Gotowe bibliografie tematyczne / Multiple sensors

Gotowa bibliografia na temat „Multiple sensors”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 6 września 2023

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Artykuły w czasopismach na temat "Multiple sensors"

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Pearlshtien, Daniela Heller, Stefano Pignatti i Eyal Ben-Dor. "Vicarious CAL/VAL Approach for Orbital Hyperspectral Sensors Using Multiple Sites". Remote Sensing 15, nr 3 (29.01.2023): 771. http://dx.doi.org/10.3390/rs15030771.

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The hyperspectral (HSR) sensors Earth Surface Mineral Dust Source Investigation (EMIT) of the National Aeronautics and Space Administration (NASA) and Environmental Mapping and Analysis Program (EnMAP) of the German Aerospace Center (DLR) were recently launched. These state-of-the-art sensors have joined the already operational HSR sensors DESIS (DLR), PRISMA (Italian Space Agency), and HISUI (developed by the Japanese Ministry of Economy, Trade, and Industry METI and Japan Aerospace Exploration Agency JAXA). The launching of more HSR sensors is being planned for the near future (e.g., SBG of NASA, and CHIME of the European Space Agency), and the challenge of monitoring and maintaining their calibration accuracy is becoming more relevant. We proposed two test sites: Amiaz Plain (AP) and Makhtesh Ramon (MR) for spectral, radiometric, and geometric calibration/validation (CAL/VAL). The sites are situated in the arid environment of southern Israel and are in the same overpass coverage. Both test sites have already demonstrated favorable results in assessing an HSR sensor’s performance and were chosen to participate in the EMIT and EnMAP validation stage. We first evaluated the feasibility of using AP and MR as CAL/VAL test sites with extensive datasets and sensors, such as the multispectral sensor Landsat (Landsat5 TM and Landsat8 OLI), the airborne HSR sensor AisaFENIX 1K, and the spaceborne HSR sensors DESIS and PRISMA. Field measurements were taken over time. The suggested methodology integrates reflectance and radiometric CAL/VAL test sites into one operational protocol. The method can highlight degradation in the spectral domain early on, help maintain quantitative applications, adjust the sensor’s radiometric calibration during its mission lifetime, and minimize uncertainties of calibration parameters. A PRISMA sensor case study demonstrates the complete operational protocol, i.e., performance evaluation, quality assessment, and cross-calibration between HSR sensors. These CAL/VAL sites are ready to serve as operational sites for other HSR sensors.
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Umeda, Kazunori, Jun Ota i Hisayuki Kimura. "Fusion of Multiple Ultrasonic Sensor Data and Image Data for Measuring an Object’s Motion". Journal of Robotics and Mechatronics 17, nr 1 (20.02.2005): 36–43. http://dx.doi.org/10.20965/jrm.2005.p0036.

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Robot sensing requires two types of observation – intensive and wide-angle. We selected multiple ultrasonic sensors for intensive observation and an image sensor for wide-angle observation in measuring a moving object’s motion with sensors in two kinds of fusion – one fusing multiple ultrasonic sensor data and the other fusing the two types of sensor data. The fusion of multiple ultrasonic sensor data takes advantage of object movement from a measurement range of an ultrasonic sensor to another sensor’s range. They are formulated in a Kalman filter framework. Simulation and experiments demonstrate the effectiveness and applicability to an actual robot system.
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Yu, Jerry. "Deflation-activated receptors, not classical inflation-activated receptors, mediate the Hering-Breuer deflation reflex". Journal of Applied Physiology 121, nr 5 (1.11.2016): 1041–46. http://dx.doi.org/10.1152/japplphysiol.00903.2015.

