Journal articles on the topic 'Limited sensing range'
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Yu, Hongjun, Peng Shi, and Cheng-Chew Lim. "Scalable formation control in stealth with limited sensing range." International Journal of Robust and Nonlinear Control 27, no. 3 (June 13, 2016): 410–33. http://dx.doi.org/10.1002/rnc.3579.
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Qi, Yifei, Peng Cheng, Jing Bai, Jiming Chen, Adrien Guenard, Ye-Qiong Song, and Zhiguo Shi. "Energy-Efficient Target Tracking by Mobile Sensors With Limited Sensing Range." IEEE Transactions on Industrial Electronics 63, no. 11 (November 2016): 6949–61. http://dx.doi.org/10.1109/tie.2016.2584000.
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Rutzinger, M., B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, R. Sailer, M. Scaioni, J. Stötter, and D. Wujanz. "CLOSE-RANGE SENSING TECHNIQUES IN ALPINE TERRAIN." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-6 (June 6, 2016): 15–22. http://dx.doi.org/10.5194/isprsannals-iii-6-15-2016.
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Early career researchers such as PhD students are a main driving force of scientific research and are for a large part responsible for research innovation. They work on specialized topics within focused research groups that have a limited number of members, but might also have limited capacity in terms of lab equipment. This poses a serious challenge for educating such students as it is difficult to group a sufficient number of them to enable efficient knowledge transfer. To overcome this problem, the Innsbruck Summer School of Alpine Research 2015 on close-range sensing techniques in Alpine terrain was organized in Obergurgl, Austria, by an international team from several universities and research centres. Of the applicants a group of 40 early career researchers were selected with interest in about ten types of specialized surveying tools, i.e. laser scanners, a remotely piloted aircraft system, a thermal camera, a backpack mobile mapping system and different grade photogrammetric equipment. During the one-week summer school, students were grouped according to their personal preference to work with one such type of equipment under guidance of an expert lecturer. All students were required to capture and process field data on a mountain-related theme like landslides or rock glaciers. The work on the assignments lasted the whole week but was interspersed with lectures on selected topics by invited experts. The final task of the summer school participants was to present and defend their results to their peers, lecturers and other colleagues in a symposium-like setting. Here we present the framework and content of this summer school which brought together scientists from close-range sensing and environmental and geosciences.
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Rutzinger, M., B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, R. Sailer, M. Scaioni, J. Stötter, and D. Wujanz. "CLOSE-RANGE SENSING TECHNIQUES IN ALPINE TERRAIN." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-6 (June 6, 2016): 15–22. http://dx.doi.org/10.5194/isprs-annals-iii-6-15-2016.
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Early career researchers such as PhD students are a main driving force of scientific research and are for a large part responsible for research innovation. They work on specialized topics within focused research groups that have a limited number of members, but might also have limited capacity in terms of lab equipment. This poses a serious challenge for educating such students as it is difficult to group a sufficient number of them to enable efficient knowledge transfer. To overcome this problem, the Innsbruck Summer School of Alpine Research 2015 on close-range sensing techniques in Alpine terrain was organized in Obergurgl, Austria, by an international team from several universities and research centres. Of the applicants a group of 40 early career researchers were selected with interest in about ten types of specialized surveying tools, i.e. laser scanners, a remotely piloted aircraft system, a thermal camera, a backpack mobile mapping system and different grade photogrammetric equipment. During the one-week summer school, students were grouped according to their personal preference to work with one such type of equipment under guidance of an expert lecturer. All students were required to capture and process field data on a mountain-related theme like landslides or rock glaciers. The work on the assignments lasted the whole week but was interspersed with lectures on selected topics by invited experts. The final task of the summer school participants was to present and defend their results to their peers, lecturers and other colleagues in a symposium-like setting. Here we present the framework and content of this summer school which brought together scientists from close-range sensing and environmental and geosciences.
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Kurita, Eisuke, Yuichi Kobayashi, and Manabu Gouko. "Motion Generation by Integration of Multiple Observation Spaces for Robots with Limited Range of Observation." Advanced Materials Research 403-408 (November 2011): 4681–88. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4681.
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Sensors of robots that act in unstructured environment sometimes do not provide complete observation, due to occlusion or limitation of sensing range. This paper presents a motion generation method for robot with multiple sensors with limited sensing ranges. The proposed method introduce extension of the action- observation mapping to outside of the sensing range of a sensor, based on the diffusion-based learning of Jacobian matrices between control input and observation variable. Multiple observation spaces can be integrated by finding correspondence between the virtual observation spaces. When a target observation variable is given to the robot, it can generate a motion from an observation space to- ward the target with another observation space using the extended observation space. The proposed framework is verified by two robot tasks, reaching motion toward the floor with a manipulator and navigation of mobile robot around the wall. In both cases, observation space by camera with limited view was extended and appropriate motion trajectories were obtained.
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Xie, Binbin, Yuqing Yin, and Jie Xiong. "Pushing the Limits of Long Range Wireless Sensing with LoRa." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–21. http://dx.doi.org/10.1145/3478080.
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Wireless sensing is an exciting new research area which enables a large variety of applications ranging from coarse-grained daily activity recognition to fine-grained vital sign monitoring. While promising in many aspects, one critical issue is the limited sensing range because weak reflection signals are used for sensing. Recently, LoRa signals are exploited for wireless sensing, moving a big step towards long-range sensing. Although promising, there is still a huge room for improvement. In this work, we qualitatively characterize the relationship between target movements and target-induced signal variations, and propose signal processing methods to enlarge the induced signal variation to achieve a longer sensing range. Experiment results show that the proposed system (1) pushes the contact-free sensing range of human walking from the state-of-the-art 50 m to 120 m; (2) achieves a sensing range of 75 m for fine-grained respiration sensing; and (3) demonstrates human respiration sensing even through seven concrete walls.
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Zeng, Youwei, Jinyi Liu, Jie Xiong, Zhaopeng Liu, Dan Wu, and Daqing Zhang. "Exploring Multiple Antennas for Long-range WiFi Sensing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (December 27, 2021): 1–30. http://dx.doi.org/10.1145/3494979.
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Despite extensive research effort on contactless WiFi sensing over the past few years, there are still significant barriers hindering its wide application. One key issue is the limited sensing range due to the intrinsic nature of employing the weak target-reflected signal for sensing and therefore the sensing range is much smaller than the communication range. In this work, we address this challenging issue, moving WiFi sensing one step closer to real-world adoption. The key idea is to effectively utilize the multiple antennas widely available on commodity WiFi access points to simultaneously strengthen the target-reflected signal and reduce the noise. Although traditional beamforming schemes can help increase the signal strength, they are designed for communication and can not be directly applied to benefit sensing. To effectively increase the WiFi sensing range using multiple antennas, we first propose a new metric that quantifies the signal sensing capability. We then propose novel signal processing methods, which lay the theoretical foundation to support beamforming-based long-range WiFi sensing. To validate the proposed idea, we develop two sensing applications: fine-grained human respiration monitoring and coarse-grained human walking tracking. Extensive experiments show that: (i) the human respiration sensing range is significantly increased from the state-of-the-art 6-8 m to 11 m;1 and (ii) human walking can be accurately tracked even when the target is 18 m away from the WiFi transceivers, outperforming the sensing range of the state-of-the-art by 50%.
