Literatura académica sobre el tema "Olfactory search"
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Artículos de revistas sobre el tema "Olfactory search"
Nams, Vilis O. "Olfactory Search Images in Striped Skunks". Behaviour 119, n.º 3-4 (1991): 267–84. http://dx.doi.org/10.1163/156853991x00472.
Texto completoBalkovsky, E. y B. I. Shraiman. "Olfactory search at high Reynolds number". Proceedings of the National Academy of Sciences 99, n.º 20 (12 de septiembre de 2002): 12589–93. http://dx.doi.org/10.1073/pnas.192393499.
Texto completoBaker, Keeley L., Michael Dickinson, Teresa M. Findley, David H. Gire, Matthieu Louis, Marie P. Suver, Justus V. Verhagen, Katherine I. Nagel y Matthew C. Smear. "Algorithms for Olfactory Search across Species". Journal of Neuroscience 38, n.º 44 (31 de octubre de 2018): 9383–89. http://dx.doi.org/10.1523/jneurosci.1668-18.2018.
Texto completoMarques, Lino, Urbano Nunes y A. T. de Almeida. "Particle swarm-based olfactory guided search". Autonomous Robots 20, n.º 3 (26 de mayo de 2006): 277–87. http://dx.doi.org/10.1007/s10514-006-7567-0.
Texto completoShcherban, Igor, Peter Kosenko, Oxana Shcherban y Paul Lobzenko. "Method of automatic search for odor-induced patterns in bioelectric activity of a rat olfactory bulb". Information and Control Systems, n.º 5 (20 de octubre de 2020): 62–69. http://dx.doi.org/10.31799/1684-8853-2020-5-62-69.
Texto completoRiman, Nour, Jonathan D. Victor, Sebastian D. Boie y Bard Ermentrout. "The Dynamics of Bilateral Olfactory Search and Navigation". SIAM Review 63, n.º 1 (enero de 2021): 100–120. http://dx.doi.org/10.1137/19m1265934.
Texto completoMontgomery, John C., Carol Diebel, Matthew B. D. Halstead y Josh Downer. "Olfactory search tracks in the Antarctic fish Trematomus bernacchii". Polar Biology 21, n.º 3 (23 de febrero de 1999): 151–54. http://dx.doi.org/10.1007/s003000050346.
Texto completoNams, V. O. "Density-dependent predation by skunks using olfactory search images". Oecologia 110, n.º 3 (18 de abril de 1997): 440–48. http://dx.doi.org/10.1007/s004420050179.
Texto completoNevitt, G. A., M. Losekoot y H. Weimerskirch. "Evidence for olfactory search in wandering albatross, Diomedea exulans". Proceedings of the National Academy of Sciences 105, n.º 12 (6 de marzo de 2008): 4576–81. http://dx.doi.org/10.1073/pnas.0709047105.
Texto completoCross, Fiona R. y Robert R. Jackson. "Olfactory search-image use by a mosquito-eating predator". Proceedings of the Royal Society B: Biological Sciences 277, n.º 1697 (26 de mayo de 2010): 3173–78. http://dx.doi.org/10.1098/rspb.2010.0596.
Texto completoTesis sobre el tema "Olfactory search"
Page, Jennifer Lynn. "The effects of plume property variation on odor plume navigation in turbulent boundary layer flows". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29752.
Texto completoCommittee Chair: Weissburg, Marc; Committee Member: Hay, Mark; Committee Member: Kubanek, Julia; Committee Member: Webster, Donald; Committee Member: Yen, Jeannette. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Marjovi, Ali. "Multi-robot and swarm olfactory search". Doctoral thesis, 2013. http://hdl.handle.net/10316/23230.