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Many airway sensory units respond to both lung inflation and deflation. Whether those responses to opposite stimuli come from one sensor (one-sensor theory) or more than one sensor (multiple-sensor theory) is debatable. One-sensor theory is commonly presumed in the literature. This article proposes a multiple-sensor theory in which a sensory unit contains different sensors for sensing different forces. Two major types of mechanical sensors operate in the lung: inflation- and deflation-activated receptors (DARs). Inflation-activated sensors can be further divided into slowly adapting receptors (SARs) and rapidly adapting receptors (RARs). Many SAR and RAR units also respond to lung deflation because they contain DARs. Pure DARs, which respond to lung deflation only, are rare in large animals but are easily identified in small animals. Lung deflation-induced reflex effects previously attributed to RARs should be assigned to DARs (including pure DARs and DARs associated with SARs and RARs) if the multiple-sensor theory is accepted. Thus, based on the information, it is proposed that activation of DARs can attenuate lung deflation, shorten expiratory time, increase respiratory rate, evoke inspiration, and cause airway secretion and dyspnea.
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Li, Sijia, Chi-Lin Chen i Kenneth J. Loh. "Laboratory Evaluation of Railroad Crosslevel Tilt Sensing Using Electrical Time Domain Reflectometry". Sensors 20, nr 16 (10.08.2020): 4470. http://dx.doi.org/10.3390/s20164470.

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Crosslevel is defined as the difference in elevation between the top surface of two railroad tracks. Severe changes in crosslevel, for example, due to earthquakes, ground settlement, or crushed ballasts, affect track geometry and can cause train derailment. Therefore, the objective of this study was to monitoring railroad crosslevel by using electrical time domain reflectometry (ETDR) to simultaneously interrogate multiple capacitive tilt sensor prototypes connected in a transmission line. ETDR works by propagating an electrical pulse signal from one end of the transmission line and then monitoring the characteristics of each reflected pulse, which is affected by the capacitance (or tilt) of the sensors. This study begins with a discussion of the capacitive tilt sensor’s design. These 3D-printed sensors were tested to characterize their tilt sensing performance. Then, multiple tilt sensors were connected in a transmission line and interrogated by ETDR. The ability to use ETDR to multiplex and interrogate sensors subjected to different angles of tilt was validated.
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Shimojima, Koji, Toshio Fukuda, Fumihito Arai i Hideo Matsuura. "Multi-Sensor Integration System utilizing Fuzzy Inference and Neural Network". Journal of Robotics and Mechatronics 4, nr 5 (20.10.1992): 416–21. http://dx.doi.org/10.20965/jrm.1992.p0416.

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Sensors are important for recognizing the system state environmental status in the intelligent robotic system. Thus, the sensor integration system (SIS) has been studied in a wide range of applications. In this paper, it is shown that the SIS can expand the measurable region of sensors with higher accuracy by multiple sensors and that operators can use the system as easily as a single high-performance sensor system. Systems which have been reported so far do not have flexibility for changing/replacing sensors. Thus, this paper presents an approach to the SIS with the knowledge data base of sensors, so the proposed SIS has the flexibility for changing/replacing sensors. This system consists of four subsystems: 1) sensors as hardware sensing devices, 2) knowledge data base of sensors (KBS), 3) fuzzy inference, and 4) neural network(NN). This system can estimate the error for the sensor’s measured value by fuzzy inference with KBS. The measured values are integrated by NN. The inferred error and measured value are put into NN. Then, NN’s output gives the integrated value of multiple sensors. The proposed system is shown to be effective through extensive experiments.
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Wei, Nannan, Limin Zhang i Xinggan Zhang. "A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase". Sensors 22, nr 17 (2.09.2022): 6649. http://dx.doi.org/10.3390/s22176649.

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The recognition of warheads in the target cloud of the ballistic midcourse phase remains a challenging issue for missile defense systems. Considering factors such as the differing dimensions of the features between sensors and the different recognition credibility of each sensor, this paper proposes a weighted decision-level fusion architecture to take advantage of data from multiple radar sensors, and an online feature reliability evaluation method is also used to comprehensively generate sensor weight coefficients. The weighted decision-level fusion method can overcome the deficiency of a single sensor and enhance the recognition rate for warheads in the midcourse phase by considering the changes in the reliability of the sensor’s performance caused by the influence of the environment, location, and other factors during observation. Based on the simulation dataset, the experiment was carried out with multiple sensors and multiple bandwidths, and the results showed that the proposed model could work well with various classifiers involving traditional learning algorithms and ensemble learning algorithms.
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Ashok, Pradeepkumar, Ganesh Krishnamoorthy i Delbert Tesar. "Guidelines for Managing Sensors in Cyber Physical Systems with Multiple Sensors". Journal of Sensors 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/321709.

