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Journal articles on the topic 'Multiple Odor Sources Localization'

Author: Grafiati

Published: 3 June 2025

Last updated: 22 June 2025

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1

Staples, Marshall, Chris Hugenholtz, Alex Serrano-Ramirez, Thomas E. Barchyn, and Mozhou Gao. "A Comparison of Multiple Odor Source Localization Algorithms." Sensors 23, no. 10 (2023): 4799. http://dx.doi.org/10.3390/s23104799.

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There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover’s distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow.

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2

Liu, Zhen Zhang, Yi Jun Wang, and Tien Fu Lu. "Odor Source Localization Using Multiple Robots in Complicated City-Like Environments." Advanced Materials Research 291-294 (July 2011): 3337–44. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.3337.

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The detection of a dangerous emission source location has the potential to be enhanced by using plume-tracing mobile robots, without endangering human life during the detection and source localization process. So far, many researchers focus on odor source localization in simple & laboratory based environments. The present study focuses on more real life odor source localization scenarios. In this study, multiple robots were used and coordinated by a supervisory program to locate an odor source in complicated city-like environments. A series of simulations has been conducted and the results demonstrated the potential of the supervisory program to effectively control a number of robots to locate a dangerous odor source in real life scenarios.

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3

Zhang, Yu Li, and Xiao Ping Ma. "Localizing Multiple Odor Sources Using Virtual Physics Based Robots." Advanced Materials Research 756-759 (September 2013): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.223.

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This paper is concerned with the problem of multiple chemical sources localization using multi-robot system. A multi-robot cooperation strategy with virtual physics force, which includes structure formation force, goal force, repulsion force and rotary force, is proposed. First, in order to test the effectiveness of the proposed strategy, two sources plume model are constructed by computation fluid dynamics simulations. Second, parallel search by two groups robots is used to locate two sources in simulation environment. With the purpose of preventing two groups from locating the same source, we proposed a rotary force which made each subgroup can locate different chemical source. Simulation experiment discussed the influence of the varied wind direction/ speed frequency and methane release frequency and different initial positions of two groups to the search performance. Finally, the comparative result about them is illustrated.

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Shigaki, Shunsuke, Mayu Yamada, Daisuke Kurabayashi, and Koh Hosoda. "Robust Moth-Inspired Algorithm for Odor Source Localization Using Multimodal Information." Sensors 23, no. 3 (2023): 1475. http://dx.doi.org/10.3390/s23031475.

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Odor-source localization, by which one finds the source of an odor by detecting the odor itself, is an important ability to possess in order to search for leaking gases, explosives, and disaster survivors. Although many animals possess this ability, research on implementing olfaction in robotics is still developing. We developed a novel algorithm that enables a robot to localize an odor source indoors and outdoors by taking inspiration from the adult male silk moth, which we used as the target organism. We measured the female-localization behavior of the silk moth by using a virtual reality (VR) system to obtain the relationship between multiple sensory stimuli and behavior during the localization behavior. The results showed that there were two types of search active and inactive depending on the direction of odor and wind detection. In an active search, the silk moth moved faster as the odor-detection frequency increased, whereas in the inactive search, they always moved slower under all odor-detection frequencies. This phenomenon was constructed as a robust moth-inspired (RMI) algorithm and implemented on a ground-running robot. Experiments on odor-source localization in three environments with different degrees of environmental complexity showed that the RMI algorithm has the best localization performance among conventional moth-inspired algorithms. Analysis of the trajectories showed that the robot could move smoothly through the odor plume even when the environment became more complex. This indicates that switching and modulating behavior based on the direction of odor and wind detection contributes to the adaptability and robustness of odor-source localization.

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Jatmiko, W., F. Jovan, R. Y. S. Dhiemas, et al. "PSO ALGORITHM FOR SINGLE AND MULTIPLE ODOR SOURCES LOCALIZATION PROBLEMS: PROGRESS AND CHALLENGE." International Journal on Smart Sensing and Intelligent Systems 9, no. 3 (2016): 1431–78. http://dx.doi.org/10.21307/ijssis-2017-925.

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Jiang, Tianshu, Hao Guo, Lingpu Ge, Fumihiro Sassa, and Kenshi Hayashi. "Inkjet-Printed Localized Surface Plasmon Resonance Subpixel Gas Sensor Array for Enhanced Identification and Visualization of Gas Spatial Distributions from Multiple Odor Sources." Sensors 24, no. 20 (2024): 6731. http://dx.doi.org/10.3390/s24206731.