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Li, Tiancheng, Victor Elvira, Hongqi Fan, and Juan M. Corchado. "Local-Diffusion-Based Distributed SMC-PHD Filtering Using Sensors With Limited Sensing Range." IEEE Sensors Journal 19, no. 4 (February 15, 2019): 1580–89. http://dx.doi.org/10.1109/jsen.2018.2882084.
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Ilic, Nemanja, Milos S. Stankovic, and Srdjan S. Stankovic. "Adaptive Consensus-Based Distributed Target Tracking in Sensor Networks With Limited Sensing Range." IEEE Transactions on Control Systems Technology 22, no. 2 (March 2014): 778–85. http://dx.doi.org/10.1109/tcst.2013.2256787.
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Mišić, Vojislav B., and Jelena Mišić. "Improving Sensing Accuracy in Cognitive PANs through Modulation of Sensing Probability." Mobile Information Systems 5, no. 2 (2009): 177–93. http://dx.doi.org/10.1155/2009/635947.
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Cognitive radio technology necessitates accurate and timely sensing of primary users' activity on the chosen set of channels. The simplest selection procedure is a simple random choice of channels to be sensed, but the impact of sensing errors with respect to primary user activity or inactivity differs considerably. In order to improve sensing accuracy and increase the likelihood of finding channels which are free from primary user activity, the selection procedure is modified by assigning different sensing probabilities to active and inactive channels. The paper presents a probabilistic analysis of this policy and investigates the range of values in which the modulation of sensing probability is capable of maintaining an accurate view of the status of the working channel set. We also present a modification of the probability modulation algorithm that allows for even greater reduction of sensing error in a limited range of the duty cycle of primary users' activity. Finally, we give some guidelines as to the optimum application ranges for the original and modified algorithm, respectively.
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He, Chenlong, Zuren Feng, and Zhigang Ren. "Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range." Sensors 18, no. 2 (February 3, 2018): 446. http://dx.doi.org/10.3390/s18020446.
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Shao, Teng. "Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range." International Journal of Distributed Sensor Networks 18, no. 7 (July 2022): 155013292211108. http://dx.doi.org/10.1177/15501329221110810.
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One of the fundamental problems in sensor networks is to estimate and track the target states of interest that evolve in the sensing field. Distributed filtering is an effective tool to deal with state estimation in which each sensor only communicates information with its neighbors in sensor networks without the requirement of a fusion center. However, in the majority of the existing distributed filters, it is assumed that typically all sensors possess unlimited field of view to observe the target states. This is quite restrictive since practical sensors have limited sensing range. In this article, we consider distributed filtering based on linear minimum mean square error criterion in sensor networks with limited sensing range. To achieve the optimal filter and consensus, two types of strategies based on linear minimum mean square error criterion are proposed, that is, linear minimum mean square error filter based on measurement and linear minimum mean square error filter based on estimate, according to the difference of the neighbor sensor information received by the sensor. In linear minimum mean square error filter based on measurement, the sensor node collects measurement from its neighbors, whereas in linear minimum mean square error filter based on estimate, the sensor node collects estimate from its neighbors. The stability and computational complexity of linear minimum mean square error filter are analyzed. Numerical experimental results further verify the effectiveness of the proposed methods.
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Dezhen Song, Chang-Young Kim, and Jingang Yi. "On the Time to Search for an Intermittent Signal Source Under a Limited Sensing Range." IEEE Transactions on Robotics 27, no. 2 (April 2011): 313–23. http://dx.doi.org/10.1109/tro.2010.2099391.
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Bai, Xia, Hejing Guo, Juan Zhao, and Tao Shan. "Compressed Sensing-Based Range-Doppler Processing Method for Passive Radar." Wireless Communications and Mobile Computing 2021 (July 22, 2021): 1–15. http://dx.doi.org/10.1155/2021/5570498.
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Passive radar (PR) systems use the existing transmitters of opportunity in the environment to perform tasks such as detection, tracking, and imaging. The classical cross-correlation based methods to obtain the range-Doppler map have the problems of high sidelobe and limited resolution due to the influence of signal bandwidth. In this paper, we propose a novel range-Doppler processing method based on compressed sensing (CS), which performs sparse reconstruction in range and Doppler dimensions to achieve high resolution and reduces sidelobe without excessive computational burden. Results from numerical simulations and experimental measurements recorded with the Chinese standard digital television terrestrial broadcasting (DTTB) based PR show that the proposed method successfully handles the range-Doppler map formatting problem for PR and outperforms the existing CS-based PR processing methods.
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Li, Dong, Jialin Liu, Sunghoon Ivan Lee, and Jie Xiong. "LASense." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 1 (March 29, 2022): 1–27. http://dx.doi.org/10.1145/3517253.
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Acoustic signals have been widely adopted in sensing fine-grained human activities, including respiration monitoring, finger tracking, eye blink detection, etc. One major challenge for acoustic sensing is the extremely limited sensing range, which becomes even more severe when sensing fine-grained activities. Different from the prior efforts that adopt multiple microphones and/or advanced deep learning techniques for long sensing range, we propose a system called LASense, which can significantly increase the sensing range for fine-grained human activities using a single pair of speaker and microphone. To achieve this, LASense introduces a virtual transceiver idea that purely leverages delicate signal processing techniques in software. To demonstrate the effectiveness of LASense, we apply the proposed approach to three fine-grained human activities, i.e., respiration, finger tapping and eye blink. For respiration monitoring, we significantly increase the sensing range from the state-of-the-art 2 m to 6 m. For finer-grained finger tapping and eye blink detection, we increase the state-of-the-art sensing range by 150% and 80%, respectively.
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Wang, Jing, and Christopher I. Smith. "A Distributed Computing Algorithm for Deployment of Mobile Robotic Agents with Limited Sensing Ranges." International Journal of Handheld Computing Research 6, no. 3 (July 2015): 46–60. http://dx.doi.org/10.4018/ijhcr.2015070104.
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In this paper, the authors present a distributed computing algorithm for an application of deploying a group of mobile robotic agents in a convex region. Each agent can be treated as an embedded system with its own sensors and actuators. Under the assumption that agents have limited sensing capabilities, the distributed computing algorithm is designed to make agents cover the convex region so that each member's individual region, the space around them, is optimized. Each agent must accommodate its kinematic constraints and can only exchange information locally based on the range of the sensors equipped. The proposed algorithm utilizes Voronoi partitions to create individual subregions and directs each member toward the centroid of their subregion. The Voronoi partitions are updated with each iteration of the algorithm. Simulation results are provided to validate the proposed algorithm.