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Cyber physical systems (CPSs) typically have numerous sensors monitoring the various physical processes involved. Some sensor failures are inevitable and may have catastrophic effects. The relational nature of the diverse measurands can be very useful in detecting faulty sensors, monitoring the health of the system, and reducing false alarms. This paper provides procedures on how one may integrate data from the various sensors, by careful design of a sensor relationship network. Once such a network has been adopted, choices become available in real time for enhancing the reliability, safety, and performance of the overall system.
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Guo, Yixuan, i Gaoyang Liang. "Perceptual Feedback Mechanism Sensor Technology in e-Commerce IoT Application Research". Journal of Sensors 2021 (28.09.2021): 1–12. http://dx.doi.org/10.1155/2021/3840103.

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With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e-commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load-balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology.
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Jayasinghe, Udeni, William S. Harwin i Faustina Hwang. "Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification". Sensors 20, nr 1 (21.12.2019): 82. http://dx.doi.org/10.3390/s20010082.

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Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.
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Nagai, Isaku, Jun Sakai i Keigo Watanabe. "Indoor Self-Localization Using Multiple Magnetic Sensors". Journal of Robotics and Mechatronics 31, nr 2 (20.04.2019): 203–11. http://dx.doi.org/10.20965/jrm.2019.p0203.

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This study proposes an indoor self-localization for the estimation of the position and posture of an instrument using multiple magnetic sensors. First, a magnetic map for the localization is efficiently created using multiple sensors and a local positioning device made from an optical sensor and a gyroscope. For the localization estimating trajectories, the measurement error of the local positioning is corrected by matching it with the magnetic map. Our instrument is composed of six magnetic sensors, and the description of the self-localization details is based on the framework of a particle filter. The experimental results show better indoor path trajectories compared with a raw trajectory without map matching. The accuracy of the instrument using various numbers of magnetic sensors for the estimation is also investigated.
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Rozprawy doktorskie na temat "Multiple sensors"

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Ribadeneira, M. Xavier. "Ball bearing diagnostics with multiple sensors". Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/18963.

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Patsikas, Dimitrios. "Track score processing of multiple dissimilar sensors". Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FPatsikas.pdf.

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Thesis (M.S. in Applied Physics and M.S. in Electrical Engineering)--Naval Postgraduate School, June 2007.
Thesis Advisor(s): Phillip E. Pace, Murali Tummala, Gamani Karunasiri. "June 2007." Includes bibliographical references (p. 57-58). Also available in print.
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De, Villiers Hendrik Barney. "Correlation and tracking using multiple radar sensors /". Link to the online version, 2006. http://hdl.handle.net/10019/1006.

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De, Villiers Hendrik Barney. "Correlation and tracking using multiple radar sensors". Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/2265.

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Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2005.
Tracking manoeuvring military airborne targets with radar is problematic due to the low scan rates and the high levels of measurement noise. Surveillance systems using multiple radars have the benefit of an increased rate of observation and noise reduction but also have the problem of correlating observations from multiple sensors. Mehtods are discussed to correlate single observations from multiple radar sensors as well as assigning observations to existing tracks. Filtering methods to reduce measurement noise of the target tracks and methods to extrapolate the predicted position of targets are also explored.
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He, Shaojun. "Integration of Multiple Sensors for Astronaut Navigation on The Lunar Surface". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1324496686.

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Boratynski, B., A. Stafiniak, A. Szyszka, M. Ramiaczek-Krasowska, R. Paszkiewicz, M. Tlaczala, A. Baranowska-Korczyc, K. Fronc i D. Elbaum. "New fabrication approach to ZnO multiple nanofiber sensors". Thesis, Sumy State University, 2011. http://essuir.sumdu.edu.ua/handle/123456789/20587.

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In the presented work, ZnO nanofiber sensor structures designed and fabricated using a standard microelectronic device technology were studied. The structures in the configuration of a resistor with chemically active ZnO multiple nanofibers deposited by electrospinning method were prepared. Investigation of inclusion in the process reactive- ly sputtered AlN insulating film to improve the robustness of the nanofibres on the substrate was undertaken. Selective wet chemical etching of AlN film using photoresist developers and a photoresist mask to define the sensor active area was studied. The Ti/Au ohmic contacts were fabricated using the lift-off photolithography process. To- pography of the sensor structure details was investigated using AFM. Electrical charac- terization by means of I-V measurements was made. Sensitivity to the physiologically relevant concentration of Bovine Serum Albumin in water solution was shown. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/20587
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Xiao, Xiangyu. "A Multiple Sensors Approach to Wood Defect Detection". Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/11145.

