieved with the support of image analysis and organizing data. The first level of abstraction is constructed by merger between several CCD camera sensors distributed in a building in a data fusion representation. Data fusion is processed based on Wavelett approach. Simulation utilizing Matlab programming shows that Wavelett fuses multi information from sensors as well. Hope that when the nuclear material out of control regions which have been predetermined before, there will arise a warning alarm and a message in the Management Information System display. Thus the nuclear material movement time event can be obtained and tracked as well. (author)

A Novel Approach to Selecting Contractor in Agent-based Multi-sensor Battlefield Reconnaissance Simulation

Directory of Open Access Journals (Sweden)

Xiong Li

2012-11-01

Full Text Available This paper presents a novel approach towards showing how contractor in agent-based simulation for complex warfare system such as multi-sensor battlefield reconnaissance system can be selected in Contract Net Protocol (CNP with high efficiency. We first analyze agent and agent-based simulation framework, CNP and collaborators, and present agents interaction chain used to actualize CNP and establish agents trust network. We then obtain contractor's importance weight and dynamic trust by presenting fuzzy similarity-based algorithm and trust modifying algorithm, thus we propose contractor selecting approach based on maximum dynamic integrative trust. We validate the feasibility and capability of this approach by implementing simulation, analyzing compared results and checking the model.

Use of multi-objective air pollution monitoring sites and online air pollution monitoring system for total health risk assessment in Hyderabad, India.

Science.gov (United States)

Anjaneyulu, Y; Jayakumar, I; Hima Bindu, V; Sagareswar, G; Mukunda Rao, P V; Rambabu, N; Ramani, K V

2005-08-01

A consensus has been emerging among public health experts in developing countries that air pollution, even at current ambient levels, aggravates respiratory and cardiovascular diseases and leads to premature mortality. Recent studies have also presented well-founded theories concerning the biological mechanisms involved and the groups of people that are probably more susceptible to health effects caused or exacerbated by inhalation of ambient particulate matter (PM.). On the basis of prognostic studies carried out in Center for Environment, JNT University, Hyderabad "it has been estimated that in Hyderabad some 1,700 to 3,000 people per year die prematurely as a result of inhaling PM". These figures reflect only the effects of acute exposure to air pollution. If the long-term effects of chronic exposure are taken into account, 10,000-15,000 people a year could die prematurely in Hyderabad. This estimate of the chronic effects is based on other studies, which are not completely comparable with the Hyderabad situation. While the study designs and analyses in these other studies may indeed be different or irrelevant to Hyderabad, the fact they were carried out in other countries is irrelevant. Taking into account these considerations, a model for total health risk assessment for the city of Hyderabad, and its state of Andhra Pradesh in India has been developed using a multi-objective air pollution monitoring network and online and real time air pollution monitoring stations. For the model studies a number of potential monitoring sites were screened for general and site-specific criteria in a geographic information system (GIS) environment that may, on a local basis, affect the representativeness of the data collected. Local features that may affect either the chemical or meteorological parameters are evaluated to assure a minimum of interference. Finally, for monitoring air pollution, an online and real-time monitoring system was designed using advanced

Compact portable QEPAS multi-gas sensor

Science.gov (United States)

Dong, Lei; Kosterev, Anatoliy A.; Thomazy, David; Tittel, Frank K.

2011-01-01

A quartz-enhanced photoacoustic spectroscopy (QEPAS) based multi-gas sensor was developed to quantify concentrations of carbon monoxide (CO), hydrogen cyanide (HCN), hydrogen chloride (HCl), and carbon dioxide (CO2) in ambient air. The sensor consists of a compact package of dimensions 25cm x 25cm x 10cm and was designed to operate at atmospheric pressure. The HCN, CO2, and HCl measurement channels are based on cw, C-band telecommunication-style packaged, fiber-coupled diode lasers, while the CO channel uses a TO can-packaged Sb diode laser as an excitation source. Moreover, the sensor incorporates rechargeable batteries and can operate on batteries for at least 8 hours. It can also operate autonomously or interact with another device (such as a computer) via a RS232 serial port. Trace gas detection limits of 7.74ppm at 4288.29cm-1 for CO, 450ppb at 6539.11 cm-1 for HCN, 1.48ppm at 5739.26 cm-1 for HCl and 97ppm at 6361.25 cm-1 for CO2 for a 1sec average time, were demonstrated.

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation

Directory of Open Access Journals (Sweden)

Dongmei Huang

2017-09-01

Full Text Available Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

Science.gov (United States)

Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi

2017-09-21

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

Assessing community exposure to hazardous air pollutants by combining optical remote sensing and "low-cost" sensor technologies

Science.gov (United States)

Pikelnaya, O.; Polidori, A.; Wimmer, R.; Mellqvist, J.; Samuelsson, J.; Marianne, E.; Andersson, P.; Brohede, S.; Izos, O.

2017-12-01

Industrial facilities such as refineries and oil processing facilities can be sources of chemicals adversely affecting human health, for example aromatic hydrocarbons and formaldehyde. In an urban setting, such as the South Coast Air Basin (SCAB), exposure to harmful air pollutants (HAP's) for residents of communities neighboring such facilities is of serious concern. Traditionally, exposure assessments are performed by modeling a community exposure using emission inventories and data collected at fixed air monitoring sites. However, recent field measurements found that emission inventories may underestimate HAP emissions from refineries; and HAP measurements data from fixed sites is lacking spatial resolution; as a result, the impact of HAP emissions on communities is highly uncertain. The next generation air monitoring technologies can help address these challenges. For example, dense "low-cost" sensors allow continuous monitoring of concentrations of pollutants within communities with high temporal- and spatial- resolution, and optical remote sensing (ORS) technologies offer measurements of emission fluxes and real-time ground-concentration mapping of HAPs. South Coast Air Quality Management District (SCAQMD) is currently conducting a multi-year study using ORS methods and "low-cost" Volatile Organic Compounds (VOCs) sensors to monitor HAP emissions from selected industrial facilities in the SCAB and their ambient concentrations in neighboring communities. For this purpose, quarterly mobile ORS surveys are conducted to quantify facility-wide emissions for VOCs, aromatic hydrocarbons and HCHO, and to collect ground-concentration profiles of these pollutants inside neighboring communities. Additionally, "low-cost" sensor nodes for deployment in neighborhood(s) downwind of the facilities have been developed in order to obtain long-term, granular data on neighborhood VOC concentrations, During this presentation we will discuss initial results of quarterly ORS

IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion

Directory of Open Access Journals (Sweden)

Omid Dehzangi

2017-11-01

Full Text Available The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF expansion of human gait cycles in order to capture joint 2 dimensional (2D spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level and late (decision score level multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class

Multi-Sensor Testing for Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory

Science.gov (United States)

Brewster, L.; Johnston, A.; Howard, R.; Mitchell, J.; Cryan, S.

2007-01-01

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as AR&D). The crewed missions may also perform rendezvous and docking operations and may require different levels of automation and/or autonomy, and must provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor proposed relative navigation sensor suite will meet the requirements. The relatively low technology readiness level of AR&D relative navigation sensors has been carried as one of the CEV Project's top risks. The AR&D Sensor Technology Project seeks to reduce the risk by the testing and analysis of selected relative navigation sensor technologies through hardware-in-the-loop testing and simulation. These activities will provide the CEV Project information to assess the relative navigation sensors maturity as well as demonstrate test methods and capabilities. The first year of this project focused on a series of"pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes that will be used to evaluate the Contractor-proposed sensors. Four candidate sensors were used in the first phase of the testing. The second phase of testing used four sensors simultaneously: two Marshall Space Flight Center (MSFC) Advanced Video Guidance Sensors (AVGS), a laser-based video sensor that uses retroreflectors attached to the target vehicle, and two commercial laser range finders. The multi-sensor testing was conducted at MSFC's Flight Robotics Laboratory (FRL

Wireless network system based multi-non-invasive sensors for smart home

Science.gov (United States)

Issa Ahmed, Rudhwan

There are several techniques that have been implemented for smart homes usage; however, most of these techniques are limited to a few sensors. Many of these methods neither meet the needs of the user nor are cost-effective. This thesis discusses the design, development, and implementation of a wireless network system, based on multi-non-invasive sensors for smart home environments. This system has the potential to be used as a means to accurately, and remotely, determine the activities of daily living by continuously monitoring relatively simple parameters that measure the interaction between users and their surrounding environment. We designed and developed a prototype system to meet the specific needs of the elderly population. Unlike audio-video based health monitoring systems (which have associated problems such as the encroachment of privacy), the developed system's distinct features ensure privacy and are almost invisible to the occupants, thus increasing the acceptance levels of this system in household environments. The developed system not only achieved high levels of accuracy, but it is also portable, easy to use, cost-effective, and requires low data rates and less power compared to other wireless devices such as Wi-Fi, Bluetooth, wireless USB, Ultra wideband (UWB), or Infrared (IR) wireless. Field testing of the prototype system was conducted at different locations inside and outside of the Minto Building (Centre for Advanced Studies in Engineering at Carleton University) as well as other locations, such as the washroom, kitchen, and living room of a prototype apartment. The main goal of the testing was to determine the range of the prototype system and the functionality of each sensor in different environments. After it was verified that the system operated well in all of the tested environments, data were then collected at the different locations for analysis and interpretation in order to identify the activities of daily living of an occupant.

A Multi-Agent-Based Intelligent Sensor and Actuator Network Design for Smart House and Home Automation

Directory of Open Access Journals (Sweden)

Fei Hu

2013-08-01

Full Text Available The smart-house technology aims to increase home automation and security with reduced energy consumption. A smart house consists of various intelligent sensors and actuators operating on different platforms with conflicting objectives. This paper proposes a multi-agent system (MAS design framework to achieve smart house automation. The novelties of this work include the developments of (1 belief, desire and intention (BDI agent behavior models; (2 a regulation policy-based multi-agent collaboration mechanism; and (3 a set of metrics for MAS performance evaluation. Simulations of case studies are performed using the Java Agent Development Environment (JADE to demonstrate the advantages of the proposed method.

Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement-The Promise and the Current Reality.

Science.gov (United States)

Broday, David M

2017-10-02

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

Measuring PM and related air pollutants using low-cost sensors

Science.gov (United States)

Emerging air quality sensors may play a key role in better characterizing levels of air pollution in a variety of settings There are a wide range of low-cost (sensors on the market, but few have been characterized. If accurate, this new generation of inexpensive sens...

Public Transportation Based Dynamic Urban Pollution Monitoring System

Directory of Open Access Journals (Sweden)

Fernando LOPEZ-PEÑA

2010-02-01

Full Text Available This paper presents the development and results of a mobile sensor based opportunistic urban pollution monitoring network that uses public transportation buses as platforms for its deployment. This work is an extended and improved version of the paper presented to the IDAACS’09 conference. It reports some aspects of the implementation of a single pollution sensor based sensing node prototype which was used for testing an opportunistic communications network and which was reported in depth elsewhere. More emphasis is given to the description of the basic sensing unit and its modular conversion into a sensing system able to acquire data on several pollutants as well as temperature, humidity and geo-location information. The software architecture developed around it in order to process the huge amounts of data the system produces is also described. The different prototypes were tested on the public transportation system of the city of Vigo and on multiple test runs around the city of A Coruña in the north-west of Spain producing very promising results.

Multi-Level Sensor Fusion Algorithm Approach for BMD Interceptor Applications

National Research Council Canada - National Science Library

Allen, Doug

1998-01-01

... through fabrication and testing of advanced sensor hardware concepts and advanced sensor fusion algorithms. Advanced sensor concepts include onboard LADAR in conjunction with a multi-color passive IR sensor...

A Portable Wireless Communication Platform Based on a Multi-Material Fiber Sensor for Real-Time Breath Detection

Directory of Open Access Journals (Sweden)

Mourad Roudjane

2018-03-01

Full Text Available In this paper, we present a new mobile wireless communication platform for real-time monitoring of an individual’s breathing rate. The platform takes the form of a wearable stretching T-shirt featuring a sensor and a detection base station. The sensor is formed by a spiral-shaped antenna made from a multi-material fiber connected to a compact transmitter. Based on the resonance frequency of the antenna at approximately 2.4 GHz, the breathing sensor relies on its Bluetooth transmitter. The contactless and non-invasive sensor is designed without compromising the user’s comfort. The sensing mechanism of the system is based on the detection of the signal amplitude transmitted wirelessly by the sensor, which is found to be sensitive to strain. We demonstrate the capability of the platform to detect the breathing rates of four male volunteers who are not in movement. The breathing pattern is obtained through the received signal strength indicator (RSSI which is filtered and analyzed with home-made algorithms in the portable system. Numerical simulations of human breath are performed to support the experimental detection, and both results are in a good agreement. Slow, fast, regular, irregular, and shallow breathing types are successfully recorded within a frequency interval of 0.16–1.2 Hz, leading to a breathing rate varying from 10 to 72 breaths per minute.

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

Science.gov (United States)

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

Concept for a solid-state multi-parameter sensor system for cell-culture monitoring

International Nuclear Information System (INIS)

Baecker, M.; Beging, S.; Biselli, M.; Poghossian, A.; Wang, J.; Zang, W.; Wagner, P.; Schoening, M.J.

2009-01-01

In this study, a concept for a silicon-based modular solid-state sensor system for inline multi-parameter monitoring of cell-culture fermentation processes is presented. The envisaged multi-parameter sensor system consists of two identical sensor modules and is intended for continuous quantification of up to five (bio-)chemical and physical parameters, namely, glucose and glutamine concentration, pH value, electrolyte conductivity and temperature by applying different transducer principles and/or different operation modes. Experimental results for the field-effect electrolyte-insulator-semiconductor (EIS) sterilisable pH sensor and electrolyte conductivity sensor based on interdigitated electrodes are presented. The ongoing autoclaving does not have any significant impact on the pH-sensitive properties of a Ta 2 O 5 -gate EIS sensor. Even after 30 autoclaving cycles, the pH sensors show a clear pH response and nearly linear calibration curve with a slope of 57 ± 1 mV/pH. Additional scanning electron microscopy and ellipsometric investigations do not show any visible surface degradation or changes in the thickness of the pH-sensitive Ta 2 O 5 layer. The preliminary results demonstrate the suitability of the developed EIS sensor for an inline pH measurement during a fermentation process. In addition, interdigitated electrodes of different geometries serving as electrolyte conductivity sensor have been tested for measurements in relatively high ionic-strength solutions.

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

Science.gov (United States)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics

Directory of Open Access Journals (Sweden)

Qiu Hao

2016-10-01

Full Text Available It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian (VB approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli (LMB filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density (VB-PHD filter and the VB cardinality balanced multi-target multi-Bernoulli (VB-CBMeMBer filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.

Wireless Intelligent Sensors Management Application Protocol-WISMAP

Directory of Open Access Journals (Sweden)

Antonio Jesus Yuste-Delgado

2010-09-01

Full Text Available Although many recent studies have focused on the development of new applications for wireless sensor networks, less attention has been paid to knowledge-based sensor nodes. The objective of this work is the development in a real network of a new distributed system in which every sensor node can execute a set of applications, such as fuzzy ruled-base systems, measures, and actions. The sensor software is based on a multi-agent structure that is composed of three components: management, application control, and communication agents; a service interface, which provides applications the abstraction of sensor hardware and other components; and an application layer protocol. The results show the effectiveness of the communication protocol and that the proposed system is suitable for a wide range of applications. As real world applications, this work presents an example of a fuzzy rule-based system and a noise pollution monitoring application that obtains a fuzzy noise indicator.

Validating novel air pollution sensors to improve exposure estimates for epidemiological analyses and citizen science.

Science.gov (United States)

Jerrett, Michael; Donaire-Gonzalez, David; Popoola, Olalekan; Jones, Roderic; Cohen, Ronald C; Almanza, Estela; de Nazelle, Audrey; Mead, Iq; Carrasco-Turigas, Glòria; Cole-Hunter, Tom; Triguero-Mas, Margarita; Seto, Edmund; Nieuwenhuijsen, Mark

2017-10-01

Low cost, personal air pollution sensors may reduce exposure measurement errors in epidemiological investigations and contribute to citizen science initiatives. Here we assess the validity of a low cost personal air pollution sensor. Study participants were drawn from two ongoing epidemiological projects in Barcelona, Spain. Participants repeatedly wore the pollution sensor - which measured carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO 2 ). We also compared personal sensor measurements to those from more expensive instruments. Our personal sensors had moderate to high correlations with government monitors with averaging times of 1-h and 30-min epochs (r ~ 0.38-0.8) for NO and CO, but had low to moderate correlations with NO 2 (~0.04-0.67). Correlations between the personal sensors and more expensive research instruments were higher than with the government monitors. The sensors were able to detect high and low air pollution levels in agreement with expectations (e.g., high levels on or near busy roadways and lower levels in background residential areas and parks). Our findings suggest that the low cost, personal sensors have potential to reduce exposure measurement error in epidemiological studies and provide valid data for citizen science studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

Directory of Open Access Journals (Sweden)

Kwang Woo Nam

2011-11-01

Full Text Available Environmental monitoring is required to understand the effects of various kinds of phenomena such as a flood, a typhoon, or a forest fire. To detect the environmental conditions in remote places, monitoring applications employ the sensor networks to detect conditions, context models to understand phenomena, and computing technology to process the large volumes of data. In this paper, we present an air pollution monitoring system to provide alarm messages about potentially dangerous areas with sensor data analysis. We design the data analysis steps to understand the detected air pollution regions and levels. The analyzed data is used to track the pollution and to give an alarm. This implemented monitoring system is used to mitigate the damages caused by air pollution.

A New Multi-Sensor Track Fusion Architecture for Multi-Sensor Information Integration

Science.gov (United States)

2004-09-01

NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION ...NAME(S) AND ADDRESS(ES) Lockheed Martin Aeronautical Systems Company,Marietta,GA,3063 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING...tracking process and degrades the track accuracy. ARCHITECHTURE OF MULTI-SENSOR TRACK FUSION MODEL The Alpha

An Improved Digital Signature Protocol to Multi-User Broadcast Authentication Based on Elliptic Curve Cryptography in Wireless Sensor Networks (WSNs

Directory of Open Access Journals (Sweden)

Hamed Bashirpour

2018-03-01

Full Text Available In wireless sensor networks (WSNs, users can use broadcast authentication mechanisms to connect to the target network and disseminate their messages within the network. Since data transfer for sensor networks is wireless, as a result, attackers can easily eavesdrop deployed sensor nodes and the data sent between them or modify the content of eavesdropped data and inject false data into the sensor network. Hence, the implementation of the message authentication mechanisms (in order to avoid changes and injecting messages into the network of wireless sensor networks is essential. In this paper, we present an improved protocol based on elliptic curve cryptography (ECC to accelerate authentication of multi-user message broadcasting. In comparison with previous ECC-based schemes, complexity and computational overhead of proposed scheme is significantly decreased. Also, the proposed scheme supports user anonymity, which is an important property in broadcast authentication schemes for WSNs to preserve user privacy and user untracking.

Clustering approaches to improve the performance of low cost air pollution sensors.

Science.gov (United States)

Smith, Katie R; Edwards, Peter M; Evans, Mathew J; Lee, James D; Shaw, Marvin D; Squires, Freya; Wilde, Shona; Lewis, Alastair C

2017-08-24

Low cost air pollution sensors have substantial potential for atmospheric research and for the applied control of pollution in the urban environment, including more localized warnings to the public. The current generation of single-chemical gas sensors experience degrees of interference from other co-pollutants and have sensitivity to environmental factors such as temperature, wind speed and supply voltage. There are uncertainties introduced also because of sensor-to-sensor response variability, although this is less well reported. The sensitivity of Metal Oxide Sensors (MOS) to volatile organic compounds (VOCs) changed with relative humidity (RH) by up to a factor of five over the range of 19-90% RH and with an uncertainty in the correction of a factor of two at any given RH. The short-term (second to minute) stabilities of MOS and electrochemical CO sensor responses were reasonable. During more extended use, inter-sensor quantitative comparability was degraded due to unpredictable variability in individual sensor responses (to either measurand or interference or both) drifting over timescales of several hours to days. For timescales longer than a week identical sensors showed slow, often downwards, drifts in their responses which diverged across six CO sensors by up to 30% after two weeks. The measurement derived from the median sensor within clusters of 6, 8 and up to 21 sensors was evaluated against individual sensor performance and external reference values. The clustered approach maintained the cost competitiveness of a sensor device, but the median concentration from the ensemble of sensor signals largely eliminated the randomised hour-to-day response drift seen in individual sensors and excluded the effects of small numbers of poorly performing sensors that drifted significantly over longer time periods. The results demonstrate that for individual sensors to be optimally comparable to one another, and to reference instruments, they would likely require

Use of Multi-Objective Air Pollution Monitoring Sites and Online Air Pollution Monitoring System for Total Health Risk Assessment in Hyderabad, India

Directory of Open Access Journals (Sweden)

K. V. Ramani

2005-08-01

Full Text Available A consensus has been emerging among public health experts in developing countries that air pollution, even at current ambient levels, aggravates respiratory and cardiovascular diseases and leads to premature mortality. Recent studies have also presented well-founded theories concerning the biological mechanisms involved and the groups of people that are probably more susceptible to health effects caused or exacerbated by inhalation of ambient particulate matter (PM.. On the basis of prognostic studies carried out in Center for Environment, JNT University, Hyderabad “it has been estimated that in Hyderabad some 1,700 to 3,000 people per year die prematurely as a result of inhaling PM”. These figures reflect only the effects of acute exposure to air pollution. If the long-term effects of chronic exposure are taken into account, 10,000–15,000 people a year could die prematurely in Hyderabad. This estimate of the chronic effects is based on other studies, which are not completely comparable with the Hyderabad situation. While the study designs and analyses in these other studies may indeed be different or irrelevant to Hyderabad, the fact they were carried out in other countries is irrelevant. Taking into account these considerations, a model for total health risk assessment for the city of Hyderabad, and its state of Andhra Pradesh in India has been developed using a multi-objective air pollution monitoring network and online and real time air pollution monitoring stations. For the model studies a number of potential monitoring sites were screened for general and site-specific criteria in a geographic information system (GIS environment that may, on a local basis, affect the representativeness of the data collected. Local features that may affect either the chemical or meteorological parameters are evaluated to assure a minimum of interference. Finally, for monitoring air pollution, an online and real

Delay/Disruption Tolerant Network-Based Message Forwarding for a River Pollution Monitoring Wireless Sensor Network Application.

Science.gov (United States)

Velásquez-Villada, Carlos; Donoso, Yezid

2016-03-25

Communications from remote areas that may be of interest is still a problem. Many innovative projects applied to remote sites face communications difficulties. The GOLDFISH project was an EU-funded project for river pollution monitoring in developing countries. It had several sensor clusters, with floating WiFi antennas, deployed along a downstream river's course. Sensor clusters sent messages to a Gateway installed on the riverbank. This gateway sent the messages, through a backhaul technology, to an Internet server where data was aggregated over a map. The communication challenge in this scenario was produced by the antennas' movement and network backhaul availability. Since the antennas were floating on the river, communications could be disrupted at any time. Also, 2G/3G availability near the river was not constant. For non-real-time applications, we propose a Delay/Disruption Tolerant Network (DTN)-based solution where all nodes have persistent storage capabilities and DTN protocols to be able to wait minutes or hours to transmit. A mechanical backhaul will periodically visit the river bank where the gateway is installed and it will automatically collect sensor data to be carried to an Internet-covered spot. The proposed forwarding protocol delivers around 98% of the messages for this scenario, performing better than other well-known DTN routing protocols.

MULTI SENSOR AND PLATFORMS SETUPS FOR VARIOUS AIRBORNE APPLICATIONS

Directory of Open Access Journals (Sweden)

G. Kemper

2016-06-01

Full Text Available To combine various sensors to get a system for specific use became popular within the last 10 years. Metric mid format cameras meanwhile reach the 100 MPix and entered the mapping market to compete with the big format sensors. Beside that also other sensors as SLR Cameras provide high resolution and enter the aerial surveying market for orthophoto production or monitoring applications. Flexibility, purchase-costs, size and weight are common aspects to design multi-sensor systems. Some sensors are useful for mapping while others are part of environmental monitoring systems. Beside classical surveying aircrafts also UL Airplanes, Para/Trikes or UAVs make use of multi sensor systems. Many of them are customer specific while other already are frequently used in the market. This paper aims to show some setup, their application, what are the results and what are the pros and cons of them are.

Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

Directory of Open Access Journals (Sweden)

Robert Behling

2014-03-01

Full Text Available Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (ETM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE and a high absolute accuracy of 23 m (RMSE for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan.

Greenidge Multi-Pollutant Control Project

Energy Technology Data Exchange (ETDEWEB)

Connell, Daniel

2008-10-18

The Greenidge Multi-Pollutant Control Project was conducted as part of the U.S. Department of Energy's Power Plant Improvement Initiative to demonstrate an innovative combination of air pollution control technologies that can cost-effectively reduce emissions of SO{sub 2}, NO{sub x}, Hg, acid gases (SO{sub 3}, HCl, and HF), and particulate matter from smaller coal-fired electric generating units (EGUs). There are about 400 units in the United States with capacities of 50-300 MW that currently are not equipped with selective catalytic reduction (SCR), flue gas desulfurization (FGD), or mercury control systems. Many of these units, which collectively represent more than 55 GW of installed capacity, are difficult to retrofit for deep emission reductions because of space constraints and unfavorable economies of scale, making them increasingly vulnerable to retirement or fuel switching in the face of progressively more stringent environmental regulations. The Greenidge Project sought to confirm the commercial readiness of an emissions control system that is specifically designed to meet the environmental compliance requirements of these smaller coal-fired EGUs by offering a combination of deep emission reductions, low capital costs, small space requirements, applicability to high-sulfur coals, mechanical simplicity, and operational flexibility. The multi-pollutant control system includes a NO{sub x}OUT CASCADE{reg_sign} hybrid selective non-catalytic reduction (SNCR)/in-duct SCR system for NO{sub x} control and a Turbosorp{reg_sign} circulating fluidized bed dry scrubbing system (with a new baghouse) for SO{sub 2}, SO{sub 3}, HCl, HF, and particulate matter control. Mercury removal is provided as a co-benefit of the in-duct SCR, dry scrubber, and baghouse, and by injection of activated carbon upstream of the scrubber, if required. The multi-pollutant control system was installed and tested on the 107-MW{sub e}, 1953-vintage AES Greenidge Unit 4 by a team including

Noncontact Sleep Study by Multi-Modal Sensor Fusion

Directory of Open Access Journals (Sweden)

Ku-young Chung

2017-07-01

Full Text Available Polysomnography (PSG is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

An Asynchronous Multi-Sensor Micro Control Unit for Wireless Body Sensor Networks (WBSNs

Directory of Open Access Journals (Sweden)

Ching-Hsing Luo

2011-07-01

Full Text Available In this work, an asynchronous multi-sensor micro control unit (MCU core is proposed for wireless body sensor networks (WBSNs. It consists of asynchronous interfaces, a power management unit, a multi-sensor controller, a data encoder (DE, and an error correct coder (ECC. To improve the system performance and expansion abilities, the asynchronous interface is created for handshaking different clock domains between ADC and RF with MCU. To increase the use time of the WBSN system, a power management technique is developed for reducing power consumption. In addition, the multi-sensor controller is designed for detecting various biomedical signals. To prevent loss error from wireless transmission, use of an error correct coding technique is important in biomedical applications. The data encoder is added for lossless compression of various biomedical signals with a compression ratio of almost three. This design is successfully tested on a FPGA board. The VLSI architecture of this work contains 2.68-K gate counts and consumes power 496-μW at 133-MHz processing rate by using TSMC 0.13-μm CMOS process. Compared with the previous techniques, this work offers higher performance, more functions, and lower hardware cost than other micro controller designs.

Dynamic Sensor Management Algorithm Based on Improved Efficacy Function

Directory of Open Access Journals (Sweden)

TANG Shujuan

2016-01-01

Full Text Available A dynamic sensor management algorithm based on improved efficacy function is proposed to solve the multi-target and multi-sensory management problem. The tracking task precision requirements (TPR, target priority and sensor use cost were considered to establish the efficacy function by weighted sum the normalized value of the three factors. The dynamic sensor management algorithm was accomplished through control the diversities of the desired covariance matrix (DCM and the filtering covariance matrix (FCM. The DCM was preassigned in terms of TPR and the FCM was obtained by the centralized sequential Kalman filtering algorithm. The simulation results prove that the proposed method could meet the requirements of desired tracking precision and adjust sensor selection according to target priority and cost of sensor source usage. This makes sensor management scheme more reasonable and effective.

Velocity Profile measurements in two-phase flow using multi-wave sensors

Science.gov (United States)

Biddinika, M. K.; Ito, D.; Takahashi, H.; Kikura, H.; Aritomi, M.

2009-02-01

Two-phase flow has been recognized as one of the most important phenomena in fluid dynamics. In addition, gas-liquid two-phase flow appears in various industrial fields such as chemical industries and power generations. In order to clarify the flow structure, some flow parameters have been measured by using many effective measurement techniques. The velocity profile as one of the important flow parameter, has been measured by using ultrasonic velocity profile (UVP) technique. This technique can measure velocity distributions along a measuring line, which is a beam formed by pulse ultrasounds. Furthermore, a multi-wave sensor can measure the velocity profiles of both gas and liquid phase using UVP method. In this study, two types of multi-wave sensors are used. A sensor has cylindrical shape, and another one has square shape. The piezoelectric elements of each sensor have basic frequencies of 8 MHz for liquid phase and 2 MHz for gas phase, separately. The velocity profiles of air-water bubbly flow in a vertical rectangular channel were measured by using these multi-wave sensors, and the validation of the measuring accuracy was performed by the comparison between the velocity profiles measured by two multi-wave sensors.

Velocity Profile measurements in two-phase flow using multi-wave sensors

International Nuclear Information System (INIS)

Biddinika, M K; Ito, D; Takahashi, H; Kikura, H; Aritomi, M

2009-01-01

Two-phase flow has been recognized as one of the most important phenomena in fluid dynamics. In addition, gas-liquid two-phase flow appears in various industrial fields such as chemical industries and power generations. In order to clarify the flow structure, some flow parameters have been measured by using many effective measurement techniques. The velocity profile as one of the important flow parameter, has been measured by using ultrasonic velocity profile (UVP) technique. This technique can measure velocity distributions along a measuring line, which is a beam formed by pulse ultrasounds. Furthermore, a multi-wave sensor can measure the velocity profiles of both gas and liquid phase using UVP method. In this study, two types of multi-wave sensors are used. A sensor has cylindrical shape, and another one has square shape. The piezoelectric elements of each sensor have basic frequencies of 8 MHz for liquid phase and 2 MHz for gas phase, separately. The velocity profiles of air-water bubbly flow in a vertical rectangular channel were measured by using these multi-wave sensors, and the validation of the measuring accuracy was performed by the comparison between the velocity profiles measured by two multi-wave sensors.

