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  • 1.
    Alhashimi, Anas
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Varagnolo, Damiano
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Calibrating distance sensors for terrestrial applications without groundtruth information2017Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 17, nr 12, s. 3698-3709, artikel-id 7911206Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move. The proposed calibration procedure exploits an approximated expectation maximization scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers, and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously. Tests in a controlled environment led to a reduction of the mean squared error of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.

  • 2.
    Concina, Isabella
    et al.
    CNR-IDASC SENSOR Laboratory.
    Falasconi, Matteo
    CNR IDASC SENSOR Lab, University of Brescia.
    Sberveglieri, Veronica
    CNR IDASC SENSOR Lab, University of Brescia.
    Electronic noses as flexible tools to assess food quality and safety: Should we trust them?2012Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 12, nr 11, s. 3232-3237, artikel-id 6189022Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents three different applications of an electronic nose (EN) based on a metal oxide sensor array, in order to illustrate the broad spectrum of potential uses of the technique in food quality control. The following scenarios are considered: 1) the screening of a typical error that may occur during the processing of tomato pulp, which leads to sensory damage of the product; 2) the detection of microbial contamination by Alicyclobacillus spp. (ACB) affecting soft drinks; and 3) the proof of evidence of extra virgin olive oil fraudulently adulterated with hazelnut oil. In each case, the EN is able to identify the spoiled product by means of the alterations in the pattern of volatile compounds, reconstructed by principal component analysis of the sensor responses.

  • 3.
    Hostettler, Roland
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nordenvaad, Magnus Lundberg
    Joint Vehicle Trajectory and Model Parameter Estimation using Road Side Sensors2015Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 15, nr 9, s. 5075-5086Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article shows how a particle smoother based system identification method can be applied for estimating the trajectory of road vehicles. As sensors, a combination of an accelerometer measuring the road surface vibrations and a magnetometer measuring magnetic disturbances mounted on the side of the road are considered. First, sensor models describing the measurements of the two sensors are introduced. It is shown that these depend on unknown, static parameters that have to be considered in the estimation. Second, the sensor models are combined with a two-dimensional constant velocity motion model. Third, the system identification algorithm is introduced which iteratively runs a Rao-Blackwellized particle smoother to estimate the vehicle trajectory followed by an expectation-maximization step to estimate the parameters. Finally, the method is applied to both simulation and measurement data. It is found that the method works well in general and some issues when real data is considered are identified as future work.

  • 4.
    Jonsson, Patrik
    et al.
    Combitech AB, Mittuniversitetet.
    Casselgren, Johan
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Strömningslära och experimentell mekanik.
    Thörnberg, Benny
    Mittuniversitetet.
    Road surface status classification using spectral analysis of NIR camera images2015Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 15, nr 3, s. 1641-1656Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There is a need for an automated road status classification system considering the vast number of weather-related accidents that occur every winter. Previous research has shown that it is possible to detect hazardous road conditions, including, for example, icy pavements, using single point infrared illumination and infrared detectors. In this paper, we extend this research into camera surveillance of a road section allowing for classification of area segments of weather-related road surface conditions such as wet, snow covered, or icy. Infrared images have been obtained using an infrared camera equipped with a set of optical wavelength filters. The images have primarily been used to develop multivariate data models and also for the classification of road conditions in each pixel. This system is a vast improvement on existing single spot road status classification systems. The resulting imaging system can reliably distinguish between dry, wet, icy, or snow covered sections on road surfaces.

  • 5.
    Ponzoni, Andrea
    et al.
    CNR-INFM SENSOR Laboratory.
    Baratto, Camilla
    CNR-INFM SENSOR Laboratory.
    Bianchi, Sebastiano
    CNR-INFM SENSOR Laboratory.
    Comini, Elisabetta
    CNR-INFM SENSOR Laboratory.
    Ferroni, Matteo
    CNR-INFM SENSOR Laboratory.
    Pardo, Matteo
    CNR-INFM SENSOR Laboratory.
    Vezzoli, Marco
    CNR-INFM SENSOR Laboratory.
    Vomiero, Alberto
    CNR-INFM SENSOR Laboratory.
    Faglia, Guido
    CNR-INFM SENSOR Laboratory.
    Sberveglieri, Giorgio
    CNR-INFM SENSOR Laboratory.
    Metal oxide nanowire and thin-film-based gas sensors for chemical warfare simulants detection2008Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 8, nr 6, s. 735-742, artikel-id 4529209Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This work concerns with metal oxide (MOX) gas sensors based on nanowires and thin films. We focus on chemical warfare agents (CWAs) detection to compare these materials from the functional point-of-view. We work with different chemicals including simulants for Sarin nerve agents, vescicant gases, cyanide agents, and analytes such as ethanol, acetone, ammonia, and carbon monoxide that can be produced by everyday activities causing false alarms. Explorative data analysis has been used to demonstrate the different sensing performances of nanowires and thin films. Within the chosen application, our analysis reveal that the introduction of nanowires inside the array composed by thin films can improve its sensing capability. Cyanide simulants have been detected at concentrations close to 1 ppm, lower than the Immediately Dangerous for Life and Health (IDLH) value of the respective warfare agent. Higher sensitivity has been obtained to simulants for Sarin and vescicant gases, which have been detected at concentrations close or even lower than 100 ppb. Results demonstrate the suitability of the proposed array to selectively detect CWA simulants with respect to some compounds produced by everyday activities. © 2008 IEEE.

