Journal articles on the topic 'Multiple sensors'
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Pearlshtien, Daniela Heller, Stefano Pignatti, and Eyal Ben-Dor. "Vicarious CAL/VAL Approach for Orbital Hyperspectral Sensors Using Multiple Sites." Remote Sensing 15, no. 3 (January 29, 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, and Hisayuki Kimura. "Fusion of Multiple Ultrasonic Sensor Data and Image Data for Measuring an Object’s Motion." Journal of Robotics and Mechatronics 17, no. 1 (February 20, 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, no. 5 (November 1, 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, and Kenneth J. Loh. "Laboratory Evaluation of Railroad Crosslevel Tilt Sensing Using Electrical Time Domain Reflectometry." Sensors 20, no. 16 (August 10, 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, and Hideo Matsuura. "Multi-Sensor Integration System utilizing Fuzzy Inference and Neural Network." Journal of Robotics and Mechatronics 4, no. 5 (October 20, 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, and Xinggan Zhang. "A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase." Sensors 22, no. 17 (September 2, 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, and 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, and Gaoyang Liang. "Perceptual Feedback Mechanism Sensor Technology in e-Commerce IoT Application Research." Journal of Sensors 2021 (September 28, 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, and Faustina Hwang. "Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification." Sensors 20, no. 1 (December 21, 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, and Keigo Watanabe. "Indoor Self-Localization Using Multiple Magnetic Sensors." Journal of Robotics and Mechatronics 31, no. 2 (April 20, 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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Bruckner, Gudrun, and Jochen Bardong. "Wireless Readout of Multiple SAW Temperature Sensors." Sensors 19, no. 14 (July 12, 2019): 3077. http://dx.doi.org/10.3390/s19143077.
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It has since long been known that surface acoustic wave (SAW) devices, resonators as well as delay lines, can be used as passive wireless sensors for physical quantities, like temperature and pressure, as well as gas sensors or identification-tags (ID-tags). The sensors are robust, work passively without a battery, can be applied at high temperatures, and provide a high resolution. Nevertheless, if the devices are used wirelessly in an industrial environment, several constraints have to be taken into account, especially when more than one quantity or device needs to be measured at the same time. The paper addresses the challenges that must be tackled when establishing multi-sensor-wireless-readout for industrial applications. Major issues here are the legal regulations for industrial, scientific and medical frequency bands (ISM-bands), as well as sampling time and costs, which impose severe restrictions to any system design. We describe several design approaches and their constraints. We successfully designed sensors based on reflective delay lines that allow the parallel readout of four independent temperature sensors in the 2.45 GHz ISM-band. These devices were fabricated and positively tested, demonstrating the applicability of SAW sensors for industrial applications.
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Yu, Jerry. "Spectrum of myelinated pulmonary afferents (III) cracking intermediate adapting receptors." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 319, no. 6 (December 1, 2020): R724—R732. http://dx.doi.org/10.1152/ajpregu.00136.2020.
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Conventional one-sensor theory (one afferent fiber connects to a single sensor) categorizes the bronchopulmonary mechanosensors into the rapidly adapting receptors (RARs), slowly adapting receptors (SARs), or intermediate adapting receptors (IARs). RARs and SARs are known to sense the rate and magnitude of mechanical change, respectively; however, there is no agreement on what IARs sense. Some investigators believe that the three types of sensors are actually one group with similar but different properties and IARs operate within that group. Other investigators (majority) believe IARs overlap with the RARs and SARs and can be classified within them according to their characteristics. Clearly, there is no consensus on IARs function. Recently, a multiple-sensor theory has been advanced in which a sensory unit may contain many heterogeneous sensors, such as both RARs and SARs. There are no IARs. Intermediate adapting unit behavior results from coexistence of RARs and SARs. Therefore, the unit can sense both rate and magnitude of changes. The purpose of this review is to provide evidence that the multiple-sensor theory better explains sensory unit behavior.
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Albishi, Ali M., Seyed H. Mirjahanmardi, Abdulbaset M. Ali, Vahid Nayyeri, Saud M. Wasly, and Omar M. Ramahi. "Intelligent Sensing Using Multiple Sensors for Material Characterization." Sensors 19, no. 21 (November 2, 2019): 4766. http://dx.doi.org/10.3390/s19214766.
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This paper presents a concept of an intelligent sensing technique based on modulating the frequency responses of microwave near-field sensors to characterize material parameters. The concept is based on the assumption that the physical parameters being extracted such as fluid concentration are constant over the range of frequency of the sensor. The modulation of the frequency response is based on the interactions between the material under test and multiple sensors. The concept is based on observing the responses of the sensors over a frequency wideband as vectors of many dimensions. The dimensions are then considered as the features for a neural network. With small datasets, the neural networks can produce highly accurate and generalized models. The concept is demonstrated by designing a microwave sensing system based on a two-port microstrip line exciting three-identical planar resonators. For experimental validation, the sensor is used to detect the concentration of a fluid material composed of two pure fluids. Very high accuracy is achieved.