Miniaturized multi-sensor for aquatic studies

DEFF Research Database (Denmark)

Birkelund, Karen; Hyldgård, Anders; Mortensen, Dennis

2011-01-01

that allows for direct exposure to the seawater and thereby more accurate measurements. The chip contains a piezo-resistive pressure sensor, a pn-junction photodiode sensitive to visible light, a four-terminal platinum resistor for temperature measurement and four conductivity electrodes for the determination...... of the salinity of saltwater. Pressure, light intensity, temperature and salinity are all essential parameters when mapping the migration route of fish. The pressure sensor has a sensitivity of S = 1.44 × 10−7 Pa−1 and is optimized to 20 bar pressure; the light sensor has a quantum efficiency between 52% and 74......We have developed and fabricated a multi-sensor chip for fisheries’ research and demonstrated the functionality under controlled conditions. The outer dimensions of the sensor chip are 3.0 × 7.4 × 0.8 mm3 and both sides of the chip are utilized for sensors. Hereby a more compact chip is achieved...

Service-oriented multi-agent systems: architecture for the sensor web

CSIR Research Space (South Africa)

Terhorst, AL

2006-03-01

Full Text Available Understanding the behaviour of ecosystems requires persistent and multi-modal observation. Advances in sensor technology and distributed computing, coupled with the development of open standards that facilitate sensor/sensor network interoperability...

A Novel Energy-Efficient Multi-Sensor Fusion Wake-Up Control Strategy Based on a Biomimetic Infectious-Immune Mechanism for Target Tracking.

Science.gov (United States)

Zhou, Jie; Liang, Yan; Shen, Qiang; Feng, Xiaoxue; Pan, Quan

2018-04-18

A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.

Observability considerations for multi-sensor and product fusion: Bias, information content, and validation (Invited)

Science.gov (United States)

Reid, J. S.; Zhang, J.; Hyer, E. J.; Campbell, J. R.; Christopher, S. A.; Ferrare, R. A.; Leptoukh, G. G.; Stackhouse, P. W.

2009-12-01

With the successful development of many aerosol products from the NASA A-train as well as new operational geostationary and polar orbiting sensors, the scientific community now has a host of new parameters to use in their analyses. The variety and quality of products has reached a point where the community has moved from basic observation-based science to sophisticated multi-component research that addresses the complex atmospheric environment. In order for these satellite data contribute to the science their uncertainty levels must move from semi-quantitative to quantitative. Initial attempts to quantify uncertainties have led to some recent debate in the community as to the efficacy of aerosol products from current and future NASA satellite sensors. In an effort to understand the state of satellite product fidelity, the Naval Research Laboratory and a newly reformed Global Energy and Water Cycle Experiment (GEWEX) aerosol panel have both initiated assessments of the nature of aerosol remote sensing uncertainty and bias. In this talk we go over areas of specific concern based on the authors’ experiences with the data, emphasizing the multi-sensor problem. We first enumerate potential biases, including retrieval, sampling/contextual, and cognitive bias. We show examples of how these biases can subsequently lead to the pitfalls of correlated/compensating errors, tautology, and confounding. The nature of bias is closely related to the information content of the sensor signal and its subsequent application to the derived aerosol quantity of interest (e.g., optical depth, flux, index of refraction, etc.). Consequently, purpose-specific validation methods must be employed, especially when generating multi-sensor products. Indeed, cloud and lower boundary condition biases in particular complicate the more typical methods of regressional bias elimination and histogram matching. We close with a discussion of sequestration of uncertainty in multi-sensor applications of

A multi-agent system architecture for sensor networks.

Science.gov (United States)

Fuentes-Fernández, Rubén; Guijarro, María; Pajares, Gonzalo

2009-01-01

The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work.

Delay/Disruption Tolerant Network-Based Message Forwarding for a River Pollution Monitoring Wireless Sensor Network Application

Directory of Open Access Journals (Sweden)

Carlos Velásquez-Villada

2016-03-01

Full Text Available Communications from remote areas that may be of interest is still a problem. Many innovative projects applied to remote sites face communications difficulties. The GOLDFISH project was an EU-funded project for river pollution monitoring in developing countries. It had several sensor clusters, with floating WiFi antennas, deployed along a downstream river’s course. Sensor clusters sent messages to a Gateway installed on the riverbank. This gateway sent the messages, through a backhaul technology, to an Internet server where data was aggregated over a map. The communication challenge in this scenario was produced by the antennas’ movement and network backhaul availability. Since the antennas were floating on the river, communications could be disrupted at any time. Also, 2G/3G availability near the river was not constant. For non-real-time applications, we propose a Delay/Disruption Tolerant Network (DTN-based solution where all nodes have persistent storage capabilities and DTN protocols to be able to wait minutes or hours to transmit. A mechanical backhaul will periodically visit the river bank where the gateway is installed and it will automatically collect sensor data to be carried to an Internet-covered spot. The proposed forwarding protocol delivers around 98% of the messages for this scenario, performing better than other well-known DTN routing protocols.

Breakdown voltage reduction by field emission in multi-walled carbon nanotubes based ionization gas sensor

Energy Technology Data Exchange (ETDEWEB)

Saheed, M. Shuaib M.; Muti Mohamed, Norani; Arif Burhanudin, Zainal, E-mail: zainabh@petronas.com.my [Centre of Innovative Nanostructures and Nanodevices, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak (Malaysia)

2014-03-24

Ionization gas sensors using vertically aligned multi-wall carbon nanotubes (MWCNT) are demonstrated. The sharp tips of the nanotubes generate large non-uniform electric fields at relatively low applied voltage. The enhancement of the electric field results in field emission of electrons that dominates the breakdown mechanism in gas sensor with gap spacing below 14 μm. More than 90% reduction in breakdown voltage is observed for sensors with MWCNT and 7 μm gap spacing. Transition of breakdown mechanism, dominated by avalanche electrons to field emission electrons, as decreasing gap spacing is also observed and discussed.

Advanced Integrated Multi-Sensor Surveillance (AIMS. Operator Machine Interface (OMI) Definition Study

National Research Council Canada - National Science Library

Baker, Kevin; Youngson, Gord

2007-01-01

To enhance the capability of airborne search and rescue (SAR) and surveillance, particularly at night and in poor weather, a multi sensor electro optical imaging system, the Advanced Integrated Multi sensor Surveillance (AIMS...

Online vehicle and atmospheric pollution monitoring using GIS and wireless sensor networks

International Nuclear Information System (INIS)

Cordova-Lopez, L E; Mason, A; Cullen, J D; Shaw, A; Al-Shamma'a, A I

2007-01-01

A Geographical Information System (GIS) is a computer system designed to integrate, store, edit, analyse, share and display geographically referenced data. A wireless sensor network (WSN) is a wireless network of spatially distributed autonomous devices using sensors to monitor physical or environmental conditions. This paper presents the integration of these two technologies to create a system able to detect measure and transmit information regarding the presence and quantities of internal combustion derived pollution and the geographical location in real time with the aim of creating pollution maps in urban environments

Vision communications based on LED array and imaging sensor

Science.gov (United States)

Yoo, Jong-Ho; Jung, Sung-Yoon

2012-11-01

In this paper, we propose a brand new communication concept, called as "vision communication" based on LED array and image sensor. This system consists of LED array as a transmitter and digital device which include image sensor such as CCD and CMOS as receiver. In order to transmit data, the proposed communication scheme simultaneously uses the digital image processing and optical wireless communication scheme. Therefore, the cognitive communication scheme is possible with the help of recognition techniques used in vision system. By increasing data rate, our scheme can use LED array consisting of several multi-spectral LEDs. Because arranged each LED can emit multi-spectral optical signal such as visible, infrared and ultraviolet light, the increase of data rate is possible similar to WDM and MIMO skills used in traditional optical and wireless communications. In addition, this multi-spectral capability also makes it possible to avoid the optical noises in communication environment. In our vision communication scheme, the data packet is composed of Sync. data and information data. Sync. data is used to detect the transmitter area and calibrate the distorted image snapshots obtained by image sensor. By making the optical rate of LED array be same with the frame rate (frames per second) of image sensor, we can decode the information data included in each image snapshot based on image processing and optical wireless communication techniques. Through experiment based on practical test bed system, we confirm the feasibility of the proposed vision communications based on LED array and image sensor.

RadMAP: The Radiological Multi-sensor Analysis Platform

International Nuclear Information System (INIS)

Bandstra, Mark S.; Aucott, Timothy J.; Brubaker, Erik; Chivers, Daniel H.; Cooper, Reynold J.; Curtis, Joseph C.; Davis, John R.; Joshi, Tenzing H.; Kua, John; Meyer, Ross; Negut, Victor; Quinlan, Michael; Quiter, Brian J.; Srinivasan, Shreyas; Zakhor, Avideh; Zhang, Richard; Vetter, Kai

2016-01-01

The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

RadMAP: The Radiological Multi-sensor Analysis Platform

Energy Technology Data Exchange (ETDEWEB)

Bandstra, Mark S., E-mail: msbandstra@lbl.gov [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Aucott, Timothy J. [Department of Nuclear Engineering, University of California Berkeley, CA (United States); Brubaker, Erik [Sandia National Laboratory, Livermore, CA (United States); Chivers, Daniel H.; Cooper, Reynold J. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Curtis, Joseph C. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Department of Nuclear Engineering, University of California Berkeley, CA (United States); Davis, John R. [Department of Nuclear Engineering, University of California Berkeley, CA (United States); Joshi, Tenzing H.; Kua, John; Meyer, Ross; Negut, Victor; Quinlan, Michael; Quiter, Brian J. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Srinivasan, Shreyas [Department of Nuclear Engineering, University of California Berkeley, CA (United States); Department of Electrical Engineering and Computer Science, University of California Berkeley, CA (United States); Zakhor, Avideh; Zhang, Richard [Department of Electrical Engineering and Computer Science, University of California Berkeley, CA (United States); Vetter, Kai [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Department of Nuclear Engineering, University of California Berkeley, CA (United States)

2016-12-21

The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

Biomagnetic Monitoring of Atmospheric Pollution: A Review of Magnetic Signatures from Biological Sensors.

Science.gov (United States)

Hofman, Jelle; Maher, Barbara A; Muxworthy, Adrian R; Wuyts, Karen; Castanheiro, Ana; Samson, Roeland

2017-06-20

Biomagnetic monitoring of atmospheric pollution is a growing application in the field of environmental magnetism. Particulate matter (PM) in atmospheric pollution contains readily measurable concentrations of magnetic minerals. Biological surfaces, exposed to atmospheric pollution, accumulate magnetic particles over time, providing a record of location-specific, time-integrated air quality information. This review summarizes current knowledge of biological material ("sensors") used for biomagnetic monitoring purposes. Our work addresses the following: the range of magnetic properties reported for lichens, mosses, leaves, bark, trunk wood, insects, crustaceans, mammal and human tissues; their associations with atmospheric pollutant species (PM, NO x , trace elements, PAHs); the pros and cons of biomagnetic monitoring of atmospheric pollution; current challenges for large-scale implementation of biomagnetic monitoring; and future perspectives. A summary table is presented, with the aim of aiding researchers and policy makers in selecting the most suitable biological sensor for their intended biomagnetic monitoring purpose.

MULTI-SENSOR NETWORK FOR LANDSLIDES SIMULATION AND HAZARD MONITORING - DESIGN AND DEPLOYMENT

Directory of Open Access Journals (Sweden)

H. Wu

2012-08-01

Full Text Available This paper describes a newly developed multi-sensor network system for landslide and hazard monitoring. Landslide hazard is one of the most destructive natural disasters, which has severely affected human safety, properties and infrastructures. We report the results of designing and deploying the multi-sensor network, based on the simulated landslide model, to monitor typical landslide areas and with a goal to predict landslide hazard and mitigate damages. The integration and deployment of the prototype sensor network were carried out in an experiment area at Tongji University in Shanghai. In order to simulate a real landslide, a contractible landslide body is constructed in the experiment area by 7m*1.5m. Then, some different kind of sensors, such as camera, GPS, crackmeter, accelerometer, laser scanning system, inclinometer, etc., are installed near or in the landslide body. After the sensors are powered, continuous sampling data will be generated. With the help of communication method, such as GPRS, and certain transport devices, such as iMesh and 3G router, all the sensor data will be transported to the server and stored in Oracle. These are the current results of an ongoing project of the center. Further research results will be updated and presented in the near future.

Real Time Assessment of Potable Water Quality in Distribution Network based on Low Cost Multi-Sensor Array

Science.gov (United States)

Bhardwaj, Jyotirmoy; Gupta, Karunesh K.; Khatri, Punit

2018-03-01

New concepts and techniques are replacing traditional methods of water quality parameters measurement systems. This paper proposed a new way of potable water quality assessment in distribution network using Multi Sensor Array (MSA). Extensive research suggests that following parameters i.e. pH, Dissolved Oxygen (D.O.), Conductivity, Oxygen Reduction Potential (ORP), Temperature and Salinity are most suitable to detect overall quality of potable water. Commonly MSA is not an integrated sensor array on some substrate, but rather comprises a set of individual sensors measuring simultaneously different water parameters all together. Based on research, a MSA has been developed followed by signal conditioning unit and finally, an algorithm for easy user interfacing. A dedicated part of this paper also discusses the platform design and significant results. The Objective of this proposed research is to provide simple, efficient, cost effective and socially acceptable means to detect and analyse water bodies regularly and automatically.

Gold nanoparticle-based optical microfluidic sensors for analysis of environmental pollutants

DEFF Research Database (Denmark)

Lafleur, Josiane P.; Senkbeil, Silja; Jensen, Thomas G.

2012-01-01

Conventional methods of environmental analysis can be significantly improved by the development of portable microscale technologies for direct in-field sensing at remote locations. This report demonstrates the vast potential of gold nanoparticle-based microfluidic sensors for the rapid, in......-field, detection of two important classes of environmental contaminants – heavy metals and pesticides. Using gold nanoparticle-based microfluidic sensors linked to a simple digital camera as the detector, detection limits as low as 0.6 μg L−1 and 16 μg L−1 could be obtained for the heavy metal mercury...... and the dithiocarbamate pesticide ziram, respectively. These results demonstrate that the attractive optical properties of gold nanoparticle probes combine synergistically with the inherent qualities of microfluidic platforms to offer simple, portable and sensitive sensors for environmental contaminants....

Development and Application of a Next Generation Air Sensor Network for the Hong Kong Marathon 2015 Air Quality Monitoring.

Science.gov (United States)

Sun, Li; Wong, Ka Chun; Wei, Peng; Ye, Sheng; Huang, Hao; Yang, Fenhuan; Westerdahl, Dane; Louie, Peter K K; Luk, Connie W Y; Ning, Zhi

2016-02-05

This study presents the development and evaluation of a next generation air monitoring system with both laboratory and field tests. A multi-parameter algorithm was used to correct for the impact of environmental conditions on the electrochemical sensors for carbon monoxide (CO) and nitrogen dioxide (NO2) pollutants. The field evaluation in an urban roadside environment in comparison to designated monitors showed good agreement with measurement error within 5% of the pollutant concentrations. Multiple sets of the developed system were then deployed in the Hong Kong Marathon 2015 forming a sensor-based network along the marathon route. Real-time air pollution concentration data were wirelessly transmitted and the Air Quality Health Index (AQHI) for the Green Marathon was calculated, which were broadcast to the public on an hourly basis. The route-specific sensor network showed somewhat different pollutant patterns than routine air monitoring, indicating the immediate impact of traffic control during the marathon on the roadside air quality. The study is one of the first applications of a next generation sensor network in international sport events, and it demonstrated the usefulness of the emerging sensor-based air monitoring technology in rapid network deployment to supplement existing air monitoring.

A Framework Based on Reference Data with Superordinate Accuracy for the Quality Analysis of Terrestrial Laser Scanning-Based Multi-Sensor-Systems.

Science.gov (United States)

Stenz, Ulrich; Hartmann, Jens; Paffenholz, Jens-André; Neumann, Ingo

2017-08-16

Terrestrial laser scanning (TLS) is an efficient solution to collect large-scale data. The efficiency can be increased by combining TLS with additional sensors in a TLS-based multi-sensor-system (MSS). The uncertainty of scanned points is not homogenous and depends on many different influencing factors. These include the sensor properties, referencing, scan geometry (e.g., distance and angle of incidence), environmental conditions (e.g., atmospheric conditions) and the scanned object (e.g., material, color and reflectance, etc.). The paper presents methods, infrastructure and results for the validation of the suitability of TLS and TLS-based MSS. Main aspects are the backward modelling of the uncertainty on the basis of reference data (e.g., point clouds) with superordinate accuracy and the appropriation of a suitable environment/infrastructure (e.g., the calibration process of the targets for the registration of laser scanner and laser tracker data in a common coordinate system with high accuracy) In this context superordinate accuracy means that the accuracy of the acquired reference data is better by a factor of 10 than the data of the validated TLS and TLS-based MSS. These aspects play an important role in engineering geodesy, where the aimed accuracy lies in a range of a few mm or less.

Integrative Multi-Spectral Sensor Device for Far-Infrared and Visible Light Fusion

Science.gov (United States)

Qiao, Tiezhu; Chen, Lulu; Pang, Yusong; Yan, Gaowei

2018-06-01

Infrared and visible light image fusion technology is a hot spot in the research of multi-sensor fusion technology in recent years. Existing infrared and visible light fusion technologies need to register before fusion because of using two cameras. However, the application effect of the registration technology has yet to be improved. Hence, a novel integrative multi-spectral sensor device is proposed for infrared and visible light fusion, and by using the beam splitter prism, the coaxial light incident from the same lens is projected to the infrared charge coupled device (CCD) and visible light CCD, respectively. In this paper, the imaging mechanism of the proposed sensor device is studied with the process of the signals acquisition and fusion. The simulation experiment, which involves the entire process of the optic system, signal acquisition, and signal fusion, is constructed based on imaging effect model. Additionally, the quality evaluation index is adopted to analyze the simulation result. The experimental results demonstrate that the proposed sensor device is effective and feasible.

Facile Fabrication of Multi-hierarchical Porous Polyaniline Composite as Pressure Sensor and Gas Sensor with Adjustable Sensitivity

Science.gov (United States)

He, Xiao-Xiao; Li, Jin-Tao; Jia, Xian-Sheng; Tong, Lu; Wang, Xiao-Xiong; Zhang, Jun; Zheng, Jie; Ning, Xin; Long, Yun-Ze

2017-08-01

A multi-hierarchical porous polyaniline (PANI) composite which could be used in good performance pressure sensor and adjustable sensitivity gas sensor has been fabricated by a facile in situ polymerization. Commercial grade sponge was utilized as a template scaffold to deposit PANI via in situ polymerization. With abundant interconnected pores throughout the whole structure, the sponge provided sufficient surface for the growth of PANI nanobranches. The flexible porous structure helped the composite to show high performance in pressure detection with fast response and favorable recoverability and gas detection with adjustable sensitivity. The sensing mechanism of the PANI/sponge-based flexible sensor has also been discussed. The results indicate that this work provides a feasible approach to fabricate efficient sensors with advantages of low cost, facile preparation, and easy signal collection.

Performance evaluation of multi-sensor data-fusion systems

Indian Academy of Sciences (India)

In this paper, the utilization of multi-sensors of different types, their characteristics, and their data-fusion in launch vehicles to achieve the goal of injecting the satellite into a precise orbit is explained. Performance requirements of sensors and their redundancy management in a typical launch vehicle are also included.

An Airborne Wireless Sensor System for Near-Real Time Air Pollution Monitoring

Directory of Open Access Journals (Sweden)

Orestis EVANGELATOS

2015-06-01

Full Text Available Over the last decades with the rapid growth of industrial zones, manufacturing plants and the substantial urbanization, environmental pollution has become a crucial health, environmental and safety concern. In particular, due to the increased emissions of various pollutants caused mainly by human sources, the air pollution problem is elevated in such extent where significant measures need to be taken. Towards the identification and the qualification of that problem, we present in this paper an airborne wireless sensor network system for automated monitoring and measuring of the ambient air pollution. Our proposed system is comprised of a pollution-aware wireless sensor network and unmanned aerial vehicles (UAVs. It is designed for monitoring the pollutants and gases of the ambient air in three-dimensional spaces without the human intervention. In regards to the general architecture of our system, we came up with two schemes and algorithms for an autonomous monitoring of a three-dimensional area of interest. To demonstrate our solution, we deployed the system and we conducted experiments in a real environment measuring air pollutants such as: NH3, CH4, CO2, O2 along with temperature, relative humidity and atmospheric pressure. Lastly, we experimentally evaluated and analyzed the two proposed schemes.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

Science.gov (United States)

Cruz-Piris, Luis; Rivera, Diego; Fernandez, Susel; Marsa-Maestre, Ivan

2018-02-02

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Game Design to Measure Reflexes and Attention Based on Biofeedback Multi-Sensor Interaction

Directory of Open Access Journals (Sweden)

Inigo de Loyola Ortiz-Vigon Uriarte

2015-03-01

Full Text Available This paper presents a multi-sensor system for implementing biofeedback as a human-computer interaction technique in a game involving driving cars in risky situations. The sensors used are: Eye Tracker, Kinect, pulsometer, respirometer, electromiography (EMG and galvanic skin resistance (GSR. An algorithm has been designed which gives rise to an interaction logic with the game according to the set of physiological constants obtained from the sensors. The results reflect a 72.333 response to the System Usability Scale (SUS, a significant difference of p = 0.026 in GSR values in terms of the difference between the start and end of the game, and an r = 0.659 and p = 0.008 correlation while playing with the Kinect between the breathing level and the energy and joy factor. All the sensors used had an impact on the end results, whereby none of them should be disregarded in future lines of research, even though it would be interesting to obtain separate breathing values from that of the cardio.

Game Design to Measure Reflexes and Attention Based on Biofeedback Multi-Sensor Interaction

Science.gov (United States)

Ortiz-Vigon Uriarte, Inigo de Loyola; Garcia-Zapirain, Begonya; Garcia-Chimeno, Yolanda

2015-01-01

This paper presents a multi-sensor system for implementing biofeedback as a human-computer interaction technique in a game involving driving cars in risky situations. The sensors used are: Eye Tracker, Kinect, pulsometer, respirometer, electromiography (EMG) and galvanic skin resistance (GSR). An algorithm has been designed which gives rise to an interaction logic with the game according to the set of physiological constants obtained from the sensors. The results reflect a 72.333 response to the System Usability Scale (SUS), a significant difference of p = 0.026 in GSR values in terms of the difference between the start and end of the game, and an r = 0.659 and p = 0.008 correlation while playing with the Kinect between the breathing level and the energy and joy factor. All the sensors used had an impact on the end results, whereby none of them should be disregarded in future lines of research, even though it would be interesting to obtain separate breathing values from that of the cardio. PMID:25789493

CONTEXT-CAPTURE MULTI-VALUED DECISION FUSION WITH FAULT TOLERANT CAPABILITY FOR WIRELESS SENSOR NETWORKS

OpenAIRE

Jun Wu; Shigeru Shimamoto

2011-01-01

Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in pract...

Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines

Directory of Open Access Journals (Sweden)

Jingjing Xu

2015-08-01

Full Text Available In this paper, a wireless sensor network (WSN technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD algorithm with particle swarm optimization (PSO, namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.

Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines.

Science.gov (United States)

Xu, Jingjing; Yang, Wei; Zhang, Linyuan; Han, Ruisong; Shao, Xiaotao

2015-08-27

In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.

A Modular Plug-And-Play Sensor System for Urban Air Pollution Monitoring: Design, Implementation and Evaluation.

Science.gov (United States)

Yi, Wei-Ying; Leung, Kwong-Sak; Leung, Yee

2017-12-22

Urban air pollution has caused public concern globally because it seriously affects human life. Modern monitoring systems providing pollution information with high spatio-temporal resolution have been developed to identify personal exposures. However, these systems' hardware specifications and configurations are usually fixed according to the applications. They can be inconvenient to maintain, and difficult to reconfigure and expand with respect to sensing capabilities. This paper aims at tackling these issues by adopting the proposed Modular Sensor System (MSS) architecture and Universal Sensor Interface (USI), and modular design in a sensor node. A compact MSS sensor node is implemented and evaluated. It has expandable sensor modules with plug-and-play feature and supports multiple Wireless Sensor Networks (WSNs). Evaluation results show that MSS sensor nodes can easily fit in different scenarios, adapt to reconfigurations dynamically, and detect low concentration air pollution with high energy efficiency and good data accuracy. We anticipate that the efforts on system maintenance, adaptation, and evolution can be significantly reduced when deploying the system in the field.

Multi-sensor control for precise assembly of optical components

Directory of Open Access Journals (Sweden)

Ma Li

2014-06-01

Full Text Available In order to perform an optical assembly accurately, a multi-sensor control strategy is developed which includes an attitude measurement system, a vision system, a loss measurement system and a force sensor. A 3-DOF attitude measuring method using linear variable differential transformers (LVDT is designed to adjust the relation of position and attitude between the spherical mirror and the resonator. A micro vision feedback system is set up to extract the light beam and the diaphragm, which can achieve the coarse positioning of the spherical mirror in the optical assembly process. A rapid self-correlation method is presented to analyze the spectrum signal for the fine positioning. In order to prevent the damage of the optical components and realize sealing of the resonator, a hybrid force-position control is constructed to control the contact force of the optical components. The experimental results show that the proposed multi-sensor control strategy succeeds in accomplishing the precise assembly of the optical components, which consists of parallel adjustment, macro coarse adjustment, macro approach, micro fine adjustment, micro approach and optical contact. Therefore, the results validate the multi-sensor control strategy.

Multi-Parameter Aerosol Scattering Sensor

Science.gov (United States)

Greenberg, Paul S.; Fischer, David G.

2011-01-01

This work relates to the development of sensors that measure specific aerosol properties. These properties are in the form of integrated moment distributions, i.e., total surface area, total mass, etc., or mathematical combinations of these moment distributions. Specifically, the innovation involves two fundamental features: a computational tool to design and optimize such sensors and the embodiment of these sensors in actual practice. The measurement of aerosol properties is a problem of general interest. Applications include, but are not limited to, environmental monitoring, assessment of human respiratory health, fire detection, emission characterization and control, and pollutant monitoring. The objectives for sensor development include increased accuracy and/or dynamic range, the inclusion in a single sensor of the ability to measure multiple aerosol properties, and developing an overall physical package that is rugged, compact, and low in power consumption, so as to enable deployment in harsh or confined field applications, and as distributed sensor networks. Existing instruments for this purpose include scattering photometers, direct-reading mass instruments, Beta absorption devices, differential mobility analyzers, and gravitational samplers. The family of sensors reported here is predicated on the interaction of light and matter; specifically, the scattering of light from distributions of aerosol particles. The particular arrangement of the sensor, e.g. the wavelength(s) of incident radiation, the number and location of optical detectors, etc., can be derived so as to optimize the sensor response to aerosol properties of practical interest. A key feature of the design is the potential embodiment as an extremely compact, integrated microsensor package. This is of fundamental importance, as it enables numerous previously inaccessible applications. The embodiment of these sensors is inherently low maintenance and high reliability by design. The novel and

Multi-Channel Multi-Radio Using 802.11 Based Media Access for Sink Nodes in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Kok-Keong Loo

2011-05-01

Full Text Available The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.

Multi-channel multi-radio using 802.11 based media access for sink nodes in wireless sensor networks.

Science.gov (United States)

Campbell, Carlene E-A; Khan, Shafiullah; Singh, Dhananjay; Loo, Kok-Keong

2011-01-01

The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.

A Multi-Agent System Architecture for Sensor Networks

Directory of Open Access Journals (Sweden)

María Guijarro

2009-12-01

Full Text Available The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work.

Multi-pollutant interactions in hyporheic zones

Science.gov (United States)

Krause, S.; Weatherill, J.; Bonet, B.; Blaen, P.; Khamis, K.; Cassidy, N. J.; Hannah, D. M.; Rivett, M. O.; Lynch, I.; Ullah, S.

2017-12-01

Hyporheic zones represent hotspots of biogeochemical reactivity, with the potential to attenuate pollutants and ameliorate their impact on ecosystem functioning. Sources and types of pollutants in streambed environments are manifold, with legacy industry contaminants, agricultural pollution and emerging pollutants such as pharmaceuticals or engineered nanoparticles entering hyporheic zones along different flow paths where they mix and potentially react with each other. Current conceptualizations of drivers and controls of biogeochemical turnover in hyporheic zones highlight primarily the role of transport and reaction times but do not account for potential interactions between different pollutants. This study presents two case studies of multi-pollutant interactions to illustrate the need to consider interferences between different pollutants, their transport and reaction pathways for adequate impact assessment. We discuss in the first instance how the natural attenuation of a Trichloroethylene (TCE) groundwater plume in an agricultural catchment is limited by high riparian and hyporheic nitrate concentrations. As nitrate outcompeted TCE in its reaction with organic carbon as electron donor, TCE attenuation was in this case limited to hyporheic denitrification hotspots. Hence any pollution control measures to reduce the impact of this TCE plume require a reduction of agricultural nitrate loads, highlighting the connectedness of legacy (TCE) and more recent (nitrate) pollution problems. In the second case, we investigate how the labile organic carbon content of streambed sediments as main control of hyporheic respiration is overridden by exposure to different silver nanoparticle concentrations, representing emerging pollutants in many of our rivers. Also in this case, the impacts of different stressors (nanoparticle exposure) and drivers (availability of organic matter, water temperature) are interacting in their impacts on hyporheic zone functioning. We argue that

A fiber optic temperature sensor based on multi-core microstructured fiber with coupled cores for a high temperature environment

Science.gov (United States)

Makowska, A.; Markiewicz, K.; Szostkiewicz, L.; Kolakowska, A.; Fidelus, J.; Stanczyk, T.; Wysokinski, K.; Budnicki, D.; Ostrowski, L.; Szymanski, M.; Makara, M.; Poturaj, K.; Tenderenda, T.; Mergo, P.; Nasilowski, T.