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The visualization of the spatial distributions of gases from various sources is essential to understanding the composition, localization, and behavior of these gases. In this study, an inkjet-printed localized surface plasmon resonance (LSPR) subpixel gas sensor array was developed to visualize the spatial distributions of gases and to differentiate between acetic acid, geraniol, pentadecane, and cis-jasmone. The sensor array, which integrates gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), and fluorescent pigments, was positioned 3 cm above the gas source. Hyperspectral imaging was used to capture the LSPR spectra across the sensor array, and these spectra were then used to construct gas information matrices. Principal component analysis (PCA) enabled effective classification of the gases and localization of their sources based on observed spectral differences. Heat maps that visualized the gas concentrations were generated using the mean squared error (MSE) between the sensor responses and reference spectra. The array identified and visualized the four gas sources successfully, thus demonstrating its potential for gas localization and detection applications. The study highlights a straightforward, cost-effective approach to gas sensing and visualization, and in future work, we intend to refine the sensor fabrication process and enhance the detection of complex gas mixtures.

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7

Luong, Duc-Nhat, and Daisuke Kurabayashi. "Odor Source Localization in Obstacle Regions Using Switching Planning Algorithms with a Switching Framework." Sensors 23, no. 3 (2023): 1140. http://dx.doi.org/10.3390/s23031140.

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Odor source localization (OSL) robots are essential for safety and rescue teams to overcome the problem of human exposure to hazardous chemical plumes. However, owing to the complicated geometry of environments, it is almost impossible to construct the dispersion model of the odor plume in practical situations to be used for probabilistic odor source search algorithms. Additionally, as time is crucial in OSL tasks, dynamically modifying the robot’s balance of emphasis between exploration and exploitation is desired. In this study, we addressed both the aforementioned problems by simplifying the environment with an obstacle region into multiple sub-environments with different resolutions. Subsequently, a framework was introduced to switch between the Infotaxis and Dijkstra algorithms to navigate the agent and enable it to reach the source swiftly. One algorithm was used to guide the agent in searching for clues about the source location, whereas the other facilitated the active movement of the agent between sub-environments. The proposed algorithm exhibited improvements in terms of success rate and search time. Furthermore, the implementation of the proposed framework on an autonomous mobile robot verified its effectiveness. Improvements were observed in our experiments with a robot when the success rate increased 3.5 times and the average moving steps of the robot were reduced by nearly 35%.

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8

Rahbar, Faezeh, Ali Marjovi, and Alcherio Martinoli. "Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization." Sensors 19, no. 3 (2019): 656. http://dx.doi.org/10.3390/s19030656.

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Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.

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LI, Ji-Gong, Jing YANG, Jie-Yong ZHOU, Jia LIU, and Guang-Da LU. "Mapping odour sources with a mobile robot in a time variant airflow environment." Austrian Contributions to Veterinary Epidemiology (ACVE) 8 (November 6, 2015): 7. https://doi.org/10.5281/zenodo.33825.

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This paper focuses on the problem of mapping odour sources using a mobile robot in a time-variant airflow environment, and provides a localization method which uses the Dempster-Shafer (D-S) theory to reason the possible locations of odour sources. In the proposed method, the robot carries out the D-S inference and iteratively updates a grid map, using the successive measurements from a gas sensor and an anemometer when the robot is cruising in the given search area. Simulations are carried out and the results in a time-variant airflow environment show that the locations of the multiple odour sources can be estimated online with the proposed method.

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10

Jain, Upma, Ritu Tiwari, and W. Wilfred Godfrey. "Multiple odor source localization using diverse-PSO and group-based strategies in an unknown environment." Journal of Computational Science 34 (May 2019): 33–47. http://dx.doi.org/10.1016/j.jocs.2019.04.008.

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Chen, Lin, Hao Guo, Cong Wang, Bin Chen, Fumihiro Sassa, and Kenshi Hayashi. "Two-Dimensional SERS Sensor Array for Identifying and Visualizing the Gas Spatial Distributions of Two Distinct Odor Sources." Sensors 24, no. 3 (2024): 790. http://dx.doi.org/10.3390/s24030790.

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The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.