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Zhang, Wen Zhao, Zhi Zhuang Liu, and Wen Zhen Zhou. "Unknown Environment Mapping Based on Multiple Micro-Robots." Applied Mechanics and Materials 556-562 (May 2014): 2496–501. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2496.
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Currently, most mapping researches are deeply dependent on long range sensors such as sonar, laser range finders or cameras, but due to power or cost constraints, which often cannot be mounted in the sensor module. In response to the no long range sensors situation, this paper presents a method to complete indoor mapping tasks in real-time only using short range sensors installed on the multiple micro-robots. The key mapping problem needed to address in the paper is that each single robot created a map based on odometer and short distance sensor data, and then maps from multiple robots were consolidated to generate a greater coherence map. A kind of SRV mobile robots were tested in an artificial rectangular environment. The experimental results show that the multiple micro-robot system has good robustness to odometer and environmental noise.
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Chen, Xin, Guo Qing Ding, Li Hua Lei, and Yuan Li. "Parameter Determination of Multiple Probes Scanning Method for Profile Measurement." Advanced Materials Research 314-316 (August 2011): 1707–12. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.1707.
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A multiple probes scanning system comprised of two displacement sensors and one angle sensor is studied for ultra-precise profile measurement. The least squares method is applied to reconstruct the measured profile with uncertainty estimation. The parameters that influence the uncertainty associated with the measured profile are investigated. In order to obtain the optimized uncertainty, the determination of the relative parameters has been discussed in two cases. The first one is that how to select suitable sensors when intervals between two displacement sensors are known. The second one is that how to select suitable intervals between two displacement sensors when the specifications of the sensors are known. The processes of the selection in two cases are illustrated respectively according to the actual situations of the measurement.
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Sannomiya, Takumi, and Christian Hafner. "Multiple Multipole Program Modelling for Nano Plasmonic Sensors." Journal of Computational and Theoretical Nanoscience 7, no. 8 (August 1, 2010): 1587–95. http://dx.doi.org/10.1166/jctn.2010.1523.
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Britton, C. L., R. L. Jones, P. I. Oden, Z. Hu, R. J. Warmack, S. F. Smith, W. L. Bryan, and J. M. Rochelle. "Multiple-input microcantilever sensors." Ultramicroscopy 82, no. 1-4 (February 2000): 17–21. http://dx.doi.org/10.1016/s0304-3991(99)00155-2.
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Liu, Peng, Zhuang Li, Yang Cong, and Yuheng Xu. "Bert-GCN: multi-sensors network prediction." ITM Web of Conferences 47 (2022): 01004. http://dx.doi.org/10.1051/itmconf/20224701004.
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With the application of neural network technologies such as GCN and GRU in sensor networks, the accuracy and robustness of multi-sensor prediction have been greatly improved. GCN effectively uses the spatial characteristics of the sensor network, and GRU effectively uses the temporal characteristics of the sensor network, so the PROPOSED T-GCN model has achieved excellent results. However, there are still shortcomings: i) The prediction is only for a single sensor feature, and multiple features cannot be trained at the same time. ii) Only the connections between sensors are considered, while the connections between multiple features of sensors are ignored. iii) Modeling for multiple features leads to the deepening of the model from 2d to 3D, resulting in slow model training and poor learning effect. To solve the above problems, this paper proposed the Bert-GCN model. Bert pre-training was added on the basis of the original GCN-GRU model to effectively improve the learning effect of multiple features of a single sensor.
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Dong, Xue Peng, Lei Han, Gang Yi Yin, and Lei Cai. "Design of Road Condition Sensor Based on Multiple Frequency Detecting Technology." Key Engineering Materials 609-610 (April 2014): 937–41. http://dx.doi.org/10.4028/www.scientific.net/kem.609-610.937.
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Road surface sensors system, plays a very important role in the highway traffic field. Comprehensive estimation of road condition, will distinctly enhance the safety of transportation. Road condition sensor is used to distinguish the coverings on road surface, such as water, snow, ice, frost and so on. In this paper, we introduce the multiple frequency detecting technology used in the road surface sensors system, and design the road condition sensor to detect coverings on road surface. The main work includes design, measurement and improvement of the sensor.
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Jiao, Libin, Rongfang Bie, Hao Wu, Yu Wei, Jixin Ma, Anton Umek, and Anton Kos. "Golf swing classification with multiple deep convolutional neural networks." International Journal of Distributed Sensor Networks 14, no. 10 (October 2018): 155014771880218. http://dx.doi.org/10.1177/1550147718802186.
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The use of smart sports equipment and body sensory systems supervising daily sports training is gradually emerging in professional and amateur sports; however, the problem of processing large amounts of data from sensors used in sport and discovering constructive knowledge is a novel topic and the focus of our research. In this article, we investigate golf swing data classification methods based on varieties of representative convolutional neural networks (deep convolutional neural networks) which are fed with swing data from embedded multi-sensors, to group the multi-channel golf swing data labeled by hybrid categories from different golf players and swing shapes. In particular, four convolutional neural classifiers are customized: “GolfVanillaCNN” with the convolutional layers, “GolfVGG” with the stacked convolutional layers, “GolfInception” with the multi-scale convolutional layers, and “GolfResNet” with the residual learning. Testing on the real-world swing dataset sampled from the system integrating two strain gage sensors, three-axis accelerometer, and three-axis gyroscope, we explore the accuracy and performance of our convolutional neural network–based classifiers from two perspectives: classification implementations and sensor combinations. Besides, we further evaluate the performance of these four classifiers in terms of classification accuracy, precision–recall curves, and F1 scores. These common classification indicators illustrate that our convolutional neural network–based classifiers can basically group the golf swing predefined by the combination of shapes and golf players correctly and outperform support vector machine method representing traditional classification methods.
