Relevant bibliographies by topics / Sensory input

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sensory input'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Sensory input"

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Santos, Bruno A., Rogerio M. Gomes, Xabier E. Barandiaran, and Phil Husbands. "Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model." Neural Computation 34, no. 3 (2022): 686–715. http://dx.doi.org/10.1162/neco_a_01471.

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Abstract A growing body of work has demonstrated the importance of ongoing oscillatory neural activity in sensory processing and the generation of sensorimotor behaviors. It has been shown, for several different brain areas, that sensory-evoked neural oscillations are generated from the modulation by sensory inputs of inherent self-sustained neural activity (SSA). This letter contributes to that strand of research by introducing a methodology to investigate how much of the sensory-evoked oscillatory activity is generated by SSA and how much is generated by sensory inputs within the context of
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Bui, Tuan V., and Robert M. Brownstone. "Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study." Journal of Neurophysiology 113, no. 7 (2015): 2824–39. http://dx.doi.org/10.1152/jn.00866.2014.

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Sensory inputs from muscle, cutaneous, and joint afferents project to the spinal cord, where they are able to affect ongoing locomotor activity. Activation of sensory input can initiate or prolong bouts of locomotor activity depending on the identity of the sensory afferent activated and the timing of the activation within the locomotor cycle. However, the mechanisms by which afferent activity modifies locomotor rhythm and the distribution of sensory afferents to the spinal locomotor networks have not been determined. Considering the many sources of sensory inputs to the spinal cord, determini
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Mao, Yu-Ting, Tian-Miao Hua, and Sarah L. Pallas. "Competition and convergence between auditory and cross-modal visual inputs to primary auditory cortical areas." Journal of Neurophysiology 105, no. 4 (2011): 1558–73. http://dx.doi.org/10.1152/jn.00407.2010.

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Sensory neocortex is capable of considerable plasticity after sensory deprivation or damage to input pathways, especially early in development. Although plasticity can often be restorative, sometimes novel, ectopic inputs invade the affected cortical area. Invading inputs from other sensory modalities may compromise the original function or even take over, imposing a new function and preventing recovery. Using ferrets whose retinal axons were rerouted into auditory thalamus at birth, we were able to examine the effect of varying the degree of ectopic, cross-modal input on reorganization of dev
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Ugawa, Yoshikazu. "Sensory input and basal ganglia." Rinsho Shinkeigaku 52, no. 11 (2012): 862–65. http://dx.doi.org/10.5692/clinicalneurol.52.862.

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Franosch, Jan-Moritz P., Sebastian Urban, and J. Leo van Hemmen. "Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions." Neural Computation 25, no. 12 (2013): 3113–30. http://dx.doi.org/10.1162/neco_a_00520.

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How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.
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Bell, C. C., V. Z. Han, Y. Sugawara, and K. Grant. "Synaptic plasticity in the mormyrid electrosensory lobe." Journal of Experimental Biology 202, no. 10 (1999): 1339–47. http://dx.doi.org/10.1242/jeb.202.10.1339.

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The mormyrid electrosensory lateral line lobe (ELL) is one of several different sensory structures in fish that behave as adaptive sensory processors. These structures generate negative images of predictable features in the sensory inflow which are added to the actual inflow to minimize the effects of predictable sensory features. The negative images are generated through a process of association between centrally originating predictive signals and sensory inputs from the periphery. In vitro studies in the mormyrid ELL show that pairing of parallel fiber input with Na+ spikes in postsynaptic c
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Etesami, Jalal, and Philipp Geiger. "Causal Transfer for Imitation Learning and Decision Making under Sensor-Shift." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10118–25. http://dx.doi.org/10.1609/aaai.v34i06.6571.

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Learning from demonstrations (LfD) is an efficient paradigm to train AI agents. But major issues arise when there are differences between (a) the demonstrator's own sensory input, (b) our sensors that observe the demonstrator and (c) the sensory input of the agent we train.In this paper, we propose a causal model-based framework for transfer learning under such “sensor-shifts”, for two common LfD tasks: (1) inferring the effect of the demonstrator's actions and (2) imitation learning. First we rigorously analyze, on the population-level, to what extent the relevant underlying mechanisms (the a
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Havrylovych, Mariia, and Valeriy Danylov. "Research of autoencoder-based user biometric verification with motion patterns." System research and information technologies, no. 2 (August 30, 2022): 128–36. http://dx.doi.org/10.20535/srit.2308-8893.2022.2.10.

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In the current research, we continue our previous study regarding motion-based user biometric verification, which consumes sensory data. Sensory-based verification systems empower the continuous authentication narrative – as physiological biometric methods mainly based on photo or video input meet a lot of difficulties in implementation. The research aims to analyze how various components of sensor data from an accelerometer affect and contribute to defining the process of unique person motion patterns and understanding how it may express the human behavioral patterns with different activity t
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Henn, V. "Sensory Input Modifying Central Motor Actions." Stereotactic and Functional Neurosurgery 49, no. 5 (1986): 251–55. http://dx.doi.org/10.1159/000100183.