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In the forest products manufacturing industry, recent price increases in the cost of high-quality lumber together with the reduced availability of this resource have forced manufacturers to utilize lower grade hardwood lumber in their manufacturing operations. This use of low quality lumber means that the labor involved in converting this lumber to usable parts is also increased because it takes more time to remove the additional defects that occur in the lower grade material. Simultaneously, labor costs have gone up and availability of skilled workers capable of getting a high yield of usable parts has markedly decreased. To face this increasingly complex and competitive environment, the industry has a critical need for efficient and cost-effective new processing equipment that can replace human operators who locate and identify defects that need to be removed in lumber and then remove these defects when cutting the lumber into rough parts. This human inspection process is laborious, inconsistent and subjective in nature due to the demands of making decisions very rapidly in a noisy and tiring environment. Hence, an automatic sawing system that could remove defects in lumber while creating maximum yield, offers significant opportunities for increasing profits of this industry. The difficult part in designing an automatic sawing system is creating an automatic inspection system that can detect critical features in wood that affect the quality of the rough parts. Many automatic inspection systems have been proposed and studied for the inspection of wood or wood products. But, most of these systems utilize a single sensing modality, e.g., a single optical sensor or an X-ray imaging system. These systems cannot detect all critical defects in wood. This research work reported in this dissertation is the first aimed at creating a vision system utilizes three imaging modalities: a color imaging system, a laser range profiling system and an X-ray imaging system. The objective of in designing this vision system is to detect and identify: 1) surface features such as knots, splits, stains; 2) geometry features such as wane, thin board; and 3) internal features such as voids, knots. The laser range profiling system is used to locate and identify geometry features. The X-ray imaging system is primarily used to detect features such as knots, splits and interior voids. The color imaging system is mainly employed to identify surface features. In this vision system a number of methodologies are used to improve processing speed and identification accuracy. The images from different sensing modalities are analyzed in a special order to offset the larger amount of image data that comes from the multiple sensors and that must be analyzed. The analysis of laser image is performed first. It is used to find defects that have insufficient thickness. These defects are then removed from consideration in the subsequent analysis of the X-ray image. Removing these defects from consideration in the analysis of the X-ray image not only improves the accuracy of detecting and identifying defects but also reduces the amount of time needed to analyze the X-ray image. Similarly, defect areas such as knot and mineral streak that are found in the analysis of the X-ray image are removed from consideration in the analysis of the color image. A fuzzy logic algorithm -- the approaching degree method-- is used to assign defect labels. The fuzzy logic approach is used to mimic human behavior in identifying defects in hardwood lumber. The initial results obtained from this vision system demonstrate the feasibility of locating and identifying all the major defects that occur in hardwood lumber. This was even true during the initial hardware development phase when only images of unsatisfactory quality from a limited lumber of samples were available. The vision system is capable of locating and identifying defects at the production speed of two linear feet per second that is typical in most hardwood secondary manufacturing plants. This vision system software was designed to run on a relative slow computer (200 MHz Pentium processor) with aid of special image processing hardware, i.e., the MORRPH board that was also designed at Virginia Tech.
Ph. D.
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Tran, Dung T. "An approach to activity recognition using multiple sensors /". Full text available, 2006. http://adt.curtin.edu.au/ETD-db/ETD-maint/view_etd?URN=adt-WCU20071219.140320.

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Tran, Tien Dung. "An approach to activity recognition using multiple sensors". Thesis, Curtin University, 2006. http://hdl.handle.net/20.500.11937/1702.