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Shao, Shuai, Kouhei Yamamoto, and Naoyuki Kubota. "An Elderly Monitoring System Based on Multiple Ultra-Sensitive Vibration and Pneumatic Sensors." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (July 20, 2021): 423–31. http://dx.doi.org/10.20965/jaciii.2021.p0423.
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In recent years, aging of the population has become a major social problem. Various types of sensor networks have been applied to elderly monitoring for addressing the care problems of the elderly living alone. We have also proposed elderly monitoring systems based on wireless sensor network devices. However, vision-based sensors can also cause a mental burden on the elderly’s privacy. Furthermore, the number of sensors must be reduced, if possible. Therefore, this study proposes an elderly monitoring system composed of two vibration sensors placed on the floor and a pneumatic sensor placed on the bed. Because both sensors include considerable measurement noise, we propose a human behavior estimation method that includes anomaly detection from time-series measurement data using an autocorrelation coefficient. Finally, we discuss the effectiveness and usability of the proposed system through several experimental results. The accuracy of walking detection reaches 94.4%, while the error of heartbeat detection is 3.01 bpm.
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Zheng, Yani, Gaurav Dhiman, Ashutosh Sharma, Amit Sharma, and Mohd Asif Shah. "An IoT-Based Water Level Detection System Enabling Fuzzy Logic Control and Optical Fiber Sensor." Security and Communication Networks 2021 (August 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/4229013.
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The usage of wireless sensors has become widespread for the collection of data for various Internet of Things (IoT) products. Specific wireless sensors use optical fiber technology as transmission media and lightwave signals as carriers, showing the advantages of antielectromagnetic interference, high sensitivity, and strong reliability. Hence, their application in IoT systems becomes a research hotspot. In this article, multiple optical fiber sensors are constructed as an IoT detection system, and a Transmission Control Protocol (TCP)/Internet Protocol (IP) communication stack is used for the sensor module. Furthermore, design of gateway module, data server, and monitoring module is established in order to run the data server in the Windows system and communicate across the network segments. Furthermore, the optical fiber sensor is connected to the development board with WiFi, meanwhile considering the optical fiber wireless network’s congestion problem. The fuzzy logic concept is introduced from the perspective of cache occupancy, and a fiber sensor’s network congestion control algorithm is proposed. In the experiment, the IoT detection system with multiple optical fiber sensors is used for water level detection, and the sensor’s real-time data detected by the User Interface (UI) are consistent with the feedback results. The proposed method is also compared with the SenTCP algorithm and the CODA algorithm, and it was observed that the proposed network congestion control algorithm based on the fuzzy logic can improve network throughput and reduce the network data packet loss.
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Chen, Linlin, Ju Huang, and Yonghua Liu. "Hand-Eye Calibration Technology of Intelligent Picking Robot Using Multiple Photoelectric Sensor Information Fusion." Journal of Nanoelectronics and Optoelectronics 16, no. 8 (August 1, 2021): 1348–56. http://dx.doi.org/10.1166/jno.2021.3083.
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Multiple photoelectric sensors are designed for information fusion, in order to restrict the movement of robot end-effecter and camera, and then construct the hand-eye equation to obtain the corresponding hand-eye pose. First, the mathematical model of hand-eye calibration (HEC) is studied; moreover, the control system based on multiple photoelectric sensors with NImyRIO-1900 microprocessor as the core is designed. Its upper layer includes infrared sensor, acoustic photoelectric sensor, and gas photoelectric sensor, which can collect the position characteristics of the robot; finally, an automatic HEC system (HECS) is designed based on the control. The system combines the control system composed of multiple photoelectric sensors and the HEC method. It can collect data in real time, expand the camera field of vision, and improve the efficiency of HEC. When the calibration result reaches the predetermined accuracy, the HECS is terminated. In the experiment, the control system based on multiple photoelectric sensors helps the automatic calibration system to complete the picking task within 15 minutes. The success rate of pose planning of the end-effector of the system is 85%, the calibration attitude error is only 0.0586, and the position error is only 12.6685 mm, which verifies that the calibration system based on multiple photoelectric sensors is effective.
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Fu, Yuan, Xiang Chen, Yu Liu, Chan Son, and Yan Yang. "Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP." Applied Sciences 12, no. 15 (July 27, 2022): 7535. http://dx.doi.org/10.3390/app12157535.