2018-02-01

Sensors based on fiber optics are irreplaceable wherever immunity to strong electro-magnetic fields or safe operation in explosive atmospheres is needed. Furthermore, it is often essential to be able to monitor high temperatures of over 500°C in such environments (e.g. in cooling systems or equipment monitoring in power plants). In order to meet this demand, we have designed and manufactured a fiber optic sensor with which temperatures up to 900°C can be measured. The sensor utilizes multi-core fibers which are recognized as the dedicated medium for telecommunication or shape sensing, but as we show may be also deployed advantageously in new types of fiber optic temperature sensors. The sensor presented in this paper is based on a dual-core microstructured fiber Michelson interferometer. The fiber is characterized by strongly coupled cores, hence it acts as an all-fiber coupler, but with an outer diameter significantly wider than a standard fused biconical taper coupler, which significantly increases the coupling region's mechanical reliability. Owing to the proposed interferometer imbalance, effective operation and high-sensitivity can be achieved. The presented sensor is designed to be used at high temperatures as a result of the developed low temperature chemical process of metal (copper or gold) coating. The hermetic metal coating can be applied directly to the silica cladding of the fiber or the fiber component. This operation significantly reduces the degradation of sensors due to hydrolysis in uncontrolled atmospheres and high temperatures.

Impact of repeated single-metal and multi-metal pollution events on soil quality.

Science.gov (United States)

Burges, Aritz; Epelde, Lur; Garbisu, Carlos

2015-02-01

Most frequently, soil metal pollution results from the occurrence of repeated single-metal and, above all, multi-metal pollution events, with concomitant adverse consequences for soil quality. Therefore, in this study, we evaluated the impact of repeated single-metal and multi-metal (Cd, Pb, Cu, Zn) pollution events on soil quality, as reflected by the values of a variety of soil microbial parameters with potential as bioindicators of soil functioning. Specifically, parameters of microbial activity (potentially mineralizable nitrogen, β-glucosidase and acid phosphatase activity) and biomass (fungal and bacterial gene abundance by RT-qPCR) were determined, in the artificially metal-polluted soil samples, at regular intervals over a period of 26 weeks. Similarly, we studied the evolution over time of CaCl2-extractable metal fractions, in order to estimate metal bioavailability in soil. Different metals showed different values of bioavailability and relative bioavailability ([metal]bio/[metal]tot) in soil throughout the experiment, under both repeated single-metal and multi-metal pollution events. Both repeated Zn-pollution and multi-metal pollution events led to a significant reduction in the values of acid phosphatase activity, and bacterial and fungal gene abundance, reflecting the negative impact of these repeated events on soil microbial activity and biomass, and, hence, soil quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multi-Sensor Testing for Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Lab

Science.gov (United States)

Brewster, Linda L.; Howard, Richard T.; Johnston, A. S.; Carrington, Connie; Mitchell, Jennifer D.; Cryan, Scott P.

2008-01-01

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as AR&D). The crewed missions may also perform rendezvous and docking operations and may require different levels of automation and/or autonomy, and must provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success ofthe Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor-proposed relative navigation sensor suite will meet the requirements. The relatively low technology readiness level of AR&D relative navigation sensors has been carried as one of the CEV Project's top risks. The AR&D Sensor Technology Project seeks to reduce the risk by the testing and analysis of selected relative navigation sensor technologies through hardware-in-the-Ioop testing and simulation. These activities will provide the CEV Project information to assess the relative navigation sensors maturity as well as demonstrate test methods and capabilities. The first year of this project focused on a series of "pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes that will be used to evaluate the Contractor-proposed sensors. Four candidate sensors were used in the first phase of the testing. The second phase of testing used four sensors simultaneously: two Marshall Space Flight Center (MSFC) Advanced Video Guidance Sensors (AVGS), a laser-based video sensor that uses retroreflectors attached to the target vehicle, and two commercial laser range finders. The multi-sensor testing was conducted at MSFC's Flight Robotics Laboratory (FRL

Greenridge Multi-Pollutant Control Project Preliminary Public Design Report

Energy Technology Data Exchange (ETDEWEB)

Connell, Daniel P

2009-01-12

The Greenidge Multi-Pollutant Control Project is being conducted as part of the U.S. Department of Energy's Power Plant Improvement Initiative to demonstrate an innovative combination of air pollution control technologies that can cost-effectively reduce emissions of SO{sub 2}, NO{sub x}, Hg, acid gases (SO{sub 3}, HCl, and HF), and particulate matter from smaller coal-fired electrical generating units (EGUs). The multi-pollutant control system includes a hybrid selective non-catalytic reduction (SNCR)/in-duct selective catalytic reduction (SCR) system to reduce NOx emissions by {ge}60%, followed by a Turbosorp{reg_sign} circulating fluidized bed dry scrubber system to reduce emissions of SO{sub 2}, SO{sub 3}, HCl, and HF by {ge}95%. Mercury removal of {ge}90% is also targeted via the co-benefits afforded by the in-duct SCR, dry scrubber, and baghouse and by injection of activated carbon upstream of the scrubber, as required. The technology is particularly well suited, because of its relatively low capital and maintenance costs and small space requirements, to meet the needs of coal-fired units with capacities of 50-300 MWe. There are about 440 such units in the United States that currently are not equipped with SCR, flue gas desulfurization (FGD), or mercury control systems. These smaller units are a valuable part of the nation's energy infrastructure, constituting about 60 GW of installed capacity. However, with the onset of the Clean Air Interstate Rule, Clean Air Mercury Rule, and various state environmental actions requiring deep reductions in emissions of SO{sub 2}, NO{sub x}, and mercury, the continued operation of these units increasingly depends upon the ability to identify viable air pollution control retrofit options for them. The large capital costs and sizable space requirements associated with conventional technologies such as SCR and wet FGD make these technologies unattractive for many smaller units. The Greenidge Project aims to confirm

A Radiosonde Using a Humidity Sensor Array with a Platinum Resistance Heater and Multi-Sensor Data Fusion

Science.gov (United States)

Shi, Yunbo; Luo, Yi; Zhao, Wenjie; Shang, Chunxue; Wang, Yadong; Chen, Yinsheng

2013-01-01

This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes. PMID:23857263

Electrochemical sensors applied to pollution monitoring: Measurement error and gas ratio bias - A volcano plume case study

Science.gov (United States)

Roberts, T. J.; Saffell, J. R.; Oppenheimer, C.; Lurton, T.

2014-06-01

There is an increasing scientific interest in the use of miniature electrochemical sensors to detect and quantify atmospheric trace gases. This has led to the development of ‘Multi-Gas' systems applied to measurements of both volcanic gas emissions, and urban air pollution. However, such measurements are subject to uncertainties introduced by sensor response time, a critical issue that has received limited attention to date. Here, a detailed analysis of output from an electrochemical SO2 sensor and two H2S sensors (contrasting in their time responses and cross-sensitivities) demonstrates how instrument errors arise under the conditions of rapidly fluctuating (by dilution) gas abundances, leading to scatter and importantly bias in the reported gas ratios. In a case study at Miyakejima volcano (Japan), electrochemical sensors were deployed at both the crater-rim and downwind locations, thereby exposed to rapidly fluctuating and smoothly varying plume gas concentrations, respectively. Discrepancies in the H2S/SO2 gas mixing ratios derived from these measurements are attributed to the sensors' differing time responses to SO2 and H2S under fluctuating plume conditions, with errors magnified by the need to correct for SO2 interference in the H2S readings. Development of a sensor response model that reproduces sensor t90 behaviour (the time required to reach 90% of the final signal following a step change in gas abundance) during calibration enabled this measurement error to be simulated numerically. The sensor response times were characterised as SO2 sensor (t90 ~ 13 s), H2S sensor without interference (t90 ~ 11 s), and H2S sensor with interference (t90 ~ 20 s to H2S and ~ 32 s to SO2). We show that a method involving data integration between periods of episodic plume exposure identifiable in the sensor output yields a less biased H2S/SO2 ratio estimate than that derived from standard analysis approaches. For the Miyakejima crater-rim dataset this method yields highly

A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

CERN Document Server

Parasuraman, Ramviyas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

2014-01-01

The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide red...

Semiconductor device-based sensors for gas, chemical, and biomedical applications

CERN Document Server

Ren, Fan

2011-01-01

Sales of U.S. chemical sensors represent the largest segment of the multi-billion-dollar global sensor market, which includes instruments for chemical detection in gases and liquids, biosensors, and medical sensors. Although silicon-based devices have dominated the field, they are limited by their general inability to operate in harsh environments faced with factors such as high temperature and pressure. Exploring how and why these instruments have become a major player, Semiconductor Device-Based Sensors for Gas, Chemical, and Biomedical Applications presents the latest research, including or

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

Directory of Open Access Journals (Sweden)

Luis Cruz-Piris

2018-02-01

Full Text Available One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

Directory of Open Access Journals (Sweden)

Xiang He

2015-12-01

Full Text Available Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer, wireless signal strength indicators (WiFi, Bluetooth, Zigbee, and visual sensors (LiDAR, camera. People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

Multi-Temporal Multi-Sensor Analysis of Urbanization and Environmental/Climate Impact in China for Sustainable Urban Development

Science.gov (United States)

Ban, Yifang; Gong, Peng; Gamba, Paolo; Taubenbock, Hannes; Du, Peijun

2016-08-01

The overall objective of this research is to investigate multi-temporal, multi-scale, multi-sensor satellite data for analysis of urbanization and environmental/climate impact in China to support sustainable planning. Multi- temporal multi-scale SAR and optical data have been evaluated for urban information extraction using innovative methods and algorithms, including KTH- Pavia Urban Extractor, Pavia UEXT, and an "exclusion- inclusion" framework for urban extent extraction, and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Various pixel- based and object-based change detection algorithms were also developed to extract urban changes. Several Chinese cities including Beijing, Shanghai and Guangzhou are selected as study areas. Spatio-temporal urbanization patterns and environmental impact at regional, metropolitan and city core were evaluated through ecosystem service, landscape metrics, spatial indices, and/or their combinations. The relationship between land surface temperature and land-cover classes was also analyzed.The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal SAR data with the KTH-Pavia Urban Extractor and UEXT. The fusion of SAR data at multiple scales from multiple sensors was proven to improve urban extraction. For urban land cover mapping, the results show that the fusion of multitemporal SAR and optical data could produce detailed land cover maps with improved accuracy than that of SAR or optical data alone. Pixel-based and object-based change detection algorithms developed with the project were effective to extract urban changes. Comparing the urban land cover results from mulitemporal multisensor data, the environmental impact analysis indicates major losses for food supply, noise reduction, runoff mitigation, waste treatment and global climate regulation services through landscape structural changes in terms of decreases in service area, edge

Multi-channel distributed coordinated function over single radio in wireless sensor networks.

Science.gov (United States)

Campbell, Carlene E-A; Loo, Kok-Keong Jonathan; Gemikonakli, Orhan; Khan, Shafiullah; Singh, Dhananjay

2011-01-01

Multi-channel assignments are becoming the solution of choice to improve performance in single radio for wireless networks. Multi-channel allows wireless networks to assign different channels to different nodes in real-time transmission. In this paper, we propose a new approach, Multi-channel Distributed Coordinated Function (MC-DCF) which takes advantage of multi-channel assignment. The backoff algorithm of the IEEE 802.11 distributed coordination function (DCF) was modified to invoke channel switching, based on threshold criteria in order to improve the overall throughput for wireless sensor networks (WSNs) over 802.11 networks. We presented simulation experiments in order to investigate the characteristics of multi-channel communication in wireless sensor networks using an NS2 platform. Nodes only use a single radio and perform channel switching only after specified threshold is reached. Single radio can only work on one channel at any given time. All nodes initiate constant bit rate streams towards the receiving nodes. In this work, we studied the impact of non-overlapping channels in the 2.4 frequency band on: constant bit rate (CBR) streams, node density, source nodes sending data directly to sink and signal strength by varying distances between the sensor nodes and operating frequencies of the radios with different data rates. We showed that multi-channel enhancement using our proposed algorithm provides significant improvement in terms of throughput, packet delivery ratio and delay. This technique can be considered for WSNs future use in 802.11 networks especially when the IEEE 802.11n becomes popular thereby may prevent the 802.15.4 network from operating effectively in the 2.4 GHz frequency band.

Multi-Sensor Based State Prediction for Personal Mobility Vehicles.

Directory of Open Access Journals (Sweden)

Jamilah Abdur-Rahim

Full Text Available This paper presents a study on multi-modal human emotional state detection while riding a powered wheelchair (PMV; Personal Mobility Vehicle in an indoor labyrinth-like environment. The study reports findings on the habituation of human stress response during self-driving. In addition, the effects of "loss of controllability", change in the role of the driver to a passenger, are investigated via an autonomous driving modality. The multi-modal emotional state detector sensing framework consists of four sensing devices: electroencephalograph (EEG, heart inter-beat interval (IBI, galvanic skin response (GSR and stressor level lever (in the case of autonomous riding. Physiological emotional state measurement characteristics are organized by time-scale, in terms of capturing slower changes (long-term and quicker changes from moment-to-moment. Experimental results with fifteen participants regarding subjective emotional state reports and commercial software measurements validated the proposed emotional state detector. Short-term GSR and heart signal characterizations captured moment-to-moment emotional state during autonomous riding (Spearman correlation; ρ = 0.6, p < 0.001. Short-term GSR and EEG characterizations reliably captured moment-to-moment emotional state during self-driving (Classification accuracy; 69.7. Finally, long-term GSR and heart characterizations were confirmed to reliably capture slow changes during autonomous riding and also of emotional state during participant resting state. The purpose of this study and the exploration of various algorithms and sensors in a structured framework is to provide a comprehensive background for multi-modal emotional state prediction experiments and/or applications. Additional discussion regarding the feasibility and utility of the possibilities of these concepts are given.

Accurate multi-robot targeting for keyhole neurosurgery based on external sensor monitoring.

Science.gov (United States)

Comparetti, Mirko Daniele; Vaccarella, Alberto; Dyagilev, Ilya; Shoham, Moshe; Ferrigno, Giancarlo; De Momi, Elena

2012-05-01

Robotics has recently been introduced in surgery to improve intervention accuracy, to reduce invasiveness and to allow new surgical procedures. In this framework, the ROBOCAST system is an optically surveyed multi-robot chain aimed at enhancing the accuracy of surgical probe insertion during keyhole neurosurgery procedures. The system encompasses three robots, connected as a multiple kinematic chain (serial and parallel), totalling 13 degrees of freedom, and it is used to automatically align the probe onto a desired planned trajectory. The probe is then inserted in the brain, towards the planned target, by means of a haptic interface. This paper presents a new iterative targeting approach to be used in surgical robotic navigation, where the multi-robot chain is used to align the surgical probe to the planned pose, and an external sensor is used to decrease the alignment errors. The iterative targeting was tested in an operating room environment using a skull phantom, and the targets were selected on magnetic resonance images. The proposed targeting procedure allows about 0.3 mm to be obtained as the residual median Euclidean distance between the planned and the desired targets, thus satisfying the surgical accuracy requirements (1 mm), due to the resolution of the diffused medical images. The performances proved to be independent of the robot optical sensor calibration accuracy.

Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

Science.gov (United States)

Cross, Eben S.; Williams, Leah R.; Lewis, David K.; Magoon, Gregory R.; Onasch, Timothy B.; Kaminsky, Michael L.; Worsnop, Douglas R.; Jayne, John T.

2017-09-01

The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ), measurements must be fast (real time), scalable, and reliable (with known accuracy, precision, and stability over time). Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform the public about pollution sources and inspire policy makers to address environmental justice issues related to air quality. In this paper, initial results obtained with a recently developed lower-cost air-quality-sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4.5-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical (EC) sensor measurements of CO, NO, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ): [CO] = 231 ± 116 ppb (spanning 84-1706 ppb), [NO] = 6.1 ± 11.5 ppb (spanning 0-209 ppb), [NO2] = 11.7 ± 8.3 ppb (spanning 0-71 ppb), and [O3] = 23.2 ± 12.5 ppb (spanning 0-99 ppb). Through the use of high-dimensional model representation (HDMR), we show that interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons (sensor flow-cell temperature = 23.4 ± 8.5 °C, spanning 4.1 to 45.2 °C; and relative humidity = 50.1 ± 15.3 %, spanning 9.8-79.9 %) can be effectively modeled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, yielding (5 min average) root

Multi-pollutants sensors based on near-IR telecom lasers and mid-IR difference frequency generation: development and applications

International Nuclear Information System (INIS)

Cousin, J.

2006-12-01

At present the detection of VOC and other anthropic trace pollutants is an important challenge in the measurement of air quality. Infrared spectroscopy, allowing spectral regions rich in molecular absorption to be probed, is a suitable technique for in-situ monitoring of the air pollution. Thus the aim of this work was to develop instruments capable of detecting multiple pollutants for in-situ monitoring by IR spectroscopy. A first project benefited from the availability of the telecommunications lasers emitting in near-IR. This instrument was based on an external cavity diode laser (1500 - 1640 nm) in conjunction with a multipass cell (100 m). The detection sensitivity was optimised by employing a balanced detection and a sweep integration procedure. The instrument developed is deployable for in-situ measurements with a sensitivity of -8 cm -1 Hz -1/2 and allowed the quantification of chemical species such as CO 2 , CO, C 2 H 2 , CH 4 and the determination of the isotopic ratio 13 CO 2 / 12 CO 2 in combustion environment The second project consisted in mixing two near-IR fiber lasers in a non-linear crystal (PPLN) in order to produce a laser radiation by difference frequency generation in the middle-IR (3.15 - 3.43 μm), where the absorption bands of the molecules are the most intense. The first studies with this source were carried out on detection of ethylene (C 2 H 4 ) and benzene (C 6 H 6 ). Developments, characterizations and applications of these instruments in the near and middle IR are detailed and the advantages of the 2 spectral ranges is highlighted. (author)

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

Directory of Open Access Journals (Sweden)

Jennifer Howcroft

Full Text Available Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521. Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

IMHRP: Improved Multi-Hop Routing Protocol for Wireless Sensor Networks

Science.gov (United States)

Huang, Jianhua; Ruan, Danwei; Hong, Yadong; Zhao, Ziming; Zheng, Hong

2017-10-01

Wireless sensor network (WSN) is a self-organizing system formed by a large number of low-cost sensor nodes through wireless communication. Sensor nodes collect environmental information and transmit it to the base station (BS). Sensor nodes usually have very limited battery energy. The batteries cannot be charged or replaced. Therefore, it is necessary to design an energy efficient routing protocol to maximize the network lifetime. This paper presents an improved multi-hop routing protocol (IMHRP) for homogeneous networks. In the IMHRP protocol, based on the distances to the BS, the CH nodes are divided into internal CH nodes and external CH nodes. The set-up phase of the protocol is based on the LEACH protocol and the minimum distance between CH nodes are limited to a special constant distance, so a more uniform distribution of CH nodes is achieved. In the steady-state phase, the routes of different CH nodes are created on the basis of the distances between the CH nodes. The energy efficiency of communication can be maximized. The simulation results show that the proposed algorithm can more effectively reduce the energy consumption of each round and prolong the network lifetime compared with LEACH protocol and MHT protocol.

A New All Solid State Approach to Gaseous Pollutant Detection

Science.gov (United States)

Brown, V.; Tamstorf, K.

1971-01-01

Recent efforts in our laboratories have concentrated on the development of an all solid state gas sensor, by combining solid electrolyte (ion exchange membrane) technology with advanced thin film deposition processes. With the proper bias magnitude and polarity these miniature electro-chemical,cells show remarkable current responses for many common pollution gases. Current activity is now focused on complementing a multiple array (matrix) of these solid state sensors, with a digital electronic scanner device possessing "scan-compare-identify-alarm: capability. This innovative approach to multi-component pollutant gas analysis may indeed be the advanced prototype for the "third generation" class of pollution analysis instrumentation so urgently needed in the decade ahead.

A hierarchical structure approach to MultiSensor Information Fusion

Energy Technology Data Exchange (ETDEWEB)

Maren, A.J. (Tennessee Univ., Tullahoma, TN (United States). Space Inst.); Pap, R.M.; Harston, C.T. (Accurate Automation Corp., Chattanooga, TN (United States))

1989-01-01

A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

A hierarchical structure approach to MultiSensor Information Fusion

Energy Technology Data Exchange (ETDEWEB)

Maren, A.J. [Tennessee Univ., Tullahoma, TN (United States). Space Inst.; Pap, R.M.; Harston, C.T. [Accurate Automation Corp., Chattanooga, TN (United States)

1989-12-31

A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

Science.gov (United States)

Deng, Xinyang; Jiang, Wen

2017-09-12

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

Development and Application of a Next Generation Air Sensor Network for the Hong Kong Marathon 2015 Air Quality Monitoring

Directory of Open Access Journals (Sweden)

Li Sun

2016-02-01

Full Text Available This study presents the development and evaluation of a next generation air monitoring system with both laboratory and field tests. A multi-parameter algorithm was used to correct for the impact of environmental conditions on the electrochemical sensors for carbon monoxide (CO and nitrogen dioxide (NO2 pollutants. The field evaluation in an urban roadside environment in comparison to designated monitors showed good agreement with measurement error within 5% of the pollutant concentrations. Multiple sets of the developed system were then deployed in the Hong Kong Marathon 2015 forming a sensor-based network along the marathon route. Real-time air pollution concentration data were wirelessly transmitted and the Air Quality Health Index (AQHI for the Green Marathon was calculated, which were broadcast to the public on an hourly basis. The route-specific sensor network showed somewhat different pollutant patterns than routine air monitoring, indicating the immediate impact of traffic control during the marathon on the roadside air quality. The study is one of the first applications of a next generation sensor network in international sport events, and it demonstrated the usefulness of the emerging sensor-based air monitoring technology in rapid network deployment to supplement existing air monitoring.

Development and Application of a Next Generation Air Sensor Network for the Hong Kong Marathon 2015 Air Quality Monitoring

Science.gov (United States)

Sun, Li; Wong, Ka Chun; Wei, Peng; Ye, Sheng; Huang, Hao; Yang, Fenhuan; Westerdahl, Dane; Louie, Peter K.K.; Luk, Connie W.Y.; Ning, Zhi

2016-01-01

This study presents the development and evaluation of a next generation air monitoring system with both laboratory and field tests. A multi-parameter algorithm was used to correct for the impact of environmental conditions on the electrochemical sensors for carbon monoxide (CO) and nitrogen dioxide (NO2) pollutants. The field evaluation in an urban roadside environment in comparison to designated monitors showed good agreement with measurement error within 5% of the pollutant concentrations. Multiple sets of the developed system were then deployed in the Hong Kong Marathon 2015 forming a sensor-based network along the marathon route. Real-time air pollution concentration data were wirelessly transmitted and the Air Quality Health Index (AQHI) for the Green Marathon was calculated, which were broadcast to the public on an hourly basis. The route-specific sensor network showed somewhat different pollutant patterns than routine air monitoring, indicating the immediate impact of traffic control during the marathon on the roadside air quality. The study is one of the first applications of a next generation sensor network in international sport events, and it demonstrated the usefulness of the emerging sensor-based air monitoring technology in rapid network deployment to supplement existing air monitoring. PMID:26861336

Sensor Data Air Pollution Prediction by Machine Learning Methods

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

submitted 25. 1. (2018) ISSN 1530-437X R&D Projects: GA ČR GA15-18108S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : machine learning * sensors * air pollution * deep neural networks * regularization networks Subject RIV: IN - Informatics, Computer Science Impact factor: 2.512, year: 2016

Sensor Deployment for Air Pollution Monitoring Using Public Transportation System

OpenAIRE

Yu, James J. Q.; Li, Victor O. K.; Lam, Albert Y. S.

2015-01-01

Air pollution monitoring is a very popular research topic and many monitoring systems have been developed. In this paper, we formulate the Bus Sensor Deployment Problem (BSDP) to select the bus routes on which sensors are deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is a recently proposed metaheuristic designed to solve a wide range of optimization problems. Using the real world data, namely Hong Kong Island bus route data, we perform a series of simulations an...

Advances in Multi-Sensor Information Fusion: Theory and Applications 2017.

Science.gov (United States)

Jin, Xue-Bo; Sun, Shuli; Wei, Hong; Yang, Feng-Bao

2018-04-11

The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

Advances in Multi-Sensor Information Fusion: Theory and Applications 2017

Directory of Open Access Journals (Sweden)

Xue-Bo Jin

2018-04-01

Full Text Available The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

BreedVision — A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding

Science.gov (United States)

Busemeyer, Lucas; Mentrup, Daniel; Möller, Kim; Wunder, Erik; Alheit, Katharina; Hahn, Volker; Maurer, Hans Peter; Reif, Jochen C.; Würschum, Tobias; Müller, Joachim; Rahe, Florian; Ruckelshausen, Arno

2013-01-01

To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (×Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies. PMID:23447014

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

Science.gov (United States)

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Cell-Based Sensor System Using L6 Cells for Broad Band Continuous Pollutant Monitoring in Aquatic Environments

Directory of Open Access Journals (Sweden)

Evamaria Stütz

2012-03-01

Full Text Available Pollution of drinking water sources represents a continuously emerging problem in global environmental protection. Novel techniques for real-time monitoring of water quality, capable of the detection of unanticipated toxic and bioactive substances, are urgently needed. In this study, the applicability of a cell-based sensor system using selected eukaryotic cell lines for the detection of aquatic pollutants is shown. Readout parameters of the cells were the acidification (metabolism, oxygen consumption (respiration and impedance (morphology of the cells. A variety of potential cytotoxic classes of substances (heavy metals, pharmaceuticals, neurotoxins, waste water was tested with monolayers of L6 cells (rat myoblasts. The cytotoxicity or cellular effects induced by inorganic ions (Ni2+ and Cu2+ can be detected with the metabolic parameters acidification and respiration down to 0.5 mg/L, whereas the detection limit for other substances like nicotine and acetaminophen are rather high, in the range of 0.1 mg/L and 100 mg/L. In a close to application model a real waste water sample shows detectable signals, indicating the existence of cytotoxic substances. The results support the paradigm change from single substance detection to the monitoring of overall toxicity.

Multi reflection of Lamb wave emission in an acoustic waveguide sensor.

Science.gov (United States)

Schmitt, Martin; Olfert, Sergei; Rautenberg, Jens; Lindner, Gerhard; Henning, Bernd; Reindl, Leonhard Michael

2013-02-27

Recently, an acoustic waveguide sensor based on multiple mode conversion of surface acoustic waves at the solid-liquid interfaces has been introduced for the concentration measurement of binary and ternary mixtures, liquid level sensing, investigation of spatial inhomogenities or bubble detection. In this contribution the sound wave propagation within this acoustic waveguide sensor is visualized by Schlieren imaging for continuous and burst operation the first time. In the acoustic waveguide the antisymmetrical zero order Lamb wave mode is excited by a single phase transducer of 1 MHz on thin glass plates of 1 mm thickness. By contact to the investigated liquid Lamb waves propagating on the first plate emit pressure waves into the adjacent liquid, which excites Lamb waves on the second plate, what again causes pressure waves traveling inside the liquid back to the first plate and so on. The Schlieren images prove this multi reflection within the acoustic waveguide, which confirms former considerations and calculations based on the receiver signal. With this knowledge the sensor concepts with the acoustic waveguide sensor can be interpreted in a better manner.

Development of paper-based electrochemical sensors for water quality monitoring

Science.gov (United States)

Smith, Suzanne; Bezuidenhout, Petroné; Mbanjwa, Mesuli; Zheng, Haitao; Conning, Mariette; Palaniyandy, Nithyadharseni; Ozoemena, Kenneth; Land, Kevin

2016-02-01

We present a method for the development of paper-based electrochemical sensors for detection of heavy metals in water samples. Contaminated water leads to serious health problems and environmental issues. Paper is ideally suited for point-of-care testing, as it is low cost, disposable, and multi-functional. Initial sensor designs were manufactured on paper substrates using combinations of inkjet printing and screen printing technologies using silver and carbon inks. Bismuth onion-like carbon nanoparticle ink was manufactured and used as the active material of the sensor for both commercial and paper-based sensors, which were compared using standard electrochemical analysis techniques. The results highlight the potential of paper-based sensors to be used effectively for rapid water quality monitoring at the point-of-need.

Multi-sensor integration for autonomous robots in nuclear power plants

International Nuclear Information System (INIS)

Mann, R.C.; Jones, J.P.; Beckerman, M.; Glover, C.W.; Farkas, L.; Bilbro, G.L.; Snyder, W.

1989-01-01

As part of a concerted RandD program in advanced robotics for hazardous environments, scientists and engineers at the Oak Ridge National Laboratory (ORNL) are performing research in the areas of systems integration, range-sensor-based 3-D world modeling, and multi-sensor integration. This program features a unique teaming arrangement that involves the universities of Florida, Michigan, Tennessee, and Texas; Odetics Corporation; and ORNL. This paper summarizes work directed at integrating information extracted from data collected with range sensors and CCD cameras on-board a mobile robot, in order to produce reliable descriptions of the robot's environment. Specifically, the paper describes the integration of two-dimensional vision and sonar range information, and an approach to integrate registered luminance and laser range images. All operations are carried out on-board the mobile robot using a 16-processor hypercube computer. 14 refs., 4 figs

Node-to-node field calibration of wireless distributed air pollution sensor network.

Science.gov (United States)

Kizel, Fadi; Etzion, Yael; Shafran-Nathan, Rakefet; Levy, Ilan; Fishbain, Barak; Bartonova, Alena; Broday, David M

2018-02-01

Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks. Copyright © 2017 Elsevier Ltd. All

Smart aggregates: multi-functional sensors for concrete structures—a tutorial and a review

International Nuclear Information System (INIS)

Song Gangbing; Gu Haichang; Mo Yilung

2008-01-01

This paper summarizes the authors' recent pioneering research work in piezoceramic-based smart aggregates and their innovative applications in concrete civil structures. The basic operating principle of smart aggregates is first introduced. The proposed smart aggregate is formed by embedding a waterproof piezoelectric patch with lead wires into a small concrete block. The proposed smart aggregates are multi-functional and can perform three major tasks: early-age concrete strength monitoring, impact detection and structural health monitoring. The proposed smart aggregates are embedded into the desired location before the casting of the concrete structure. The concrete strength development is monitored by observing the high frequency harmonic wave response of the smart aggregate. Impact on the concrete structure is detected by observing the open-circuit voltage of the piezoceramic patch in the smart aggregate. For structural health monitoring purposes, a smart aggregate-based active sensing system is designed for the concrete structure. Wavelet packet analysis is used as a signal-processing tool to analyze the sensor signal. A damage index based on the wavelet packet analysis is used to determine the structural health status. To better describe the time-history and location information of damage, two types of damage index matrices are proposed: a sensor-history damage index matrix and an actuator–sensor damage index matrix. To demonstrate the multi-functionality of the proposed smart aggregates, different types of concrete structures have been used as test objects, including concrete bridge bent-caps, concrete cylinders and a concrete frame. Experimental results have verified the effectiveness and the multi-functionality of the proposed smart aggregates. The multi-functional smart aggregates have the potential to be applied to the comprehensive monitoring of concrete structures from their earliest stages and throughout their lifetime. (topical review)

Design and Evaluation of a Wireless Sensor Network Based Aircraft Strength Testing System

Science.gov (United States)

Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

2009-01-01

The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system. PMID:22408521

Design and evaluation of a wireless sensor network based aircraft strength testing system.