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Building smart home environments which automatically or semi-automatically assist and comfort occupants is an important topic in the pervasive computing field, especially with the coming of cheap, easy-to-install sensors. This has given rise to the indispensable need for human activity recognition from ubiquitous sensors whose purpose is to observe and understand what occupants are trying to do from sensory data. The main approach to the problem of human activity recognition is a probabilistic one so as to handle the complication of uncertainty, the overlapping of human behaviours and environmental noise. This thesis develops a probabilistic model as a framework for human activity recognition using multiple multi-modal sensors in complex pervasive environments. The probabilistic model to be developed is adapted and based on the abstract hidden Markov model (AHMM) with one layer to fuse multiple sensors. The concept of factored state representation is employed in the model to parsimoniously represent the state transitions for reducing the number of required parameters. The exact method is used in learning the model’s parameters and performing inference. To be able to incorporate a large number of sensors, several more parsimonious representations including the mixtures of smaller multinomials and sigmoid functions are investigated to model the state transitions, resulting in a reduction of the number of parameters and time required for training.We examine the approximate variational method to significantly reduce the time required for training the model instead of using the exact method. A system of fixed point equations is derived to iteratively update the free variational parameters. We also present the factored model in the case where all variables are continuous with the use of the conditional Gaussian distribution to model state transitions. The variational method is still employed in this case to speed up the model’s training process. The developed model is implemented and applied in recognizing daily activity in our smart home and the Nokia lab from multiple sensors. The experimental results show that the model is appropriate for fusing multiple sensors in activity recognition with a reasonable recognition performance.
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Tran, Tien Dung. "An approach to activity recognition using multiple sensors". Curtin University of Technology, School of Computing, 2006. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=17568.

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Building smart home environments which automatically or semi-automatically assist and comfort occupants is an important topic in the pervasive computing field, especially with the coming of cheap, easy-to-install sensors. This has given rise to the indispensable need for human activity recognition from ubiquitous sensors whose purpose is to observe and understand what occupants are trying to do from sensory data. The main approach to the problem of human activity recognition is a probabilistic one so as to handle the complication of uncertainty, the overlapping of human behaviours and environmental noise. This thesis develops a probabilistic model as a framework for human activity recognition using multiple multi-modal sensors in complex pervasive environments. The probabilistic model to be developed is adapted and based on the abstract hidden Markov model (AHMM) with one layer to fuse multiple sensors. The concept of factored state representation is employed in the model to parsimoniously represent the state transitions for reducing the number of required parameters. The exact method is used in learning the model’s parameters and performing inference. To be able to incorporate a large number of sensors, several more parsimonious representations including the mixtures of smaller multinomials and sigmoid functions are investigated to model the state transitions, resulting in a reduction of the number of parameters and time required for training.
We examine the approximate variational method to significantly reduce the time required for training the model instead of using the exact method. A system of fixed point equations is derived to iteratively update the free variational parameters. We also present the factored model in the case where all variables are continuous with the use of the conditional Gaussian distribution to model state transitions. The variational method is still employed in this case to speed up the model’s training process. The developed model is implemented and applied in recognizing daily activity in our smart home and the Nokia lab from multiple sensors. The experimental results show that the model is appropriate for fusing multiple sensors in activity recognition with a reasonable recognition performance.
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Książki na temat "Multiple sensors"

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Aranda, Miguel, Gonzalo López-Nicolás i Carlos Sagüés. Control of Multiple Robots Using Vision Sensors. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57828-6.

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Vernede, L. Multiple acoustic sensor detection system. Manchester: UMIST, 1993.

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Ireland), Sense (Northern. Report on dual & multiple sensory handicapped people. Belfast: Sense NorthernIreland, 1991.

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I, Rumiati Raffaella, i Caramazza Alfonso, red. The Multiple functions of sensory-motor representations. Hove: Psychology Press, 2005.

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Glover, M. H. Evaluation of a multiple-sensor weigh-in-motion system. Crowthorne, Berks: Transport and Road Research Laboratory, Vehicles Group, Vehicles and Environment Division, 1991.

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F, Keane John. An analysis of multiple sensor system payloads for unmanned aerial vehicles. Monterey, Calif: Naval Postgraduate School, 1993.

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Wang, Yue. Search and Classification Using Multiple Autonomous Vehicles: Decision-Making and Sensor Management. Wyd. 2. London: Springer London, 2012.

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C, Lee Thomas H., red. The new and the multiple: Sung senses of the past. Hong Kong: Chinese University Press, 2004.

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Fowler, Susan. Sensory stimulation: Theory and activity ideas (for adults with physical and multiple disabilities). [Victoria]: Spastic Society of Victoria, 1997.

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Foster, Timothy A. C² information management : data fusion and track ID's in a multiple sensor environment. Monterey, Calif: Naval Postgraduate School, 1992.