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Deval, Sapna, Luke Ritchie, Martin Reisslein, and Andrea W. Richa. "Evaluation of Physical Carrier Sense Based Backbone Maintenance in Mobile Ad Hoc Networks." International Journal of Vehicular Technology 2009 (July 5, 2009): 1–13. http://dx.doi.org/10.1155/2009/958056.
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Physical carrier sensing has to date mainly been exploited for improving medium access control in wireless networks. Recently, a parallel algorithm striving to extensively exploit physical carrier sensing for constructing and maintaining a connected dominating set (CDS), which is also known as spanner, backbone, or overlay network in wireless ad hoc networks with interference ranges larger than transmission ranges has been proposed. Existing evaluations of this algorithm are limited to theoretical asymptotic bounds and simulations of static networks. In this paper, we evaluate the physical carrier sensing-based CDS maintenance for mobile ad hoc networks through discrete event simulations. For a wide range of node speeds and node densities, we evaluate the CDS characteristics and message exchanges required for maintaining the CDS. We find that the algorithm maintains a stable leader set dominating all nodes in the network for a wide range of mobility levels but struggles to maintain connectivity at high mobility levels. We also quantify the portions of the control messages for CDS maintenance that are exchanged through physical carrier sensing. We find that the parallel algorithm manages to greatly reduce the reliance on intact message receptions.
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Zhao, Zheng, Deliang Zeng, Yong Hu, and Shan Gao. "Soft sensing of coal quality." Thermal Science 19, no. 1 (2015): 231–42. http://dx.doi.org/10.2298/tsci131207024z.
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This study presents a soft sensing model of coal quality for utility boilers. This model is based on the coal quality information obtained from exhaust gas. The mechanism modeling method combined with data driving theory is used in the modeling process. The procedure for solving the nonlinear equations for coal quality applies the inner loop iteration of dry ash-free basis of S (Sdaf) and outside loop iteration of dry ash-free basis of N (Ndaf) within a limited range, and dry ash-free basis of C (Cdaf) is searched from the entire range during outside loop iteration. The upper and lower limits of Ndaf are defined according to the NOX content in the exhaust gas, thereby solving the iterative initial value selection problem. Finally, the effectiveness of the proposed method is verified via several simulations and comparisons, the results show that this method is credible and effective and it can be used in power plant for control system optimization.
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Hashimoto, Masafumi, Yosuke Matsui, and Kazuhiko Takahashi. "Moving-Object Tracking with In-Vehicle Multi-Laser Range Sensors." Journal of Robotics and Mechatronics 20, no. 3 (June 20, 2008): 367–77. http://dx.doi.org/10.20965/jrm.2008.p0367.
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This paper presents a method for moving-object tracking with in-vehicle 2D laser range sensor (LRS) in a cluttered environment. A sensing area of one LRS is limited in orientation, and hence the mobile robot is equipped with multi-LRSs for omnidirectional sensing. Since each LRS takes the laser image on its own local coordinate frame, the laser image is mapped onto a reference coordinate frame so that the object tracking can be achieved by cooperation of multi-LRSs. For mapping the coordinate frames of multi-LRSs are calibrated, that is, the relative positions and orientations of the multi-LRSs are estimated. The calibration is based on Kalman filter and chi-hypothesis testing. Moving-object tracking is achieved by two steps: detection and tracking. Each LRS finds moving objects from its own laser image via a heuristic rule and an occupancy grid based method. It tracks the moving objects via Kalman filter and the assignment algorithm based data association. When the moving objects exist in the overlapped sensing areas of the LRSs, these LRSs exchange the tracking data and fuse them in a decentralized manner. A rule based track management is embedded into the tracking system in order to enhance the tracking performance. The experimental result of three walking-people tracking in an indoor environment validates the proposed method.
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Feng, Kunpeng, Jiwen Cui, Yihua Jin, Xun Sun, Dong Jiang, Hong Dang, Yizhao Niu, and Jiubin Tan. "Enhancement of the Performance and Data Processing Rate of an Optical Frequency Domain Reflectometer Distributed Sensing System Using A Limited Swept Wavelength Range." Sensors 18, no. 10 (October 16, 2018): 3480. http://dx.doi.org/10.3390/s18103480.
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A novel optical frequency domain reflectometer (OFDR) processing algorithm is proposed to enhance the measurable range and data processing rate using a narrow swept spectrum range and reducing the time consuming of the process distributed sensing results. To reduce the swept wavelength range and simultaneously enhance strain measurable range, the local similarity characteristics of Rayleigh scattering fingerprint spectrum is discovered and a new similarity evaluation function based on least-square method is built to improve the data processing rate and sensing performance. By this method, the strain measurable range is raised to 3000 µε under a highest spatial resolution of 3 mm when the swept spectrum range is only 10 nm and the data processing rate is improved by at least 10 times. Experimental results indicate that a nonlinearity of less than 0.5%, a strain resolution of better than 10 µε, a repeatability at zero strain of below ±0.4 GHz and a full-scale accuracy is lower than 0.85 GHz under a highest spatial resolution of 3 mm can be achieved. Advantages of this method are fast processing rate, large strain measurable range, high SNR, and applicability with current OFDR systems.
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Yang, Haolin, Meilin Ji, Xueying Xiu, Haochen Lv, Alex Gu, and Songsong Zhang. "AlScN Film Based Piezoelectric Micromechanical Ultrasonic Transducer for an Extended Long-Range Detection." Micromachines 13, no. 11 (November 10, 2022): 1942. http://dx.doi.org/10.3390/mi13111942.
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Piezoelectric micromachined ultrasonic transducers (PMUTs) have been widely applied in distance sensing. However, the sensing distance of currently reported miniaturized ultrasonic sensors (e.g., PMUTs or CMUT) is still limited up to a certain range (e.g., ≤5 m) compared to conventional bulk ultrasonic devices. This paper reports a PMUT array design using scandium-doped aluminum nitride (AlScN) as its piezoelectric layer for an extended long-range detection purpose. To minimize air attenuation, our device is resonating at 66 kHz for a high receive sensitivity of 5.7 mV/Pa. The proposed PMUT array can generate a sound pressure level (SPL) as high as 120 dB at a distance of 10 cm without beam forming. This PMUT design is catered for a pin-to-pin replacement of the current commercial bulk ultrasonic ranging sensor and works directly with the conventional range finding system (e.g., TI PGA460). In comparison with the common bulk transducer, the size of our device is 80% smaller. With the identical ranging detection setup, the proposed PMUT array improves the system SNR by more than 5 dB even at a distance as far as 6.8 m. The result of extended sensing distance validates our miniaturized PMUT array as the optimized candidate for most ultrasonic ranging applications. With the progressive development of piezoelectric MEMS, we believe that the PMUT technology could be a game changer in future long-range sensing applications.