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In order to deal with the shortcomings (such as poor robustness) of the traditional single-channel vibration signal in the comprehensive monitoring of the gearbox fault state, a multi-channel decision-level fusion algorithm was proposed based on symmetrized dot pattern (SDP) analysis, with the visual geometry group 16 network (VGG16) fault diagnosis model. Firstly, the SDP method was used to convert the vibration signal of a single multi-channel sensor into an imaging arm. Secondly, the obtained image arm was input into the VGG16 convolutional neural network in order to train the fault diagnosis model that can be obtained. Then, the SDP images of the signals that were to be measured from multiple multi-channel sensors were input into the fault diagnosis model, and the diagnosis results of multiple multi-channel sensors could then be obtained. Experimentally, it was demonstrated that the diagnostic results of multi-channel sensors one, two, and three were more accurate than those of single-channel sensors one, two, and three, by 3.01%, 16.7%, and 5.17%, respectively. However, the fault generation was not generated in a single direction, but rather multiple directions. In order to improve the comprehensiveness of the raw vibration data, a fusion method using DS (Dempster–Shafer) evidence theory was proposed in order to fuse multiple multi-channel sensors, in which the accuracy achieved 99.93% when sensor one and sensor two were fused, which was an improvement of 8.88% and 1.02% over single sensors one and two, respectively. When sensor one and sensor three were fused, the accuracy reached 99.31%, which was an improvement of 8.31% and 6.17% over single sensors one and three, respectively. When sensor two and sensor three were fused, the accuracy reached 99.91%, which was an improvement of 1.00% and 6.74% over single sensors two and three, respectively. When three sensors were fused simultaneously, the accuracy reached 99.99%, which was 8.93%, 1.08%, and 6.81% better than single sensors one, two, and three, respectively. Therefore, it can be proved that the number of sensor channels has a great influence on the diagnosis results.
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Hammer, Christof, Johannes Warmer, Sebastian Sporrer, Peter Kaul, Ronald Thoelen, and Norbert Jung. "A Compact, Reliable and Efficient 16 Channel Power Supply for the Automated Screening of Semiconducting Metal Oxide Gas Sensors." Electronics 8, no. 8 (August 9, 2019): 882. http://dx.doi.org/10.3390/electronics8080882.
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The choice of suitable semiconducting metal oxide (MOX) gas sensors for the detection of a specific gas or gas mixture is time-consuming since the sensor’s sensitivity needs to be characterized at multiple temperatures to find its optimal operating conditions. To obtain reliable measurement results, it is very important that the power for the sensor’s integrated heater is stable, regulated and error-free (or error-tolerant). Especially the error-free requirement can be only be achieved if the power supply implements failure-avoiding and failure-detection methods. The biggest challenge is deriving multiple different voltages from a common supply in an efficient way while keeping the system as small and lightweight as possible. This work presents a reliable, compact, embedded system that addresses the power supply requirements for fully automated simultaneous sensor characterization for up to 16 sensors at multiple temperatures. The system implements efficient (avg. 83.3% efficiency) voltage conversion with low ripple output (<32 mV) and supports static or temperature-cycled heating modes. Voltage and current of each channel are constantly monitored and regulated to guarantee reliable operation. To evaluate the proposed design, 16 sensors were screened. The results are shown in the experimental part of this work.
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Huang, Yunbao, Haiyan Li, Qifu Wang, and Liping Chen. "Integrating multiple sensors for the closed-loop three-dimensional digitization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 226, no. 11 (February 1, 2012): 2816–34. http://dx.doi.org/10.1177/0954406212436442.
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In this article, we present a set of novel techniques, which include (1) sensor-stage calibration, (2) high-level script programming for the flexible control of data sensing, and (3) a framework for dynamic data sensing, visualization and shape modeling. It enables the integration of multiple three-dimensional sensors into one controlled three-dimensional digitization system for the closed-loop of data sensing and shape modeling. In this system, point, line, and area sensors with different sensing coverage, accuracies, and resolutions can be integrated into a multi-stage platform on which data acquisition from each sensor can be programmed in a controlled manner. In this manner, the closed-loop of data sensing and shape modeling can be achieved, which can significantly improve the overall digitization efficiency and shape modeling quality. Finally, two three-dimensional digitization examples of such multi-sensor system are demonstrated, (1) complementary integration of multiple sensors for three-dimensional digitization and (2) the closed-loop of dynamic data sensing and shape modeling to significantly improve overall sensing efficiency and shape modeling quality.
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Park, Sunghoon, Hyejoon Kheel, Gun-Joo Sun, Taegyung Ko, Wan In Lee, and Chongmu Lee. "Acetone Gas Sensing Properties of a Multiple-Networked Fe2O3-Functionalized CuO Nanorod Sensor." Journal of Nanomaterials 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/830127.
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Fe2O3-decorated CuO nanorods were prepared by Cu thermal oxidation followed by Fe2O3decoration via a solvothermal route. The acetone gas sensing properties of multiple-networked pristine and Fe2O3-decorated CuO nanorod sensors were examined. The optimal operating temperature of the sensors was found to be 240°C. The pristine and Fe2O3-decorated CuO nanorod sensors showed responses of 586 and 1,090%, respectively, to 1,000 ppm of acetone at 240°C. The Fe2O3-decorated CuO nanorod sensor also showed faster response and recovery than the latter sensor. The acetone gas sensing mechanism of the Fe2O3-decorated CuO nanorod sensor is discussed in detail. The origin of the enhanced sensing performance of the multiple-networked Fe2O3-decorated CuO nanorod sensor to acetone gas was explained by modulation of the potential barrier at the Fe2O3-CuO interface, highly catalytic activity of Fe2O3for acetone oxidation, and the creation of active adsorption sites by Fe2O3nanoparticles.