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Części książek na temat "Multiple sensors"

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Marchant, Roman, Pablo Guerrero i Javier Ruiz-del-Solar. "Cooperative Global Tracking Using Multiple Sensors". W RoboCup 2012: Robot Soccer World Cup XVI, 310–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39250-4_28.

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Maida, Gabriele, i Marco Morana. "Gait Analysis Using Multiple Kinect Sensors". W Advances in Intelligent Systems and Computing, 167–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03992-3_12.

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Song, Wei, Seoungjae Cho, Kyungeun Cho i Kyhyun Um. "Three-Dimensional Scene Reconstruction Using Multiple Sensors". W Lecture Notes in Electrical Engineering, 463–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55038-6_72.

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Bečanović, Vlatko, Ramin Hosseiny i Giacomo Indiveri. "Object Tracking Using Multiple Neuromorphic Vision Sensors". W RoboCup 2004: Robot Soccer World Cup VIII, 426–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32256-6_36.

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Matos, Nuno, António Santos i Ana Vasconcelos. "A Virtual Rehabilitation Solution Using Multiple Sensors". W Communications in Computer and Information Science, 143–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48645-0_13.

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Aranda, Miguel, Gonzalo López-Nicolás i Carlos Sagüés. "Introduction". W Control of Multiple Robots Using Vision Sensors, 1–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57828-6_1.

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Aranda, Miguel, Gonzalo López-Nicolás i Carlos Sagüés. "Angle-Based Navigation Using the 1D Trifocal Tensor". W Control of Multiple Robots Using Vision Sensors, 19–51. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57828-6_2.

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Aranda, Miguel, Gonzalo López-Nicolás i Carlos Sagüés. "Vision-Based Control for Nonholonomic Vehicles". W Control of Multiple Robots Using Vision Sensors, 53–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57828-6_3.

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Aranda, Miguel, Gonzalo López-Nicolás i Carlos Sagüés. "Controlling Mobile Robot Teams from 1D Homographies". W Control of Multiple Robots Using Vision Sensors, 79–102. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57828-6_4.

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Aranda, Miguel, Gonzalo López-Nicolás i Carlos Sagüés. "Control of Mobile Robot Formations Using Aerial Cameras". W Control of Multiple Robots Using Vision Sensors, 103–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57828-6_5.

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Streszczenia konferencji na temat "Multiple sensors"

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Lin, Hsing-Ying, Chen-Han Huang, Yu-Chia Liu, Kuo-Wei Huang i Lai-Kwan Chau. "Multiplex fiber-optic biosensor using multiple particle plasmon resonances". W Asia Pacific Optical Sensors Conference, redaktorzy John Canning i Gangding Peng. SPIE, 2012. http://dx.doi.org/10.1117/12.914383.

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Tajti, Tibor, i Nagy Benedek. "Motion sensor data correction using multiple sensors and multiple measurements". W 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2016. http://dx.doi.org/10.1109/sami.2016.7423022.

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Jangampet, Vinay Dutt, Rahul Dixit, David Klotzkin i Ian Papautsky. "Simultaneous, single-detector fluorescence detection of multiple fluorescent dyes". W 2010 Ninth IEEE Sensors Conference (SENSORS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icsens.2010.5690305.

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Fonollosa, J., A. Vergara i R. Huerta. "Sensor failure mitigation based on multiple kernels". W 2012 IEEE Sensors. IEEE, 2012. http://dx.doi.org/10.1109/icsens.2012.6411124.

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Williams, Jason L. "Experiments with graphical model implementations of multiple target multiple Bernoulli filters". W 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146620.

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Seeley, Charles E., i Sai S. Sarva. "Development of a Multi-Function Sensors". W ASME 2009 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2009. http://dx.doi.org/10.1115/smasis2009-1319.

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Sensors are increasingly used to determine the operational and environmental state of aerospace structures while in service. Typically, each sensor is tasked with a single function. The measurement of multiple physical quantities is usually associated with multiple sensors. The number of sensors on a structure may become prohibitive due to cost, weight or power constraints. This paper investigates the development and application of a multi-functional sensor where a single sensor performs multiple functions. A flexible MFC based piezoelectric sensor is embedded in a corrugated composite plate to determine the state of stress, temperature and vibration, all from the same device. Development of the sensor and preliminary test results are discussed in detail.
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Yun, Hansik, Hyeonsoo Park i Byoungho Lee. "Detection of Heterogeneous Characteristics Using Multiple Surface Plasmon Resonance Dips". W Optical Sensors. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/sensors.2013.st4b.4.