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Narla, Avaneesh V., David Bruce Borenstein, and Ned S. Wingreen. "A biophysical limit for quorum sensing in biofilms." Proceedings of the National Academy of Sciences 118, no. 21 (May 18, 2021): e2022818118. http://dx.doi.org/10.1073/pnas.2022818118.
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Bacteria grow on surfaces in complex immobile communities known as biofilms, which are composed of cells embedded in an extracellular matrix. Within biofilms, bacteria often interact with members of their own species and cooperate or compete with members of other species via quorum sensing (QS). QS is a process by which microbes produce, secrete, and subsequently detect small molecules called autoinducers (AIs) to assess their local population density. We explore the competitive advantage of QS through agent-based simulations of a spatial model in which colony expansion via extracellular matrix production provides greater access to a limiting diffusible nutrient. We note a significant difference in results based on whether AI production is constitutive or limited by nutrient availability: If AI production is constitutive, simple QS-based matrix-production strategies can be far superior to any fixed strategy. However, if AI production is limited by nutrient availability, QS-based strategies fail to provide a significant advantage over fixed strategies. To explain this dichotomy, we derive a biophysical limit for the dynamic range of nutrient-limited AI concentrations in biofilms. This range is remarkably small (less than 10-fold) for the realistic case in which a growth-limiting diffusible nutrient is taken up within a narrow active growth layer. This biophysical limit implies that for QS to be most effective in biofilms AI production should be a protected function not directly tied to metabolism.
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Liu, Jianli, Xiaohan Liao, Huping Ye, Huanyin Yue, Yong Wang, Xiang Tan, and Dongliang Wang. "UAV Swarm Scheduling Method for Remote Sensing Observations during Emergency Scenarios." Remote Sensing 14, no. 6 (March 15, 2022): 1406. http://dx.doi.org/10.3390/rs14061406.
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Recently, unmanned aerial vehicle (UAV) remote sensing has been widely used in emergency scenarios; the operating mode has transitioned from one UAV to multiple UAVs. However, the current multiple-UAV remote sensing mode is characterized by high labor costs and limited operational capabilities; meanwhile, there is no suitable UAV swarm scheduling method that can be applied to remote sensing in emergency scenarios. To solve these problems, this study proposes a UAV swarm scheduling method. Firstly, the tasks were formulated and decomposed according to the data requirements and the maximum flight range of a UAV; then, the task sets were decomposed according to the maximum flight range of the UAV swarm to form task subsets; finally, aiming at the shortest total flight range of the task subsets and to balance the flight ranges of each UAV, taking the complete execution of the tasks as the constraint, the task allocation model was constructed, and the model was solved via a particle swarm optimization algorithm to obtain the UAV swarm scheduling scheme. Compared with the direct allocation method and the manual scheduling methods, the results show that the proposed method has high usability and efficiency.
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Liaw, Shien-Kuei, Chi-Wen Liao, Meng-Hsuan Tsai, Dong-Chang Li, Shu-Ming Yang, Zhu-Yong Xia, Chien-Hung Yeh, and Wen-Fung Liu. "Hybrid Fiber-Optic Sensing Integrating Brillouin Optical Time-Domain Analysis and Fiber Bragg Grating for Long-Range Two-Parameter Measurement." Sensors 21, no. 12 (June 20, 2021): 4224. http://dx.doi.org/10.3390/s21124224.
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Distributed fiber sensing (DFS) can provide real-time signals and warnings. The entire length of fiber optic cable can act as a sensing element, but the accuracy is sometimes limited. On the other hand, point-to-point fiber sensing (PPFS) is usually implemented using one or more fiber Bragg gratings (FBGs) at specific positions along with the fiber for the monitoring of specific parameters (temperature, strain, pressure, and so on). However, the cost becomes expensive when the number of FBGs increases. A hybrid fiber sensing scheme is thus proposed, combining the advantages of DFS and PPFS. It is based on a Brillouin optical time-domain analysis (BOTDA) fiber system with additional FBGs embedded at certain positions where it is necessary to detect specific parameters. The hybrid fiber sensing system has the advantages of full sensing coverage at essential locations that need to be carefully monitored. In our work, the test results showed that the proposed system could achieve a sensing distance of 16 km with the single-mode fiber with a 2 m spatial resolution. For FBG parameter measurements, the temperature variation was 52 °C, from 25 °C to 77 °C, with a temperature sensitivity of 23 pm/°C, and the strain was from 0 to 400 µε, with a strain sensitivity of 0.975 pm/µε, respectively, using two FBGs.
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Li, Yang, Dan Wu, Jie Zhang, Xuhai Xu, Yaxiong Xie, Tao Gu, and Daqing Zhang. "DiverSense." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (July 4, 2022): 1–28. http://dx.doi.org/10.1145/3536393.
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The ubiquity of Wi-Fi infrastructure has facilitated the development of a range of Wi-Fi based sensing applications. Wi-Fi sensing relies on weak signal reflections from the human target and thus only supports a limited sensing range, which significantly hinders the real-world deployment of the proposed sensing systems. To extend the sensing range, traditional algorithms focus on suppressing the noise introduced by the imperfect Wi-Fi hardware. This paper picks a different direction and proposes to enhance the quality of the sensing signal by fully exploiting the signal diversity provided by the Wi-Fi hardware. We propose DiverSense, a system that combines sensing signal received from all subcarriers and all antennas in the array, to fully utilize the spatial and frequency diversity. To guarantee the diversity gain after signal combining, we also propose a time-diversity based signal alignment algorithm to align the phase of the multiple received sensing signals. We implement the proposed methods in a respiration monitoring system using commodity Wi-Fi devices and evaluate the performance in diverse environments. Extensive experimental results demonstrate that DiverSense is able to accurately monitor the human respiration even when the sensing signal is under noise floor, and therefore boosts sensing range to 40 meters, which is a 3x improvement over the current state-of-the-art. DiverSense also works robustly under NLoS scenarios, e.g., DiverSense is able to accurately monitor respiration even when the human and the Wi-Fi transceivers are separated by two concrete walls with wooden doors.
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Li, Changcheng, Deyun Chen, Chengjun Xie, and You Tang. "Algorithm for wireless sensor networks in ginseng field in precision agriculture." PLOS ONE 17, no. 2 (February 7, 2022): e0263401. http://dx.doi.org/10.1371/journal.pone.0263401.
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In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng.
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Endo, Takahiro, Ryuma Maeda, and Fumitoshi Matsuno. "Stability Analysis of Swarm Heterogeneous Robots with Limited Field of View." Informatics and Automation 19, no. 5 (October 13, 2020): 942–66. http://dx.doi.org/10.15622/ia.2020.19.5.2.