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12

Tariq, Mohammad F., Scott C. Sterrett, Sidney Moore, Lane, David J. Perkel, and David H. Gire. "Dynamics of odor-source localization: Insights from real-time odor plume recordings and head-motion tracking in freely moving mice." PLOS ONE 19, no. 9 (2024): e0310254. http://dx.doi.org/10.1371/journal.pone.0310254.

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Animals navigating turbulent odor plumes exhibit a rich variety of behaviors, and employ efficient strategies to locate odor sources. A growing body of literature has started to probe this complex task of localizing airborne odor sources in walking mammals to further our understanding of neural encoding and decoding of naturalistic sensory stimuli. However, correlating the intermittent olfactory information with behavior has remained a long-standing challenge due to the stochastic nature of the odor stimulus. We recently reported a method to record real-time olfactory information available to freely moving mice during odor-guided navigation, hence overcoming that challenge. Here we combine our odor-recording method with head-motion tracking to establish correlations between plume encounters and head movements. We show that mice exhibit robust head-pitch motions in the 5-14Hz range during an odor-guided navigation task, and that these head motions are modulated by plume encounters. Furthermore, mice reduce their angles with respect to the source upon plume contact. Head motions may thus be an important part of the sensorimotor behavioral repertoire during naturalistic odor-source localization.

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13

Bowker, Robert P. G., Michael A. McGinley, and James Schubert. "ANALYSIS OF AMBIENT ODOR DATA FROM AN INDUSTRIAL AREA WITH MULTIPLE ODOR SOURCES." Proceedings of the Water Environment Federation 2004, no. 3 (2004): 374–93. http://dx.doi.org/10.2175/193864704784327061.

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14

Yang, Jen-Chih, Pao-Erh Chang, Chi-Chang Ho, and Chang-Fu Wu. "Application of factor and cluster analyses to determine source–receptor relationships of industrial volatile organic odor species in a dual-optical sensing system." Atmospheric Measurement Techniques 12, no. 10 (2019): 5347–62. http://dx.doi.org/10.5194/amt-12-5347-2019.

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Abstract. Most odor nuisance investigations rely on either human olfactory examination or on-site sampling and analytical techniques, but these methods are often subject to spatial and temporal limitations and thus impractical for locating odor emission sources. This study developed an alternative approach with a dual-optical sensing system, a meteorological station, and the combination of factor and cluster analyses to identify and characterize emission sources of multiple air contaminants. Factor and cluster analyses were employed to establish the emission profile of multiple odorous substances from each emission source. Both receptor and source monitoring data were collected to characterize the emission sources of various odorous substances. Open-path Fourier transform infrared (OP-FTIR) as a receptor path detected concurrent trends of several organic solvents with concentrations higher than the reference odor threshold values, indicating that these compounds were potential causes of odor nuisance. Qualitative source apportionment by factor and cluster analyses suggested that these odorous substances were used as organic solvents in surface coating or painting processes. Closed-cell Fourier transform infrared (CC-FTIR) at two nearby surface-coating companies indicated that only one company's stack exhibited the same odorous substance profile found by the OP-FTIR receptor path. The major odor emission source was thus identified in this study. This study demonstrated the feasibility of using the alternative investigative framework to successfully identify emission sources from an industrial odor nuisance site. The major emission sources were identified, and future enforcement plans can be conducted to enhance odor investigation efficiency and improve overall air quality.

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15

Jewett, Donald. "LOCALIZATION OF MULTIPLE TEMPORALLY-OVERLAPPING SOURCES." Journal of Clinical Neurophysiology 16, no. 2 (1999): 189. http://dx.doi.org/10.1097/00004691-199903000-00077.

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16

Jatmiko, W., W. Pambuko, A. Febrian, et al. "RANGED SUBGROUP PARTICLE SWARM OPTIMIZATION FOR LOCALIZING MULTIPLE ODOR SOURCES." International Journal on Smart Sensing and Intelligent Systems 3, no. 3 (2010): 411–42. http://dx.doi.org/10.21307/ijssis-2017-401.

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17

Blacodon, D., M. Caplot, and G. Elias. "Source localization technique for impulsive multiple sources." Journal of Aircraft 26, no. 2 (1989): 154–56. http://dx.doi.org/10.2514/3.45737.

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18

Sheinvald, J., M. Wax, and A. J. Meiss. "Localization of multiple sources with moving arrays." IEEE Transactions on Signal Processing 46, no. 10 (1998): 2736–43. http://dx.doi.org/10.1109/78.720375.