Science.gov (United States)

Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

2009-01-01

The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system.

A Smart Multi-parameter Sensor with Online Monitoring for the Aquaculture in China

OpenAIRE

Peng , Fa; Wang , Jinxing; Liu , Shuangxi; Li , Daoliang; Xu , Dan; Wang , Yang

2013-01-01

International audience; PH, DO,ORP, EC and water-level are important parameters of the aquaculture monitoring. But the high cost of foreign sensors and high-energy consumption of Chinese sensors make it impossible for wide use in China. This paper uses MCU STM8L152 to realize the ultralow power design. With simple hardware structure design, the cost of the multi-parameter sensor can be reduced .The experiment data of the multi-parameter sensor contrasting with the results obtained by Hach mul...

Pollution source localization in an urban water supply network based on dynamic water demand.

Science.gov (United States)

Yan, Xuesong; Zhu, Zhixin; Li, Tian

2017-10-27

Urban water supply networks are susceptible to intentional, accidental chemical, and biological pollution, which pose a threat to the health of consumers. In recent years, drinking-water pollution incidents have occurred frequently, seriously endangering social stability and security. The real-time monitoring for water quality can be effectively implemented by placing sensors in the water supply network. However, locating the source of pollution through the data detection obtained by water quality sensors is a challenging problem. The difficulty lies in the limited number of sensors, large number of water supply network nodes, and dynamic user demand for water, which leads the pollution source localization problem to an uncertainty, large-scale, and dynamic optimization problem. In this paper, we mainly study the dynamics of the pollution source localization problem. Previous studies of pollution source localization assume that hydraulic inputs (e.g., water demand of consumers) are known. However, because of the inherent variability of urban water demand, the problem is essentially a fluctuating dynamic problem of consumer's water demand. In this paper, the water demand is considered to be stochastic in nature and can be described using Gaussian model or autoregressive model. On this basis, an optimization algorithm is proposed based on these two dynamic water demand change models to locate the pollution source. The objective of the proposed algorithm is to find the locations and concentrations of pollution sources that meet the minimum between the analogue and detection values of the sensor. Simulation experiments were conducted using two different sizes of urban water supply network data, and the experimental results were compared with those of the standard genetic algorithm.

Multi-Functional Sensor System for Heart Rate, Body Position and Movement Intensity Analysis

Directory of Open Access Journals (Sweden)

Michael MAO

2008-12-01

Full Text Available A novel multi-functional wearable sensor has been developed with multi-axis accelerometer, disposable hydro-gel electrodes, and analog filtering components. This novel sensor implementation can be used for detecting common body positions, movement intensity, and measures bio-potential signals for ECG and heart rate analysis. Based on the novel sensor principle, a prototype combines position detection, heart rate detection, and motion intensity level detection together in a handheld device that records the physiological information and wirelessly transmits the signals through Bluetooth to a mobile phone. Static body positions such as standing/sitting, lying supine, prone, and on the sides have been detected with high accuracy (97.7 % during the subject tests. Further, an algorithm that detects body movement intensity that can potentially be applied in real-time monitoring physical activity level is proposed based on average variance values. Motion intensity results show variance values increase and exercise intensity increases for almost all of the cases. A clear relation between movement intensity level shown by an increase in frequency and/or speed of exercise increases the variance values detected in all three spatial axes.

Miniaturized Planar Room Temperature Ionic Liquid Electrochemical Gas Sensor for Rapid Multiple Gas Pollutants Monitoring.

Science.gov (United States)

Wan, Hao; Yin, Heyu; Lin, Lu; Zeng, Xiangqun; Mason, Andrew J

2018-02-01

The growing impact of airborne pollutants and explosive gases on human health and occupational safety has escalated the demand of sensors to monitor hazardous gases. This paper presents a new miniaturized planar electrochemical gas sensor for rapid measurement of multiple gaseous hazards. The gas sensor features a porous polytetrafluoroethylene substrate that enables fast gas diffusion and room temperature ionic liquid as the electrolyte. Metal sputtering was utilized for platinum electrodes fabrication to enhance adhesion between the electrodes and the substrate. Together with carefully selected electrochemical methods, the miniaturized gas sensor is capable of measuring multiple gases including oxygen, methane, ozone and sulfur dioxide that are important to human health and safety. Compared to its manually-assembled Clark-cell predecessor, this sensor provides better sensitivity, linearity and repeatability, as validated for oxygen monitoring. With solid performance, fast response and miniaturized size, this sensor is promising for deployment in wearable devices for real-time point-of-exposure gas pollutant monitoring.

Multi-image acquisition-based distance sensor using agile laser spot beam.

Science.gov (United States)

Riza, Nabeel A; Amin, M Junaid

2014-09-01

We present a novel laser-based distance measurement technique that uses multiple-image-based spatial processing to enable distance measurements. Compared with the first-generation distance sensor using spatial processing, the modified sensor is no longer hindered by the classic Rayleigh axial resolution limit for the propagating laser beam at its minimum beam waist location. The proposed high-resolution distance sensor design uses an electronically controlled variable focus lens (ECVFL) in combination with an optical imaging device, such as a charged-coupled device (CCD), to produce and capture different laser spot size images on a target with these beam spot sizes different from the minimal spot size possible at this target distance. By exploiting the unique relationship of the target located spot sizes with the varying ECVFL focal length for each target distance, the proposed distance sensor can compute the target distance with a distance measurement resolution better than the axial resolution via the Rayleigh resolution criterion. Using a 30 mW 633 nm He-Ne laser coupled with an electromagnetically actuated liquid ECVFL, along with a 20 cm focal length bias lens, and using five spot images captured per target position by a CCD-based Nikon camera, a proof-of-concept proposed distance sensor is successfully implemented in the laboratory over target ranges from 10 to 100 cm with a demonstrated sub-cm axial resolution, which is better than the axial Rayleigh resolution limit at these target distances. Applications for the proposed potentially cost-effective distance sensor are diverse and include industrial inspection and measurement and 3D object shape mapping and imaging.

Building Twilight "Light Sensors" to Study the Effects of Light Pollution on Fireflies

Science.gov (United States)

Thancharoen, Anchana; Branham, Marc A.; Lloyd, James E.

2008-01-01

Light pollution negatively affects many nocturnal organisms. We outline two experiments that can be conducted by students to examine the effects of light pollution on firefly behavior. Inexpensive electronic light sensors, which are easy to construct and calibrate, are used to sample light levels along transects in spaces where fireflies are…

A Low Power, Parallel Wearable Multi-Sensor System for Human Activity Evaluation.

Science.gov (United States)

Li, Yuecheng; Jia, Wenyan; Yu, Tianjian; Luan, Bo; Mao, Zhi-Hong; Zhang, Hong; Sun, Mingui

2015-04-01

In this paper, the design of a low power heterogeneous wearable multi-sensor system, built with Zynq System-on-Chip (SoC), for human activity evaluation is presented. The powerful data processing capability and flexibility of this SoC represent significant improvements over our previous ARM based system designs. The new system captures and compresses multiple color images and sensor data simultaneously. Several strategies are adopted to minimize power consumption. Our wearable system provides a new tool for the evaluation of human activity, including diet, physical activity and lifestyle.

Micro-controller based air pressure monitoring instrumentation system using optical fibers as sensor

Science.gov (United States)

Hazarika, D.; Pegu, D. S.

2013-03-01

This paper describes a micro-controller based instrumentation system to monitor air pressure using optical fiber sensors. The principle of macrobending is used to develop the sensor system. The instrumentation system consists of a laser source, a beam splitter, two multi mode optical fibers, two Light Dependent Resistance (LDR) based timer circuits and a AT89S8252 micro-controller. The beam splitter is used to divide the laser beam into two parts and then these two beams are launched into two multi mode fibers. One of the multi mode fibers is used as the sensor fiber and the other one is used as the reference fiber. The use of the reference fiber is to eliminate the environmental effects while measuring the air pressure magnitude. The laser beams from the sensor and reference fibers are applied to two identical LDR based timer circuits. The LDR based timer circuits are interfaced to a micro-controller through its counter pins. The micro-controller samples the frequencies of the timer circuits using its counter-0 and counter-1 and the counter values are then processed to provide the measure of air pressure magnitude.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch.

Science.gov (United States)

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-19

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

Directory of Open Access Journals (Sweden)

Tao Huang

2016-01-01

Full Text Available Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

Science.gov (United States)

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-01

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture. PMID:26797616

Network Coding on Heterogeneous Multi-Core Processors for Wireless Sensor Networks

Science.gov (United States)

Kim, Deokho; Park, Karam; Ro, Won W.

2011-01-01

While network coding is well known for its efficiency and usefulness in wireless sensor networks, the excessive costs associated with decoding computation and complexity still hinder its adoption into practical use. On the other hand, high-performance microprocessors with heterogeneous multi-cores would be used as processing nodes of the wireless sensor networks in the near future. To this end, this paper introduces an efficient network coding algorithm developed for the heterogenous multi-core processors. The proposed idea is fully tested on one of the currently available heterogeneous multi-core processors referred to as the Cell Broadband Engine. PMID:22164053

Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network.

Science.gov (United States)

Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

2017-07-08

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods.

Direct media exposure of MEMS multi-sensor systems using a potted-tube packaging concept

DEFF Research Database (Denmark)

Hyldgård, Anders; Birkelund, Karen; Janting, Jakob

2008-01-01

in the filling material is measured. The packaging concept is used to encapsulate a microfabricated multi-sensor (measuring temperature, water conductivity, pressure and light intensity). The direct exposure of the sensors results in high sensitivity and fast response time. The design of an elongated multi-sensor......A packaging concept for Data Storage Tags is presented. A potted-tube packaging concept, using a polystyrene tube and different epoxies as filling material that allows for direct sensor exposure is investigated. The curing temperature, water uptake and the diffusion coefficient for water...... is described and effectiveness of the packaging is demonstrated with the precise measurement of water conductivity using the packaged multi-sensor. The packaging concept is very promising for high accuracy measurements in harsh environments....

Multi-sensors multi-baseline mapping system for mobile robot using stereovision camera and laser-range device

Directory of Open Access Journals (Sweden)

Mohammed Faisal

2016-06-01

Full Text Available Countless applications today are using mobile robots, including autonomous navigation, security patrolling, housework, search-and-rescue operations, material handling, manufacturing, and automated transportation systems. Regardless of the application, a mobile robot must use a robust autonomous navigation system. Autonomous navigation remains one of the primary challenges in the mobile-robot industry; many control algorithms and techniques have been recently developed that aim to overcome this challenge. Among autonomous navigation methods, vision-based systems have been growing in recent years due to rapid gains in computational power and the reliability of visual sensors. The primary focus of research into vision-based navigation is to allow a mobile robot to navigate in an unstructured environment without collision. In recent years, several researchers have looked at methods for setting up autonomous mobile robots for navigational tasks. Among these methods, stereovision-based navigation is a promising approach for reliable and efficient navigation. In this article, we create and develop a novel mapping system for a robust autonomous navigation system. The main contribution of this article is the fuse of the multi-baseline stereovision (narrow and wide baselines and laser-range reading data to enhance the accuracy of the point cloud, to reduce the ambiguity of correspondence matching, and to extend the field of view of the proposed mapping system to 180°. Another contribution is the pruning the region of interest of the three-dimensional point clouds to reduce the computational burden involved in the stereo process. Therefore, we called the proposed system multi-sensors multi-baseline mapping system. The experimental results illustrate the robustness and accuracy of the proposed system.

Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research.

Science.gov (United States)

Larkin, A; Hystad, P

2017-12-01

We present a review of emerging technologies and how these can transform personal air pollution exposure assessment and subsequent health research. Estimating personal air pollution exposures is currently split broadly into methods for modeling exposures for large populations versus measuring exposures for small populations. Air pollution sensors, smartphones, and air pollution models capitalizing on big/new data sources offer tremendous opportunity for unifying these approaches and improving long-term personal exposure prediction at scales needed for population-based research. A multi-disciplinary approach is needed to combine these technologies to not only estimate personal exposures for epidemiological research but also determine drivers of these exposures and new prevention opportunities. While available technologies can revolutionize air pollution exposure research, ethical, privacy, logistical, and data science challenges must be met before widespread implementations occur. Available technologies and related advances in data science can improve long-term personal air pollution exposure estimates at scales needed for population-based research. This will advance our ability to evaluate the impacts of air pollution on human health and develop effective prevention strategies.

Limited Scope Design Study for Multi-Sensor Towbody

Science.gov (United States)

2016-06-01

ports 2 Leak sensors 1 Electrical Surface supply voltage 300 V nominal (250–425 Vdc) Towbody output voltages 48/24/12 Vdc Load power...shallow water (អ m) at thousands of current and former Department of Defense (DoD) sites encompassing millions of acres. This design study...addresses the munitions remediation in shallow water problem with a system that uses a Multi-Sensor Towbody (MuST) and surface vessel with support

A Secure Network Coding-based Data Gathering Model and Its Protocol in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Qian Xiao

2012-09-01

Full Text Available To provide security for data gathering based on network coding in wireless sensor networks (WSNs, a secure network coding-based data gathering model is proposed, and a data-privacy preserving and pollution preventing (DPPaamp;PP protocol using network coding is designed. DPPaamp;PP makes use of a new proposed pollution symbol selection and pollution (PSSP scheme based on a new obfuscation idea to pollute existing symbols. Analyses of DPPaamp;PP show that it not only requires low overhead on computation and communication, but also provides high security on resisting brute-force attacks.

A Vision for an International Multi-Sensor Snow Observing Mission

Science.gov (United States)

Kim, Edward

2015-01-01

Discussions within the international snow remote sensing community over the past two years have led to encouraging consensus regarding the broad outlines of a dedicated snow observing mission. The primary consensus - that since no single sensor type is satisfactory across all snow types and across all confounding factors, a multi-sensor approach is required - naturally leads to questions about the exact mix of sensors, required accuracies, and so on. In short, the natural next step is to collect such multi-sensor snow observations (with detailed ground truth) to enable trade studies of various possible mission concepts. Such trade studies must assess the strengths and limitations of heritage as well as newer measurement techniques with an eye toward natural sensitivity to desired parameters such as snow depth and/or snow water equivalent (SWE) in spite of confounding factors like clouds, lack of solar illumination, forest cover, and topography, measurement accuracy, temporal and spatial coverage, technological maturity, and cost.

Intensive time series data exploitation: the Multi-sensor Evolution Analysis (MEA) platform

Science.gov (United States)

Mantovani, Simone; Natali, Stefano; Folegani, Marco; Scremin, Alessandro

2014-05-01

The monitoring of the temporal evolution of natural phenomena must be performed in order to ensure their correct description and to allow improvements in modelling and forecast capabilities. This assumption, that is obvious for ground-based measurements, has not always been true for data collected through space-based platforms: except for geostationary satellites and sensors, that allow providing a very effective monitoring of phenomena with geometric scale from regional to global; smaller phenomena (with characteristic dimension lower than few kilometres) have been monitored with instruments that could collect data only with a time interval in the order of several days; bi-temporal techniques have been the most used ones for years, in order to characterise temporal changes and try identifying specific phenomena. The more the number of flying sensor has grown and their performance improved, the more their capability of monitoring natural phenomena at a smaller geographic scale has grown: we can now count on tenth of years of remotely sensed data, collected by hundreds of sensors that are now accessible from a wide users' community, and the techniques for data processing have to be adapted to move toward a data intensive exploitation. Starting from 2008, the European Space Agency has initiated the development of the Multi-sensor Evolution Analysis (MEA) platform (https://mea.eo.esa.int), whose first aim was to permit the access and exploitation of long term remotely sensed satellite data from different platforms: 15 years of global (A)ATSR data together with 5 years of regional AVNIR-2 data were loaded into the system and were used, through a web-based graphic user interface, for land cover change analysis. The MEA data availability has grown during years integrating multi-disciplinary data that feature spatial and temporal dimensions: so far tenths of Terabytes of data in the land and atmosphere domains are available and can be visualized and exploited, keeping the

Pollution management system

DEFF Research Database (Denmark)

2015-01-01

A pollution management system comprises an array of one or more inlets and at least one outlet. The one or more inlets are arranged to collect polluted air and supply said polluted air to a polluted air treatment element. The one or more inlets each comprise a respective inlet sensor for measuring...... a level of pollution at the inlet, and the at least one outlet comprises an outlet sensor for measuring a level of pollution at the outlet. The inlet sensors and the outlet sensor are arranged to provide feedback to the polluted air treatment element....

Accuracy of different sensors for the estimation of pollutant concentrations (total suspended solids, total and dissolved chemical oxygen demand) in wastewater and stormwater.

Science.gov (United States)

Lepot, Mathieu; Aubin, Jean-Baptiste; Bertrand-Krajewski, Jean-Luc

2013-01-01

Many field investigations have used continuous sensors (turbidimeters and/or ultraviolet (UV)-visible spectrophotometers) to estimate with a short time step pollutant concentrations in sewer systems. Few, if any, publications compare the performance of various sensors for the same set of samples. Different surrogate sensors (turbidity sensors, UV-visible spectrophotometer, pH meter, conductivity meter and microwave sensor) were tested to link concentrations of total suspended solids (TSS), total and dissolved chemical oxygen demand (COD), and sensors' outputs. In the combined sewer at the inlet of a wastewater treatment plant, 94 samples were collected during dry weather, 44 samples were collected during wet weather, and 165 samples were collected under both dry and wet weather conditions. From these samples, triplicate standard laboratory analyses were performed and corresponding sensors outputs were recorded. Two outlier detection methods were developed, based, respectively, on the Mahalanobis and Euclidean distances. Several hundred regression models were tested, and the best ones (according to the root mean square error criterion) are presented in order of decreasing performance. No sensor appears as the best one for all three investigated pollutants.

A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

Directory of Open Access Journals (Sweden)

Lin Zhang

2017-03-01

Full Text Available With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR. This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.

A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

Science.gov (United States)

Zhang, Lin; Yin, Na; Fu, Xiong; Lin, Qiaomin; Wang, Ruchuan

2017-01-01

With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service. PMID:28282894

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety

Directory of Open Access Journals (Sweden)

Zutao Zhang

2016-06-01

Full Text Available Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.

Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

Directory of Open Access Journals (Sweden)

E. S. Cross

2017-09-01

Full Text Available The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ, measurements must be fast (real time, scalable, and reliable (with known accuracy, precision, and stability over time. Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform the public about pollution sources and inspire policy makers to address environmental justice issues related to air quality. In this paper, initial results obtained with a recently developed lower-cost air-quality-sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4.5-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical (EC sensor measurements of CO, NO, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ: [CO]  =  231 ± 116 ppb (spanning 84–1706 ppb, [NO]  =  6.1 ± 11.5 ppb (spanning 0–209 ppb, [NO2]  =  11.7 ± 8.3 ppb (spanning 0–71 ppb, and [O3]  =  23.2 ± 12.5 ppb (spanning 0–99 ppb. Through the use of high-dimensional model representation (HDMR, we show that interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons (sensor flow-cell temperature  =  23.4 ± 8.5 °C, spanning 4.1 to 45.2 °C; and relative humidity  =  50.1 ± 15.3 %, spanning 9.8–79.9�

Direction sensitive bending sensors based on multi-wall carbon nanotube/epoxy nanocomposites

International Nuclear Information System (INIS)

Wichmann, Malte H G; Buschhorn, Samuel T; Boeger, Lars; Schulte, Karl; Adelung, Rainer

2008-01-01

In the present work, a direction sensitive bending strain sensor consisting of a single block of epoxy/multi-wall carbon nanotube composite was developed. Moreover, the manufacturing could be realized in a straightforward single-step processing route. The directional sensitivity to bending deformations is related to the change in electrical resistance, which becomes positive or negative, depending on the direction of bending deflection. This effect is achieved by generating a gradient in electrical conductivity throughout the material. The resistance versus strain behaviour of these devices is investigated in detail and related to the microstructure of the nanocomposites.

Cluster-based Data Gathering in Long-Strip Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

FANG, W.

2012-02-01

Full Text Available This paper investigates a special class of wireless sensor networks that are different from traditional ones in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a long-strip topology. According to hardware capabilities of current sensor nodes, a cluster-based protocol for reliable and efficient data gathering in long-strip wireless sensor networks (LSWSN is proposed. A well-distributed cluster-based architecture is first formed in the whole network through contention-based cluster head election. Cluster heads are responsible for coordination among the nodes within their clusters and aggregation of their sensory data, as well as transmission the data to the sink node on behalf of their own clusters. The intra-cluster coordination is based on the traditional TDMA schedule, in which the inter-cluster interference caused by the border nodes is solved by the multi-channel communication technique. The cluster reporting is based on the CSMA contention, in which a connected overlay network is formed by relay nodes to forward the data from the cluster heads through multi-hops to the sink node. The relay nodes are non-uniformly deployed to resolve the energy-hole problem which is extremely serious in the LSWSN. Extensive simulation results illuminate the distinguished performance of the proposed protocol.

An early warning system for groundwater pollution based on the assessment of groundwater pollution risks.

Science.gov (United States)

Zhang, Weihong.; Zhao, Yongsheng; Hong, Mei; Guo, Xiaodong

2009-04-01

Groundwater pollution usually is complex and concealed, remediation of which is difficult, high cost, time-consuming, and ineffective. An early warning system for groundwater pollution is needed that detects groundwater quality problems and gets the information necessary to make sound decisions before massive groundwater quality degradation occurs. Groundwater pollution early warning were performed by considering comprehensively the current groundwater quality, groundwater quality varying trend and groundwater pollution risk . The map of the basic quality of the groundwater was obtained by fuzzy comprehensive evaluation or BP neural network evaluation. Based on multi-annual groundwater monitoring datasets, Water quality state in sometime of the future was forecasted using time-sequenced analyzing methods. Water quality varying trend was analyzed by Spearman's rank correlative coefficient.The relative risk map of groundwater pollution was estimated through a procedure that identifies, cell by cell,the values of three factors, that is inherent vulnerability, load risk of pollution source and contamination hazard. DRASTIC method was used to assess inherent vulnerability of aquifer. Load risk of pollution source was analyzed based on the potential of contamination and pollution degree. Assessment index of load risk of pollution source which involves the variety of pollution source, quantity of contaminants, releasing potential of pollutants, and distance were determined. The load risks of all sources considered by GIS overlay technology. Early warning model of groundwater pollution combined with ComGIS technology organically, the regional groundwater pollution early-warning information system was developed, and applied it into Qiqiha'er groundwater early warning. It can be used to evaluate current water quality, to forecast water quality changing trend, and to analyze space-time influencing range of groundwater quality by natural process and human activities. Keywords

A user's guide to the MultiMet Sensor Management and Calibration Facility

OpenAIRE

Pascal, R.W.; Williams, A.; Ahmed, R.

1991-01-01

The report describes the operating instructions and procedures for the MultiMet Sensor Management and Calibration Facility. This includes a description of the Meteological database ME'IDB, and the Sensor Management database which organises the large number of sensors required by the Multimet System. Calibration procedures and policies are also described for the various types of sensors used.

Design and simulation of material-integrated distributed sensor processing with a code-based agent platform and mobile multi-agent systems.

Science.gov (United States)

Bosse, Stefan

2015-02-16

Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques.

Design and Simulation of Material-Integrated Distributed Sensor Processing with a Code-Based Agent Platform and Mobile Multi-Agent Systems

Directory of Open Access Journals (Sweden)

Stefan Bosse

2015-02-01

Full Text Available Multi-agent systems (MAS can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques.

New Approach to Monitor Transboundary Particulate Pollution over Northeast Asia

Science.gov (United States)

Park, M. E.; Song, C. H.; Park, R. S.; Lee, Jaehwa; Kim, J.; Lee, S.; Woo, J. H.; Carmichael, G. R.; Eck, Thomas F.; Holben, Brent N.;

Parallel multi-join query optimization algorithm for distributed sensor network in the internet of things

Science.gov (United States)

Zheng, Yan

2015-03-01

Internet of things (IoT), focusing on providing users with information exchange and intelligent control, attracts a lot of attention of researchers from all over the world since the beginning of this century. IoT is consisted of large scale of sensor nodes and data processing units, and the most important features of IoT can be illustrated as energy confinement, efficient communication and high redundancy. With the sensor nodes increment, the communication efficiency and the available communication band width become bottle necks. Many research work is based on the instance which the number of joins is less. However, it is not proper to the increasing multi-join query in whole internet of things. To improve the communication efficiency between parallel units in the distributed sensor network, this paper proposed parallel query optimization algorithm based on distribution attributes cost graph. The storage information relations and the network communication cost are considered in this algorithm, and an optimized information changing rule is established. The experimental result shows that the algorithm has good performance, and it would effectively use the resource of each node in the distributed sensor network. Therefore, executive efficiency of multi-join query between different nodes could be improved.

Creation of 3D Multi-Body Orthodontic Models by Using Independent Imaging Sensors

Directory of Open Access Journals (Sweden)

Armando Viviano Razionale

2013-02-01

Full Text Available In the field of dental health care, plaster models combined with 2D radiographs are widely used in clinical practice for orthodontic diagnoses. However, complex malocclusions can be better analyzed by exploiting 3D digital dental models, which allow virtual simulations and treatment planning processes. In this paper, dental data captured by independent imaging sensors are fused to create multi-body orthodontic models composed of teeth, oral soft tissues and alveolar bone structures. The methodology is based on integrating Cone-Beam Computed Tomography (CBCT and surface structured light scanning. The optical scanner is used to reconstruct tooth crowns and soft tissues (visible surfaces through the digitalization of both patients’ mouth impressions and plaster casts. These data are also used to guide the segmentation of internal dental tissues by processing CBCT data sets. The 3D individual dental tissues obtained by the optical scanner and the CBCT sensor are fused within multi-body orthodontic models without human supervisions to identify target anatomical structures. The final multi-body models represent valuable virtual platforms to clinical diagnostic and treatment planning.

Innovative multi-cantilever array sensor system with MOEMS read-out

Science.gov (United States)

Ivaldi, F.; Bieniek, T.; Janus, P.; Grabiec, P.; Majstrzyk, W.; Kopiec, D.; Gotszalk, T.

2016-11-01

Cantilever based sensor system are a well-established sensor family exploited in several every-day life applications as well as in high-end research areas. The very high sensitivity of such systems and the possibility to design and functionalize the cantilevers to create purpose built and highly selective sensors have increased the interest of the scientific community and the industry in further exploiting this promising sensors type. Optical deflection detection systems for cantilever sensors provide a reliable, flexible method for reading information from cantilevers with the highest sensitivity. However the need of using multi-cantilever arrays in several fields of application such as medicine, biology or safety related areas, make the optical method less suitable due to its structural complexity. Working in the frame of a the Joint Undertaking project Lab4MEMS II our group proposes a novel and innovative approach to solve this issue, by integrating a Micro-Opto-Electro-Mechanical-System (MOEMS) with dedicated optics, electronics and software with a MOEMS micro-mirror, ultimately developed in the frame of Lab4MEMSII. In this way we are able to present a closely packed, lightweight solution combining the advantages of standard optical read-out systems with the possibility of recording multiple read-outs from large cantilever arrays quasi simultaneously.

Disposable Multi-Sensor Unattended Ground Sensor Systems for Detecting Personnel (Systemes de detection multi-capteurs terrestres autonome destines a detecter du personnel)

Science.gov (United States)

2015-02-01

the set of DCT coefficients for all the training data corresponding to the people. Then, the matrix ][ pX can be written as: ][][][ −+ −= ppp XXX ...deployed on two types of ground conditions. This included ARL multi-modal sensors, video and acoustic sensors from the Universities of Memphis and...Mississippi, SASNet from Canada, video from Night Vision Laboratory and Pearls of Wisdom system from Israel operated in conjunction with ARL personnel. This

A Single Rod Multi-modality Multi-interface Level Sensor Using an AC Current Source

Directory of Open Access Journals (Sweden)

Abdulgader Hwili

2008-05-01

Full Text Available Crude oil separation is an important process in the oil industry. To make efficient use of the separators, it is important to know their internal behaviour, and to measure the levels of multi-interfaces between different materials, such as gas-foam, foam-oil, oil-emulsion, emulsion-water and water-solids. A single-rod multi-modality multi-interface level sensor is presented, which has a current source, and electromagnetic modalities. Some key issues have been addressed, including the effect of salt content and temperature i.e. conductivity on the measurement.

An export coefficient based inexact fuzzy bi-level multi-objective programming model for the management of agricultural nonpoint source pollution under uncertainty

Science.gov (United States)

Cai, Yanpeng; Rong, Qiangqiang; Yang, Zhifeng; Yue, Wencong; Tan, Qian

2018-02-01

In this research, an export coefficient based inexact fuzzy bi-level multi-objective programming (EC-IFBLMOP) model was developed through integrating export coefficient model (ECM), interval parameter programming (IPP) and fuzzy parameter programming (FPP) within a bi-level multi-objective programming framework. The proposed EC-IFBLMOP model can effectively deal with the multiple uncertainties expressed as discrete intervals and fuzzy membership functions. Also, the complexities in agricultural systems, such as the cooperation and gaming relationship between the decision makers at different levels, can be fully considered in the model. The developed model was then applied to identify the optimal land use patterns and BMP implementing levels for agricultural nonpoint source (NPS) pollution management in a subcatchment in the upper stream watershed of the Miyun Reservoir in north China. The results of the model showed that the desired optimal land use patterns and implementing levels of best management of practices (BMPs) would be obtained. It is the gaming result between the upper- and lower-level decision makers, when the allowable discharge amounts of NPS pollutants were limited. Moreover, results corresponding to different decision scenarios could provide a set of decision alternatives for the upper- and lower-level decision makers to identify the most appropriate management strategy. The model has a good applicability and can be effectively utilized for agricultural NPS pollution management.