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Jiao, Wan, Gayle Hagler, Ronald Williams, Robert Sharpe, Ryan Brown, Daniel Garver, Robert Judge, et al. "Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States." Atmospheric Measurement Techniques 9, no. 11 (November 1, 2016): 5281–92. http://dx.doi.org/10.5194/amt-9-5281-2016.
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Abstract. Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ∼ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, and −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results – some sensors had very high agreement (e.g., r = 0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.
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Asundi, Sharanabasaweshwara, Norman Fitz-Coy, and Haniph Latchman. "Evaluation of Murrell’s EKF-Based Attitude Estimation Algorithm for Exploiting Multiple Attitude Sensor Configurations." Sensors 21, no. 19 (September 27, 2021): 6450. http://dx.doi.org/10.3390/s21196450.
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Pico- and nano-satellites, due to their form factor and size, are limited in accommodating multiple or redundant attitude sensors. For such satellites, Murrell’s implementation of the extended Kalman filter (EKF) can be exploited to accommodate multiple sensor configurations from a set of non redundant attitude sensors. The paper describes such an implementation involving a sun sensor suite and a magnetometer as attitude sensors. The implementation exploits Murrell’s EKF to enable three sensor configurations, which can be operationally commanded, for satellite attitude estimation. Among the three attitude estimation schemes, (i) sun sensor suite and magnetometer, (ii) magnetic field vector and its time derivative and (iii) magnetic field vector, it is shown that the third configuration is better suited for attitude estimation in terms of precision and accuracy, but can consume more time to converge than the other two.
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Rhee, Joon, and Jiwon Seo. "Low-Cost Curb Detection and Localization System Using Multiple Ultrasonic Sensors." Sensors 19, no. 6 (March 21, 2019): 1389. http://dx.doi.org/10.3390/s19061389.
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Curb detection and localization systems constitute an important aspect of environmental recognition systems of autonomous driving vehicles. This is because detecting curbs can provide information about the boundary of a road, which can be used as a safety system to prevent unexpected intrusions into pedestrian walkways. Moreover, curb detection and localization systems enable the autonomous vehicle to recognize the surrounding environment and the lane in which the vehicle is driving. Most existing curb detection and localization systems use multichannel light detection and ranging (lidar) as a primary sensor. However, although lidar demonstrates high performance, it is too expensive to be used for commercial vehicles. In this paper, we use ultrasonic sensors to implement a practical, low-cost curb detection and localization system. To compensate for the relatively lower performance of ultrasonic sensors as compared to other higher-cost sensors, we used multiple ultrasonic sensors and applied a series of novel processing algorithms that overcome the limitations of a single ultrasonic sensor and conventional algorithms. The proposed algorithms consisted of a ground reflection elimination filter, a measurement reliability calculation, and distance estimation algorithms corresponding to the reliability of the obtained measurements. The performance of the proposed processing algorithms was demonstrated by a field test under four representative curb scenarios. The availability of reliable distance estimates from the proposed methods with three ultrasonic sensors was significantly higher than that from the other methods, e.g., 92.08% vs. 66.34%, when the test vehicle passed a trapezoidal-shaped road shoulder. When four ultrasonic sensors were used, 96.04% availability and 13.50 cm accuracy (root mean square error) were achieved.
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Choe, K., and H. Baruh. "Sensor Failure Detection in Flexible Structures Using Modal Observers." Journal of Dynamic Systems, Measurement, and Control 115, no. 3 (September 1, 1993): 411–18. http://dx.doi.org/10.1115/1.2899117.
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A procedure is developed to detect sensor failures in flexible structures by means of observers designed at the modal level. Estimates of the modal coordinates generated by the modal observers are used to estimate the system output at the sensor’s locations. These estimates of the system output are then compared with the sensors’ measurements to detect failure. It is shown that, when the observer gains are properly selected, failure of a certain sensor primarily affects the estimate of that sensor, and it affects the estimates of the operational sensors much less. This makes it possible to detect multiple sensor failures. Because the observers are designed for each mode individually, one can obtain closed-form expressions for the observer poles, making the failure detection procedure applicable to high-order systems.
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Zhu, Yusi, Zhan Zhao, Zhen Fang, and Lidong Du. "Dual-Resonator-Based (DRB) and Multiple-Resonator-Based (MRB) MEMS Sensors: A Review." Micromachines 12, no. 11 (November 4, 2021): 1361. http://dx.doi.org/10.3390/mi12111361.