Texto completoSearching for olfactory targets with mobile robots has received much attention in the recent years due to its applications in chemical leak detection, environmental monitoring, pollution monitoring, inspection of landfills, and search and rescue operations. Some of these tasks are done in scenarios extremely dangerous for humans, making it more desirable to use robots instead. The use of autonomous robots to assist such tasks reduces the risks involved in these operations. This thesis aims to address the problem of searching for olfactory targets with a group of mobile robots. A group of robots with on-board sensors can actually form a mobile sensor network, so they can cover larger area in a shorter time and they show better performance in several scenarios (e.g., in the face of noisy and large environments) in comparison to a single robot. Two types of robotic groups are considered in this thesis; “multi-robot systems” and “swarm robotic systems.” For each of these systems a certain objective is defined to address several challenges in robotic olfactory search. “Olfactory search and exploration with multiple robots in structured unknown environments” and “swarm robotics olfactory search in unstructured unknown environments” are the two defined objectives of this thesis. In both objectives, the problem of “odor plume finding” that is the first step to localize odor sources is considered as the main problem. To fulfill the first objective, this thesis presents a multi-robot olfactory search and exploration method designed for structured environments. The method is a decentralized frontier based algorithm enhanced by a cost=utility evaluation function that considers the gas cues (odor concentration and airflow) at each frontier which makes the robots to try to find the odor sources as fast as possible. Several challenges namely “task sharing and cooperation”, “localization and multi-robot mapping”, “programming and processing” in addition to “olfactory search challenges” are addressed. The proposed method is evaluated in simulations and in small-scaled realistic structured environments. The results of real world experiments confirm the e ect of gas cues on the behavior of the robots and show that using the proposed algorithm, robots firstly explore the areas with higher probability of existence of odor sources. Simulation results show that having more robots is more advantageous in a complex environment than in a simple environment. We address the second objective, “swarm robotics olfactory search in unstructured unknown environments”, through an analytical optimization approach. Defining single and multiple (stationary and mobile) gas sensors coverage and finding the optimal configuration of N mobile sensors in di erent environmental conditions are among the main novelties of this thesis. We found that the topology of optimal solutions is in the form of line configuration, with equal distance between each pair of neighboring sensors. Regardless of number of sensors, the optimal distance between neighboring pairs depends on the wind speed. A mathematical function that can accurately estimate the optimal distances based on the wind speed was computed by nonlinear regression estimation. Moreover, based on the results of optimizations, we present and design a set of wind-biased virtual attractive/repulsive control forces for the swarm robots such that their emergent behavior converges to the optimal formations. The optimization results were validated and evaluated by experiments in realistic environments. Finally, this dissertation proposes a method for “swarm robotics odor plume tracking and source declaration.” We present the implementation of the proposed method in simulations and provide some preliminary results of its validations and discuss the future works of this work.
A procura de alvos olfactivos com recurso a robˆos m´oveis tem recebido bastante atenc¸ ˜ao por parte da comunidade cient´ıfica nos ´ultimos anos. Isto deve-se, em parte `as poss´ıveis aplicac¸ ˜oes em cen´arios tais como a detecc¸ ˜ao de fugas qu´ımicas, monitorizac¸ ˜ao ambiental, monitorizac¸ ˜ao de agentes poluentes, inspecc¸ ˜ao de aterros e operac¸ ˜oes de busca e salvamento. Algumas destas tarefas s˜ao realizadas em ambientes extremamente perigosos para os humanos, o que torna o recurso a robˆos ainda mais apelativo. A assistˆencia de robˆos aut´onomos em tarefas como estas reduz o risco envolvido para os humanos nestas operac¸ ˜oes. Esta tese tem como objectivo ir de encontro ao problema da busca de alvos olfactivos atrav´es do uso de um grupo de robˆos m´oveis, equipados com sensores de g´as. Este grupo pode formar uma rede de sensores m´ovel, permitindo a cobertura de ´areas mais extensas num per´ıodo de tempo mais curto, o que se traduz num melhor desempenho em diversos ambientes (e.g., na presenc¸a de ambientes ruidosos e amplos) quando comparado com uma soluc¸ ˜ao que utiliza apenas um robˆo. Nesta tese s˜ao considerados dois tipos de grupos de robˆos: “sistemas multirob ˆo” e “sistemas de enxames de robˆos”. Para cada um destes sistemas, define-se um objectivo de forma a ir ao encontro de v´arios desafios no ˆambito da busca