  • 6.
    Sakai, Kazuya
    et al.
    Department of Information and Communication Systems, Tokyo Metropolitan University.
    Sun, Min-Te
    Department of Computer Science and Information Engineering, National Central University, Taoyuan.
    Ku, Wei-Shinn
    Department of Computer Science and Software Engineering, Auburn University.
    Lai, Ten H.
    Department of Computer Science and Engineering, The Ohio State University, Columbus.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Framework for the Optimal k-Coverage Deployment Patterns of Wireless Sensors2015Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 15, nr 12, s. 7273-7283Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The strategy for node deployment to achieve multiple connectivity and coverage plays an important role in various wireless senor network applications. To alleviate the operational cost, the number of nodes to be deployed needs to be reduced. While the optimal k-connectivity deployment patterns (k <= 6) and the multiple k-coverage problem (k <= 3) have been extensively studied for 2-D networks, a general method to identify the optimal deployment pattern for any given sensor coverage requirement has yet to be found. Considering the ease of sensor deployment and operation, the deployment patterns should be identical and symmetric in the deployment region. This implies that the Voronoi diagram of the optimal deployment is a regular tessellation. Based on the fact that there exist only three regular tessellations, we propose a framework, namely, range elimination scheme (RES), to compute the optimal k-coverage deployment pattern for any given k value to accommodate various wireless sensor application requirements. We apply RES to show the optimal k-coverage deployment patterns for 4 <= k <= 9. Our analytical and simulation results show that our proposed framework successfully identifies the optimal deployment patterns and significantly reduces the number of sensors to be deployed

  • 7.
    Sarkar, Chayan
    et al.
    Delft University of Technology.
    Rao, Vijay S.
    Delft University of Technology.
    Prasad, R. Venkatesha
    Delft University of Technology.
    Das, Sankar Narayan
    IIT Kanpur.
    Misra, Sudip
    IIT Kharagpur.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    VSF: An Energy-Efficient Sensing Framework Using Virtual Sensors2016Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 16, nr 12, s. 5046-5059, artikel-id 7440786Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    this paper, we describe virtual sensing framework (VSF), which reduces sensing and data transmission activities of nodes in a sensor network without compromising on either the sensing interval or data quality. VSF creates virtual sensors (VSs) at the sink to exploit the temporal and spatial correlations amongst sensed data. Using an adaptive model at every sensing iteration, the VSs can predict multiple consecutive sensed data for all the nodes with the help of sensed data from a few active nodes. We show that even when the sensed data represent different physical parameters (e.g., temperature and humidity), our proposed technique still works making it independent of physical parameter sensed. Applying our technique can substantially reduce data communication among the nodes leading to reduced energy consumption per node yet maintaining high accuracy of the sensed data. In particular, using VSF on the temperature data from IntelLab and GreenOrb data set, we have reduced the total data traffic within the network up to 98% and 79%, respectively. Corresponding average root mean squared error of the predicted data per node is as low as 0.36 degrees C and 0.71 degrees C, respectively. This paper is expected to support deployment of many sensors as part of Internet of Things in large scales.

  • 8.
    Wan, Jiafu
    et al.
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou.
    Tang, Shenglog
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou.
    Shu, Zhaogang
    Fujian Agriculture and Forestry University, Fuzhou, China, College of Computer and Information Sciences, Fujian Agriculture and Forestry University.
    Li, Di
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou.
    Wang, Shiyong
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou.
    Imran, Muhammad Al
    College of Computer and Information Sciences, Almuzahmiyah, King Saud University.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Software-Defined Industrial Internet of Things in the Context of Industry 4.02016Ingår i: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 16, nr 20, s. 7373-7380Artikel i tidskrift (Refereegranskat)
1 - 8 av 8
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