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19

Pessentheiner, Hannes, Martin Hagmuller, and Gernot Kubin. "Localization and Characterization of Multiple Harmonic Sources." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 8 (2016): 1348–63. http://dx.doi.org/10.1109/taslp.2016.2556282.

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20

Zhong, Xuan, Liang Sun, and William Yost. "Active binaural localization of multiple sound sources." Robotics and Autonomous Systems 85 (November 2016): 83–92. http://dx.doi.org/10.1016/j.robot.2016.07.008.

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21

Hassan, Sunzid, Lingxiao Wang, and Khan Raqib Mahmud. "Integrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization." Sensors 24, no. 24 (2024): 7875. https://doi.org/10.3390/s24247875.

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Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent’s sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional ‘olfaction-only’ OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities. The algorithm leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the ‘High-level Reasoning’ module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the ‘Low-level Action’ module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our algorithm, we implemented it on a mobile robot in a real-world environment with non-unidirectional airflow environments and obstacles to mimic a complex, practical search environment. We compared the performance of our proposed algorithm to single-sensory-modality-based ‘olfaction-only’ and ‘vision-only’ navigation algorithms, and a supervised learning-based ‘vision and olfaction fusion’ (Fusion) navigation algorithm. The experimental results show that the proposed LLM-based algorithm outperformed the other algorithms in terms of success rates and average search times in both unidirectional and non-unidirectional airflow environments.

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22

Zhang, Yankui, Daming Wang, Weijia Cui, Jiangdong You, Hao Li, and Fei Liu. "DOA-Based Localization Method with Multiple Screening K-Means Clustering for Multiple Sources." Wireless Communications and Mobile Computing 2019 (September 11, 2019): 1–7. http://dx.doi.org/10.1155/2019/5643752.

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The existing angle-based localization methods are mainly suitable for the single source. Actually, there often exists situation which contains multiple target sources. To solve the problem of localization of multitarget sources, this paper presents a K-means clustering method based on multiple screening, which can effectively realize the localization of multiple sources based on DOA (direction of arrival) parameters. The method firstly establishes a cost function of position coordinates by using DOA parameters from the measuring position coordinates and then solves the cost function to obtain a complete set of real position coordinates and fuzzy position coordinates. As the distribution of real target coordinates is concentrated and the fuzzy target positions are scattered, the K-means clustering method is adopted to classify the coordinate set. In order to improve the positioning accuracy, a multiscreening process is introduced to screen the input samples before each clustering, and it can be finally concluded that clustering centers are the position coordinates of the target sources. Meanwhile, the complexity analysis and performance verification of this method are proposed. Simulation experiments show that this method can efficiently realize ambiguity-free, highly precise localization of multitarget sources.

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23

McQueen, Rachel H., and Sara Vaezafshar. "Odor in textiles: A review of evaluation methods, fabric characteristics, and odor control technologies." Textile Research Journal 90, no. 9-10 (2019): 1157–73. http://dx.doi.org/10.1177/0040517519883952.

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During use, textile items can develop unpleasant odors that arise from many different sources, both internal and external to the human body. Laundering is not always effective at removing odors, with odor potentially building up over time due to incomplete removal of soils and odorous compounds and/or malodors transferred during the laundering process. Textile odor can lead to consumer dissatisfaction, particularly as there are high expectations that clothing and textile products meet multiple aesthetic and functional needs. The problem of odor in textiles is complex and multi-faceted, with odorous volatile compounds, microorganisms, and precursors to odor, such as sweat, being transferred to, and retained by, fabrics. This article reviews the literature that specifically relates to odor within textiles. Methods for evaluating odor in textiles, including methods for collecting odor on textile substrates, as well as sensory and instrumental methods of odor detection, were reviewed. Literature that examined differences among fabrics that varied by fabric properties were reviewed. As well, the effectiveness of specific odor controlling finishing technologies to control malodor within textiles was also examined.

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24

Liu, Shiqi, Yan Zhang, and Shurui Fan. "Adaptive Space-Aware Infotaxis II as a Strategy for Odor Source Localization." Entropy 26, no. 4 (2024): 302. http://dx.doi.org/10.3390/e26040302.