A market-based optimization approach to sensor and resource management

Science.gov (United States)

Schrage, Dan; Farnham, Christopher; Gonsalves, Paul G.

2006-05-01

Dynamic resource allocation for sensor management is a problem that demands solutions beyond traditional approaches to optimization. Market-based optimization applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents' bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. This paper makes two contributions to the field of market-based optimization: First, we develop a market protocol to handle heterogeneous goods in a dynamic setting. Second, we develop arbitrage agents to improve the efficiency in the market in light of its dynamic nature.

A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis

Directory of Open Access Journals (Sweden)

Fuyuan Xiao

2017-10-01

Full Text Available The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence highly conflict with each other. To handle this problem, a novel multi-sensor data fusion approach on the basis of the distance of evidence, belief entropy and fuzzy preference relation analysis is proposed. A function of evidence distance is first leveraged to measure the conflict degree among the pieces of evidence; thus, the support degree can be obtained to represent the reliability of the evidence. Next, the uncertainty of each piece of evidence is measured by means of the belief entropy. Based on the quantitative uncertainty measured above, the fuzzy preference relations are applied to represent the relative credibility preference of the evidence. Afterwards, the support degree of each piece of evidence is adjusted by taking advantage of the relative credibility preference of the evidence that can be utilized to generate an appropriate weight with respect to each piece of evidence. Finally, the modified weights of the evidence are adopted to adjust the bodies of the evidence in the advance of utilizing Dempster’s combination rule. A numerical example and a practical application in fault diagnosis are used as illustrations to demonstrate that the proposal is reasonable and efficient in the management of conflict and fault diagnosis.

The research and development of an air pollutant monitoring system based on DOAS technology

Science.gov (United States)

Li, Hua; Liu, Han-peng; Zheng, Ming; Meng, Xiao-feng

2009-07-01

This article illuminates a kind of sensor used in measuring the concentrations of the main pollutants in flue gas streams (Dust, SO2 and NOx) based on the UV-DOAS technology in air pollutant monitoring. Using the high-level embedded microprocessors and complex programmable logic device, the sensor completes system measurement, management and signal communication, and spectrum inversion and data saving are processed by PC at the same time. Differential optical absorption spectroscopy (DOAS) technology is used in the flue gas pollutant factor analysis through the sensor construction. The absorption spectra of SO2, NOx and smoke dust are inverted to reduce the interference of other factors in flue gas streams. At the same time, the effect of light source fluctuation and optical transmission ratio is considered and removed in the measurement system. The result shows that the monitoring accuracy of concentration of sulfur dioxide and smoke dust achieves +/-2%, the concentration of nitrogen oxides accuracy achieves +/-3%, which meets the requirements of the national standard. The sensor can be directly installed in a flue. As a result, process of measuring is simplified and measurement accuracy is improved. Further more, this method increases the stability of the system and reduces the maintenance costs. Measurement data is transferred through data bus between the sensor and upper PC to realize remote control and real-time measurement. Considering the severe conditions in measuring the main pollutants in flue gas streams, applications of anti-interference and anti-corrosion etc. are taken in the system design.

Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm

Directory of Open Access Journals (Sweden)

Bin Yong

2015-01-01

Full Text Available The impact of one or two missing passive microwave (PMW input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA products were reproduced and re-released by National Aeronautics and Space Administration (NASA Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16 input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the “early” and “late” Version-7 TMPA real-time (TMPA-RT precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km2. The retrieval counts and retrieval frequency of various PMW and Infrared (IR sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers’ current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG. This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and

Gas sensors for ammonia detection based on polyaniline-coated multi-wall carbon nanotubes

International Nuclear Information System (INIS)

He Lifang; Jia Yong; Meng Fanli; Li Minqiang; Liu Jinhuai

2009-01-01

Polyaniline-coated multi-wall carbon nanotubes (PANI-coated MWNTs) were prepared by in situ polymerization method. Field emission scanning electron microscopy, transmission electron microscopy, Raman spectroscopy, and thermogravimetric analysis were used to characterize the as-prepared PANI-coated MWNTs. Obtained results indicated that PANI was uniformly coated on MWNTs, and the thickness of the coatings can be controlled by changing the weight ratios of aniline monomer and MWNTs in the polymerization process. Sensors were fabricated by spin-coating onto pre-patterned electrodes, and ammonia gas sensing properties of the as-prepared PANI-coated MWNTs were studied. The results showed a good response and reproducibility towards ammonia at room temperature. In addition, PANI-coated MWNTs exhibited a linear response to ammonia in the range of 0.2-15 ppm. The effects of the thickness of PANI coatings on the gas sensing properties were also investigated in detail. The results suggest a potential application of PANI-coated MWNTs in gas sensor for detecting ammonia.

ESAM: Endocrine inspired Sensor Activation Mechanism for multi-target tracking in WSNs

Science.gov (United States)

Adil Mahdi, Omar; Wahab, Ainuddin Wahid Abdul; Idris, Mohd Yamani Idna; Znaid, Ammar Abu; Khan, Suleman; Al-Mayouf, Yusor Rafid Bahar

2016-10-01

Target tracking is a significant application of wireless sensor networks (WSNs) in which deployment of self-organizing and energy efficient algorithms is required. The tracking accuracy increases as more sensor nodes are activated around the target but more energy is consumed. Thus, in this study, we focus on limiting the number of sensors by forming an ad-hoc network that operates autonomously. This will reduce the energy consumption and prolong the sensor network lifetime. In this paper, we propose a fully distributed algorithm, an Endocrine inspired Sensor Activation Mechanism for multi target-tracking (ESAM) which reflecting the properties of real life sensor activation system based on the information circulating principle in the endocrine system of the human body. Sensor nodes in our network are secreting different hormones according to certain rules. The hormone level enables the nodes to regulate an efficient sleep and wake up cycle of nodes to reduce the energy consumption. It is evident from the simulation results that the proposed ESAM in autonomous sensor network exhibits a stable performance without the need of commands from a central controller. Moreover, the proposed ESAM generates more efficient and persistent results as compared to other algorithms for tracking an invading object.

Multi-scale dynamic modeling of atmospheric pollution in urban environment

International Nuclear Information System (INIS)

Thouron, Laetitia

2017-01-01

Urban air pollution has been identified as an important cause of health impacts, including premature deaths. In particular, ambient concentrations of gaseous pollutants such as nitrogen dioxide (NO 2 ) and particulate matter (PM10 and PM2.5) are regulated, which means that emission reduction strategies must be put in place to reduce these concentrations in places where the corresponding regulations are not respected. Besides, air pollution can contribute to the contamination of other media, for example through the contribution of atmospheric deposition to runoff contamination. The multifactorial and multi-scale aspects of urban make the pollution sources difficult to identify. Indeed, the urban environment is a heterogeneous space characterized by complex architectural structures (old buildings alongside a more modern building, residential, commercial, industrial zones, roads, etc.), non-uniform atmospheric pollutant emissions and therefore the population exposure to pollution is variable in space and time. The modeling of urban air pollution aims to understand the origin of pollutants, their spatial extent and their concentration/deposition levels. Some pollutants have long residence times and can stay several weeks in the atmosphere (PM2.5) and therefore be transported over long distances, while others are more local (NO x in the vicinity of traffic). The spatial distribution of a pollutant will therefore depend on several factors, and in particular on the surfaces encountered. Air quality depends strongly on weather, buildings (canyon-street) and emissions. The aim of this thesis is to address some of these aspects by modeling: (1) urban background pollution with a transport-chemical model (Polyphemus / POLAIR3D), which makes it possible to estimate atmospheric pollutants by type of urban surfaces (roofs, walls and roadways), (2) street-level pollution by explicitly integrating the effects of the building in a three-dimensional way with a multi-scale model of

A Satellite-Based Multi-Pollutant Index of Global Air Quality

Science.gov (United States)

Cooper, Mathew J.; Martin, Randall V.; vanDonkelaar, Aaron; Lamsal, Lok; Brauer, Michael; Brook, Jeffrey R.

2012-01-01

Air pollution is a major health hazard that is responsible formillions of annual excess deaths worldwide. Simpleindicators are useful for comparative studies and to asses strends over time. The development of global indicators hasbeen impeded by the lack of ground-based observations in vast regions of the world. Recognition is growing of the need for amultipollutant approach to air quality to better represent human exposure. Here we introduce the prospect of amultipollutant air quality indicator based on observations from satellite remote sensing.

TOPICAL REVIEW: Smart aggregates: multi-functional sensors for concrete structures—a tutorial and a review

Science.gov (United States)

Song, Gangbing; Gu, Haichang; Mo, Yi-Lung

2008-06-01

This paper summarizes the authors' recent pioneering research work in piezoceramic-based smart aggregates and their innovative applications in concrete civil structures. The basic operating principle of smart aggregates is first introduced. The proposed smart aggregate is formed by embedding a waterproof piezoelectric patch with lead wires into a small concrete block. The proposed smart aggregates are multi-functional and can perform three major tasks: early-age concrete strength monitoring, impact detection and structural health monitoring. The proposed smart aggregates are embedded into the desired location before the casting of the concrete structure. The concrete strength development is monitored by observing the high frequency harmonic wave response of the smart aggregate. Impact on the concrete structure is detected by observing the open-circuit voltage of the piezoceramic patch in the smart aggregate. For structural health monitoring purposes, a smart aggregate-based active sensing system is designed for the concrete structure. Wavelet packet analysis is used as a signal-processing tool to analyze the sensor signal. A damage index based on the wavelet packet analysis is used to determine the structural health status. To better describe the time-history and location information of damage, two types of damage index matrices are proposed: a sensor-history damage index matrix and an actuator-sensor damage index matrix. To demonstrate the multi-functionality of the proposed smart aggregates, different types of concrete structures have been used as test objects, including concrete bridge bent-caps, concrete cylinders and a concrete frame. Experimental results have verified the effectiveness and the multi-functionality of the proposed smart aggregates. The multi-functional smart aggregates have the potential to be applied to the comprehensive monitoring of concrete structures from their earliest stages and throughout their lifetime.

Effect of Using an Indoor Air Quality Sensor on Perceptions of and Behaviors Toward Air Pollution (Pittsburgh Empowerment Library Study): Online Survey and Interviews.

Science.gov (United States)

Wong-Parodi, Gabrielle; Dias, M Beatrice; Taylor, Michael

2018-03-08

Air quality affects us all and is a rapidly growing concern in the 21st century. We spend the majority of our lives indoors and can be exposed to a number of pollutants smaller than 2.5 microns (particulate matter, PM 2.5 ) resulting in detrimental health effects. Indoor air quality sensors have the potential to provide people with the information they need to understand their risk and take steps to reduce their exposure. One such sensor is the Speck sensor developed at the Community Robotics, Education and Technology Empowerment Lab at Carnegie Mellon University. This sensor provides users with continuous real-time and historical PM 2.5 information, a Web-based platform where people can track their PM 2.5 levels over time and learn about ways to reduce their exposure, and a venue (blog post) for the user community to exchange information. Little is known about how the use of such monitors affects people's knowledge, attitudes, and behaviors with respect to indoor air pollution. The aim of this study was to assess whether using the sensor changes what people know and do about indoor air pollution. We conducted 2 studies. In the first study, we recruited 276 Pittsburgh residents online and through local branches of the Carnegie Library of Pittsburgh, where the Speck sensor was made available by the researchers in the library catalog. Participants completed a 10- to 15-min survey on air pollution knowledge (its health impact, sources, and mitigation options), perceptions of indoor air quality, confidence in mitigation, current behaviors toward air quality, and personal empowerment and creativity in the spring and summer of 2016. In our second study, we surveyed 26 Pittsburgh residents in summer 2016 who checked out the Speck sensor for 3 weeks on the same measures assessed in the first study, with additional questions about the perception and use of the sensor. Follow-up interviews were conducted with a subset of those who used the Speck sensor. A series of paired t

3D Reconstruction and Restoration Monitoring of Sculptural Artworks by a Multi-Sensor Framework

Directory of Open Access Journals (Sweden)

Sandro Barone

2012-12-01

Full Text Available Nowadays, optical sensors are used to digitize sculptural artworks by exploiting various contactless technologies. Cultural Heritage applications may concern 3D reconstructions of sculptural shapes distinguished by small details distributed over large surfaces. These applications require robust multi-view procedures based on aligning several high resolution 3D measurements. In this paper, the integration of a 3D structured light scanner and a stereo photogrammetric sensor is proposed with the aim of reliably reconstructing large free form artworks. The structured light scanner provides high resolution range maps captured from different views. The stereo photogrammetric sensor measures the spatial location of each view by tracking a marker frame integral to the optical scanner. This procedure allows the computation of the rotation-translation matrix to transpose the range maps from local view coordinate systems to a unique global reference system defined by the stereo photogrammetric sensor. The artwork reconstructions can be further augmented by referring metadata related to restoration processes. In this paper, a methodology has been developed to map metadata to 3D models by capturing spatial references using a passive stereo-photogrammetric sensor. The multi-sensor framework has been experienced through the 3D reconstruction of a Statue of Hope located at the English Cemetery in Florence. This sculptural artwork has been a severe test due to the non-cooperative environment and the complex shape features distributed over a large surface.

Probe Sensor Using Nanostructured Multi-Walled Carbon Nanotube Yarn for Selective and Sensitive Detection of Dopamine

Directory of Open Access Journals (Sweden)

Wed Al-Graiti

2017-04-01

Full Text Available The demands for electrochemical sensor materials with high strength and durability in physiological conditions continue to grow and novel approaches are being enabled by the advent of new electromaterials and novel fabrication technologies. Herein, we demonstrate a probe-style electrochemical sensor using highly flexible and conductive multi-walled carbon nanotubes (MWNT yarns. The MWNT yarn-based sensors can be fabricated onto micro Pt-wire with a controlled diameter varying from 100 to 300 µm, and then further modified with Nafion via a dip-coating approach. The fabricated micro-sized sensors were characterized by electron microscopy, Raman, FTIR, electrical, and electrochemical measurements. For the first time, the MWNT/Nafion yarn-based probe sensors have been assembled and assessed for high-performance dopamine sensing, showing a significant improvement in both sensitivity and selectivity in dopamine detection in presence of ascorbic acid and uric acid. It offers the potential to be further developed as implantable probe sensors.

Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network

OpenAIRE

Gezaq Abror; Rusminto Tjatur Widodo; M. Udin Harun Al Rasyid

2018-01-01

Wireless Sensor Network (WSN) can be applied for Air Pollution Level Monitoring System that have been determined by the Environmental Impact Management Agency which is  PM10, SO2, O3, NO2 and CO. In WSN, node system is constrained to a limited power supply, so that the node system has a lifetime. To doing lifetime maximization, power management scheme is required and sensor nodes should use energy efficiently. This paper proposes dynamic sleep scheduling using Time Category-Fuzzy Logic (Time-...

Flexible Graphene-Based Wearable Gas and Chemical Sensors.

Science.gov (United States)

Singh, Eric; Meyyappan, M; Nalwa, Hari Singh

2017-10-11

Wearable electronics is expected to be one of the most active research areas in the next decade; therefore, nanomaterials possessing high carrier mobility, optical transparency, mechanical robustness and flexibility, lightweight, and environmental stability will be in immense demand. Graphene is one of the nanomaterials that fulfill all these requirements, along with other inherently unique properties and convenience to fabricate into different morphological nanostructures, from atomically thin single layers to nanoribbons. Graphene-based materials have also been investigated in sensor technologies, from chemical sensing to detection of cancer biomarkers. The progress of graphene-based flexible gas and chemical sensors in terms of material preparation, sensor fabrication, and their performance are reviewed here. The article provides a brief introduction to graphene-based materials and their potential applications in flexible and stretchable wearable electronic devices. The role of graphene in fabricating flexible gas sensors for the detection of various hazardous gases, including nitrogen dioxide (NO 2 ), ammonia (NH 3 ), hydrogen (H 2 ), hydrogen sulfide (H 2 S), carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), and humidity in wearable technology, is discussed. In addition, applications of graphene-based materials are also summarized in detecting toxic heavy metal ions (Cd, Hg, Pb, Cr, Fe, Ni, Co, Cu, Ag), and volatile organic compounds (VOCs) including nitrobenzene, toluene, acetone, formaldehyde, amines, phenols, bisphenol A (BPA), explosives, chemical warfare agents, and environmental pollutants. The sensitivity, selectivity and strategies for excluding interferents are also discussed for graphene-based gas and chemical sensors. The challenges for developing future generation of flexible and stretchable sensors for wearable technology that would be usable for the Internet of Things (IoT) are also highlighted.

Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments

OpenAIRE

Xing, Yuxin; Vincent, Timothy A.; Cole, Marina; Gardner, Julian W.; Fan, Han; Hernandez Bennetts, Victor; Schaffernicht, Erik; Lilienthal, Achim

2017-01-01

In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operati...

Multi-channel electronically scanned cryogenic pressure sensor

Science.gov (United States)

Chapman, John J. (Inventor); Hopson, Purnell, Jr. (Inventor); Kruse, Nancy M. H. (Inventor)

1995-01-01

A miniature, multi-channel, electronically scanned pressure measuring device uses electrostatically bonded silicon dies in a multielement array. These dies are bonded at specific sites on a glass, prepatterned substrate. Thermal data is multiplexed and recorded on each individual pressure measuring diaphragm. The device functions in a cryogenic environment without the need of heaters to keep the sensor at constant temperatures.

Robust Target Tracking with Multi-Static Sensors under Insufficient TDOA Information.

Science.gov (United States)

Shin, Hyunhak; Ku, Bonhwa; Nelson, Jill K; Ko, Hanseok

2018-05-08

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

A multi-criteria approach to Great Barrier Reef catchment (Queensland, Australia) diffuse-source pollution problem.

Science.gov (United States)

Greiner, R; Herr, A; Brodie, J; Haynes, D

2005-01-01

This paper presents a multi-criteria based tool for assessing the relative impact of diffuse-source pollution to the Great Barrier Reef (GBR) from the river basins draining into the GBR lagoon. The assessment integrates biophysical and ecological data of water quality and pollutant concentrations with socio-economic information pertaining to non-point source pollution and (potential) pollutant impact. The tool generates scores for each river basin against four criteria, thus profiling the basins and enabling prioritization of management alternatives between and within basins. The results support policy development for pollution control through community participation, scientific data integration and expert knowledge contributed by people from across the catchment. The results specifically provided support for the Reef Water Quality Protection Plan, released in October 2003. The aim of the plan is to provide a framework for reducing discharge of sediment, nutrient and other diffuse-source loads and (potential) impact of that discharge and for prioritising management actions both between and within river basins.

Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN

Directory of Open Access Journals (Sweden)

Rohini Sharma

2016-04-01

Full Text Available In wireless sensor networks (WSNs, efficient energy conservation is required to prolong the lifetime of the network. In this work, we have given emphasis on balanced energy consumption and energy holes avoidance. This paper proposes a multi-gateway-based approach to reduce the transmission distance between the sender and the sink node. The area to be monitored is divided into regions and gateway nodes are deployed at optimal positions. We have designed a transmission scheme, in which sensors in the sink region communicate directly to the sink, sensors in the gateway region communicate directly to the gateway, and sensors in the cluster region transmit their data directly to their respective cluster head which transmits data to the gateway in its region. If the distance between a cluster head and the sink is less than the distance between the cluster head and the gateway node, the cluster head transmits data to the sink instead of the gateway node. We have compared the proposed protocol with Low-Energy Adaptive Clustering Hierarchy (LEACH, Gateway Based Energy Aware Multi-Hop Routing (M-GEAR, and Gateway Based Stable Election Protocol (GSEP protocols. The protocol performs better than other protocols in terms of throughput, stability period, lifetime, residual energy, and the packet transmitted to the sink.

Multi sensor satellite imagers for commercial remote sensing

Science.gov (United States)

Cronje, T.; Burger, H.; Du Plessis, J.; Du Toit, J. F.; Marais, L.; Strumpfer, F.

2005-10-01

This paper will discuss and compare recent refractive and catodioptric imager designs developed and manufactured at SunSpace for Multi Sensor Satellite Imagers with Panchromatic, Multi-spectral, Area and Hyperspectral sensors on a single Focal Plane Array (FPA). These satellite optical systems were designed with applications to monitor food supplies, crop yield and disaster monitoring in mind. The aim of these imagers is to achieve medium to high resolution (2.5m to 15m) spatial sampling, wide swaths (up to 45km) and noise equivalent reflectance (NER) values of less than 0.5%. State-of-the-art FPA designs are discussed and address the choice of detectors to achieve these performances. Special attention is given to thermal robustness and compactness, the use of folding prisms to place multiple detectors in a large FPA and a specially developed process to customize the spectral selection with the need to minimize mass, power and cost. A refractive imager with up to 6 spectral bands (6.25m GSD) and a catodioptric imager with panchromatic (2.7m GSD), multi-spectral (6 bands, 4.6m GSD), hyperspectral (400nm to 2.35μm, 200 bands, 15m GSD) sensors on the same FPA will be discussed. Both of these imagers are also equipped with real time video view finding capabilities. The electronic units could be subdivided into the Front-End Electronics and Control Electronics with analogue and digital signal processing. A dedicated Analogue Front-End is used for Correlated Double Sampling (CDS), black level correction, variable gain and up to 12-bit digitizing and high speed LVDS data link to a mass memory unit.

Robust driver heartbeat estimation: A q-Hurst exponent based automatic sensor change with interactive multi-model EKF.

Science.gov (United States)

Vrazic, Sacha

2015-08-01

Preventing car accidents by monitoring the driver's physiological parameters is of high importance. However, existing measurement methods are not robust to driver's body movements. In this paper, a system that estimates the heartbeat from the seat embedded piezoelectric sensors, and that is robust to strong body movements is presented. Multifractal q-Hurst exponents are used within a classifier to predict the most probable best sensor signal to be used in an Interactive Multi-Model Extended Kalman Filter pulsation estimation procedure. The car vibration noise is reduced using an autoregressive exogenous model to predict the noise on sensors. The performance of the proposed system was evaluated on real driving data up to 100 km/h and with slaloms at high speed. It is shown that this method improves by 36.7% the pulsation estimation under strong body movement compared to static sensor pulsation estimation and appears to provide reliable pulsation variability information for top-level analysis of drowsiness or other conditions.

Appraising city-scale pollution monitoring capabilities of multi-satellite datasets using portable pollutant monitors

Science.gov (United States)

Aliyu, Yahaya A.; Botai, Joel O.

2018-04-01

The retrieval characteristics for a city-scale satellite experiment was explored over a Nigerian city. The study evaluated carbon monoxide and aerosol contents in the city atmosphere. We utilized the MSA Altair 5× gas detector and CW-HAT200 particulate counter to investigate the city-scale monitoring capabilities of satellite pollution observing instruments; atmospheric infrared sounder (AIRS), measurement of pollution in the troposphere (MOPITT), moderate resolution imaging spectroradiometer (MODIS), multi-angle imaging spectroradiometer (MISR) and ozone monitoring instrument (OMI). To achieve this, we employed the Kriging interpolation technique to collocate the satellite pollutant estimations over 19 ground sample sites for the period of 2015-2016. The portable pollutant devices were validated using the WHO air filter sampling model. To determine the city-scale performance of the satellite datasets, performance indicators: correlation coefficient, model efficiency, reliability index and root mean square error, were adopted as measures. The comparative analysis revealed that MOPITT carbon monoxide (CO) and MODIS aerosol optical depth (AOD) estimates are the appropriate satellite measurements for ground equivalents in Zaria, Nigeria. Our findings were within the acceptable limits of similar studies that utilized reference stations. In conclusion, this study offers direction to Nigeria's air quality policy organizers about available alternative air pollution measurements for mitigating air quality effects within its limited resource environment.

A proteorhodopsin-based biohybrid light-powering pH sensor.

Science.gov (United States)

Rao, Siyuan; Guo, Zhibin; Liang, Dawei; Chen, Deliang; Wei, Yen; Xiang, Yan

2013-10-14

The biohybrid sensor is an emerging technique for multi-functional detection that utilizes the instinctive responses or interactions of biomolecules. We develop a biohybrid pH sensor by taking advantage of the pH-dependent photoelectric characteristics of proteorhodopsin (pR). The transient absorption kinetics study indicates that the photoelectric behavior of pR is attributed to the varying lifetime of the M intermediate at different environmental pH values. This pR-based biohybrid light-powering sensor with microfluidic design can achieve real-time pH detection with quick response and high sensitivity. The results of this work would shed light on pR and its potential applications.

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

Science.gov (United States)

Eide, Ingvar; Westad, Frank

2018-01-01

A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

A multi-criteria evaluation system for marine litter pollution based on statistical analyses of OSPAR beach litter monitoring time series.

Science.gov (United States)

Schulz, Marcus; Neumann, Daniel; Fleet, David M; Matthies, Michael

2013-12-01

During the last decades, marine pollution with anthropogenic litter has become a worldwide major environmental concern. Standardized monitoring of litter since 2001 on 78 beaches selected within the framework of the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) has been used to identify temporal trends of marine litter. Based on statistical analyses of this dataset a two-part multi-criteria evaluation system for beach litter pollution of the North-East Atlantic and the North Sea is proposed. Canonical correlation analyses, linear regression analyses, and non-parametric analyses of variance were used to identify different temporal trends. A classification of beaches was derived from cluster analyses and served to define different states of beach quality according to abundances of 17 input variables. The evaluation system is easily applicable and relies on the above-mentioned classification and on significant temporal trends implied by significant rank correlations. Copyright © 2013 Elsevier Ltd. All rights reserved.

Solving Multi-Pollutant Emission Dispatch Problem Using Computational Intelligence Technique

Directory of Open Access Journals (Sweden)

Nur Azzammudin Rahmat

2016-06-01

Full Text Available Economic dispatch is a crucial process conducted by the utilities to correctly determine the satisfying amount of power to be generated and distributed to the consumers. During the process, the utilities also consider pollutant emission as the consequences of fossil-fuel consumption. Fossil-fuel includes petroleum, coal, and natural gas; each has its unique chemical composition of pollutants i.e. sulphur oxides (SOX, nitrogen oxides (NOX and carbon oxides (COX. This paper presents multi-pollutant emission dispatch problem using computational intelligence technique. In this study, a novel emission dispatch technique is formulated to determine the amount of the pollutant level. It utilizes a pre-developed optimization technique termed as differential evolution immunized ant colony optimization (DEIANT for the emission dispatch problem. The optimization results indicated high level of COX level, regardless of any type of fossil fuel consumed.

Design and Implementation of a Laser-Based Ammonia Breath Sensor for Medical Applications

KAUST Repository

Owen, Kyle

2012-06-01

Laser-based sensors can be used as non-invasive monitoring tools to measure parts per billion (ppb) levels of trace gases. Ammonia sensors are useful for applications in environmental pollutant monitoring, atmospheric and combustion kinetic studies, and medical diagnostics. This sensor was specifically designed to measure ammonia in exhaled breath to be used as a medical diagnostic and monitoring tool, however, it can also be extended for use in other applications. Although ammonia is a naturally occurring species in exhaled breath, abnormally elevated levels can be an indication of adverse medical conditions. Laser-based breath diagnostics have many benefits since they are cost effective, non-invasive, painless, real time monitors. They have the potential to improve the quality of medical care by replacing currently used blood tests and providing immediate feedback to physicians. This sensor utilizes a Quantum Cascade Laser and Wavelength Modulation Spectroscopy with second harmonic normalized by first harmonic detection in a 76 m multi-pass absorption cell to measure ppb levels of ammonia with improved sensitivity over previous sensors. Initial measurements to determine the ammonia absorption line parameters were performed using direct absorption spectroscopy. This is the first experimental study of the ammonia absorption line transitions near 1103.46 cm1 with absorption spectroscopy. The linestrengths were measured with uncertainties less than 10%. The collisional broadening coefficients for each of the ammonia lines with nitrogen, oxygen, water vapor, and carbon dioxide were also measured, many of which had uncertainties less than 5%. The sensor was characterized to show a detectability limit of 10 ppb with an uncertainty of less than 5% at typical breath ammonia levels. Initial breath test results showed that some of the patients with chronic kidney disease had elevated ammonia levels while others had ammonia levels in the same range as expected for healthy

Optimal sensor configuration for flexible structures with multi-dimensional mode shapes

International Nuclear Information System (INIS)

Chang, Minwoo; Pakzad, Shamim N

2015-01-01

A framework for deciding the optimal sensor configuration is implemented for civil structures with multi-dimensional mode shapes, which enhances the applicability of structural health monitoring for existing structures. Optimal sensor placement (OSP) algorithms are used to determine the best sensor configuration for structures with a priori knowledge of modal information. The signal strength at each node is evaluated by effective independence and modified variance methods. Euclidean norm of signal strength indices associated with each node is used to expand OSP applicability into flexible structures. The number of sensors for each method is determined using the threshold for modal assurance criterion (MAC) between estimated (from a set of observations) and target mode shapes. Kriging is utilized to infer the modal estimates for unobserved locations with a weighted sum of known neighbors. A Kriging model can be expressed as a sum of linear regression and random error which is assumed as the realization of a stochastic process. This study presents the effects of Kriging parameters for the accurate estimation of mode shapes and the minimum number of sensors. The feasible ranges to satisfy MAC criteria are investigated and used to suggest the adequate searching bounds for associated parameters. The finite element model of a tall building is used to demonstrate the application of optimal sensor configuration. The dynamic modes of flexible structure at centroid are appropriately interpreted into the outermost sensor locations when OSP methods are implemented. Kriging is successfully used to interpolate the mode shapes from a set of sensors and to monitor structures associated with multi-dimensional mode shapes. (paper)

Interpreting Mobile and Handheld Air Sensor Readings in Relation to Air Quality Standards and Health Effect Reference Values: Tackling the Challenges

Directory of Open Access Journals (Sweden)

George M. Woodall

2017-09-01

Full Text Available The US Environmental Protection Agency (EPA and other federal agencies face a number of challenges in interpreting and reconciling short-duration (seconds to minutes readings from mobile and handheld air sensors with the longer duration averages (hours to days associated with the National Ambient Air Quality Standards (NAAQS for the criteria pollutants-particulate matter (PM, ozone, carbon monoxide, lead, nitrogen oxides, and sulfur oxides. Similar issues are equally relevant to the hazardous air pollutants (HAPs where chemical-specific health effect reference values are the best indicators of exposure limits; values which are often based on a lifetime of continuous exposure. A multi-agency, staff-level Air Sensors Health Group (ASHG was convened in 2013. ASHG represents a multi-institutional collaboration of Federal agencies devoted to discovery and discussion of sensor technologies, interpretation of sensor data, defining the state of sensor-related science across each institution, and provides consultation on how sensors might effectively be used to meet a wide range of research and decision support needs. ASHG focuses on several fronts: improving the understanding of what hand-held sensor technologies may be able to deliver; communicating what hand-held sensor readings can provide to a number of audiences; the challenges of how to integrate data generated by multiple entities using new and unproven technologies; and defining best practices in communicating health-related messages to various audiences. This review summarizes the challenges, successes, and promising tools of those initial ASHG efforts and Federal agency progress on crafting similar products for use with other NAAQS pollutants and the HAPs. NOTE: The opinions expressed are those of the authors and do not necessary represent the opinions of their Federal Agencies or the US Government. Mention of product names does not constitute endorsement.