olfactiva com robˆos m´oveis. “Busca olfactiva e explorac¸ ˜ao com recurso a m´ultiplos robˆos em ambientes desconhecidos e estruturados” e “busca olfactiva com recurso a enxames de robˆos em ambientes desconhecidos e n˜ao-estruturados” s˜ao os dois objectivos principais desta tese. Em ambos os objectivos o problema de “encontrar a pluma de odor”, o primeiro passo na localizac¸ ˜ao de fontes de odor, ´e considerado como o problema principal. Para cumprir o primeiro objectivo, esta tese apresenta um m´etodo multi-robˆo de procura olfactiva desenvolvido para ambientes estruturados. Este consiste num algoritmo de fronteira, descentralizado, optimizado por uma func¸ ˜ao de avaliac¸ ˜ao de custo/utilidade que considera pistas de odor (concentrac¸ ˜ao de odor e informac¸ ˜ao de correntes de ar) em cada fronteira, o que leva a que o robˆo tente encontrar fontes de odor o mais depressa poss´ıvel. Para al´em do desafio da “procura olfactiva”, v´arios desafios tais como a “cooperac¸ ˜ao e partilha de tarefas”, “localizac¸ ˜ao e mapeamento multi-robot” e “programac¸ ˜ao e processamento” foram abordados. O m´etodo proposto foi avaliado em simulac¸ ˜oes e num ambiente estruturado real de pequena escala. Os resultados das experiˆencias reais confirmam o efeito que as pistas de odor exercem no comportamento dos robˆos e mostram que ao utilizar o algoritmo proposto, os robˆos exploram primeiro as ´areas com maior probabilidade de existˆencia de fontes de odor. Os resultados das simulac¸ ˜oes confirmam a vantagem da utilizac¸ ˜ao de mais robˆos em ambientes mais complexos. O segundo objectivo, “busca olfactiva com recurso a enxames de robˆos em ambientes desconhecidos e n˜ao-estruturados”, ´e abordado atrav´es de um algoritmo de optimizac¸ ˜ao anal´ıtico. Definindo uma cobertura singular e m´ultipla (fixa e m´ovel) para os sensores de g´as, encontrou-se a configurac¸ ˜ao ´optima para N sensores m´oveis em diferentes ambientes, uma das principais contribuic¸ ˜oes desta tese. Descobriu-se que a topologia ´optima consiste numa configurac¸ ˜ao em linha, com os sensores equidistantes. Independentemente do n´umero de sensores, a distˆancia ´optima entre pares de sensores vizinhos depende da velocidade do vento. calculou-se uma func¸ ˜ao matem´atica capaz de estimar de forma ´optima a distˆancia entre pares vizinhos de sensores, com base na velocidade do vento atrav´es de estimac¸ ˜ao por regress˜ao n˜ao-linear. Com base nos resultados das optimizac¸ ˜oes, desenvolveram-se e apresentaram-se um conjunto de forc¸as de controlo de atracc¸ ˜ao/repuls˜ao para enxames de robots influenciadas pelo vento. O comportamento emergente, converge para as formac¸ ˜oes ´optimas. Os resultados das optimizac¸ ˜oes foram validados e avaliados por experiˆencias em ambientes realistas. Finalmente esta dissertac¸ ˜ao prop˜oe um m´etodo para o “seguimento de plumas de odor e declarac¸ ˜ao de fontes de odor com exames de robˆos”. Apresenta-se a implementac¸ ˜ao do m´etodo proposto em simulac¸ ˜oes e apresentam-se alguns resultados preliminares da sua validac¸ ˜ao bem como a discuss˜ao de trabalho futuro nesta ´area.
VanHoven, Miri K. "Olfactory neuron differentiation in C. elegans /". 2005. http://wwwlib.umi.com/dissertations/search.
Texto completoChee-Ruiter, Christine Wai Jun. "The Biological Sense of Smell: Olfactory Search Behavior and a Metabolic View for Olfactory Perception". Thesis, 2000. https://thesis.library.caltech.edu/7595/1/Chee-Ruiter%202000.pdf.
Texto completoPart I of the thesis describes the olfactory searching and scanning behaviors of rats in a wind tunnel, and a detailed movement analysis of terrestrial arthropod olfactory scanning behavior. Olfactory scanning behaviors in rats may be a behavioral correlate to hippocampal place cell activity.
Part II focuses on the organization of olfactory perception, what it suggests about a natural order for chemicals in the environment, and what this in tum suggests about the organization of the olfactory system. A model of odor quality space (analogous to the "color wheel") is presented. This model defines relationships between odor qualities perceived by human subjects based on a quantitative similarity measure. Compounds containing Carbon, Nitrogen, or Sulfur elicit odors that are contiguous in this odor representation, which thus allows one to predict the broad class of odor qualities a compound is likely to elicit. Based on these findings, a natural organization for olfactory stimuli is hypothesized: the order provided by the metabolic process. This hypothesis is tested by comparing compounds that are structurally similar, perceptually similar, and metabolically similar in a psychophysical cross-adaptation paradigm. Metabolically similar compounds consistently evoked shifts in odor quality and intensity under cross-adaptation, while compounds that were structurally similar or perceptually similar did not. This suggests that the olfactory system may process metabolically similar compounds using the same neural pathways, and that metabolic similarity may be the fundamental metric about which olfactory processing is organized. In other words, the olfactory system may be organized around a biological basis.