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Mobile robot olfaction of toxic and hazardous odor sources is of great significance in anti-terrorism, disaster prevention, and control scenarios. Aiming at the problems of low search efficiency and easily falling into a local optimum of the current odor source localization strategies, the paper proposes the adaptive space-aware Infotaxis II algorithm. To improve the tracking efficiency of robots, a new reward function is designed by considering the space information and emphasizing the exploration behavior of robots. Considering the enhancement in exploratory behavior, an adaptive navigation-updated mechanism is proposed to adjust the movement range of robots in real time through information entropy to avoid an excessive exploration behavior during the search process, which may lead the robot to fall into a local optimum. Subsequently, an improved adaptive cosine salp swarm algorithm is applied to confirm the optimal information adaptive parameter. Comparative simulation experiments between ASAInfotaxis II and the classical search strategies are carried out in 2D and 3D scenarios regarding the search efficiency and search behavior, which show that ASAInfotaxis II is competent to improve the search efficiency to a larger extent and achieves a better balance between exploration and exploitation behaviors.

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Jiang, Jiacai. "MULTIPLE SOURCES LOCALIZATION BASED ON INDEPENDENT DOUBLETS ARRAY." Progress In Electromagnetics Research C 115 (2021): 95–110. http://dx.doi.org/10.2528/pierc21062606.

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Liu, Chen, Bruce C. Wheeler, William D. O’Brien, Robert C. Bilger, Charissa R. Lansing, and Albert S. Feng. "Localization of multiple sound sources with two microphones." Journal of the Acoustical Society of America 108, no. 4 (2000): 1888–905. http://dx.doi.org/10.1121/1.1290516.

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Mcgill, Kathleen, and Stephen Taylor. "Robot algorithms for localization of multiple emission sources." ACM Computing Surveys 43, no. 3 (2011): 1–25. http://dx.doi.org/10.1145/1922649.1922652.

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Lu, Lu, Hongting Zhang, and Hsiao-Chun Wu. "Novel Energy-Based Localization Technique for Multiple Sources." IEEE Systems Journal 8, no. 1 (2014): 142–50. http://dx.doi.org/10.1109/jsyst.2013.2260628.

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Lin, Meiyan, Yonghui Huang, Baozhu Li, Zhen Huang, Zihan Zhang, and Wenjie Zhao. "Deep Learning-Based Multiple Co-Channel Sources Localization Using Bernoulli Heatmap." Electronics 11, no. 10 (2022): 1551. http://dx.doi.org/10.3390/electronics11101551.

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Multiple sources localization (MSL) has received considerable attention in scenarios of commercial, industrial, and defense areas. In this paper, a novel deep learning-based approach with observations of received signal strength (RSS) is proposed for the localization of multiple co-channel sources. The proposed method, named MSLocNet, formulates the MSL problem as a Bernoulli heatmap regression problem, solved by a fully convolutional network (FCN). The proposed MSLocNet enables simultaneous localization of variable numbers of sources, and exhibits better localization performance. Simulations, under complex environments with shadow fading, are conducted to validate the improved localization accuracy of the proposed method over other benchmark schemes. Moreover, experiments are carried out in a real environment to verify the feasibility of the proposed method.

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Mišković, Milan, Nenad Vukmirović, Dragan Golubović, and Miljko Erić. "Method for Direct Localization of Multiple Impulse Acoustic Sources in Outdoor Environment." Electronics 11, no. 16 (2022): 2509. http://dx.doi.org/10.3390/electronics11162509.

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A method for the direct outdoor localization of multiple impulse acoustic sources by a distributed microphone array is proposed. This localization problem is of great interest for gunshot, firecracker and explosion detection localization in a civil environment, as well as for gun, mortar, small arms, artillery, sniper detection localization in military battlefield monitoring systems. Such a kind of localization is a complicated technical problem in many aspects. In such a scenario, the permutation of impulse arrivals on distributed microphones occurs, so the application of classical two-step localization methods, such as time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), fingerprint methods, etc., is faced with the so-called association problem, which is difficult to solve. The association problem does not exist in the proposed method for direct (one-step) localization, so the proposed method is more suitable for localization in a given acoustic scenario than the mentioned two-step localization methods. Furthermore, in the proposed method, direct localization is performed impulse by impulse. The observation interval used for the localization could not be arbitrarily chosen; it is limited by the duration of impulses. In the mathematical model formulated in the paper, atmospheric factors in acoustic signal propagation (temperature, pressure, etc.) are included. The results of simulations show that by using the proposed method, centimeter localization accuracy can be achieved.