Three dimensional multi perspective imaging with randomly distributed sensors

International Nuclear Information System (INIS)

DaneshPanah, Mehdi; Javidi, Bahrain

2008-01-01

In this paper, we review a three dimensional (3D) passive imaging system that exploits the visual information captured from the scene from multiple perspectives to reconstruct the scene voxel by voxel in 3D space. The primary contribution of this work is to provide a computational reconstruction scheme based on randomly distributed sensor locations in space. In virtually all of multi perspective techniques (e.g. integral imaging, synthetic aperture integral imaging, etc), there is an implicit assumption that the sensors lie on a simple, regular pickup grid. Here, we relax this assumption and suggest a computational reconstruction framework that unifies the available methods as its special cases. The importance of this work is that it enables three dimensional imaging technology to be implemented in a multitude of novel application domains such as 3D aerial imaging, collaborative imaging, long range 3D imaging and etc, where sustaining a regular pickup grid is not possible and/or the parallax requirements call for a irregular or sparse synthetic aperture mode. Although the sensors can be distributed in any random arrangement, we assume that the pickup position is measured at the time of capture of each elemental image. We demonstrate the feasibility of the methods proposed here by experimental results.

Genetic Algorithm and its Application in Optimal Sensor Layout

Directory of Open Access Journals (Sweden)

Xiang-Yang Chen

2015-05-01

Full Text Available This paper aims at the problem of multi sensor station distribution, based on multi- sensor systems of different types as the research object, in the analysis of various types of sensors with different application background, different indicators of demand, based on the different constraints, for all kinds of multi sensor station is studied, the application of genetic algorithms as a tool for the objective function of the models optimization, then the optimal various types of multi sensor station distribution plan, improve the performance of the system, and achieved good military effect. In the field of application of sensor radar, track measuring instrument, the satellite, passive positioning equipment of various types, specific problem, use care indicators and station arrangement between the mathematical model of geometry, using genetic algorithm to get the optimization results station distribution, to solve a variety of practical problems provides useful help, but also reflects the improved genetic algorithm in electronic weapon system based on multi sensor station distribution on the applicability and effectiveness of the optimization; finally the genetic algorithm for integrated optimization of multi sensor station distribution using the good to the training exercise tasks based on actual in, and have achieved good military effect.

Windows pollution problems of the dust concentration measurement based on scattering method

International Nuclear Information System (INIS)

Zhao Yanjun; Zhang Yongtao; Shi Xinyue; Xu Chuanlong; Wang Shimin

2009-01-01

The windows are separated the measurement system from the dust space in the light Scattering dust concentration measurement system. The windows are polluted unavoidably by the dust and the measurement error is produced. Based on the Mie Scattering theory, the measurement error is researched in this paper. The numerical simulation results show that the measurement error is related to the particles diameter distribution and the refractive index, but is independent of the particles average diameter. A novel photoelectricity sensor is developed in this paper in order to solve the measurement error by the windows pollution. The calculated method is brought out which can amend the measurement errors by the windows pollution and improve the measurement accuracy.

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor

Directory of Open Access Journals (Sweden)

Xin Li

2018-02-01

Full Text Available Dynamic thermal management (DTM mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE by 1.2 ∘ C and increase the signal-to-noise ratio (SNR by 15.8 dB (with a very small average runtime overhead compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.

Science.gov (United States)

Li, Xin; Ou, Xingtao; Li, Zhi; Wei, Henglu; Zhou, Wei; Duan, Zhemin

2018-02-02

Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 ∘ C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

Multi-media pollution prevention: A case study of a refinery

International Nuclear Information System (INIS)

Schmitt, R.E.; Podar, M.K.

1994-01-01

In late 1989, Amoco Corporation and the United States Environmental Protection Agency began a voluntary, joint project to study pollution prevention opportunities at an industrial facility. Amoco proposed use of its refinery at Yorktown, Virginia, to conduct a multi-media assessment of releases to the environment, then to develop and evaluate options to reduce these releases. A Workgroup composed of state, federal, and Amoco representatives provided oversight to the Project. Monthly Workgroup meetings provided Project oversight, a forum for presentations on different Project components, and an opportunity for informal discussion of different viewpoints about environmental management. The Workgroup identified four objectives for this study: (1) Inventory refinery releases to the environment to define their chemical type, quantity, source, and medium of release; (2) develop options to reduce selected releases identified, as well as rank and prioritize the options based on a variety of criteria and perspectives; (3) identify and evaluate factors as technical, legislative, regulatory, institutional, permitting, and economic, that impede or encourage pollution prevention; and (4) enhance participates' knowledge of refinery and regulatory systems

Attitude Control of Quad-rotor by Improving the Reliability of Multi-Sensor System

Energy Technology Data Exchange (ETDEWEB)

Yu, Dong Hyeon; Chong, Kil To [Chon-bok National University, Jeonju (Korea, Republic of); Park, Jong Ho [Seonam University, Namwon (Korea, Republic of); Ryu, Ji Hyoung [ETRI, Daejeon (Korea, Republic of)

2015-05-15

This paper presents the results of study for improving the reliability of quadrotor attitude control by applying a multi-sensor along with a data fusion algorithm. First, a mathematical model of the quadrotor dynamics was developed. Then, using the quadrotor mathematical model, simulations were performed using the improved reliability multi-sensor data as the inputs. From the simulation results, we designed a Gimbal-equipped quadrotor system. With the quadrotor in a hover state, we performed experiments according to the angle change of the user's specifications . We then calculated the attitude control data from the actual experimental data. Furthermore, with additional simulations, we verified the performance of the designed quadrotor attitude control system with multiple sensors.

Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors

Science.gov (United States)

Dutton, Neale A. W.; Gyongy, Istvan; Parmesan, Luca; Henderson, Robert K.

2016-01-01

SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors by evaluating the peak separation and width (PSW) of single photon peaks in a photon counting histogram (PCH). The technique is used to identify and analyse cumulative noise in analogue integrating SPC SPAD-based pixels. The DSERN of our SPAD image sensor is exploited to confirm recent multi-photon threshold quanta image sensor (QIS) theory. Finally, various single and multiple photon spatio-temporal oversampling techniques are reviewed. PMID:27447643

Multi-dimensional fiber-optic radiation sensor for ocular proton therapy dosimetry

International Nuclear Information System (INIS)

Jang, K.W.; Yoo, W.J.; Moon, J.; Han, K.T.; Park, B.G.; Shin, D.; Park, S-Y.; Lee, B.

2012-01-01

In this study, we fabricated a multi-dimensional fiber-optic radiation sensor, which consists of organic scintillators, plastic optical fibers and a water phantom with a polymethyl methacrylate structure for the ocular proton therapy dosimetry. For the purpose of sensor characterization, we measured the spread out Bragg-peak of 120 MeV proton beam using a one-dimensional sensor array, which has 30 fiber-optic radiation sensors with a 1.5 mm interval. A uniform region of spread out Bragg-peak using the one-dimensional fiber-optic radiation sensor was obtained from 20 to 25 mm depth of a phantom. In addition, the Bragg-peak of 109 MeV proton beam was measured at the depth of 11.5 mm of a phantom using a two-dimensional sensor array, which has 10×3 sensor array with a 0.5 mm interval.

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

Directory of Open Access Journals (Sweden)

Sheng Wang

2007-10-01

Full Text Available The recent availability of low cost and miniaturized hardware has allowedwireless sensor networks (WSNs to retrieve audio and video data in real worldapplications, which has fostered the development of wireless multimedia sensor networks(WMSNs. Resource constraints and challenging multimedia data volume makedevelopment of efficient algorithms to perform in-network processing of multimediacontents imperative. This paper proposes solving problems in the domain of WMSNs fromthe perspective of multi-agent systems. The multi-agent framework enables flexible networkconfiguration and efficient collaborative in-network processing. The focus is placed ontarget classification in WMSNs where audio information is retrieved by microphones. Todeal with the uncertainties related to audio information retrieval, the statistical approachesof power spectral density estimates, principal component analysis and Gaussian processclassification are employed. A multi-agent negotiation mechanism is specially developed toefficiently utilize limited resources and simultaneously enhance classification accuracy andreliability. The negotiation is composed of two phases, where an auction based approach isfirst exploited to allocate the classification task among the agents and then individual agentdecisions are combined by the committee decision mechanism. Simulation experiments withreal world data are conducted and the results show that the proposed statistical approachesand negotiation mechanism not only reduce memory and computation requi

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks.

Science.gov (United States)

Artuñedo, Antonio; Del Toro, Raúl M; Haber, Rodolfo E

2017-04-26

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller ( TLC ) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

Directory of Open Access Journals (Sweden)

Antonio Artuñedo

2017-04-01

Full Text Available Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

Head ballistocardiogram based on wireless multi-location sensors.

Science.gov (United States)

Onizuka, Kohei; Sodini, Charles G

2015-08-01

Recently a wearable BCG monitoring technique based on an accelerometer worn at the ear was demonstrated to replace a conventional bulky BCG acquisition system. In this work, a multi-location wireless vital signs monitor was developed, and at least two common acceleration vectors correlating to sitting-BCG were found in the supine position by using head PPG signal as a reference for eight healthy human subjects. The head side amplitude in the supine position is roughly proportional to the sitting amplitude that is in turn proportional to the stroke volume. Signal processing techniques to identify J-waves in a subject having small amplitude was also developed based on the two common vectors at the head side and top.

UV Radiation Detection Using Optical Sensor Based on Eu3+ Doped PMMA

Directory of Open Access Journals (Sweden)

Miluski Piotr

2016-12-01

Full Text Available Progress in UV treatment applications requires new compact and sensor constructions. In the paper a hybrid (organic-inorganic rare-earth-based polymeric UV sensor construction is proposed. The efficient luminescence of poly(methyl methacrylate (PMMA matrix doped by europium was used for testing the optical sensor (optrode construction. The europium complex assures effective luminescence in the visible range with well determined multi-peak spectrum emission enabling construction of the optrode. The fabricated UV optical fibre sensor was used for determination of Nd:YAG laser intensity measurements at the third harmonic (355 nm in the radiation power range 5.0-34.0 mW. The multi-peak luminescence spectrum was used for optimization of the measurement formula. The composition of luminescent peak intensity enables to increase the slope of sensitivity up to −2.8 mW-1. The obtained results and advantages of the optical fibre construction enable to apply it in numerous UV detection systems.

Multi-objective ant algorithm for wireless sensor network positioning

International Nuclear Information System (INIS)

Fidanova, S.; Shindarov, M.; Marinov, P.

2013-01-01

It is impossible to imagine our modern life without telecommunications. Wireless networks are a part of telecommunications. Wireless sensor networks (WSN) consist of spatially distributed sensors, which communicate in wireless way. This network monitors physical or environmental conditions. The objective is the full coverage of the monitoring region and less energy consumption of the network. The most appropriate approach to solve the problem is metaheuristics. In this paper the full coverage of the area is treated as a constrain. The objectives which are optimized are a minimal number of sensors and energy (lifetime) of the network. We apply multi-objective Ant Colony Optimization to solve this important telecommunication problem. We chose MAX-MIN Ant System approach, because it is proven to converge to the global optima

Multi-Sensor Geomagnetic Prospection: A Case Study from Neolithic Thessaly, Greece

Directory of Open Access Journals (Sweden)

Tuna Kalaycı

2016-11-01

Full Text Available Multi-sensor prospecting is a fast-emerging paradigm in archaeological geophysics. Given suitable ground conditions for navigation, sensor arrays drastically increase efficiency in data collection. In particular, geomagnetic prospecting benefits from this development. Despite these advancements, data processing still lacks a best-practice approach. Conventional processing methods developed for gridded data has been challenged by sensor arrays “roaming” in the landscape. In realization of the issue, the Innovative Geophysical Approaches for the Study of Early Agricultural Villages of Neolithic Thessaly (IGEAN Project explored various innovative techniques for the betterment of the multi-sensor geomagnetic data processing. As a result, a modular pipeline is produced with minimal user intervention. In addition to standard steps, such as data clipping, various other algorithms have been introduced. This pipeline is tested over 20 Neolithic settlements in Thessaly, Greece, three of which are presented here in detail. The proposed workflow provides drastic improvements over raw data. As a result of these improvements, the IGEAN project revealed astonishing details on architectural elements, settlement enclosures, and paleolandscapes, changing completely the existing perspective of the Neolithic habitation in Thessaly.

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

Directory of Open Access Journals (Sweden)

Ingvar Eide

Full Text Available A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors, salinity (calculated from temperature and conductivity, biomass at three different depth intervals (5-50, 50-120, 120-250 m, and current speed measured in two directions (east and north using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA. Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

Directory of Open Access Journals (Sweden)

Jinbeum Jang

2015-03-01

Full Text Available This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF algorithm consists of four steps: (i acquisition of left and right images using AF points in the region-of-interest; (ii feature extraction in the left image under low illumination and out-of-focus blur; (iii the generation of two feature images using the phase difference between the left and right images; and (iv estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Multi-shelled ZnCo2O4 yolk-shell spheres for high-performance acetone gas sensor

Science.gov (United States)

Xiong, Ya; Zhu, Zongye; Ding, Degong; Lu, Wenbo; Xue, Qingzhong

2018-06-01

In the present study, multi-shelled ZnCo2O4 yolk-shell spheres have been successfully prepared by using carbonaceous microspheres as templates. It is found that the multi-shelled ZnCo2O4 yolk-shell spheres based sensor shows optimal sensing performances (response value of 38.2, response/recovery time of 19 s/71 s) toward 500 ppm acetone at 200 °C. In addition, this sensor exhibits a low detection limit of 0.5 ppm acetone (response value of 1.36) and a good selectivity toward hydrogen, methane, ethanol, ammonia and carbon dioxide. Furthermore, it is demonstrated that acetone gas response of multi-shelled ZnCo2O4 yolk-shell spheres is significantly better than that of ZnCo2O4 nanotubes and ZnCo2O4 nanosheets. High acetone response of the multi-shelled ZnCo2O4 yolk-shell spheres is attributed to the enhanced gas accessibility of the multi-shell morphology caused by the small crystalline size and high specific surface area while the short response/recovery time is mainly related to the rapid gas diffusion determined by the highly porous structure. Our work puts forward an exciting opportunity in designing various yolk-shelled structures for multipurpose applications.

Integration of Multi-sensor Data for Desertification Monitoring

Science.gov (United States)

Lin, S.; Kim, J.

2010-12-01

The desert area has been rapidly expanding globally due to reasons such as climate change, uninhibited human activities, etc. The continuous desertification has seriously affected in (and near) desert area all over the world. As sand dune activity has been recognised as an essential indicator of desertification (it is the signature and the consequence of desertification), an accurate monitoring of desert dune movement hence becomes crucial for understanding and modelling the progress of desertification. In order to determine dune’s moving speed and tendency, also to understand the propagation occurring in transition region between desert and soil rich area, a monitoring system applying multi-temporal and multi-sensor remote sensed data are proposed and implemented. Remote sensed data involved in the monitoring scheme include space-borne optical image, Synthetic Aperture Radar (SAR) data, multi- and hyper-spectral image, and terrestrial close range image. In order to determine the movement of dunes, a reference terrain surface is required. To this end, a digital terrain model (DTM) covering the test site is firstly produced using high resolution optical stereo satellite images. Subsequently, ERS-1/2 SAR imagery are employed as another resource for dune field observation. Through the interferometric SAR (InSAR) technique combining with image-based stereo DTM, the surface displacements are obtained. From which the movement and speed of the dunes can be determined. To understand the effect of desertification combating activities, the correlation between dune activities and the landcover change is also an important issue to be covered in the monitoring scheme. The task is accomplished by tracing soil and vegetation canopy variation with the multi and hyper spectral image analysis using Hyperion and Ali imagery derived from Earth Observation Mission 1 (EO-1). As a result, the correlation between the soil restorations, expanding of vegetation canopy and the ceasing of

Multi-year simulations of air pollution in two cities

Science.gov (United States)

Zink, Katrin; Berchet, Antoine; Emmenegger, Lukas; Brunner, Dominik

2016-04-01

As more and more people are living in urban areas world wide, air quality monitoring and forecasting at the city scale becomes increasingly critical. Due to the proximity to sources and the complex, fine-scale structure of the flow and turbulence in the built environment, air pollutant concentrations vary strongly in cities both spatially and temporally. Studies assessing the effect of air pollution on human health would greatly benefit from accurate knowledge of individual exposure, but given the high variability of concentrations and the mobility of the population, this is a marvellous task requiring highly-resolved, city-wide information on air pollutant concentrations. The Swiss Nano-Tera project OpenSense II addresses these issues using statistical and physical modeling of air pollution at very high resolution combined with long-term air pollution measurements and mobile networks of low-cost sensors. In the framework of this project, we have set up the nested meteorology and dispersion model system GRAMM/GRAL the cities of Lausanne and Zurich and improved several computational aspects of the system. Using the mesoscale model GRAMM, we simulate the flow in a larger domain around the two cities at 100 m resolution taking the complex topography and influences of different land cover on surface-atmosphere exchange of heat and momentum into account. These flow fields serve as initial and boundary conditions for the nested model GRAL, which simulates the flow inside the city at building-resolving scale (5 m resolution) based on the Reynolds-Averaged-Navier-Stokes equations, and computes the transport and dispersion of air pollutants in a Lagrangian framework. For computational efficiency, both GRAMM and GRAL simulations are run for a fixed catalog of 1008 weather situations varying in terms of background wind speed, direction and stability. Hourly time-series of meteorology and air pollutants are constructed from these steady-state solutions by selecting, for each

Study of Water Pollution Early Warning Framework Based on Internet of Things

Science.gov (United States)

Chengfang, H.; Xiao, X.; Dingtao, S.; Bo, C.; Xiongfei, W.

2016-06-01

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

A novel framework for feature extraction in multi-sensor action potential sorting.

Science.gov (United States)

Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

2015-09-30

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources

Directory of Open Access Journals (Sweden)

Xiang Gao

2016-07-01

Full Text Available This paper addresses the problem of mapping odor distribution derived from a chemical source using multi-sensor integration and reasoning system design. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require a mobile vehicle to follow an odor plume along its entire path, which is time consuming and may be especially difficult in a cluttered environment. To solve both of the above challenges, this paper proposes a novel algorithm that combines data from odor and anemometer sensors, and combine sensors’ data at different positions. Initially, a multi-sensor integration method, together with the path of airflow was used to map the pattern of odor particle movement. Then, more sensors are introduced at specific regions to determine the probable location of the odor source. Finally, the results of odor source location simulation and a real experiment are presented.

Multi-robot system using low-cost infrared sensors

Directory of Open Access Journals (Sweden)

Anubhav Kakkar

2013-03-01

Full Text Available This paper presents a proposed set of the novel technique, methods, and algorithm for simultaneous path planning, area exploration, area retrieval, obstacle avoidance, object detection, and object retrieval   autonomously by a multi-robot system. The proposed methods and algorithms are built considering the use of low cost infrared sensors with the ultimate function of efficiently exploring the given unknown area and simultaneously identifying desired objects by analyzing the physical characteristics of several of the objects that come across during exploration. In this paper, we have explained the scenario by building a coordinative multi-robot system consisting of two autonomously operated robots equipped with low-cost and low-range infrared sensors to perform the assigned task by analyzing some of the sudden changes in their environment. Along with identifying and retrieving the desired object, the proposed methodology also provide an inclusive analysis of the area being explored. The novelties presented in the paper may significantly provide a cost-effective solution to the problem of area exploration and finding a known object in an unknown environment by demonstrating an innovative approach of using the infrared sensors instead of high cost long range sensors and cameras. Additionally, the methodology provides a speedy and uncomplicated method of traversing a complicated arena while performing all the necessary and inter-related tasks of avoiding the obstacles, analyzing the area as well as objects, and reconstructing the area using all these information collected and interpreted for an unknown environment. The methods and algorithms proposed are simulated over a complex arena to depict the operations and manually tested over a physical environment which provided 78% correct results with respect to various complex parameters set randomly.

MULTI ELEMENT SI SENSOR WITH READOUT ASIC FOR EXAFS SPECTROSCOPY.

Energy Technology Data Exchange (ETDEWEB)

DE GERONIMO,G.; O CONNOR,P.; BEUTTENMULLER,R.H.; LI,Z.; KUCZEWSKI,A.J.; SIDDONS,D.P.

2002-09-09

Extended X-ray Absorption Fine Structure (EXAFS) experiments impose stringent requirements on a detection system, due to the need for processing ionizing events at a high rate, typically above of 10Mcps/cm{sup 2}, and with a high resolution, typically better than 300eV. The detection system here presented is being developed targeting these stringent requirements. It is the result of a cooperation between the Instrumentation Division and the National Synchrotron Light Source (NSLS) of the Brookhaven National Laboratory (BNL). The system is composed of a multi-element Si sensor with dedicated per pixel electronics. The combination of high rate, high resolution and moderate complexity makes this system attractive when compared to other multi-element solutions. In sections 2, 3 and 4 the sensor, the interconnect and the electronics are briefly described. Section 5 reports on the first experimental results.

Facile Fabrication of Multi-hierarchical Porous Polyaniline Composite as Pressure Sensor and Gas Sensor with Adjustable Sensitivity

OpenAIRE

He, Xiao-Xiao; Li, Jin-Tao; Jia, Xian-Sheng; Tong, Lu; Wang, Xiao-Xiong; Zhang, Jun; Zheng, Jie; Ning, Xin; Long, Yun-Ze

2017-01-01

A multi-hierarchical porous polyaniline (PANI) composite which could be used in good performance pressure sensor and adjustable sensitivity gas sensor has been fabricated by a facile in situ polymerization. Commercial grade sponge was utilized as a template scaffold to deposit PANI via in situ polymerization. With abundant interconnected pores throughout the whole structure, the sponge provided sufficient surface for the growth of PANI nanobranches. The flexible porous structure helped the co...

A Multi-Agent Framework Manages a Representative Sensor Web

Science.gov (United States)

Suri, D.; Schmidt, D.; Biswas, G.; Kinnebrew, J.; Otte, W.; Shankaran, N.

2008-12-01

NASA's vision of a Sensor Web (which includes a distributed global observation system) consists of a large number of elements, such as remote spacecraft hosting multiple instruments, in situ terrestrial and oceanic sensor networks, and airborne assets. Researchers and developers of a Sensor web face a number of challenges that arise from (1) the inherent heterogeneous and geographical distributed nature of the Sensor web; (2) the myriad mission goals and objectives that must be satisfied by the Sensor web, ranging from an improved understanding of earth science, weather forecasting, and disaster management to an alleviation of societal problems; and (3) the need to support myriad operational modes, such as long and short-term monitoring and targeted observations. Resolving these challenges requires some form of autonomy - typically embodied in software. Agent technology has emerged both as a salient purveyor of entities that exhibit autonomous behavior and also as a paradigm for constructing complex software systems with a large number of interacting heterogeneous components. This paper describes our experiences integrating the Multi-agent Architecture for Coordinated Responsive Observations (MACRO) into the SouthEast Alaska MOnitoring Network for Science, Telecommunications, Education, and Research (SEAMONSTER). MACRO provides agents at (1) the mission level, where agents interact with users to define science goals and then translate these goals into a set of prioritized tasks that have to be executed to achieve these goals, and (2) the resource level, where agents translate tasks into activities related to data collection, data analysis, and data communication. As a representative small-scale sensor web situated in multiple locations on the Juneau Icefield, SEAMONSTER affords an unparalleled opportunity to develop, mature, and showcase MACRO's multi-level agent capabilities. MACRO is developed by the Lockheed Martin Space System Company's Advanced Technology

A multi-channel humidity control system based on LabVIEW

International Nuclear Information System (INIS)

Zhang Aiwu; Xie Yuguang; Liu Hongbang; Liu Yingbiao; Cai Xiao; Yu Boxiang; Lu Junguang; Zhou Li

2011-01-01

A real time multi-channel humidity control system was designed based on LabVIEW, using the dry air branch of BESⅢ drying system. The hardware of this control system consist of mini humidity and temperature sensors, intelligent collection module, switch quantity controller and electromagnetic valves. The humidity can be controlled at arbitrary value from air humidity to 3% with accuracy better than 2%. Multi microenvironment with different humidity can be easily controlled and monitored in real time by this system. It can also be extended to hybrid control of multi channel humidity and temperature. (authors)

Dynamic Task Allocation in Multi-Hop Multimedia Wireless Sensor Networks with Low Mobility

Directory of Open Access Journals (Sweden)

Klaus Moessner

2013-10-01

Full Text Available This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.

Multi-parameter fibre Bragg grating sensor-array for thermal vacuum cycling test

Science.gov (United States)

Cheng, L.; Ahlers, B.; Toet, P.; Casarosa, G.; Appolloni, M.

2017-11-01

Fibre Bragg Grating (FBG) sensor systems based on optical fibres are gaining interest in space applications. Studies on Structural Health Monitoring (SHM) of the reusable launchers using FBG sensors have been carried out in the Future European Space Transportation Investigations Programme (FESTIP). Increasing investment in the development on FBG sensor applications is foreseen for the Future Launchers Preparatory Programme (FLPP). TNO has performed different SHM measurements with FBGs including on the VEGA interstage [1, 2] in 2006. Within the current project, a multi-parameter FBG sensor array demonstrator system for temperature and strain measurements is designed, fabricated and tested under ambient as well as Thermal Vacuum (TV) conditions in a TV chamber of the European Space Agency (ESA), ESTEC site. The aim is the development of a multi-parameters measuring system based on FBG technology for space applications. During the TV tests of a Space Craft (S/C) or its subsystems, thermal measurements, as well as strain measurements are needed by the engineers in order to verify their prediction and to validate their models. Because of the dimensions of the test specimen and the accuracy requested to the measurement, a large number of observation/measuring points are needed. Conventional sensor systems require a complex routing of the cables connecting the sensors to their acquisition unit. This will add extra weight to the construction under test. FBG sensors are potentially light-weight and can easily be multiplexed in an array configuration. The different tasks comply of a demonstrator system design; its component selection, procurement, manufacturing and finally its assembly. The temperature FBG sensor is calibrated in a dedicated laboratory setup down to liquid nitrogen (LN2) temperature at TNO. A temperature-wavelength calibration curve is generated. After a test programme definition a setup in thermal vacuum is realised at ESA premises including a mechanical

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

Science.gov (United States)

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-09-25

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

Development of the smartphone-based colorimetry for multi-analyte sensing arrays.

Science.gov (United States)

Hong, Jong Il; Chang, Byoung-Yong

2014-05-21

Here we report development of a smartphone app (application) that digitizes the colours of a colorimetric sensor array. A conventional colorimetric sensor array consists of multiple paper-based sensors, and reports the detection results in terms of colour change. Evaluation of the colour changes is normally done by the naked eye, which may cause uncertainties due to personal subjectivity and the surrounding conditions. Solutions have been particularly sought in smartphones as they are capable of spectrometric functions. Our report specifically focuses on development of a practical app for immediate point-of-care (POC) multi-analyte sensing without additional devices. First, the individual positions of the sensors are automatically identified by the smartphone; second, the colours measured at each sensor are digitized based on a correction algorithm; and third, the corrected colours are converted to concentration values by pre-loaded calibration curves. All through these sequential processes, the sensor array taken in a smartphone snapshot undergoes laboratory-level spectrometry. The advantages of inexpensive and convenient paper-based colorimetry and the ubiquitous smartphone are tied to achieve a ready-to-go POC diagnosis.

Multi-scale Analysis of MEMS Sensors Subject to Drop Impacts

Directory of Open Access Journals (Sweden)

Sarah Zerbini

2007-09-01

Full Text Available The effect of accidental drops on MEMS sensors are examined within the frame-work of a multi-scale finite element approach. With specific reference to a polysilicon MEMSaccelerometer supported by a naked die, the analysis is decoupled into macro-scale (at dielength-scale and meso-scale (at MEMS length-scale simulations, accounting for the verysmall inertial contribution of the sensor to the overall dynamics of the device. Macro-scaleanalyses are adopted to get insights into the link between shock waves caused by the impactagainst a target surface and propagating inside the die, and the displacement/acceleration his-tories at the MEMS anchor points. Meso-scale analyses are adopted to detect the most stresseddetails of the sensor and to assess whether the impact can lead to possible localized failures.Numerical results show that the acceleration at sensor anchors cannot be considered an ob-jective indicator for drop severity. Instead, accurate analyses at sensor level are necessary toestablish how MEMS can fail because of drops.

AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

Directory of Open Access Journals (Sweden)

Daniel Scheffler

2017-07-01

Full Text Available Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software, a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

PASSIVE WIRELESS MULTI-SENSOR TEMPERATURE AND PRESSURE SENSING SYSTEM USING ACOUSTIC WAVE DEVICES, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — This proposal describes the development of passive surface acoustic wave (SAW) sensors and multi-sensor systems for NASA application to remote wireless sensing of...

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

Science.gov (United States)

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability. PMID:28903223

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments.

Science.gov (United States)

Roy, Nirmalya; Misra, Archan; Cook, Diane

2016-02-01

Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional 'hidden' context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions.

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments

Science.gov (United States)

Misra, Archan; Cook, Diane

2016-01-01

Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional ‘hidden’ context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions. PMID:27042240

Multi-sensor measurement system for robotic drilling

OpenAIRE

Frommknecht, Andreas; Kühnle, Jens; Pidan, Sergej; Effenberger, Ira

2015-01-01

A multi-sensor measurement system for robotic drilling is presented. The system enables a robot to measure its 6D pose with respect to the work piece and to establish a reference coordinate system for drilling. The robot approaches the drill point and performs an orthogonal alignment with the work piece. Although the measurement systems are readily capable of achieving high position accuracy and low deviation to perpendicularity, experiments show that inaccuracies in the robot's 6D-pose and e...