The idea of a biological basis for olfactory perception represents a shift in how olfaction is understood. The biological view has predictive power while the current chemical view does not, and the biological view provides explanations for some of the most basic questions in olfaction, that are unanswered in the chemical view. Existing data do not disprove a biological view, and are consistent with basic hypotheses that arise from this viewpoint.
Capítulos de libros sobre el tema "Olfactory search"
Hudson, R. y H. Distel. "Olfactory Guidance of Nipple-Search Behaviour in Newborn Rabbits". En Ontogeny of Olfaction, 243–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-71576-1_19.
Texto completoMcComas, Alan J. "Anatomy of the Limbic System". En Aranzio's Seahorse and the Search for Memory and Consciousness, 209—C32.N14. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192868244.003.0034.
Texto completoMcComas, Alan J. "Final Note: The Multi-Tasking Hippocampus". En Aranzio's Seahorse and the Search for Memory and Consciousness, 289—C44.N4. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192868244.003.0046.
Texto completo"- Reactive and Cognitive Search Strategies for Olfactory Robots". En Neuromorphic Olfaction, 168–87. CRC Press, 2016. http://dx.doi.org/10.1201/b14670-10.
Texto completoMcComas, Alan J. "The Evolutionary Argument". En Aranzio's Seahorse and the Search for Memory and Consciousness, 251—C36.N5. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192868244.003.0038.
Texto completoMcComas, Alan J. "Speculations on Spatial Maps and Other Issues". En Aranzio's Seahorse and the Search for Memory and Consciousness, 283—C43.N15. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192868244.003.0045.
Texto completoMcComas, Alan J. "Recognition of the ‘Limbic System’". En Aranzio's Seahorse and the Search for Memory and Consciousness, 201—C31.N14. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192868244.003.0033.
Texto completo"In Search of Lost Scents: The Olfactory Dimension of Italian Futurism". En 2020, 247–75. De Gruyter, 2020. http://dx.doi.org/10.1515/9783110702200-010.
Texto completoStevens, Martin. "Smelling in Stereo". En Secret Worlds, 136–59. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198813675.003.0006.
Texto completoActas de conferencias sobre el tema "Olfactory search"
Matsukura, Haruka, Hironori Hashiguchi y Hiroshi Ishida. "Olfactory search behavior of human wearing olfactory assist mask". En 2014 IEEE Sensors. IEEE, 2014. http://dx.doi.org/10.1109/icsens.2014.6985420.
Texto completoZhang, Siqi, Rongxin Cui y Demin Xu. "Swarm olfactory search in turbulence environment". En 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). IEEE, 2014. http://dx.doi.org/10.1109/mfi.2014.6997701.
Texto completoIshida, Hanako, Ryuichi Takemura, Haruka Matsukura y Hiroshi Ishida. "Experimental Observation of Olfactory Search Behavior of Crayfish in Seven Arm Maze". En 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). IEEE, 2019. http://dx.doi.org/10.1109/isoen.2019.8823519.
Texto completoGhinea, Gheorghita y Oluwakemi Ademoye. "Olfactory media impact on task performance: The case of a word search game". En 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL). IEEE, 2015. http://dx.doi.org/10.1109/imctl.2015.7359606.
Texto completoPanagiotou, C. F., Davide Cerizza, Tamer A. Zaki y Yosuke Hasegawa. "EVALUATION OF OLFACTORY SEARCH ALGORITHMS USING DIRECT NUMERICAL SIMULATION OF TURBULENT SCALAR TRANSPORT". En Tenth International Symposium on Turbulence and Shear Flow Phenomena. Connecticut: Begellhouse, 2017. http://dx.doi.org/10.1615/tsfp10.800.
Texto completoFernandes, Lucca Ferdinando Queiroz, Raiana Carol de Medeiros Dantas, Maria Clara Medeiros Araújo y Lucas de Oliveira Araújo Andrade. "Non-motor clinical manifestations of Parkinson’s disease and its relevance in early diagnosis". En XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.100.
Texto completoNunes, Alícia Malta Brandão. "COVID-19 and neuroinvasion: a systematic review". En XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.747.
Texto completoHernandez-Reyes, Cesar, Shunsuke Shigaki y Daisuke Kurabayashi. "Effect of the agent size on the performance of an infotactic and a hybrid olfactory search based on the burstiness of odor pulses". En 2021 18th International Conference on Ubiquitous Robots (UR). IEEE, 2021. http://dx.doi.org/10.1109/ur52253.2021.9494650.
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