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This paper presents a stability analysis of swarm robots, a group of multiple robots. In particular, we focus on robot swarms with heterogeneous abilities, in which each robot has a different sensing range and physical limitations, including maximum velocity and acceleration. In addition, each robot has a unique sensing region with a limited angle field of view. We previously proposed a decentralized navigation method for such heterogeneous swarm robots consisting of one leader and multiple followers. With the decentralized navigation method, a single leader can navigate for followers while maintaining connectivity and satisfying the physical limitations unique to each robot; i.e., each follower has a target robot and follows it without violating its physical limitations. In this paper, we focus on a stability analysis of such swarm robots. When the leader moves at a constant velocity, we mathematically prove that the shape and orientations of all robots eventually converge to the equilibrium state. For this, we must first prove that the equilibrium state exists. Then, we show the convergence of the state to its equilibrium. Finally, we carry out experiments and numerical simulations to confirm the stability analysis, i.e., the convergence of the swarm robots to the equilibrium states.
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Wang, Kuiwu, Qin Zhang, and Xiaolong Hu. "Improved Distributed Multisensor Fusion Method Based on Generalized Covariance Intersection." Journal of Sensors 2022 (October 28, 2022): 1–22. http://dx.doi.org/10.1155/2022/6348938.
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In response to the multitarget tracking problem of distributed sensors with a limited detection range, a distributed sensor measurement complementary Gaussian component correlation GCI fusion tracking method is proposed on the basis of the probabilistic hypothesis density filtering tracking theory. First, the sensor sensing range is extended by complementing the measurements. In this case, the multitarget density product is used to classify whether the measurements belong to the intersection region of the detection range. The local intersection region is complemented only once to reduce the computational cost. Secondly, each sensor runs a probabilistic hypothesis density filter separately and floods the filtering posterior with the neighboring sensors so that each sensor obtains the posterior information of the neighboring sensors. Subsequently, Gaussian components are correlated by distance division, and Gaussian components corresponding to the same target are correlated into the same subset. GCI fusion is performed on each correlated subset to complete the fusion state estimation. Simulation experiments show that the proposed method can effectively perform multitarget tracking in a distributed sensor network with a limited sensing range.
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Wang, Jian, Jing-Yang Liu, and Hong Yi. "Formation Control of Unmanned Surface Vehicles with Sensing Constraints Using Exponential Remapping Method." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/7619086.
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This paper presents a formation control strategy for unmanned surface vehicles (USVs) with sensing constraints moving in a leader-follower formation. Each USV is assumed to be equipped with a vision-based sensor, which is able to get the line-of-sight (LOS) range and bearing information. Most existing literature assumes that the USVs in formation control are with no sensing limitations or with 360-degree sensing fields; however, in our research, the vision-based sensor’s capability is restricted due to limited Field of View (FOV) and visual range. We consider that each USV in formation problem is equipped with a sector-like sensing field sensor for the leader-follower formation in two-dimensional space. The formation controller is developed by employing backstepping control technique and exponential remapping. The backstepping controller is designed to stabilize the triangular formation of three USVs, and the proposed exponential remapping method is to deal with the sector-like sensing constraint problem. Comparative analysis with three exponential remapping methods using numerical simulations is given to demonstrate the effectiveness of the proposed method.
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Jurjević, Luka, Mateo Gašparović, Xinlian Liang, and Ivan Balenović. "Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest." Remote Sensing 13, no. 11 (May 24, 2021): 2063. http://dx.doi.org/10.3390/rs13112063.
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Digital terrain models (DTMs) are important for a variety of applications in geosciences as a valuable information source in forest management planning, forest inventory, hydrology, etc. Despite their value, a DTM in a forest area is typically lower quality due to inaccessibility and limited data sources that can be used in the forest environment. In this paper, we assessed the accuracy of close-range remote sensing techniques for DTM data collection. In total, four data sources were examined, i.e., handheld personal laser scanning (PLShh, GeoSLAM Horizon), terrestrial laser scanning (TLS, FARO S70), unmanned aerial vehicle (UAV) photogrammetry (UAVimage), and UAV laser scanning (ULS, LS Nano M8). Data were collected within six sample plots located in a lowland pedunculate oak forest. The reference data were of the highest quality available, i.e., total station measurements. After normality and outliers testing, both robust and non-robust statistics were calculated for all close-range remote sensing data sources. The results indicate that close-range remote sensing techniques are capable of achieving higher accuracy (root mean square error < 15 cm; normalized median absolute deviation < 10 cm) than airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data that are generally understood to be the best data sources for DTM on a large scale.
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Wang, Xuanzhi, Kai Niu, Jie Xiong, Bochong Qian, Zhiyun Yao, Tairong Lou, and Daqing Zhang. "Placement Matters." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 1 (March 29, 2022): 1–25. http://dx.doi.org/10.1145/3517237.
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WiFi-based contactless sensing has found numerous applications in the fields of smart home and health care owning to its low-cost, non-intrusive and privacy-preserving characteristics. While promising in many aspects, the limited sensing range and interference issues still exist, hindering the adoption of WiFi sensing in real world. In this paper, inspired by the SNR (signal-to-noise ratio) metric in communication theory, we propose a new metric named SSNR (sensing-signal-to-noise-ratio) to quantify the sensing capability of WiFi systems. We theoretically model the effect of transmitter-receiver distance on sensing coverage. We show that in LoS scenario, the sensing coverage area increases first from a small oval to a maximal one and then decreases. When the transmitter-receiver distance further increases, the coverage area is separated into two ovals located around the two transceivers respectively. We demonstrate that, instead of applying complex signal processing scheme or advanced hardware, by just properly placing the transmitter and receiver, the two well-known issues in WiFi sensing (i.e., small range and severe interference) can be greatly mitigated. Specifically, by properly placing the transmitter and receiver, the coverage of human walking sensing can be expanded by around 200%. By increasing the transmitter-receiver distance, a target's fine-grained respiration can still be accurately sensed with one interferer sitting just 0.5 m away.
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Mishra, Richa, Vivekanand Jha, Rajeev K. Tripathi, and Ajay K. Sharma. "Corona based node distribution scheme targeting energy balancing in wireless sensor networks for the sensors having limited sensing range." Wireless Networks 26, no. 2 (September 12, 2018): 879–96. http://dx.doi.org/10.1007/s11276-018-1834-9.
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Wang, Jiaqi, Xuelei Fu, Hui Gao, Xin Gui, Honghai Wang, and Zhengying Li. "FPGA-Based Dynamic Wavelength Interrogation System for Thousands of Identical FBG Sensors." Photonics 9, no. 2 (January 29, 2022): 79. http://dx.doi.org/10.3390/photonics9020079.