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Li, Xiang, Yangyang Liu, Chengli Zhao, Xue Zhang, and Dongyun Yi. "Locating Multiple Sources of Contagion in Complex Networks under the SIR Model." Applied Sciences 9, no. 20 (2019): 4472. http://dx.doi.org/10.3390/app9204472.

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Simultaneous outbreaks of contagion are a great threat against human life, resulting in great panic in society. It is urgent for us to find an efficient multiple sources localization method with the aim of studying its pathogenic mechanism and minimizing its harm. However, our ability to locate multiple sources is strictly limited by incomplete information about nodes and the inescapable randomness of the propagation process. In this paper, we present a valid approach, namely the Potential Concentration Label method, which helps locate multiple sources of contagion faster and more accurately in complex networks under the SIR(Susceptible-Infected-Recovered) model. Through label assignment in each node, our aim is to find the nodes with maximal value after several iterations. The experiments demonstrate that the accuracy of our multiple sources localization method is high enough. With the number of sources increasing, the accuracy of our method declines gradually. However, the accuracy remains at a slight fluctuation when average degree and network scale make a change. Moreover, our method still keeps a high multiple sources localization accuracy with noise of various intensities, which shows its strong anti-noise ability. I believe that our method provides a new perspective for accurate and fast multi-sources localization in complex networks.

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Pu, Henglin, Chao Cai, Menglan Hu, Tianping Deng, Rong Zheng, and Jun Luo. "Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming." Sensors 21, no. 2 (2021): 532. http://dx.doi.org/10.3390/s21020532.

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Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

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Wang, Fangzhou, and Xi Pan. "Acoustic Sources Localization in 3D Using Multiple Spherical Arrays." Journal of Electrical Engineering and Technology 11, no. 3 (2016): 759–68. http://dx.doi.org/10.5370/jeet.2016.11.3.759.

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Tam, Kai-Chung, Siu-Kit Lau, and Shiu-Keung Tang. "Multiple sources localization with microphone array: A subarray approach." Journal of the Acoustical Society of America 130, no. 4 (2011): 2451. http://dx.doi.org/10.1121/1.3654845.

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Neilsen, Tracianne B. "Localization of multiple acoustic sources in the shallow ocean." Journal of the Acoustical Society of America 118, no. 5 (2005): 2944–53. http://dx.doi.org/10.1121/1.2041307.

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Ziskind, I., and M. Wax. "Maximum likelihood localization of multiple sources by alternating projection." IEEE Transactions on Acoustics, Speech, and Signal Processing 36, no. 10 (1988): 1553–60. http://dx.doi.org/10.1109/29.7543.

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Fabrikov, A. V. "Array signal processing for localization of multiple radiation sources." Measurement Techniques 40, no. 5 (1997): 405–12. http://dx.doi.org/10.1007/bf02504209.

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Singh, A. V., M. Yu, A. K. Gupta, and K. M. Bryden. "Localization of multiple acoustic sources in a room environment." Applied Energy 109 (September 2013): 171–81. http://dx.doi.org/10.1016/j.apenergy.2013.03.046.

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39

Kikuta, S., K. Sato, H. Kashiwadani, K. Tsunoda, T. Yamasoba, and K. Mori. "Neurons in the anterior olfactory nucleus pars externa detect right or left localization of odor sources." Proceedings of the National Academy of Sciences 107, no. 27 (2010): 12363–68. http://dx.doi.org/10.1073/pnas.1003999107.

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Zhang, Liting, Hao Huan, Tao Ran, Shangyu Zhang, Yushu Zhang, and Hao Ding. "A Spaceborne Passive Localization Algorithm Based on MSD-HOUGH for Multiple Signal Sources." Remote Sensing 16, no. 22 (2024): 4303. http://dx.doi.org/10.3390/rs16224303.

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Recently, the passive synthetic aperture (PSA) technique has been used in passive localization to improve the position accuracy of single source by estimating the Doppler parameter of the received signal. However, in the presence of multiple sources, time-frequency aliasing will lead to serious cross-term interference during Doppler signal extraction, resulting in low localization performance. To solve this problem, a spaceborne passive synthetic aperture localization algorithm based on the multiple-stay detector HOUGH transform (MSD-HOUGH) is proposed in this paper. Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. Then, the PSA Doppler equations are established in the HOUGH domain, which can suppress the cross-term interference of the multiple emitters. Meanwhile, a multiple-stay detector (MSD) is designed to reduce the pseudo-peaks in HOUGH domain. The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. Experimental results show that compared with other localization methods, the proposed algorithm has a significant improvement for multiple signal source localization.