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Single-resonator-based (SRB) sensors have thrived in many sensing applications. However, they cannot meet the high-sensitivity requirement of future high-end markets such as ultra-small mass sensors and ultra-low accelerometers, and are vulnerable to environmental influences. It is fortunate that the integration of dual or multiple resonators into a sensor has become an effective way to solve such issues. Studies have shown that dual-resonator-based (DRB) and multiple-resonator-based (MRB) MEMS sensors have the ability to reject environmental influences, and their sensitivity is tens or hundreds of times that of SRB sensors. Hence, it is worth understanding the state-of-the-art technology behind DRB and MRB MEMS sensors to promote their application in future high-end markets.
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Lee, Jae Hoon, Takashige Yano, Tomoshi Yamashita, and Shingo Okamoto. "Monitoring Bicycle Riding Motion with Multiple Inertial Sensors." Applied Mechanics and Materials 541-542 (March 2014): 1398–402. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.1398.
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This paper presents a novel sensory system for monitoring situations of riding bicycle. The proposed system can be used to measure and save in real-time not only the motion of bicycle rider but also the situation near the vehicle. Multiple inertial sensors being attached to human body are employed to measure the motion of the rider. Two laser scanners installed in the front of the bicycle and two cameras of wide view angle were used to detect the environmental change including pedestrians and static/dynamic objects. The system configuration was designed for the synchronization of multiple sensors according to the position information of the vehicle. Particularly, the human motion of riding bicycle is captured with the system and analyzed with the measurement data in this paper.
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Shen, Tzung-Sz, Jianbing Huang, and Chia-Hsiang Menq. "Multiple-Sensor Planning and Information Integration for Automatic Coordinate Metrology." Journal of Computing and Information Science in Engineering 1, no. 2 (May 1, 2001): 167–79. http://dx.doi.org/10.1115/1.1385827.
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Multiple-sensor integration of vision and touch probe sensors has been shown to be a feasible approach for rapid and high-precision coordinate acquisition [Shen, T. S., Huang, J., and Meng, C. H., 2000, “Multiple-sensor integration for rapid and high-precision coordinate metrology,” IEEE/ASME Trans. Mechatron. 5, pp. 110–121]. However, the automation of coordinate measurements is still hindered by unknown surface areas that cannot be digitized using the vision system due to occlusions. It is identified that the estimation and reasoning of unknown surface areas, and automatic sensor planning using multiple sensors are two key issues. In order to advance multiple-sensor integration technologies toward a fully automatic and agile coordinate metrology, information integration algorithms for estimating and reasoning unknown surface areas, and an automatic multiple-sensor planning environment are developed in this paper. Experimental and simulation results are also demonstrated.
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Dobrovolskis, Algirdas, Doc Audronė Janavičiutė, Prof Doc Egidijus Kazanavičius, and Doc Agnius Liutkevičius. "METHOD FOR INDOOR HUMAN POSITION TRACKING USING MULTIPLE DEPTH SENSORS." EPH - International Journal of Science And Engineering 4, no. 1 (March 27, 2018): 32–38. http://dx.doi.org/10.53555/eijse.v4i1.155.
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In this article a positioning method for covering room area was proposed. Multiple Kinect depth sensors were used to work around narrow field of view of one Kinect sensor and cover the room area to prevent blind spots. Affine transformation was used to convert coordinates of the Kinect sensors to the coordinates of the room. Indoor human tracking application was developed in research. During application testing, average aggregated error of 15cm was determined.
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Yue, Rui, Hao Xu, Jianqing Wu, Renjuan Sun, and Changwei Yuan. "Data Registration with Ground Points for Roadside LiDAR Sensors." Remote Sensing 11, no. 11 (June 5, 2019): 1354. http://dx.doi.org/10.3390/rs11111354.
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The Light Detection and Ranging (LiDAR) sensors are being considered as new traffic infrastructure sensors to detect road users’ trajectories for connected/autonomous vehicles and other traffic engineering applications. A LiDAR-enhanced traffic infrastructure system requires multiple LiDAR sensors around intersections, along with road segments, which can provide a seamless detection range at intersections or along arterials. Each LiDAR sensor generates cloud points of surrounding objects in a local coordinate system with the sensor at the origin, so it is necessary to integrate multiple roadside LiDAR sensors’ data into the same coordinate system. None of existing methods can integrate the data from roadside LiDAR sensors, because the extensive detection range of roadside sensors generates low-density cloud points and the alignment of roadside sensors is different from mapping scans or autonomous sensing systems. This paper presents a method to register datasets from multiple roadside LiDAR sensors. This approach innovatively integrates LiDAR datasets with 3D cloud points of road surface and 2D reference point features, so the method is abbreviated as RGP (Registration with Ground and Points). The RGP method applies optimization algorithms to identify the optimized linear coordinate transformation. This research considered the genetic algorithm (global optimization) and the hill climbing algorithm (local optimization). The performance of the RGP method and the different optimization algorithms was evaluated with field LiDAR sensors data. When the developed process can integrate data from roadside sensors, it can also register LiDAR sensors’ data on an autonomous vehicle or a robot.
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Jing, Junmin, Zengxing Zhang, Zhiwei Liao, Bin Yao, Yuzhen Guo, Wenjun Zhang, Yanbo Xu, and Chenyang Xue. "A Hardware System for Synchronous Processing of Multiple Marine Dynamics MEMS Sensors." Micromachines 13, no. 12 (December 2, 2022): 2135. http://dx.doi.org/10.3390/mi13122135.