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Shi, Hongyang, Xinda Qi, Yunqi Cao, Nelson Sepúlveda, Chuan Wang i Xiaobo Tan. "Highly Stretchable Resistive Strain Sensors Using Multiple Viscous Conductive Materials". W ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/smasis2020-2321.

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Abstract This paper proposes a highly stretchable strain sensor using viscous conductive materials as resistive element and introduces a simple and economic fabrication process by encapsulating the conductive materials between two layers of silicone rubbers Ecoflex 00-30. The fabrication process of the strain sensor is presented, and the properties of the viscous conductive materials are studied. Characterization shows that the sensor with conductive gels, toothpastes, carbon paint, and carbon grease can sustain a maximum tensile strain of 200% and retain good repeatability, with a strain gauge factor of 2.0, 1.75, 3.0, and 7.5, respectively. Furthermore, strain sensors with graphite and carbon nanotubes mixed with conductive gels are fabricated to explore how to improve the gauge factor. With a focus on the most promising material, conductive carbon grease, cyclic stretching tests are conducted and show good repeatability at 100% strain for 100 cycles. Lastly, it is demonstrated that the stretchable strain sensor made of carbon grease is capable of measuring finger bending. With its easy and low-cost fabrication process, large strain detection range and good gauge factor, the conductive materials-based strain sensors are promising for future biomedical, wearable electronics and rehabilitation applications.
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Saad, Nor Hayati, Carl J. Anthony, Raya Al-Dadah i Michael C. L. Ward. "Exploitation of multiple sensor arrays in electronic nose". W 2009 IEEE Sensors. IEEE, 2009. http://dx.doi.org/10.1109/icsens.2009.5398487.

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Lee, Boon-Giin, Kyung-Hoon Do i Wan-Young Chung. "WSN based 3D mobile indoor multiple user tracking". W 2009 IEEE Sensors. IEEE, 2009. http://dx.doi.org/10.1109/icsens.2009.5398494.

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Raporty organizacyjne na temat "Multiple sensors"

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Keeney, James T. Multiple Usage of Existing Satellite Sensors. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2006. http://dx.doi.org/10.21236/ada459023.

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Murray, Steven A. Human-Machine Interaction With Multiple Autonomous Sensors. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1995. http://dx.doi.org/10.21236/ada422501.

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Keeney, James T. Multiple Usage of Existing Satellite Sensors (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, styczeń 2006. http://dx.doi.org/10.21236/ada459022.

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Lee, T. S. Non-Model Based Estimation with Applications to Sensor Calibration Using Multiple Sensors. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 1985. http://dx.doi.org/10.21236/ada154736.

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Chau, Y. A., i E. Geraniotis. Distributed Detection from Multiple Sensors with Correlated Observations. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1989. http://dx.doi.org/10.21236/ada454790.

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Tugnait, Jitendra K. Tracking of Multiple Maneuvering Targets in Clutter Using Multiple Sensors, IMM and JPDA Coupled Filtering. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 2003. http://dx.doi.org/10.21236/ada417569.

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Kibbe, Marion P., i Scott A. Weisgerber. Targeting Decisions Using Multiple Imaging Sensors: Operator Performance and Calibration. Fort Belvoir, VA: Defense Technical Information Center, luty 1990. http://dx.doi.org/10.21236/ada224277.

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Geranlotis, E., i Y. A. Chau. Distributed Detection of Weak Signals From Multiple Sensors With Correlated Observations. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1988. http://dx.doi.org/10.21236/ada452624.

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Maurer, Uwe, Asim Smailagic, Daniel P. Siewiorek i Michael Deisher. Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 2006. http://dx.doi.org/10.21236/ada534437.

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Zakhor, Avideh. (DCT-FY08) Target Detection Using Multiple Modality Airborne and Ground Based Sensors. Fort Belvoir, VA: Defense Technical Information Center, marzec 2013. http://dx.doi.org/10.21236/ada580839.

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