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Thrift, William John, Antony Cabuslay, Andrew Benjamin Laird, Saba Ranjbar, Allon I. Hochbaum, and Regina Ragan. "Surface-Enhanced Raman Scattering-Based Odor Compass: Locating Multiple Chemical Sources and Pathogens." ACS Sensors 4, no. 9 (2019): 2311–19. http://dx.doi.org/10.1021/acssensors.9b00809.

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Davis, Dorian, Samba Gaye, Lirane Mandjoupa, et al. "Locating impulsive sound sources in microscale urban spaces." Journal of the Acoustical Society of America 152, no. 4 (2022): A57. http://dx.doi.org/10.1121/10.0015529.

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In this work, techniques for localizing impulsive acoustic sources in an urban environment are presented. Of particular interest is the localization of sources in urban street canyons and enclosed urban areas. Sound propagation in an urban environment is strongly influenced by multiple reflections. In urban street canyons, multiple reflections tend to amplify with decreasing canyon width. A numerical investigation is performed to study the role multiple reflections on time difference of arrival (TDOA) and beamforming source localization techniques. Results of various urban street canyon and enclosed space geometries are investigated. The results and limitations of the TDOA and beamforming techniques for estimating source position in urban environments are discussed.

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Sobol, �ukasz, and Arkadiusz Dyjakon. "Biochar as a Sustainable Product for the Removal of Odor Emissions - Mini Literature Review." Revista de Chimie 73, no. 4 (2022): 86–96. http://dx.doi.org/10.37358/rc.22.4.8557.

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Odors are considered as one of the most important environmental problems that may adversely influence the quality of life of people affected by their interaction. Due to the multiple sources of emissions, combined with the abundance of chemical compounds classified odor nuisance, new methods of reducing odor emissions are sought while maintaining environmental neutrality. One potential option is to use biochar, a sustainable, carbonized solid product resulting from the thermo-chemical treatment of biomass and/or organic waste. Due to its valuable properties (in particular, high specific surface area, and microporous structure), this material can be used for sorption purposes. Although the issue of using biochar to remove odor emissions is relatively new, in recent years new research directions have been undertaken to determine the sorption efficiency of biochar not only as a direct adsorbent but also in alternative applications. Therefore, this paper aimed to review the most important directions of biochar management in the removal of odor-causing compounds and to highlight the current advances undertaken in this direction. It was distinguished that biochar can enhance odor mitigation by being an additive to compost, a biofiltration medium, a direct adsorbent, a soil additive, a substrate for the production of the odor-absorbing product, a dietary supplement, and a biocover. However, further research is needed to strengthen the range of greater use of biochar in practice.

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Salvati, Daniele, and Sergio Canazza. "Incident Signal Power Comparison for Localization of Concurrent Multiple Acoustic Sources." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/582397.

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In this paper, a method to solve the localization of concurrent multiple acoustic sources in large open spaces is presented. The problem of the multisource localization in far-field conditions is to correctly associate the direction of arrival (DOA) estimated by a network array system to the same source. The use of systems implementing a Bayesian filter is a traditional approach to address the problem of localization in multisource acoustic scenario. However, in a real noisy open space the acoustic sources are often discontinuous with numerous short-duration events and thus the filtering methods may have difficulty to track the multiple sources. Incident signal power comparison (ISPC) is proposed to compute DOAs association. ISPC is based on identifying the incident signal power (ISP) of the sources on a microphone array using beamforming methods and comparing the ISP between different arrays using spectral distance (SD) measurement techniques. This method solves the ambiguities, due to the presence of simultaneous sources, by identifying sounds through a minimization of an error criterion on SD measures of DOA combinations. The experimental results were conducted in an outdoor real noisy environment and the ISPC performance is reported using different beamforming techniques and SD functions.

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Jia, Maoshen, Yuxuan Wu, Changchun Bao, and Jing Wang. "Multiple Sound Sources Localization with Frame-by-Frame Component Removal of Statistically Dominant Source." Sensors 18, no. 11 (2018): 3613. http://dx.doi.org/10.3390/s18113613.