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Temperature, depth, conductivity, and turbulence are fundamental parameters of marine dynamics in the field of ocean science. These closely correlated parameters require time-synchronized observations to provide feedback on marine environmental problems, which requires using sensors with synchronized power supply, multi-path data solving, recording, and storage performances. To address this challenge, this work proposes a hardware system capable of synchronously processing temperature, depth, conductivity, and turbulence data on marine dynamics collected by sensors. The proposed system uses constant voltage sources to excite temperature and turbulence sensors, a constant current source to drive a depth sensor, and an alternating current (AC) constant voltage source to drive a conductivity sensor. In addition, the proposed system uses a high-precision analog-digital converter to acquire the direct current (DC) signals from temperature, depth, and turbulence sensors, as well as the AC signals from conductivity sensors. Since the sampling frequency of turbulence sensors is different from that of the other sensors, the proposed system stores the generated data at different storage rates as multiple-files. Further, the proposed hardware system manages these files through a file system (file allocation tab) to reduce the data parsing difficulty. The proposed sensing and hardware logic system is verified and compared with the standard conductivity-temperature-depth measurement system in the National Center of Ocean Standards and Metrology. The results indicate that the proposed system achieved National Verification Level II Standard. In addition, the proposed system has a temperature indication error smaller than 0.02 °C, a conductivity error less than 0.073 mS/cm, and a pressure error lower than 0.8‰ FS. The turbulence sensor shows good response and consistency. Therefore, for observation methods based on a single point, single line, and single profile, it is necessary to study multi-parameter data synchronous acquisition and processing in the time and spatial domains to collect fundamental physical quantities of temperature, salt, depth, and turbulence. The four basic physical parameters collected by the proposed system are beneficial to the in-depth research on physical ocean motion, heat transfer, energy transfer, mass transfer, and heat-energy-mass coupling and can help to realize accurate simulation, inversion, and prediction of ocean phenomena.
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Marek, Gary W., Thomas H. Marek, Kevin R. Heflin, Dana O. Porter, Jerry E. Moorhead, Robert C. Schwartz, and David K. Brauer. "Factory-Calibrated Soil Water Sensor Performance Using Multiple Installation Orientations and Depths." Applied Engineering in Agriculture 36, no. 1 (2020): 39–54. http://dx.doi.org/10.13031/aea.13448.
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Abstract. The use of soil water sensors is commonly advocated to aid and improve irrigation management in crop production systems. However, there are concerns about how sensor type, installation technique, sensor orientation, and soil texture may affect sensor accuracy. A field study was conducted to compare the performance of three commercially available soil water sensors (Acclima 315L, Decagon GS1, and Campbell Scientific 655) and a soil water potential sensor (Watermark 200SS) using different installation orientations of horizontal insertion, laid horizontal placement, and vertical insertion at depths of 15, 46, and 76 cm (6, 18, and 30 in.) in an irrigated clay loam soil field. Results indicated all sensors demonstrated similar trends of soil water content in response to wetting events (precipitation and irrigation) at the 15 cm depth following a 4-month settling period prior from the start of the growing season. Comparatively, the Acclima 315L performed well using horizontal insertion compared to calibrated neutron moisture meters (NMMs) at depths of 46 and 76 cm with R2 of 0.73 and 0.96 and slopes of 1.36 and 1.47, respectively. In addition, water storage in the 0.9 m soil profile integrated using the horizontally inserted Acclima 315L across the three depths matched closely with profile water storage determined by the NMMs with a mean difference (MD) and root mean square error (RMSE) of 25.7 and 36.4 mm. However, site-specific corrections or calibrations for each sensor type are required for accurate soil water content estimations with this clay loam soil for irrigation management applications. Keywords: Corn, Irrigation management, Neutron moisture meter, Soil water content, Soil water sensors, Semi-arid region.
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Upadhyaya, Vikas, and Soharab Hossain Shaikh. "Traffic Monitoring Using Multiple Sensors." International Journal of Sensors, Wireless Communications and Control 8, no. 1 (August 10, 2018): 72–84. http://dx.doi.org/10.2174/2210327908666180307154048.
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Shekhar, Shashank, Oussama Khatib, and Makoto Shimojo. "Object Localization with Multiple Sensors." International Journal of Robotics Research 7, no. 6 (December 1988): 34–44. http://dx.doi.org/10.1177/027836498800700604.
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Wu, Wei, Marco Anisetti, Chehri Abdellah, and Gwanggil Jeon. "Image Enlargement Using Multiple Sensors." Journal of Sensors 2016 (2016): 1–3. http://dx.doi.org/10.1155/2016/1498796.
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Chakraborty, Anirban, and Cheng Luo. "Multiple conducting polymer microwire sensors." Microsystem Technologies 15, no. 10-11 (June 19, 2009): 1737–45. http://dx.doi.org/10.1007/s00542-009-0885-3.
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Jung, Minwoo, Dae-Young Kim, and Seokhoon Kim. "A System Architecture of a Fusion System for Multiple LiDARs Image Processing." Applied Sciences 12, no. 19 (September 20, 2022): 9421. http://dx.doi.org/10.3390/app12199421.