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Under realistic scenarios, more fiber Bragg gratings (FBGs) are always expected to be multiplexed in one sensor array to share the expensive optical components and electrical devices. However, either the sensing number or the interrogation frequency is limited in previous works due to the huge amount of data generated from large-scale sensing arrays. This paper presents a field-programmable gate array (FPGA)-based dynamic wavelength interrogation system for thousands of identical FBGs. With the advantages of parallel controlling and pipeline processing, FPGA can accelerate the data-processing rate of the wavelength interrogation, realizing a continuous-running and real-time sensing system. The signal-processing system precisely synchronizes the generation of interrogation pulses, the acquisition of reflected signals, and the processing of the wavelength-related data, making the interrogation frequency fundamentally limited by the round-trip time of light pulses traveling in the fiber. Multiple sensing arrays can be independently carried out simultaneously, affecting hardly the interrogation frequency. Experimental results show that over 4000 FBGs with a 3-m spatial resolution in four channels are interrogated with a 150-Hz sensing frequency, 3-nm dynamic range, and ±5.9-pm sensing precision, greatly improving the interrogation frequency while ensuring the multiplexing number.
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Zadenoori, Mohammad Amin, and Enrico Vicario. "Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities." Sensors 22, no. 12 (June 14, 2022): 4491. http://dx.doi.org/10.3390/s22124491.
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The operation of a variety of natural or man-made systems subject to uncertainty is maintained within a range of safe behavior through run-time sensing of the system state and control actions selected according to some strategy. When the system is observed from an external perspective, the control strategy may not be known and it should rather be reconstructed by joint observation of the applied control actions and the corresponding evolution of the system state. This is largely hurdled by limitations in the sensing of the system state and different levels of noise. We address the problem of optimal selection of control actions for a stochastic system with unknown dynamics operating under a controller with unknown strategy, for which we can observe trajectories made of the sequence of control actions and noisy observations of the system state which are labeled by the exact value of some reward functions. To this end, we present an approach to train an Input–Output Hidden Markov Model (IO-HMM) as the generative stochastic model that describes the state dynamics of a POMDP by the application of a novel optimization objective adopted from the literate. The learning task is hurdled by two restrictions: the only available sensed data are the limited number of trajectories of applied actions, noisy observations of the system state, and system state; and, the high failure costs prevent interaction with the online environment, preventing exploratory testing. Traditionally, stochastic generative models have been used to learn the underlying system dynamics and select appropriate actions in the defined task. However, current state of the art techniques, in which the state dynamics of the POMDP is first learned and then strategies are optimized over it, frequently fail because the model that best fits the data may not be well suited for controlling. By using the aforementioned optimization objective, we try to to tackle the problems related to model mis-specification. The proposed methodology is illustrated in a scenario of failure avoidance for a multi component system. The quality of the decision making is evaluated by using the collected reward on the test data and compared against the previous literature usual approach.
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Liu, Jialin, Dong Li, Lei Wang, Fusang Zhang, and Jie Xiong. "Enabling Contact-free Acoustic Sensing under Device Motion." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 3 (September 6, 2022): 1–27. http://dx.doi.org/10.1145/3550329.
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Recent years have witnessed increasing attention from both academia and industry on contact-free acoustic sensing. Due to the pervasiveness of audio devices and fine granularity of acoustic sensing, it has been applied in numerous fields, including human-computer interaction and contact-free health sensing. Though promising, the limited working range hinders the wide adoption of acoustic sensing in real life. To break the sensing range limit, we propose to deploy the acoustic device on a moving platform (i.e., a robot) to support applications that require larger coverage and continuous sensing. In this paper, we propose SonicBot, a system that enables contact-free acoustic sensing under device motion. We propose a sequence of signal processing schemes to eliminate the impact of device motion and then obtain clean target movement information that is previously overwhelmed by device movement. We implement SonicBot using commercial audio devices and conduct extensive experiments to evaluate the performance of the proposed system. Experiment results show that our system can achieve a median error of 1.11 cm and 1.31 mm for coarse-grained and fine-grained tracking, respectively. To showcase the applicability of our proposed system in real-world settings, we perform two field studies, including coarse-grained gesture sensing and fine-grained respiration monitoring when the acoustic device moves along with a robot.
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Xu, Huatao, Pengfei Zhou, Rui Tan, Mo Li, and Guobin Shen. "LIMU-BERT." GetMobile: Mobile Computing and Communications 26, no. 3 (October 7, 2022): 39–42. http://dx.doi.org/10.1145/3568113.3568124.
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Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for a wide range of sensing applications. Most existing works require substantial amounts of wellcurated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. This article presents a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. With the representations learned via our model, task-specific models trained with limited labeled samples can achieve superior performances in typical IMU sensing applications, such as Human Activity Recognition (HAR).
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Fiorentino, Jonathan, and Antonio Scialdone. "The role of cell geometry and cell-cell communication in gradient sensing." PLOS Computational Biology 18, no. 3 (March 14, 2022): e1009552. http://dx.doi.org/10.1371/journal.pcbi.1009552.
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Cells can measure shallow gradients of external signals to initiate and accomplish a migration or a morphogenetic process. Recently, starting from mathematical models like the local-excitation global-inhibition (LEGI) model and with the support of empirical evidence, it has been proposed that cellular communication improves the measurement of an external gradient. However, the mathematical models that have been used have over-simplified geometries (e.g., they are uni-dimensional) or assumptions about cellular communication, which limit the possibility to analyze the gradient sensing ability of more complex cellular systems. Here, we generalize the existing models to study the effects on gradient sensing of cell number, geometry and of long- versus short-range cellular communication in 2D systems representing epithelial tissues. We find that increasing the cell number can be detrimental for gradient sensing when the communication is weak and limited to nearest neighbour cells, while it is beneficial when there is long-range communication. We also find that, with long-range communication, the gradient sensing ability improves for tissues with more disordered geometries; on the other hand, an ordered structure with mostly hexagonal cells is advantageous with nearest neighbour communication. Our results considerably extend the current models of gradient sensing by epithelial tissues, making a step further toward predicting the mechanism of communication and its putative mediator in many biological processes.
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Liu, Jiao, Guanlong Teng, and Feng Hong. "Human Activity Sensing with Wireless Signals: A Survey." Sensors 20, no. 4 (February 22, 2020): 1210. http://dx.doi.org/10.3390/s20041210.
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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.
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Chandrasekhar, K., Dr Hamsapriye, and V. K. Lakshmeesha. "Analysis of Pisarenko Harmonic Distortion (PHD) based subNyquist rate spectrum sensing for broadband Cognitive Radio." Defence Science Journal 67, no. 1 (December 23, 2016): 80. http://dx.doi.org/10.14429/dsj.67.9770.
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The essential part of Cognitive Radio (CR) is spectrum sensing, so that the underutilized spectrum could be detected to improve the spectrum efficiency. For this purpose, a wide range of frequency bands are considered and locations of multiple occupied spectrum subbands are focused. A major challenge related with such broadband spectrum sensing is that it is either extravagant or impracticable to perform Nyquist sampling on the broadband signal. In this study, a broadband spectrum sensing method, that takes advantage of subNyquist sampling wherein the sampling rate is considerably reduced. The correlation matrix of a limited number of noisy samples is computed and is used to estimate the frequency function of the PHD method to detect the occupied and unoccupied channels. The salient feature of this approach as compared to other methods is that, no prior knowledge of signal properties (which would lead to uncertainly problems) is necessary. Further, the efficiency of this method is assessed by calculating the detection probability of the occupied channel as a function of the limited number of samples and the signal to noise ratio of random input signals. The simulation results demonstrate a reliable detection, even with limited samples and a low SNR.