Towards a social and context-aware multi-sensor fall detection and risk assessment platform.

Science.gov (United States)

De Backere, F; Ongenae, F; Van den Abeele, F; Nelis, J; Bonte, P; Clement, E; Philpott, M; Hoebeke, J; Verstichel, S; Ackaert, A; De Turck, F

2015-09-01

For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms. In this paper, a social- and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task. The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. The combination of several systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm

Science.gov (United States)

Zhang, Shou-ping; Xin, Xiao-kang

2017-07-01

Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.

Corrosion Sensor Development for Condition-Based Maintenance of Aircraft

Directory of Open Access Journals (Sweden)

Gino Rinaldi

2012-01-01

Full Text Available Aircraft routinely operate in atmospheric environments that, over time, will impact their structural integrity. Material protection and selection schemes notwithstanding, recurrent exposure to chlorides, pollution, temperature gradients, and moisture provide the necessary electrochemical conditions for the development and profusion of corrosion in aircraft structures. For aircraft operators, this becomes an important safety matter as corrosion found in a given aircraft must be assumed to be present in all of that type of aircraft. This safety protocol and its associated unscheduled maintenance requirement drive up the operational costs of the fleet and limit the availability of the aircraft. Hence, there is an opportunity at present for developing novel sensing technologies and schemes to aid in shifting time-based maintenance schedules towards condition-based maintenance procedures. In this work, part of the ongoing development of a multiparameter integrated corrosion sensor is presented. It consists of carbon nanotube/polyaniline polymer sensors and commercial-off-the-shelf sensors. It is being developed primarily for monitoring environmental and material factors for the purpose of providing a means to more accurately assess the structural integrity of aerospace aluminium alloys through fusion of multiparameter sensor data. Preliminary experimental test results are presented for chloride ion concentration, hydrogen gas evolution, humidity variations, and material degradation.

STUDY OF WATER POLLUTION EARLY WARNING FRAMEWORK BASED ON INTERNET OF THINGS

Directory of Open Access Journals (Sweden)

H. Chengfang

2016-06-01

Full Text Available In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

Reconnaissance blind multi-chess: an experimentation platform for ISR sensor fusion and resource management

Science.gov (United States)

Newman, Andrew J.; Richardson, Casey L.; Kain, Sean M.; Stankiewicz, Paul G.; Guseman, Paul R.; Schreurs, Blake A.; Dunne, Jeffrey A.

2016-05-01

This paper introduces the game of reconnaissance blind multi-chess (RBMC) as a paradigm and test bed for understanding and experimenting with autonomous decision making under uncertainty and in particular managing a network of heterogeneous Intelligence, Surveillance and Reconnaissance (ISR) sensors to maintain situational awareness informing tactical and strategic decision making. The intent is for RBMC to serve as a common reference or challenge problem in fusion and resource management of heterogeneous sensor ensembles across diverse mission areas. We have defined a basic rule set and a framework for creating more complex versions, developed a web-based software realization to serve as an experimentation platform, and developed some initial machine intelligence approaches to playing it.

Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots

Science.gov (United States)

Emter, Thomas; Petereit, Janko

2014-05-01

An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.

Design and implementation of PAVEMON: A GIS web-based pavement monitoring system based on large amounts of heterogeneous sensors data

Science.gov (United States)

Shahini Shamsabadi, Salar

A web-based PAVEment MONitoring system, PAVEMON, is a GIS oriented platform for accommodating, representing, and leveraging data from a multi-modal mobile sensor system. Stated sensor system consists of acoustic, optical, electromagnetic, and GPS sensors and is capable of producing as much as 1 Terabyte of data per day. Multi-channel raw sensor data (microphone, accelerometer, tire pressure sensor, video) and processed results (road profile, crack density, international roughness index, micro texture depth, etc.) are outputs of this sensor system. By correlating the sensor measurements and positioning data collected in tight time synchronization, PAVEMON attaches a spatial component to all the datasets. These spatially indexed outputs are placed into an Oracle database which integrates seamlessly with PAVEMON's web-based system. The web-based system of PAVEMON consists of two major modules: 1) a GIS module for visualizing and spatial analysis of pavement condition information layers, and 2) a decision-support module for managing maintenance and repair (Mℝ) activities and predicting future budget needs. PAVEMON weaves together sensor data with third-party climate and traffic information from the National Oceanic and Atmospheric Administration (NOAA) and Long Term Pavement Performance (LTPP) databases for an organized data driven approach to conduct pavement management activities. PAVEMON deals with heterogeneous and redundant observations by fusing them for jointly-derived higher-confidence results. A prominent example of the fusion algorithms developed within PAVEMON is a data fusion algorithm used for estimating the overall pavement conditions in terms of ASTM's Pavement Condition Index (PCI). PAVEMON predicts PCI by undertaking a statistical fusion approach and selecting a subset of all the sensor measurements. Other fusion algorithms include noise-removal algorithms to remove false negatives in the sensor data in addition to fusion algorithms developed for

An Architecture Offering Mobile Pollution Sensing with High Spatial Resolution

Directory of Open Access Journals (Sweden)

Oscar Alvear

2016-01-01

Full Text Available Mobile sensing is becoming the best option to monitor our environment due to its ease of use, high flexibility, and low price. In this paper, we present a mobile sensing architecture able to monitor different pollutants using low-end sensors. Although the proposed solution can be deployed everywhere, it becomes especially meaningful in crowded cities where pollution values are often high, being of great concern to both population and authorities. Our architecture is composed of three different modules: a mobile sensor for monitoring environment pollutants, an Android-based device for transferring the gathered data to a central server, and a central processing server for analyzing the pollution distribution. Moreover, we analyze different issues related to the monitoring process: (i filtering captured data to reduce the variability of consecutive measurements; (ii converting the sensor output to actual pollution levels; (iii reducing the temporal variations produced by mobile sensing process; and (iv applying interpolation techniques for creating detailed pollution maps. In addition, we study the best strategy to use mobile sensors by first determining the influence of sensor orientation on the captured values and then analyzing the influence of time and space sampling in the interpolation process.

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks.

Science.gov (United States)

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-12-04

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

Multi-sensor analysis to study turbidity patterns in the Guadalquivir estuary

OpenAIRE

I. Caballero; G. Navarro

2016-01-01

Revista oficial de la Asociación Española de Teledetección [EN] A detailed study of the mechanisms generated through the turbidity plume and its variability at the Guadalquivir estuary has been carried out with remote sensing and in situ data. Several sensors with different characteristics have been required (spatial, temporal and spectral resolution), thereby providing information for a multi-sensor analysis. The main objective was to determine the water quality parameters (suspended soli...

Imprinted sol-gel electrochemical sensor for the determination of benzylpenicillin based on Fe3O4/SiO2 multi-walled carbon nanotubes-chitosans nanocomposite film modified carbon electrode

International Nuclear Information System (INIS)

Hu Yufang; Li Jiaxing; Zhang Zhaohui; Zhang Huabin; Luo Lijuan; Yao Shouzhuo

2011-01-01

Graphical abstract: A novel imprinted sol-gel electrochemical sensor based on Fe 3 O 4 /SiO 2 -MWNTs-CTS nanocomposite film and a thin MIP film has been developed on a carbon electrode. Highlights: → A novel imprinted sol-gel electrochemical sensor based on Fe 3 O 4 /SiO 2 -MWNTs-CTS nanocomposites has been developed. → Fe 3 O 4 /SiO 2 -MWNTs-CTS nanocomposites act as 'electronic wires' to enhance the electron transfer. → The inherent specificity of the MIPs brings about highly selectivity. The imprinted sensor detects benzylpenicillin in real samples successfully. - Abstract: Herein, a novel imprinted sol-gel electrochemical sensor based on multi-walled carbon nanotubes (MWNTs) doped with chitosan film on a carbon electrode has been developed. Prior to doped, the MWNTs have been decorated with Fe 3 O 4 nanoparticles which have been coated uniformly with SiO 2 layer. The characterization of imprinted sensor has been carried out by X-ray diffraction and scanning electron microscopy. The performance of the proposed imprinted sensor has been investigated using cyclic voltammetry and differential pulse voltammetry. The imprinted sensor offers a fast response and sensitive benzylpenicillin quantification. The fabricated benzylpenicillin imprinted sensor exhibits a linear response from 5.0 x 10 -8 to 1.0 x 10 -3 mol L -1 with a detection limit of 1.5 x 10 -9 mol L -1 . For samples analysis, perfect recoveries of the imprinted sensor for benzylpenicillin indicated that the imprinted sensor was able to detect benzylpenicillin in real samples successfully.

Synergistic multi-sensor and multi-frequency retrieval of cloud ice water path constrained by CloudSat collocations

International Nuclear Information System (INIS)

Islam, Tanvir; Srivastava, Prashant K.

2015-01-01

The cloud ice water path (IWP) is one of the major parameters that have a strong influence on earth's radiation budget. Onboard satellite sensors are recognized as valuable tools to measure the IWP in a global scale. Albeit, active sensors such as the Cloud Profiling Radar (CPR) onboard the CloudSat satellite has better capability to measure the ice water content profile, thus, its vertical integral, IWP, than any passive microwave (MW) or infrared (IR) sensors. In this study, we investigate the retrieval of IWP from MW and IR sensors, including AMSU-A, MHS, and HIRS instruments on-board the N19 satellite, such that the retrieval is consistent with the CloudSat IWP estimates. This is achieved through the collocations between the passive satellite measurements and CloudSat scenes. Potential benefit of synergistic multi-sensor multi-frequency retrieval is investigated. Two modeling approaches are explored for the IWP retrieval – generalized linear model (GLM) and neural network (NN). The investigation has been carried out over both ocean and land surface types. The MW/IR synergy is found to be retrieved more accurate IWP than the individual AMSU-A, MHS, or HIRS measurements. Both GLM and NN approaches have been able to exploit the synergistic retrievals. - Highlights: • MW/IR synergy is investigated for IWP retrieval. • The IWP retrieval is modeled using CloudSat collocations. • Two modeling approaches are explored – GLM and ANN. • MW/IR synergy performs better than the MW or IR only retrieval

Multi-Channel Electronically Scanned Cryogenic Pressure Sensor And Method For Making Same

Science.gov (United States)

Chapman, John J. (Inventor); Hopson, Purnell, Jr. (Inventor); Holloway, Nancy M. (Inventor)

2001-01-01

A miniature, multi-channel, electronically scanned pressure measuring device uses electrostatically bonded silicon dies in a multi-element array. These dies are bonded at specific sites on a glass, pre-patterned substrate. Thermal data is multiplexed and recorded on each individual pressure measuring diaphragm. The device functions in a cryogenic environment without the need of heaters to keep the sensor at constant temperatures.

Multi Sensor Approach to Address Sustainable Development

Science.gov (United States)

Habib, Shahid

2007-01-01

The main objectives of Earth Science research are many folds: to understand how does this planet operates, can we model her operation and eventually develop the capability to predict such changes. However, the underlying goals of this work are to eventually serve the humanity in providing societal benefits. This requires continuous, and detailed observations from many sources in situ, airborne and space. By and large, the space observations are the way to comprehend the global phenomena across continental boundaries and provide credible boundary conditions for the mesoscale studies. This requires a multiple sensors, look angles and measurements over the same spot in accurately solving many problems that may be related to air quality, multi hazard disasters, public health, hydrology and more. Therefore, there are many ways to address these issues and develop joint implementation, data sharing and operating strategies for the benefit of the world community. This is because for large geographical areas or regions and a diverse population, some sound observations, scientific facts and analytical models must support the decision making. This is crucial for the sustainability of vital resources of the world and at the same time to protect the inhabitants, endangered species and the ecology. Needless to say, there is no single sensor, which can answer all such questions effectively. Due to multi sensor approach, it puts a tremendous burden on any single implementing entity in terms of information, knowledge, budget, technology readiness and computational power. And, more importantly, the health of planet Earth and its ability to sustain life is not governed by a single country, but in reality, is everyone's business on this planet. Therefore, with this notion, it is becoming an impractical problem by any single organization/country to bear this colossal responsibility. So far, each developed country within their means has proceeded along satisfactorily in implementing

Multi-Sensor Mud Detection

Science.gov (United States)

Rankin, Arturo L.; Matthies, Larry H.

2010-01-01

Robust mud detection is a critical perception requirement for Unmanned Ground Vehicle (UGV) autonomous offroad navigation. A military UGV stuck in a mud body during a mission may have to be sacrificed or rescued, both of which are unattractive options. There are several characteristics of mud that may be detectable with appropriate UGV-mounted sensors. For example, mud only occurs on the ground surface, is cooler than surrounding dry soil during the daytime under nominal weather conditions, is generally darker than surrounding dry soil in visible imagery, and is highly polarized. However, none of these cues are definitive on their own. Dry soil also occurs on the ground surface, shadows, snow, ice, and water can also be cooler than surrounding dry soil, shadows are also darker than surrounding dry soil in visible imagery, and cars, water, and some vegetation are also highly polarized. Shadows, snow, ice, water, cars, and vegetation can all be disambiguated from mud by using a suite of sensors that span multiple bands in the electromagnetic spectrum. Because there are military operations when it is imperative for UGV's to operate without emitting strong, detectable electromagnetic signals, passive sensors are desirable. JPL has developed a daytime mud detection capability using multiple passive imaging sensors. Cues for mud from multiple passive imaging sensors are fused into a single mud detection image using a rule base, and the resultant mud detection is localized in a terrain map using range data generated from a stereo pair of color cameras.

Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution.

Science.gov (United States)

Hu, Bifeng; Chen, Songchao; Hu, Jie; Xia, Fang; Xu, Junfeng; Li, Yan; Shi, Zhou

2017-01-01

Rapid heavy metal soil surveys at large scale with high sampling density could not be conducted with traditional laboratory physical and chemical analyses because of the high cost, low efficiency and heavy workload involved. This study explored a rapid approach to assess heavy metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were successfully predicted by PXRF (R2 >0.90 and RPD >2.50) while As and Ni were predicted with less accuracy (R2 heavy metals contamination grades in farmland soils was conducted based on previous results; the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was an effective and rapid method to determine the degree of pollution with soil heavy metals. This study provides a new approach to assess soil heavy metals pollution; this method will facilitate large-scale surveys of soil heavy metal pollution.

Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution

Science.gov (United States)

Hu, Bifeng; Chen, Songchao; Hu, Jie; Xia, Fang; Xu, Junfeng; Li, Yan; Shi, Zhou

2017-01-01

Rapid heavy metal soil surveys at large scale with high sampling density could not be conducted with traditional laboratory physical and chemical analyses because of the high cost, low efficiency and heavy workload involved. This study explored a rapid approach to assess heavy metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were successfully predicted by PXRF (R2 >0.90 and RPD >2.50) while As and Ni were predicted with less accuracy (R2 heavy metals contamination grades in farmland soils was conducted based on previous results; the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was an effective and rapid method to determine the degree of pollution with soil heavy metals. This study provides a new approach to assess soil heavy metals pollution; this method will facilitate large-scale surveys of soil heavy metal pollution. PMID:28234944

A bio-image sensor for simultaneous detection of multi-neurotransmitters.

Science.gov (United States)

Lee, You-Na; Okumura, Koichi; Horio, Tomoko; Iwata, Tatsuya; Takahashi, Kazuhiro; Hattori, Toshiaki; Sawada, Kazuaki

2018-03-01

We report here a new bio-image sensor for simultaneous detection of spatial and temporal distribution of multi-neurotransmitters. It consists of multiple enzyme-immobilized membranes on a 128 × 128 pixel array with read-out circuit. Apyrase and acetylcholinesterase (AChE), as selective elements, are used to recognize adenosine 5'-triphosphate (ATP) and acetylcholine (ACh), respectively. To enhance the spatial resolution, hydrogen ion (H + ) diffusion barrier layers are deposited on top of the bio-image sensor and demonstrated their prevention capability. The results are used to design the space among enzyme-immobilized pixels and the null H + sensor to minimize the undesired signal overlap by H + diffusion. Using this bio-image sensor, we can obtain H + diffusion-independent imaging of concentration gradients of ATP and ACh in real-time. The sensing characteristics, such as sensitivity and detection of limit, are determined experimentally. With the proposed bio-image sensor the possibility exists for customizable monitoring of the activities of various neurochemicals by using different kinds of proton-consuming or generating enzymes. Copyright © 2017 Elsevier B.V. All rights reserved.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

Science.gov (United States)

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-01-01

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Jinsong Gui

2016-09-01

Full Text Available Multi-Input Multi-Output (MIMO can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs, clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO, which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

Cleaning up coal-fired plants : multi-pollutant technology

Energy Technology Data Exchange (ETDEWEB)

Granson, E.

2009-06-15

Coal is the source of 41 per cent of the world's electricity. Emission reduction technologies are needed to address the rapid growth of coal-fired plants in developing countries. This article discussed a multi-pollutant technology currently being developed by Natural Resources Canada's CANMET Energy Technology Centre. The ECO technology was designed to focus on several types of emissions, including sulfur oxides (SOx), nitrogen oxides (NOx), mercury and particulates, as well as acid gases and other metals from the exhaust gas of coal-fired plants. The ECO process converts and absorbs incoming pollutants in a wet electrostatic precipitator while at the same time producing a valuable fertilizer. The ECO system is installed as part of the plant's existing particulate control device and treats flue gas in 3 process steps: (1) a dielectric barrier discharge reactor oxidizes gaseous pollutants to higher oxides; (2) an ammonia scrubber then removes sulfur dioxide (SO{sub 2}) not converted by the reactor while also removing the NOx; and (3) the wet electrostatic precipitator captures acid aerosols produced by the discharge reactor. A diagram of the ECO process flow was included. It was concluded that the systems will be installed in clean coal plants by 2015. 2 figs.

A capacitive CMOS-MEMS sensor designed by multi-physics simulation for integrated CMOS-MEMS technology

Science.gov (United States)

Konishi, Toshifumi; Yamane, Daisuke; Matsushima, Takaaki; Masu, Kazuya; Machida, Katsuyuki; Toshiyoshi, Hiroshi

2014-01-01

This paper reports the design and evaluation results of a capacitive CMOS-MEMS sensor that consists of the proposed sensor circuit and a capacitive MEMS device implemented on the circuit. To design a capacitive CMOS-MEMS sensor, a multi-physics simulation of the electromechanical behavior of both the MEMS structure and the sensing LSI was carried out simultaneously. In order to verify the validity of the design, we applied the capacitive CMOS-MEMS sensor to a MEMS accelerometer implemented by the post-CMOS process onto a 0.35-µm CMOS circuit. The experimental results of the CMOS-MEMS accelerometer exhibited good agreement with the simulation results within the input acceleration range between 0.5 and 6 G (1 G = 9.8 m/s2), corresponding to the output voltages between 908.6 and 915.4 mV, respectively. Therefore, we have confirmed that our capacitive CMOS-MEMS sensor and the multi-physics simulation will be beneficial method to realize integrated CMOS-MEMS technology.

Laser-Based and Ultra-Portable Gas Sensor for Indoor and Outdoor Formaldehyde (HCHO) Monitoring

Science.gov (United States)

Shutter, J. D.; Allen, N.; Paul, J.; Thiebaud, J.; So, S.; Scherer, J. J.; Keutsch, F. N.

2017-12-01

While used as a key tracer of oxidative chemistry in the atmosphere, formaldehyde (HCHO) is also a known human carcinogen and is listed and regulated by the United States EPA as a hazardous air pollutant. Combustion processes and photochemical oxidation of volatile organic compounds (VOCs) are the major outdoor sources of HCHO, and building materials and household products are ubiquitous sources of indoor HCHO. Due to the ease with which humans can be exposed to HCHO, it is imperative to monitor levels of both indoor and outdoor HCHO exposure in both short and long-term studies.High-quality direct and indirect methods of quantifying HCHO mixing ratios exist, but instrument size and user-friendliness can make them cumbersome or impractical for certain types of indoor and long-term outdoor measurements. In this study, we present urban HCHO measurements by using a new, commercially-available, ppbv-level accurate HCHO gas sensor (Aeris Technologies' MIRA Pico VOC Laser-Based Gas Analyzer) that is highly portable (29 cm x 20 cm x 10 cm), lightweight (3 kg), easy-to-use, and has low power (15 W) consumption. Using an ultra-compact multipass cell, an absorption path length of 13 m is achieved, resulting in a sensor capable of achieving ppbv/s sensitivity levels with no significant spectral interferences.To demonstrate the utility of the gas sensor for emissions measurements, a GPS was attached to the sensor's housing in order to map mobile HCHO measurements in real-time around the Boston, Massachusetts, metro area. Furthermore, the sensor was placed in residential and industrial environments to show its usefulness for indoor and outdoor pollution measurements. Lastly, we show the feasibility of using the HCHO sensor (or a network of them) in long-term monitoring stations for hazardous air pollutants.

Multi-Sensor Architectures

DEFF Research Database (Denmark)

Hussain, Dil Muhammad Akbar; Ahmed, Zaki; Khan, M. Z.

2012-01-01

The use of multiple sensors typically requires the fusion of data from different type of sensors. The combined use of such a data has the potential to give an efficient, high quality and reliable estimation. Input data from different sensors allows the introduction of target attributes (target ty...

Nanostructures in environmental pollution detection, monitoring, and remediation

Directory of Open Access Journals (Sweden)

A. Vaseashta et al

2007-01-01

Full Text Available We present preliminary results of our joint investigations to monitor and mitigate environmental pollution, a leading contributor to chronic and deadly health disorders and diseases affecting millions of people each year. Using nanotechnology-based gas sensors; pollution is monitored at several ground stations. The sensor unit is portable, provides instantaneous ground pollution concentrations accurately, and can be readily deployed to disseminate real-time pollution data to a web server providing a topological overview of monitored locations. We are also employing remote sensing technologies with high-spatial and spectral resolution to model urban pollution using satellite images and image processing. One of the objectives of this investigation is to develop a unique capability to acquire, display and assimilate these valuable sources of data to accurately assess urban pollution by real-time monitoring using commercial sensors fabricated using nanofabrication technologies and satellite imagery. This integrated tool will be beneficial towards prediction processes to support public awareness and establish policy priorities for air quality in polluted areas. The complex nature of environmental pollution data mining requires computing technologies that integrate multiple sources and repositories of data over multiple networking systems and platforms that must be accurate, secure, and reliable. An evaluation of information security risks and strategies within an environmental information system is presented. In addition to air pollution, we explore the efficacy of nanostructured materials in the detection and remediation of water pollution. We present our results of sorption on advanced nanomaterials-based sorbents that have been found effective in the removal of cadmium and arsenic from water streams.

A Capacitive Humidity Sensor Based on Multi-Wall Carbon Nanotubes (MWCNTs

Directory of Open Access Journals (Sweden)

Zhen-Gang Zhao

2009-09-01

Full Text Available A new type of capacitive humidity sensor is introduced in this paper. The sensor consists of two plate electrodes coated with MWCNT films and four pieces of isolating medium at the four corners of the sensor. According to capillary condensation, the capacitance signal of the sensor is sensitive to relative humidity (RH, which could be transformed to voltage signal by a capacitance to voltage converter circuit. The sensor is tested using different saturated saline solutions at the ambient temperature of 25 °C, which yielded approximately 11% to 97% RH, respectively. The function of the MWCNT films, the effect of electrode distance, the temperature character and the repeatability of the sensor are discussed in this paper.

Design and implementation of a sensor for environmental control based on fiber optics

International Nuclear Information System (INIS)

Navarro Becerra, Yamelys; Orlando Torres, Cesar; Giacometto, Francisco J

2011-01-01

In this paper, we developed and implemented experimentally a prototype sensor, with application to environmental monitoring, The prototype sensor is constituted by a capillary in which the gaseous fluid is set to move (gas or pollutant) and using the intensity of light transmitted through an optical fiber, we could obtain measurements of light intensity according to the presence of the pollutant, in our case correlated measures or levels of intensity of SO2 with the intensity of light that the sensor designed was able to measure, the sensor had two indicators of pollution lighting a red color when levels exceed the regulations (Resolution 909 of 2008) and green when everything is in its normal, the signal was issued electronically by a phototransistor fdt317 reference. With intensity meter Newport. And respectively a detector upon detection of the presence of the contaminant to infrared light showed an amount proportional to the rate of contamination.

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

Science.gov (United States)

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

Extracellular Bio-imaging of Acetylcholine-stimulated PC12 Cells Using a Calcium and Potassium Multi-ion Image Sensor.

Science.gov (United States)

Matsuba, Sota; Kato, Ryo; Okumura, Koichi; Sawada, Kazuaki; Hattori, Toshiaki

2018-01-01

In biochemistry, Ca 2+ and K + play essential roles to control signal transduction. Much interest has been focused on ion-imaging, which facilitates understanding of their ion flux dynamics. In this paper, we report a calcium and potassium multi-ion image sensor and its application to living cells (PC12). The multi-ion sensor had two selective plasticized poly(vinyl chloride) membranes containing ionophores. Each region on the sensor responded to only the corresponding ion. The multi-ion sensor has many advantages including not only label-free and real-time measurement but also simultaneous detection of Ca 2+ and K + . Cultured PC12 cells treated with nerve growth factor were prepared, and a practical observation for the cells was conducted with the sensor. After the PC12 cells were stimulated by acetylcholine, only the extracellular Ca 2+ concentration increased while there was no increase in the extracellular K + concentration. Through the practical observation, we demonstrated that the sensor was helpful for analyzing the cell events with changing Ca 2+ and/or K + concentration.

Error Modelling for Multi-Sensor Measurements in Infrastructure-Free Indoor Navigation

Directory of Open Access Journals (Sweden)

Laura Ruotsalainen

2018-02-01

Full Text Available The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU, sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi-sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF, which is a sophisticated option for integrating measurements emerging from pedestrian motion having non-Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is

Practical Application of Aptamer-Based Biosensors in Detection of Low Molecular Weight Pollutants in Water Sources

Directory of Open Access Journals (Sweden)

Wei Zhang

2018-02-01

Full Text Available Water pollution has become one of the leading causes of human health problems. Low molecular weight pollutants, even at trace concentrations in water sources, have aroused global attention due to their toxicity after long-time exposure. There is an increased demand for appropriate methods to detect these pollutants in aquatic systems. Aptamers, single-stranded DNA or RNA, have high affinity and specificity to each of their target molecule, similar to antigen-antibody interaction. Aptamers can be selected using a method called Systematic Evolution of Ligands by EXponential enrichment (SELEX. Recent years we have witnessed great progress in developing aptamer selection and aptamer-based sensors for low molecular weight pollutants in water sources, such as tap water, seawater, lake water, river water, as well as wastewater and its effluents. This review provides an overview of aptamer-based methods as a novel approach for detecting low molecular weight pollutants in water sources.

Approach for Self-Calibrating CO₂ Measurements with Linear Membrane-Based Gas Sensors.

Science.gov (United States)

Lazik, Detlef; Sood, Pramit

2016-11-17

Linear membrane-based gas sensors that can be advantageously applied for the measurement of a single gas component in large heterogeneous systems, e.g., for representative determination of CO₂ in the subsurface, can be designed depending on the properties of the observation object. A resulting disadvantage is that the permeation-based sensor response depends on operating conditions, the individual site-adapted sensor geometry, the membrane material, and the target gas component. Therefore, calibration is needed, especially of the slope, which could change over several orders of magnitude. A calibration-free approach based on an internal gas standard is developed to overcome the multi-criterial slope dependency. This results in a normalization of sensor response and enables the sensor to assess the significance of measurement. The approach was proofed on the example of CO₂ analysis in dry air with tubular PDMS membranes for various CO₂ concentrations of an internal standard. Negligible temperature dependency was found within an 18 K range. The transformation behavior of the measurement signal and the influence of concentration variations of the internal standard on the measurement signal were shown. Offsets that were adjusted based on the stated theory for the given measurement conditions and material data from the literature were in agreement with the experimentally determined offsets. A measurement comparison with an NDIR reference sensor shows an unexpectedly low bias (sensor response, and comparable statistical uncertainty.

3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion.

Science.gov (United States)

Dou, Qingxu; Wei, Lijun; Magee, Derek R; Atkins, Phil R; Chapman, David N; Curioni, Giulio; Goddard, Kevin F; Hayati, Farzad; Jenks, Hugo; Metje, Nicole; Muggleton, Jennifer; Pennock, Steve R; Rustighi, Emiliano; Swingler, Steven G; Rogers, Christopher D F; Cohn, Anthony G

2016-11-02

We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed "multi-utility multi-sensor" system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation.

Environmental pollution: influence on the operation of a sensor of radioactive aerosols

International Nuclear Information System (INIS)

Duarte Rodriguez, X.; Hernandez Armas, J.; Martin Delgado, J.; Rodriguez Perestelo, N.; Perez Lopez, M.; Catalan Acosta, A.; Fernandez de Aldecoa, J. c.

2013-01-01

The content of radioactive aerosols in the air is an important component to estimate the ambient radiation dose. In the laboratories of environmental radioactivity, measurements of radionuclides in air they are performed using sensors. The flow picked up by the equipment can be changed if the degree of air pollution changes for some reason. It handles this study and the population doses are estimated due to inhalation of ambient air. (Author)

Biochemical component identification by plasmonic improved whispering gallery mode optical resonance based sensor

Science.gov (United States)

Saetchnikov, Vladimir A.; Tcherniavskaia, Elina A.; Saetchnikov, Anton V.; Schweiger, Gustav; Ostendorf, Andreas

2014-05-01

Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements, antibiotic of different generation etc. in both single and multi component solutions under varied in wide range concentration analyzed on the light scattering parameters of whispering gallery mode optical resonance based sensor are represented. Multiplexing on parameters and components has been realized using developed fluidic sensor cell with fixed in adhesive layer dielectric microspheres and data processing. Biochemical component identification has been performed by developed network analysis techniques. Developed approach is demonstrated to be applicable both for single agent and for multi component biochemical analysis. Novel technique based on optical resonance on microring structures, plasmon resonance and identification tools has been developed. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive. Both biomolecule and nanoparticle injections caused considerable changes of optical resonance spectra. Plasmonic gold layers under optimized thickness also improve parameters of optical resonance spectra. Biochemical component identification has been also performed by developed network analysis techniques both for single and for multi component solution. So advantages of plasmon enhancing optical microcavity resonance with multiparameter identification tools is used for development of a new platform for ultra sensitive label-free biomedical sensor.