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LiDAR sensors are extensively used in autonomous vehicles and their optimal use is a critical concern. In this paper, we propose an embedded software architecture for multiple LiDAR sensors, i.e., a fusion system that acts as an embedded system for processing data from multiple LiDAR sensors. The fusion system software comprises multiple clients and a single server. The client and server are connected through inter-process communication. Multiple clients create processes to process the data from each LiDAR sensor via a multiprocessing method. Our approach involves a scheduling method for efficient multiprocessing. The server uses multithreading to optimize the internal functions. For internal communication within the fusion system, multiple clients and a single server are connected using the socket method. In sequential processing, the response time increases in proportion to the number of connected LiDAR sensors. By contrast, in the proposed software architecture, the response time decreases in inverse proportion to the number of LiDAR sensors. As LiDAR sensors become increasingly popular in the field of autonomous driving, the results of this study can be expected to make a substantial contribution to technology development in this domain.
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Zhu, Chu, Frank V. Bright, and Gary M. Hieftje. "Simultaneous Determination of Br− and I− with a Multiple Fiber-Optic Fluorescence Sensor." Applied Spectroscopy 44, no. 1 (January 1990): 59–63. http://dx.doi.org/10.1366/0003702904085813.
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A dual fiber-optic sensor for the simultaneous determination of bromide and iodide is successfully demonstrated. The device is based upon the dependence of quenching effects on individual fluorophores (quinine and harmane) and quenchers (bromide and iodide). From the point of view of practical application, n unknown analyte species should be able to be determined with a sensor array which contains n sensor elements on which n fluorophores are immobilized. The relative error of the new sensors for the determination of bromide and iodide is less than 5%. The sensor combination is reversible and exhibits an overall response time of 2 minutes. The interference of several ions on the sensors has been studied.
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Guo, Yanyan, Xiangqian Che, Xiangli Meng, and Li Bian. "A Globe Calibration Method for Optical Multisensor in 3D Complex Surface Measurement System." Journal of Sensors 2022 (May 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/8989768.
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There are few existing omnipotent sensors that handle a complex surface inspection task in an accurate and effective way. The prevailing solution is integrating multiple sensors and taking advantage of their strengths. One key task is the extrinsic parameter calibration (global calibration) of the multiple sensors before measurement. This paper proposes a method of optimal extrinsic calibration for a structured light sensor (SLS) and conoscopic holography sensor (CHS). In adopting this method, a common planar calibration board is placed with different poses in front of the multisensory system, and the extrinsic calibration problem is solved through a three-dimensional reconstruction of the calibration board and using geometric constraints of the views from the SLS and CHS. This calibration method, which uses only the plane calibration board, is simple. Physical experiments demonstrate that the proposed method is robust and accurate in the calibration of multiple inhomogeneous optical sensors for the measurement of a complex surface.
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Zhang, Zhaoxi, Prince Michael Amegbor, and Clive Eric Sabel. "Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring." Sensors 21, no. 22 (November 19, 2021): 7693. http://dx.doi.org/10.3390/s21227693.
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The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.
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Lima, Jeovano de Jesus Alves de, Leonardo Felipe Maldaner, and José Paulo Molin. "Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester." Sensors 21, no. 13 (July 1, 2021): 4530. http://dx.doi.org/10.3390/s21134530.
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Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s−1, representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows.
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Jeon, Heegyun, Sungmin Aum, Hyungbo Shim, and Yongsoon Eun. "Resilient State Estimation for Control Systems Using Multiple Observers and Median Operation." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3750264.
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This paper addresses the problem of state estimation for linear dynamic systems that is resilient against malicious attacks on sensors. By “resiliency” we mean the capability of correctly estimating the state despite external attacks. We propose a state estimation with a bank of observers combined through median operations and show that the proposed method is resilient in the sense that estimated states asymptotically converge to the true state despite attacks on sensors. In addition, the effect of sensor noise and process disturbance is also considered. For bounded sensor noise and process disturbance, the proposed method eliminates the effect of attack and achieves state estimation error within a bound proportional to those of sensor noise and disturbance. While existing methods are computationally heavy because online solution of nonconvex optimization is needed, the proposed approach is computationally efficient by using median operation in the place of the optimization. It should be pointed out that the proposed method requires the system states being observable with every sensor, which is not a necessary condition for the existing methods. From resilient system design point of view, however, this fact may not be critical because sensors can be chosen for resiliency in the design stage. The gained computational efficiency helps real-time implementation in practice.
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Tsukada, K., Y. Miyahara, Y. Shibata, and H. Miyagi. "An integrated chemical sensor with multiple ion and gas sensors." Sensors and Actuators B: Chemical 2, no. 4 (October 1990): 291–95. http://dx.doi.org/10.1016/0925-4005(90)80156-t.
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Sebastian, Abu, and Angeliki Pantazi. "Nanopositioning with multiple sensors: MISO control and inherent sensor fusion." IFAC Proceedings Volumes 44, no. 1 (January 2011): 2012–17. http://dx.doi.org/10.3182/20110828-6-it-1002.03044.
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