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Payacán, S., F. D. Alfaro, W. Pérez-Martínez, and I. Briceño-de-Urbaneja. "Potential distribution model of Leontochir ovallei using remote sensing data." Revista de Teledetección, no. 54 (December 23, 2019): 59. http://dx.doi.org/10.4995/raet.2019.12792.
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<p>Predicting the potential distribution of short-lived species with a narrow natural distribution range is a difficult task, especially when there is limited field data. The possible distribution of <em>L. ovallei</em> was modeled using the maximum entropy approach. This species has a very restricted distribution along the hyperarid coastal desert in northern Chile. Our results showed that local and regional environmental factors define its distribution. Changes in altitude and microhabitat related to the landforms are of critical importance at the local scale, whereas cloud cover variations associated with coastal fog was the principal factor determining the presence <em>of L. ovallei</em> at the regional level. This study verified the value of the maximum entropy in understanding the factors that influence the distribution of plant species with restricted distribution ranges.</p>
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Zuo, Yan, Yerun Gao, Shiyu Qin, Zhenye Wang, De Zhou, Zhen Li, Yu Yu, Ming Shao, and Xinliang Zhang. "Broadband multi-wavelength optical sensing based on photothermal effect of 2D MXene films." Nanophotonics 9, no. 1 (November 23, 2019): 123–31. http://dx.doi.org/10.1515/nanoph-2019-0338.
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AbstractTwo-dimensional (2D) materials were widely used in sensing owing to the tunable physical or chemical properties. For years, optical sensing attracted a massive amount of attention on account of high accuracy, high security, non-invasive measurement, and strong anti-interference ability. Among the various optical sensing schemes, multi-wavelength optical sensing (MWOS) is an important branch and widely adopted in optical image, spectroscopy, or bio/chemical research. However, no spectral selectivity, limited working wavelength range, or intrinsic instability makes conventional 2D materials unsuitable for MWOS. A new class of 2D materials, known as MXene, exhibits outstanding electronic, optical, and thermal properties, leading to new applications in optical sensing. In this paper, we propose an integrated photothermal optical sensor (PHOS) using Ti3C2Tx MXene films. Thanks to the inherent spectral dependence of Ti3C2Tx MXene over a broadband range, the proposed PHOS can respond to different wavelengths from visible to short-wavelength infrared. Because of the efficient photothermal conversion, the PHOS has a control efficiency up to 0.19 π · mW−1 · mm−1 under 980-nm laser pumping and shows a higher control efficiency under red light (690 nm) irradiation. The measured response time of the proposed PHOS is 23.4 μs. This paper brings MXene into chip-integrated optical sensing fields for the first time and shows the potential applications.
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Quinn, Alexander, Yil-Hwan You, and Michael J. McShane. "Hydrogel Microdomain Encapsulation of Stable Functionalized Silver Nanoparticles for SERS pH and Urea Sensing." Sensors 19, no. 16 (August 12, 2019): 3521. http://dx.doi.org/10.3390/s19163521.
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Conceptual and commercial examples of implantable sensors have been limited to a relatively small number of target analytes, with a strong focus on glucose monitoring. Recently, surface-enhanced Raman spectroscopy (SERS) pH sensors were demonstrated to track acid-producing enzymatic reactions targeting specific analytes. We show here that SERS pH tracking in the basic regime is also possible, and can be used to monitor urea concentration. To accomplish this, we developed a hydrogel consisting of polyelectrolyte multilayer microcapsules containing a SERS-sensitive pH reporter (4-mercapopyridine capped silver nanoparticles modified with bovine serum albumin). This pH sensing material exhibited a sensitive Raman scattering response to a wide range of pH from 6.5–9.7. By incorporating urease into the hydrogel matrix, the new sensor was capable of distinguishing urea concentrations of 0, 0.1, 1, and 10 mM. We also found that bovine serum albumin (BSA) prevented severe aggregation of the nanoparticle-based pH sensor, which improved sensing range and sensitivity. Furthermore, BSA safeguarded the pH sensor during the encapsulation procedure. Together, the combination of materials represents a novel approach to enabling optical sensing of reactions that generate pH changes in the basic range.
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Zhou, Haonan, Xiaoping Du, Lurui Xia, and Sen Li. "Self-Learning for Few-Shot Remote Sensing Image Captioning." Remote Sensing 14, no. 18 (September 15, 2022): 4606. http://dx.doi.org/10.3390/rs14184606.
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Large-scale caption-labeled remote sensing image samples are expensive to acquire, and the training samples available in practical application scenarios are generally limited. Therefore, remote sensing image caption generation tasks will inevitably fall into the dilemma of few-shot, resulting in poor qualities of the generated text descriptions. In this study, we propose a self-learning method named SFRC for few-shot remote sensing image captioning. Without relying on additional labeled samples and external knowledge, SFRC improves the performance in few-shot scenarios by ameliorating the way and efficiency of the method of learning on limited data. We first train an encoder for semantic feature extraction using a supplemental modified BYOL self-supervised learning method on a small number of unlabeled remote sensing samples, where the unlabeled remote sensing samples are derived from caption-labeled samples. When training the model for caption generation in a small number of caption-labeled remote sensing samples, the self-ensemble yields a parameter-averaging teacher model based on the integration of intermediate morphologies of the model over a certain training time horizon. The self-distillation uses the self-ensemble-obtained teacher model to generate pseudo labels to guide the student model in the next generation to achieve better performance. Additionally, when optimizing the model by parameter back-propagation, we design a baseline incorporating self-critical self-ensemble to reduce the variance during gradient computation and weaken the effect of overfitting. In a range of experiments only using limited caption-labeled samples, the performance evaluation metric scores of SFRC exceed those of recent methods. We conduct percentage sampling few-shot experiments to test the performance of the SFRC method in few-shot remote sensing image captioning with fewer samples. We also conduct ablation experiments on key designs in SFRC. The results of the ablation experiments prove that these self-learning designs we generated for captioning in sparse remote sensing sample scenarios are indeed fruitful, and each design contributes to the performance of the SFRC method.
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Tovar, Pedro, Yuan Wang, Liang Chen, and Xiaoyi Bao. "Distributed birefringence sensing at 10−9 accuracy over ultra-long PMF by optical frequency comb and distributed Brillouin amplifier." Optics Express 30, no. 18 (August 25, 2022): 33156. http://dx.doi.org/10.1364/oe.466212.