The measurement of gas–liquid two-phase flows in a small diameter pipe using a dual-sensor multi-electrode conductance probe

International Nuclear Information System (INIS)

Zhai, Lu-Sheng; Bian, Peng; Han, Yun-Feng; Gao, Zhong-Ke; Jin, Ning-De

2016-01-01

We design a dual-sensor multi-electrode conductance probe to measure the flow parameters of gas–liquid two-phase flows in a vertical pipe with an inner diameter of 20 mm. The designed conductance probe consists of a phase volume fraction sensor (PVFS) and a cross-correlation velocity sensor (CCVS). Through inserting an insulated flow deflector in the central part of the pipe, the gas–liquid two-phase flows are forced to pass through an annual space. The multiple electrodes of the PVFS and the CCVS are flush-mounted on the inside of the pipe wall and the outside of the flow deflector, respectively. The geometry dimension of the PVFS is optimized based on the distribution characteristics of the sensor sensitivity field. In the flow loop test of vertical upward gas–liquid two-phase flows, the output signals from the dual-sensor multi-electrode conductance probe are collected by a data acquisition device from the National Instruments (NI) Corporation. The information transferring characteristics of local flow structures in the annular space are investigated using the transfer entropy theory. Additionally, the kinematic wave velocity is measured based on the drift velocity model to investigate the propagation behavior of the stable kinematic wave in the annular space. Finally, according to the motion characteristics of the gas–liquid two-phase flows, the drift velocity model based on the flow patterns is constructed to measure the individual phase flow rate with higher accuracy. (paper)

Composite multi-modal vibration control for a stiffened plate using non-collocated acceleration sensor and piezoelectric actuator

International Nuclear Information System (INIS)

Li, Shengquan; Li, Juan; Mo, Yueping; Zhao, Rong

2014-01-01

A novel active method for multi-mode vibration control of an all-clamped stiffened plate (ACSP) is proposed in this paper, using the extended-state-observer (ESO) approach based on non-collocated acceleration sensors and piezoelectric actuators. Considering the estimated capacity of ESO for system state variables, output superposition and control coupling of other modes, external excitation, and model uncertainties simultaneously, a composite control method, i.e., the ESO based vibration control scheme, is employed to ensure the lumped disturbances and uncertainty rejection of the closed-loop system. The phenomenon of phase hysteresis and time delay, caused by non-collocated sensor/actuator pairs, degrades the performance of the control system, even inducing instability. To solve this problem, a simple proportional differential (PD) controller and acceleration feed-forward with an output predictor design produce the control law for each vibration mode. The modal frequencies, phase hysteresis loops and phase lag values due to non-collocated placement of the acceleration sensor and piezoelectric patch actuator are experimentally obtained, and the phase lag is compensated by using the Smith Predictor technology. In order to improve the vibration control performance, the chaos optimization method based on logistic mapping is employed to auto-tune the parameters of the feedback channel. The experimental control system for the ACSP is tested using the dSPACE real-time simulation platform. Experimental results demonstrate that the proposed composite active control algorithm is an effective approach for suppressing multi-modal vibrations. (paper)

An electrochemical sensor modified with poly(3,4-ethylenedioxythiophene)-wrapped multi-walled carbon nanotubes for enzyme inhibition-based determination of organophosphates

International Nuclear Information System (INIS)

Kaur, Navpreet; Thakur, Himkusha; Prabhakar, Nirmal; Kumar, Rajesh

2016-01-01

We report on an acetylcholinesterase (AChE) based sensor for the determination of organophosphates (OPs). A nanocomposite consisting of poly(3,4-ethylenedioxythiophene) (PEDOT; a conducting polymer) and functionalized multi-walled carbon nanotubes (f-MWCNTs) was used as a host matrix for covalent immobilization of AChE. The unique properties of PEDOT and f-MWCNTs proved to be highly efficient for retaining the activity and stability of AChE. The effects of pH value of the phosphate buffer, concentration of enzyme, substrate concentration and incubation time were optimized using chlorpyrifos-methyl as a model OP as it exerts a strong inhibitory effect on AChE. Differential pulse voltammetry (DPV) analysis performed at +0.15 V revealed a linear relation of current in respect to chlorpyrifos-methyl concentration range (0.001 to 50 ppb) with 0.001 ppb as a detection limit. The sensor can be regenerated to 97% of its activity by applying 2-pyridine aldoxime methiodide (2-PAM) as a regenerating agent. It can be re-used up to six times and stored up to one month. The sensor was applied to the determination of chlorpyrifos-methyl in spiked lettuce where it gave recoveries ranging between 95 and 97%. (author)

Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters

Directory of Open Access Journals (Sweden)

Stéfani Novoa

2017-01-01

Full Text Available The accurate measurement of suspended particulate matter (SPM concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρw tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i an assessment of existing atmospheric correction (AC algorithms developed for turbid coastal waters; and (ii a switching method that automatically selects the most sensitive SPM vs. ρw relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE with a method based on multi-temporal analyses of atmospheric constituents (MACCS. For the selected scenes, the ACOLITE-MACCS difference was

Information-based self-organization of sensor nodes of a sensor network

Science.gov (United States)

Ko, Teresa H [Castro Valley, CA; Berry, Nina M [Tracy, CA

2011-09-20

A sensor node detects a plurality of information-based events. The sensor node determines whether at least one other sensor node is an information neighbor of the sensor node based on at least a portion of the plurality of information-based events. The information neighbor has an overlapping field of view with the sensor node. The sensor node sends at least one communication to the at least one other sensor node that is an information neighbor of the sensor node in response to at least one information-based event of the plurality of information-based events.

Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

Directory of Open Access Journals (Sweden)

Dong Sun

2012-01-01

Full Text Available The human hand has multiple degrees of freedom (DOF for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.

Hand motion classification using a multi-channel surface electromyography sensor.

Science.gov (United States)

Tang, Xueyan; Liu, Yunhui; Lv, Congyi; Sun, Dong

2012-01-01

The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.

Wireless Sensor Network Based Smart Parking System

Directory of Open Access Journals (Sweden)

Jeffrey JOSEPH

2014-01-01

Full Text Available Ambient Intelligence is a vision in which various devices come together and process information from multiple sources in order to exert control on the physical environment. In addition to computation and control, communication plays a crucial role in the overall functionality of such a system. Wireless Sensor Networks are one such class of networks, which meet these criteria. These networks consist of spatially distributed sensor motes which work in a co-operative manner to sense and control the environment. In this work, an implementation of an energy-efficient and cost-effective, wireless sensor networks based vehicle parking system for a multi-floor indoor parking facility has been introduced. The system monitors the availability of free parking slots and guides the vehicle to the nearest free slot. The amount of time the vehicle has been parked is monitored for billing purposes. The status of the motes (dead/alive is also recorded. Information like slot allocated, directions to the slot and billing data is sent as a message to customer’s mobile phones. This paper extends our previous work 1 with the development of a low cost sensor mote, about one tenth the cost of a commercially available mote, keeping in mind the price sensitive markets of the developing countries.

Advances in Understanding Air Pollution and Cardiovascular Diseases: The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

Science.gov (United States)

Kaufman, Joel D.; Spalt, Elizabeth W.; Curl, Cynthia L.; Hajat, Anjum; Jones, Miranda R.; Kim, Sun-Young; Vedal, Sverre; Szpiro, Adam A.; Gassett, Amanda; Sheppard, Lianne; Daviglus, Martha L.; Adar, Sara D.

2016-01-01

The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) leveraged the platform of the MESA cohort into a prospective longitudinal study of relationships between air pollution and cardiovascular health. MESA Air researchers developed fine-scale, state-of-the-art air pollution exposure models for the MESA Air communities, creating individual exposure estimates for each participant. These models combine cohort-specific exposure monitoring, existing monitoring systems, and an extensive database of geographic and meteorological information. Together with extensive phenotyping in MESA—and adding participants and health measurements to the cohort—MESA Air investigated environmental exposures on a wide range of outcomes. Advances by the MESA Air team included not only a new approach to exposure modeling but also biostatistical advances in addressing exposure measurement error and temporal confounding. The MESA Air study advanced our understanding of the impact of air pollutants on cardiovascular disease and provided a research platform for advances in environmental epidemiology. PMID:27741981

Robust site security using smart seismic array technology and multi-sensor data fusion

Science.gov (United States)

Hellickson, Dean; Richards, Paul; Reynolds, Zane; Keener, Joshua

2010-04-01

Traditional site security systems are susceptible to high individual sensor nuisance alarm rates that reduce the overall system effectiveness. Visual assessment of intrusions can be intensive and manually difficult as cameras are slewed by the system to non intrusion areas or as operators respond to nuisance alarms. Very little system intrusion performance data are available other than discrete sensor alarm indications that provide no real value. This paper discusses the system architecture, integration and display of a multi-sensor data fused system for wide area surveillance, local site intrusion detection and intrusion classification. The incorporation of a novel seismic array of smart sensors using FK Beamforming processing that greatly enhances the overall system detection and classification performance of the system is discussed. Recent test data demonstrates the performance of the seismic array within several different installations and its ability to classify and track moving targets at significant standoff distances with exceptional immunity to background clutter and noise. Multi-sensor data fusion is applied across a suite of complimentary sensors eliminating almost all nuisance alarms while integrating within a geographical information system to feed a visual-fusion display of the area being secured. Real-time sensor detection and intrusion classification data is presented within a visual-fusion display providing greatly enhanced situational awareness, system performance information and real-time assessment of intrusions and situations of interest with limited security operator involvement. This approach scales from a small local perimeter to very large geographical area and can be used across multiple sites controlled at a single command and control station.

A Synchronous Multi-Body Sensor Platform in a Wireless Body Sensor Network: Design and Implementation

Science.gov (United States)

Gil, Yeongjoon; Wu, Wanqing; Lee, Jungtae

2012-01-01

Background Human life can be further improved if diseases and disorders can be predicted before they become dangerous, by correctly recognizing signals from the human body, so in order to make disease detection more precise, various body-signals need to be measured simultaneously in a synchronized manner. Object This research aims at developing an integrated system for measuring four signals (EEG, ECG, respiration, and PPG) and simultaneously producing synchronous signals on a Wireless Body Sensor Network. Design We designed and implemented a platform for multiple bio-signals using Bluetooth communication. Results First, we developed a prototype board and verified the signals from the sensor platform using frequency responses and quantities. Next, we designed and implemented a lightweight, ultra-compact, low cost, low power-consumption Printed Circuit Board. Conclusion A synchronous multi-body sensor platform is expected to be very useful in telemedicine and emergency rescue scenarios. Furthermore, this system is expected to be able to analyze the mutual effects among body signals. PMID:23112605

A Synchronous Multi-Body Sensor Platform in a Wireless Body Sensor Network: Design and Implementation

Directory of Open Access Journals (Sweden)

Jungtae Lee

2012-07-01

Full Text Available Background: Human life can be further improved if diseases and disorders can be predicted before they become dangerous, by correctly recognizing signals from the human body, so in order to make disease detection more precise, various body-signals need to be measured simultaneously in a synchronized manner. Object: This research aims at developing an integrated system for measuring four signals (EEG, ECG, respiration, and PPG and simultaneously producing synchronous signals on a Wireless Body Sensor Network. Design: We designed and implemented a platform for multiple bio-signals using Bluetooth communication. Results: First, we developed a prototype board and verified the signals from the sensor platform using frequency responses and quantities. Next, we designed and implemented a lightweight, ultra-compact, low cost, low power-consumption Printed Circuit Board. Conclusion: A synchronous multi-body sensor platform is expected to be very useful in telemedicine and emergency rescue scenarios. Furthermore, this system is expected to be able to analyze the mutual effects among body signals.

Detecting marine hazardous substances and organisms: sensors for pollutants, toxins, and pathogens

Directory of Open Access Journals (Sweden)

O. Zielinski

2009-09-01

Full Text Available Marine environments are influenced by a wide diversity of anthropogenic and natural substances and organisms that may have adverse effects on human health and ecosystems. Real-time measurements of pollutants, toxins, and pathogens across a range of spatial scales are required to adequately monitor these hazards, manage the consequences, and to understand the processes governing their magnitude and distribution. Significant technological advancements have been made in recent years for the detection and analysis of such marine hazards. In particular, sensors deployed on a variety of mobile and fixed-point observing platforms provide a valuable means to assess hazards. In this review, we present state-of-the-art of sensor technology for the detection of harmful substances and organisms in the ocean. Sensors are classified by their adaptability to various platforms, addressing large, intermediate, or small areal scales. Current gaps and future demands are identified with an indication of the urgent need for new sensors to detect marine hazards at all scales in autonomous real-time mode. Progress in sensor technology is expected to depend on the development of small-scale sensor technologies with a high sensitivity and specificity towards target analytes or organisms. However, deployable systems must comply with platform requirements as these interconnect the three areal scales. Future developments will include the integration of existing methods into complex and operational sensing systems for a comprehensive strategy for long-term monitoring. The combination of sensor techniques on all scales will remain crucial for the demand of large spatial and temporal coverage.

Multi-sensor radiation detector system

International Nuclear Information System (INIS)

Foster, R.G.; Cyboron, R.D.

1975-01-01

The invention is a multi-sensor radiation detection system including a self-powered detector and an ion or fission chamber, preferably joined as a unitary structure, for removable insertion into a nuclear reactor. The detector and chamber are connected electrically in parallel, requiring but two conductors extending out of the reactor to external electrical circuitry which includes a load impedance, a voltage source, and switch means. The switch means are employed to alternately connect the detector and chamber either with th load impedance or with the load impedance and the voltage source. In the former orientation, current through the load impedance indicates flux intensity at the self-powered detector and in the latter orientation, the current indicates flux intensity at the detector and fission chamber, though almost all of the current is contributed by the fission chamber. (auth)

Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

International Nuclear Information System (INIS)

Hou, Dibo; He, Huimei; Huang, Pingjie; Zhang, Guangxin; Loaiciga, Hugo

2013-01-01

This study presents a method for detecting contamination events of sources of drinking water based on the Dempster–Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events. (paper)

A multi-indicator approach for identifying shoreline sewage pollution hotspots adjacent to coral reefs.

Science.gov (United States)

Abaya, Leilani M; Wiegner, Tracy N; Colbert, Steven L; Beets, James P; Carlson, Kaile'a M; Kramer, K Lindsey; Most, Rebecca; Couch, Courtney S

2018-04-01

Sewage pollution is contributing to the global decline of coral reefs. Identifying locations where it is entering waters near reefs is therefore a management priority. Our study documented shoreline sewage pollution hotspots in a coastal community with a fringing coral reef (Puakō, Hawai'i) using dye tracer studies, sewage indicator measurements, and a pollution scoring tool. Sewage reached shoreline waters within 9 h to 3 d. Fecal indicator bacteria concentrations were high and variable, and δ 15 N macroalgal values were indicative of sewage at many stations. Shoreline nutrient concentrations were two times higher than those in upland groundwater. Pollution hotspots were identified with a scoring tool using three sewage indicators. It confirmed known locations of sewage pollution from dye tracer studies. Our study highlights the need for a multi-indicator approach and scoring tool to identify sewage pollution hotspots. This approach will be useful for other coastal communities grappling with sewage pollution. Copyright © 2018 Elsevier Ltd. All rights reserved.

A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

KAUST Repository

Nassar, Joanna M.

2017-02-07

We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

KAUST Repository

Nassar, Joanna M.; Sevilla, Galo T.; Velling, Seneca J.; Cordero, Marlon D.; Hussain, Muhammad Mustafa

2017-01-01

We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

Science.gov (United States)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

2013-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will

Test and evaluation of multiple lambda-sensors for use in combustion gases; Multi {lambda}-sond - Utveckling och test av enkel teknik foer O{sub 2}-maetning oever tvaersnitt

Energy Technology Data Exchange (ETDEWEB)

Kallner, Per; Gaardman, Lennart; Engel, Erik [Vattenfall Utveckling AB, Stockholm (Sweden)

2001-03-01

Fluctuations and zones with fuel rich conditions are the main causes of CO-emissions and abnormal deposit formation. This project was initiated to try and trace such disturbances. 'Multi-sensor-probes' is a quick and easy way of mapping fluctuations in many points along an axis simultaneously. This project is concentrated on such measurement systems, based on lambda({lambda})-sensor technology. The project was run in a few steps: - The use of {lambda}-sensors for O{sub 2} measurements in hot flue gases (>800 deg C), - Development and testing of probe-constructions for multi-point measurements, and - Performing measurement series in Vaermeforsk-related boilers, to identify fluctuations and fuel rich zones. This project shows how standard {lambda}-sensors can be utilised also in hot flue gases. In-situ measurements with a single-sensor probe is demonstrated in CFB, PF and grate boilers. It is a water-cooled probe, with the {lambda}-sensor mounted inside the tip of the probe. To perform multi-point measurements with the standard {lambda}-sensors, an extractive probe design had to be developed. With the standard {lambda}-sensors no sensible design for multi-point in-situ measurements could be found. A way to achieve such designs would be development of {lambda}-sensors where the zirconium oxide measurement cell is separated from the electrical wiring. This would give much smaller pieces to include in the probe and especially solve the problem of low temperature demand for the parts just behind the measurement cell in standard {lambda}-sensors. This could mean a multi-probe cooled to just 500-600 deg C. The construction of the extractive multi-probe tested in this project suffered from leakage in the suction lines when exposed inside the PF boiler. A proposed design with better function in this aspect is presented in this report. The results show that by combining a cooled probe-design with standard {lambda}-sensor components a tool for measurements of O{sub 2

EAGLE 2006 – Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest

Directory of Open Access Journals (Sweden)

Z. Su

2009-06-01

Full Text Available EAGLE2006 – an intensive field campaign for the advances in land surface hydrometeorological processes – was carried out in the Netherlands from 8th to 18th June 2006, involving 16 institutions with in total 67 people from 16 different countries. In addition to the acquisition of multi-angle and multi-sensor satellite data, several airborne instruments – an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were deployed and extensive ground measurements were conducted over one grassland site at Cabauw and two forest sites at Loobos and Speulderbos in the central part of the Netherlands. The generated data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative land surface parameter estimation and land surface hydrometeorological process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation (ITC and originated from the combination of a number of initiatives supported by different funding agencies. The objectives of the EAGLE2006 campaign were closely related to the objectives of other European Space Agency (ESA campaign activities (SPARC2004, SEN2FLEX2005 and especially AGRISAR2006. However, one important objective of the EAGLE2006 campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band and at hyperspectral optical and thermal bands acquired simultaneously over contrasting vegetated fields (forest and grassland. As such, all activities were related to algorithm development for future satellite missions such as the Sentinels and for validation of retrievals of land surface parameters with optical and thermal and microwave sensors onboard current and future satellite missions. This contribution describes the campaign objectives and

Wireless distributed environmental sensor networks for air pollution measurement-the promise and the current reality

Czech Academy of Sciences Publication Activity Database

Broday, D. M.; Arpaci, A.; Bartoňová, A.; Castell-Balaguer, N.; Cole-Hunter, T.; Dauge, F.R.; Fishbain, B.; Jones, R.L.; Galea, K.; Jovasevic-Stojanovic, M.; Kocman, D.; Martinez-Iniguez, T.; Nieuwenhuijsen, M.; Robinson, J.; Švecová, Vlasta; Thai, P.

2017-01-01

Roč. 17, č. 10 (2017), s. 2263 ISSN 1424-8220 Institutional support: RVO:68378041 Keywords : air pollution * in situ field calibration * micro sensing units Subject RIV: EI - Biotechnology ; Bionics OBOR OECD: Biomaterials (as related to medical implants, devices, sensors) Impact factor: 2.677, year: 2016

A scalable pressure sensor based on an electrothermally and electrostatically operated resonator

KAUST Repository

Hajjaj, Amal Z.

2017-11-29

We present a pressure sensor based on the convective cooling of the air surrounding an electrothermally heated resonant bridge. Unlike conventional pressure sensors that rely on diaphragm deformation in response to pressure, the sensor does not require diaphragms of the large surface area, and hence is scalable and can be realized even at the nanoscale. The concept is demonstrated using both straight and arch microbeam resonators driven and sensed electrostatically. The change in the surrounding pressure is shown to be accurately tracked by monitoring the change in the resonance frequency of the structure. The sensitivity of the sensor, which is controllable by the applied electrothermal load, is shown near 57 811 ppm/mbar for a pressure range from 1 to 10 Torr. We show that a straight beam operated near the buckling threshold leads to the maximum sensitivity of the device. The experimental data and simulation results, based on a multi-physics finite element model, demonstrate the feasibility and simplicity of the pressure sensor. Published by AIP Publishing.

Aplikasi Musik Orkestra Angklung Multi Oktaf Berbasis Android Dengan Sensor Accelerometer

Directory of Open Access Journals (Sweden)

Mardiyono Mardiyono

2017-07-01

Full Text Available The development of android application of music instrument called Angklung is explored in several features and techniques. Previous applications allow the user to play angklung by touching and shaking, learn, answer the quiz, customize the application environment such as changing the background sound, character asset, etc. There are the problems of android angklung application including the amount of tones generally 1 octave, utilizing of accelerometer sensor, and recording system. This paper describes the development of angklung android multi octaves using accelerometer sensor. The method used is waterfall involving requirements definition, system and software design, implementation and unit testing, integration and system testing and operational and maintenance. The application is tested each functionalities (black box and questionnaire. The result show that the application has successfully worked in android device (Jelly Bean, Kit Kat and Lollipop that provides the innovations features to play angklung in 18 tones, play different octave by shaking the device in different way, and record the angklung orchestra sound using android. Based on the questionnaire result, the user satisfaction rate is 82.8%.

Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network.

Science.gov (United States)

Han, Changcai; Yang, Jinsheng

2017-10-30

The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes.

Compressed air leak detection based on time delay estimation using a portable multi-sensor ultrasonic detector

International Nuclear Information System (INIS)

Liao, Pingping; Cai, Maolin; Shi, Yan; Fan, Zichuan

2013-01-01

The conventional ultrasonic method for compressed air leak detection utilizes a directivity-based ultrasonic leak detector (DULD) to locate the leak. The location accuracy of this method is low due to the limit of the nominal frequency and the size of the ultrasonic sensor. In order to overcome this deficiency, a method based on time delay estimation (TDE) is proposed. The method utilizes three ultrasonic sensors arranged in an equilateral triangle to simultaneously receive the ultrasound generated by the leak. The leak can be located according to time delays between every two sensor signals. The theoretical accuracy of the method is analyzed, and it is found that the location error increases linearly with delay estimation error and the distance from the leak to the sensor plane, and the location error decreases with the distance between sensors. The average square difference function delay estimator with parabolic fitting is used and two practical techniques are devised to remove the anomalous delay estimates. Experimental results indicate that the location accuracy using the TDE-based ultrasonic leak detector is 6.5–8.3 times as high as that using the DULD. By adopting the proposed method, the leak can be located more accurately and easily, and then the detection efficiency is improved. (paper)

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks.

Science.gov (United States)

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X

2015-12-26

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Liu Yang

2015-12-01

Full Text Available Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs in the network act as routers to transmit data to base station (BS cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

Multi-Source Multi-Sensor Information Fusion

Indian Academy of Sciences (India)

M. Senthilkumar (Newgen Imaging) 1461 1996 Oct 15 13:05:22

applications in biomedical, industrial automation, aerospace systems and environmental ... performance evaluation and achievable accuracy, mainly for aerospace ... system and sensors, sensor data processing and performance requirement, ...

A METEOROLOGICAL RISK ASSESSMENT METHOD FOR POWER LINES BASED ON GIS AND MULTI-SENSOR INTEGRATION

Directory of Open Access Journals (Sweden)

Z. Lin

2016-06-01

Full Text Available Power lines, exposed in the natural environment, are vulnerable to various kinds of meteorological factors. Traditional research mainly deals with the influence of a single meteorological condition on the power line, which lacks of comprehensive effects evaluation and analysis of the multiple meteorological factors. In this paper, we use multiple meteorological monitoring data obtained by multi-sensors to implement the meteorological risk assessment and early warning of power lines. Firstly, we generate meteorological raster map from discrete meteorological monitoring data using spatial interpolation. Secondly, the expert scoring based analytic hierarchy process is used to compute the power line risk index of all kinds of meteorological conditions and establish the mathematical model of meteorological risk. By adopting this model in raster calculator of ArcGIS, we will have a raster map showing overall meteorological risks for power line. Finally, by overlaying the power line buffer layer to that raster map, we will get to know the exact risk index around a certain part of power line, which will provide significant guidance for power line risk management. In the experiment, based on five kinds of observation data gathered from meteorological stations in Guizhou Province of China, including wind, lightning, rain, ice, temperature, we carry on the meteorological risk analysis for the real power lines, and experimental results have proved the feasibility and validity of our proposed method.

Reputation-based secure sensor localization in wireless sensor networks.

Science.gov (United States)

He, Jingsha; Xu, Jing; Zhu, Xingye; Zhang, Yuqiang; Zhang, Ting; Fu, Wanqing

2014-01-01

Location information of sensor nodes in wireless sensor networks (WSNs) is very important, for it makes information that is collected and reported by the sensor nodes spatially meaningful for applications. Since most current sensor localization schemes rely on location information that is provided by beacon nodes for the regular sensor nodes to locate themselves, the accuracy of localization depends on the accuracy of location information from the beacon nodes. Therefore, the security and reliability of the beacon nodes become critical in the localization of regular sensor nodes. In this paper, we propose a reputation-based security scheme for sensor localization to improve the security and the accuracy of sensor localization in hostile or untrusted environments. In our proposed scheme, the reputation of each beacon node is evaluated based on a reputation evaluation model so that regular sensor nodes can get credible location information from highly reputable beacon nodes to accomplish localization. We also perform a set of simulation experiments to demonstrate the effectiveness of the proposed reputation-based security scheme. And our simulation results show that the proposed security scheme can enhance the security and, hence, improve the accuracy of sensor localization in hostile or untrusted environments.

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications.

Science.gov (United States)

Shao, Chenzhong; Tanaka, Shuji; Nakayama, Takahiro; Hata, Yoshiyuki; Bartley, Travis; Nonomura, Yutaka; Muroyama, Masanori

2017-08-28

Robot tactile sensation can enhance human-robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as "sensor platform LSI") as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated.

A Multi-Sensor Acquisition Architecture and Real-Time Reference for Sensor and Fusion Methods Benchmarking

OpenAIRE

KAIS, Mikaël; MILLESCAMPS, Damien; BETAILLE, David; LUSETTI, Benoît; CHAPELON, Antoine

2006-01-01

Localization is a key functionality for Advance Driving Assistance Systems (ADAS) as well as for Vehicle-Vehicle or Vehicle-Infrastructure cooperation. Indeed, depending on the accuracy and integrity of the localization process, applications such as driver information, driver assistance or even fully autonomous driving can be performed. This paper presents a multi-sensor acquisition architecture for localization. Special attention has been given to main parameters that can affect the accuracy...

Multi-electrode gas sensor system - MEGAS. Final report; Multi-Elektroden-Gassensorsystem - MEGAS. Abschlussbericht

Energy Technology Data Exchange (ETDEWEB)

Heidtkamp, C.

2002-07-01

A tungsten/titanium - mixed-oxide based sensor for selective exhaust gas measurement of e.g. diesel engines (NO{sub x}, CO, hydrocarbons, NH{sub 3},..) is described. The special design of the used sensors should allow operation at high ambient temperature with the potential of quantitative determination of different exhaust gas components with only one sensor. Several batches of sensor prototypes are characterised according to sensitivity and stability. (orig.)

Performance Evaluation of "Low-cost" Sensors for Measuring Gaseous and Particle Air Pollutants: Results from Two Years of Field and Laboratory Testing

Science.gov (United States)

Feenstra, B. J.; Polidori, A.; Tisopulos, L.; Papapostolou, V.; Zhang, H.; Pathmanabhan, J.

2016-12-01

In recent years great progress has been made in development of low-cost miniature air quality sensing technologies. Such low-cost sensors offer a prospect of providing a real-time spatially dense information on pollutants, however, the quality of the data produced by these sensors is so far untested. In an effort to inform the general public about the actual performance of commercially available low-cost air quality sensors, in June 2014 the South Coast Air Quality Management District (SCAQMD) has established the Air Quality Sensor Performance Evaluation Center (AQ-SPEC). This program performs a thorough characterization of low-cost sensors under ambient (in the field) and controlled (in the laboratory) conditions. During the field testing, air quality sensors are operated side-by-side with Federal Reference Methods and Federal Equivalent Methods (FRM and FEM, respectively), which are routinely used to measure the ambient concentration of gaseous or particle pollutants for regulatory purposes. Field testing is conducted at two of SCAQMD's existing air monitoring stations, one in Rubidoux and one near the I-710 freeway. Sensors that demonstrate an acceptable performance in the field are brought back to the lab where a "characterization chamber" is used to challenge these devices with known concentrations of different particle and gaseous pollutants under different temperature and relative humidity levels. Testing results for each sensor are then summarized in a technical report and, along with other relevant information, posted online on a dedicated website (www.aqmd.gov/aq-spec) to educate the public about the capabilities of commercially available sensors and their potential applications. During this presentation, the results from two years of field and laboratory testing will be presented. The major strengths and weaknesses of some of the most commonly available particle and gaseous sensors will be discussed.

Single-event transient imaging with an ultra-high-speed temporally compressive multi-aperture CMOS image sensor.

Science.gov (United States)

Mochizuki, Futa; Kagawa, Keiichiro; Okihara, Shin-ichiro; Seo, Min-Woong; Zhang, Bo; Takasawa, Taishi; Yasutomi, Keita; Kawahito, Shoji

2016-02-22

In the work described in this paper, an image reproduction scheme with an ultra-high-speed temporally compressive multi-aperture CMOS image sensor was demonstrated. The sensor captures an object by compressing a sequence of images with focal-plane temporally random-coded shutters, followed by reconstruction of time-resolved images. Because signals are modulated pixel-by-pixel during capturing, the maximum frame rate is defined only by the charge transfer speed and can thus be higher than those of conventional ultra-high-speed cameras. The frame rate and optical efficiency of the multi-aperture scheme are discussed. To demonstrate the proposed imaging method, a 5×3 multi-aperture image sensor was fabricated. The average rising and falling times of the shutters were 1.53 ns and 1.69 ns, respectively. The maximum skew among the shutters was 3 ns. The sensor observed plasma emission by compressing it to 15 frames, and a series of 32 images at 200 Mfps was reconstructed. In the experiment, by correcting disparities and considering temporal pixel responses, artifacts in the reconstructed images were reduced. An improvement in PSNR from 25.8 dB to 30.8 dB was confirmed in simulations.