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Multiple sound sources localization is a hot topic in audio signal processing and is widely utilized in many application areas. This paper proposed a multiple sound sources localization method based on a statistically dominant source component removal (SDSCR) algorithm by soundfield microphone. The existence of the statistically weak source (SWS) among soundfield microphone signals is validated by statistical analysis. The SDSCR algorithm with joint an intra-frame and inter-frame statistically dominant source (SDS) discriminations is designed to remove the component of SDS while reserve the SWS component. The degradation of localization accuracy caused by the existence of the SWS is resolved using the SDSCR algorithm. The objective evaluation of the proposed method is conducted in simulated and real environments. The results show that the proposed method achieves a better performance compared with the conventional SSZ-based method both in sources localization and counting.

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Yu, Yan S. W., Matthew M. Graff, Chris S. Bresee, Yan B. Man, and Mitra J. Z. Hartmann. "Whiskers aid anemotaxis in rats." Science Advances 2, no. 8 (2016): e1600716. http://dx.doi.org/10.1126/sciadv.1600716.

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Observation of terrestrial mammals suggests that they can follow the wind (anemotaxis), but the sensory cues underlying this ability have not been studied. We identify a significant contribution to anemotaxis mediated by whiskers (vibrissae), a modality previously studied only in the context of direct tactile contact. Five rats trained on a five-alternative forced-choice airflow localization task exhibited significant performance decrements after vibrissal removal. In contrast, vibrissal removal did not disrupt the performance of control animals trained to localize a light source. The performance decrement of individual rats was related to their airspeed threshold for successful localization: animals that found the task more challenging relied more on the vibrissae for localization cues. Following vibrissal removal, the rats deviated more from the straight-line path to the air source, choosing sources farther from the correct location. Our results indicate that rats can perform anemotaxis and that whiskers greatly facilitate this ability. Because air currents carry information about both odor content and location, these findings are discussed in terms of the adaptive significance of the interaction between sniffing and whisking in rodents.

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Sekiguchi, Kouhei, Yoshiaki bando, Keisuke Nakamura, Kazuhiro Nakadai, Katsutoshi Itoyama, and Kazuyoshi Yoshii. "Online Localization of Multiple Sound Sources and Multiple Robots with Asynchronous Microphone Arrays." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2016 (2016): 1A2–09b5. http://dx.doi.org/10.1299/jsmermd.2016.1a2-09b5.

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Hunter, Cameron C., Kendra S. Siebert, Damien J. Downes, et al. "Multiple Nuclear Localization Signals Mediate Nuclear Localization of the GATA Transcription Factor AreA." Eukaryotic Cell 13, no. 4 (2014): 527–38. http://dx.doi.org/10.1128/ec.00040-14.

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ABSTRACTTheAspergillus nidulansGATA transcription factor AreA activates transcription of nitrogen metabolic genes in response to nitrogen limitation and is known to accumulate in the nucleus during nitrogen starvation. Sequence analysis of AreA revealed multiple nuclear localization signals (NLSs), five putative classical NLSs conserved in fungal AreA orthologs but not in theSaccharomyces cerevisiaefunctional orthologs Gln3p and Gat1p, and one putative noncanonical RRX33RXR bipartite NLS within the DNA-binding domain. In order to identify the functional NLSs in AreA, we constructedareAmutants with mutations in individual putative NLSs or combinations of putative NLSs and strains expressing green fluorescent protein (GFP)-AreA NLS fusion genes. Deletion of all five classical NLSs individually or collectively did not affect utilization of nitrogen sources or AreA-dependent gene expression and did not prevent AreA nuclear localization. Mutation of the bipartite NLS conferred the inability to utilize alternative nitrogen sources and abolished AreA-dependent gene expression likely due to effects on DNA binding but did not prevent AreA nuclear localization. Mutation of all six NLSs simultaneously prevented AreA nuclear accumulation. The bipartite NLS alone strongly directed GFP to the nucleus, whereas the classical NLSs collaborated to direct GFP to the nucleus. Therefore, AreA contains multiple conserved NLSs, which show redundancy and together function to mediate nuclear import. The noncanonical bipartite NLS is conserved in GATA factors fromAspergillus, yeast, and mammals, indicating an ancient origin.

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Ðurković, Marko, Tim Habigt, Martin Rothbucher, and Klaus Diepold. "Low latency localization of multiple sound sources in reverberant environments." Journal of the Acoustical Society of America 130, no. 6 (2011): EL392—EL398. http://dx.doi.org/10.1121/1.3659146.

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Wax, M., and I. Ziskind. "On unique localization of multiple sources by passive sensor arrays." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 7 (1989): 996–1000. http://dx.doi.org/10.1109/29.32277.

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