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Brillouin dynamic gratings (BDG) can measure the distributed birefringence of polarization-maintaining fibers (PMF), however, its sensing range is limited by both stimulated Brillouin scattering depletion and fiber losses in PMF, which are significantly higher than those in standard single-mode fibers. In this work, we theoretically and experimentally verify that BDG can be sustained over ultra-long distances when assisted by distributed Brillouin amplification, significantly extending the distributed birefringence measurement distance. Using an optical frequency comb pumped by a narrow linewidth laser to both generate and interrogate the amplified BDG, a birefringence measurement accuracy of 7.5 × 10−9 was achieved over 7 km sensing length, more than double the longest range reported. This opens a new opportunity to investigate small birefringence changes due to nonlinear optics effects and monitoring fiber network security from eavesdropping.
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Zhong, Tianfen, Nanchao Wang, Xue Shen, Da Xiao, Zhen Xiang, and Dong Liu. "Determination of Planetary Boundary Layer height with Lidar Signals Using Maximum Limited Height Initialization and Range Restriction (MLHI-RR)." Remote Sensing 12, no. 14 (July 15, 2020): 2272. http://dx.doi.org/10.3390/rs12142272.
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The planetary boundary layer height (PBLH) is a vital parameter to characterize the surface convection, which determines the diffusion of air pollutants. The accurate inversion of PBLH is extremely important for the study of aerosol concentrations, in order to predict air quality and provide weather forecast. Aerosol lidar, a powerful remote sensing instrument for detecting the characteristics of atmospheric temporal and spatial evolution, can continuously retrieve the planetary boundary layer (PBL) and obtain high resolution measurements. However, multi-layer conditions, including one or more layers of aerosol, or cloud above the PBL, can seriously interfere the accuracy of PBLH determined by lidar. A new technique of maximum limited height initialization and range restriction (MLHI-RR) is proposed to eliminate the impact of multi-layer conditions on PBLH determination. Four widely used methods for deriving PBLH are utilized, in addition to the MLHI-RR constraint. Comparisons demonstrate that the proposed technique can determine the PBLH in multi-layer conditions with higher accuracy. The proposed technique requires no affiliate information besides lidar signals, which provide a convenient method for PBLH determination under complicated conditions.
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Silva, Mariana M., Paulo E. Lopes, Yilong Li, Petra Pötschke, Fernando N. Ferreira, and Maria C. Paiva. "Polylactic Acid/Carbon Nanoparticle Composite Filaments for Sensing." Applied Sciences 11, no. 6 (March 15, 2021): 2580. http://dx.doi.org/10.3390/app11062580.
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Polylactic acid (PLA) is a bio-based, biodegradable polymer that presents high potential for biomedical and sensing applications. Ongoing works reported in the literature concern mainly applications based on 3D printing, while textile applications are hindered by the limited flexibility of PLA and its composite filaments. In the present work, PLA/multiwall carbon nanotube (MWCNT) composite filaments were produced with enhanced flexibility and electrical conductivity, which may be applied on a textile structure. A biodegradable plasticizer was incorporated in the nanocomposites, aiming at improving MWCNT dispersion and increasing the flexibility of the filaments. Filaments were produced with a range of compositions and their morphology was characterized as well as their thermal, thermomechanical, and electrical properties. Selected compositions were tested for sensing activity using saturated acetone vapor, demonstrating a suitable response and potential for the application in fabrics with sensing capacity.
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Perarasi, T., M. Leeban Moses, and C. Raju. "Energy optimization by green sensing methodology for channel design in a cognitive radio network." Journal of Applied Research and Technology 19, no. 2 (April 30, 2021): 153–62. http://dx.doi.org/10.22201/icat.24486736e.2021.19.2.1582.
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An inventive innovation for access of underutilized spectrum and unused range is cognitive radio (CR). Spectrum sensing is the capacity of a gadget to effectively detect the environment and to extricate data from utilizing its radio. A CR framework utilizing hypothesis testing channel banks and the enhancement of the range detecting limit for the predefined requirements on probabilities of missed discovery and of the false alert are proposed to optimize the energy in the system. The limit is additionally upgraded to limit the range detecting mistake subject to the predetermined limitations. The regions of convergence for different estimations of clients are found, and furthermore, the absolute error rate has been broken down for different number of clients going from 1 to 10 and has supported the upgraded estimation of range detecting with least vitality misfortune. The exhibition of a helpful range detecting by different voting standard like OR, AND and M out of N voting rule are applied. From the outcomes, it is seen that the proposed conspire displays exceptionally least impedance for the essential clients. Wastage of energy is limited in the system which characterizes the term green sensing.
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Yrttimaa, Tuomas, Ninni Saarinen, Ville Kankare, Niko Viljanen, Jari Hynynen, Saija Huuskonen, Markus Holopainen, Juha Hyyppä, Eija Honkavaara, and Mikko Vastaranta. "Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands." ISPRS International Journal of Geo-Information 9, no. 5 (May 7, 2020): 309. http://dx.doi.org/10.3390/ijgi9050309.
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Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.
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Rohs, Michael, and Georg Essl. "Sensing-Based Interaction for Information Navigation on Handheld Displays." Advances in Human-Computer Interaction 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/450385.
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Information navigation on handheld displays is characterized by the small display dimensions and limited input capabilities of today’s mobile devices. Special strategies are required to help users navigate to off-screen content and develop awareness of spatial layouts despite the small display. Yet, handheld devices offer interaction possibilities that desktop computers do not. Handheld devices can easily be moved in space and used as a movable window into a large virtual workspace. We investigate different information navigation methods for small-scale handheld displays using a range of sensor technologies for spatial tracking. We compare user performance in an abstract map navigation task and discuss the tradeoffs of the different sensor and visualization techniques.
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Krorakai, Karnpimon, Supannika Klangphukhiew, Sirinan Kulchat, and Rina Patramanon. "Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing." Applied Sciences 11, no. 1 (January 3, 2021): 392. http://dx.doi.org/10.3390/app11010392.
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Most electrochemical sensing requires affordable, portable and easy-to-use electrochemical devices for use in point-of-care testing and resource-limited settings. This work presents the design and evaluates the analytical performance of a near-field communication (NFC) potentiostat, a flat card-sized electrochemical device containing a microchip for electrical analysis and an NFC antenna for smartphone connection. The NFC interface is a wireless connection between the microchip and smartphone to simplify measuring units and make the potentiostat into a passive operated device, running without a battery. The proposed potentiostat can perform the common electrochemical techniques including cyclic voltammetry and chronoamperometry with a current range and voltage range of ±20 µA and ±0.8 V. The performance of the NFC potentiostat is compared to a commercial benchtop potentiostat using ferricyanide as a standard solution. The results show that the NFC potentiostat is comparable to a commercial benchtop potentiostat for both cyclic voltammetry and chronoamperometry measurements. The application of the proposed potentiostat is demonstrated by measuring ascorbic acid concentration. As described, the NFC potentiostat, which is compatible with a smartphone, is low-cost, small in size and user-friendly. Thus, the device can be developed for on-site measurement to apply in various fields.