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Stolz, Thomas, Max Diesner, Susanne Neupert, et al. "Descending octopaminergic neurons modulate sensory-evoked activity of thoracic motor neurons in stick insects." Journal of Neurophysiology 122, no. 6 (2019): 2388–413. http://dx.doi.org/10.1152/jn.00196.2019.

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Neuromodulatory neurons located in the brain can influence activity in locomotor networks residing in the spinal cord or ventral nerve cords of invertebrates. How inputs to and outputs of neuromodulatory descending neurons affect walking activity is largely unknown. With the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and immunohistochemistry, we show that a population of dorsal unpaired median (DUM) neurons descending from the gnathal ganglion to thoracic ganglia of the stick insect Carausius morosus contains the neuromodulatory amine octopamine. These
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Dissertations / Theses on the topic "Sensory input"

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McNair, Nicolas A. "Input-specificity of sensory-induced neural plasticity in humans." Thesis, University of Auckland, 2008. http://hdl.handle.net/2292/3285.

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The aim of this thesis was to investigate the input-specificity of sensory-induced plasticity in humans. This was achieved by varying the characteristics of sine gratings so that they selectively targeted distinct populations of neurons in the visual cortex. In Experiments 1-3, specificity was investigated with electroencephalography using horizontally- and vertically-oriented sine gratings (Experiment 1) or gratings of differing spatial frequency (Experiments 2 & 3). Increases in the N1b potential were observed only for sine gratings that were the same in orientation or spatial frequency as t
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Nargis, Sultana Mahbuba. "Sensory Input and Mental Imagery in Second Language Acquisition." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418370678.

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Kim, Jung-Kyong. "Sensory substitution learning using auditory input: Behavioral and neural correlates." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96695.

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Sensory substitution refers to the replacement of one sensory input with another. This concept, originally developed to aid the blind, presents a scientific opportunity to study crossmodal perceptual learning and neural plasticity. Using a technique that translates vision into sound, the present dissertation examined sensory substitution learning. Four studies tested the hypotheses that mental representations of spatial information such as shape are abstract, and that they are based on involvement of common brain regions independently of sensory modality. Study 1 aimed to develop a trainin
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Lovell, Nathan, and N/A. "Machine Vision as the Primary Sensory Input for Mobile, Autonomous Robots." Griffith University. School of Information and Communication Technology, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20070911.152447.

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Image analysis, and its application to sensory input (computer vision) is a fairly mature field, so it is surprising that its techniques are not extensively used in robotic applications. The reason for this is that, traditionally, robots have been used in controlled environments where sophisticated computer vision was not necessary, for example in car manufacturing. As the field of robotics has moved toward providing general purpose robots that must function in the real world, it has become necessary that the robots be provided with robust sensors capable of understanding the complex world aro
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Xin, Yifei. "Exploring the Chinese Room: Parallel Sensory Input in Second Language Learning." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1333762798.

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Lovell, Nathan. "Machine Vision as the Primary Sensory Input for Mobile, Autonomous Robots." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/367107.

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Image analysis, and its application to sensory input (computer vision) is a fairly mature field, so it is surprising that its techniques are not extensively used in robotic applications. The reason for this is that, traditionally, robots have been used in controlled environments where sophisticated computer vision was not necessary, for example in car manufacturing. As the field of robotics has moved toward providing general purpose robots that must function in the real world, it has become necessary that the robots be provided with robust sensors capable of understanding the complex world aro
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Ortman, Robert L. "Sensory input encoding and readout methods for in vitro living neuronal networks." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44856.

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Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different
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Chakrabarty, Arnab. "Role of sensory input in structural plasticity of dendrites in adult neuronal networks." Diss., lmu, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-155241.

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Zhao, Yifan. "Language Learning through Dialogs:Mental Imagery and Parallel Sensory Input in Second Language Learning." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1396634043.

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MacBride, Claire Ann MacBride. "Mental Imagery as a Substitute for Parallel Sensory Input in the Field of SLA." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525379740507044.

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Books on the topic "Sensory input"

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Proebster, Walter E. Peripherie von Informationssystemen: Technologie und Anwendung : Eingabe, Tastatur, Sensoren, Sprache etc. : Ausgabe, Drucker, Bildschirm, Anzeigen etc. : externe Speicher, Magnetik, Optik etc. Springer-Verlag, 1987.

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AIPR Workshop (26th 1997 Washington, D.C.). Exploiting new image sources and sensors: 26th AIPR Workshop, 15-17 October 1997, Washington, D.C. Edited by Selander J. Michael 1952-, Society of Photo-optical Instrumentation Engineers., and AIPR Executive Committee. SPIE, 1998.

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Tyagi, Amit Kumar. Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality. IGI Global, 2021.

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Tyagi, Amit Kumar. Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality. IGI Global, 2021.

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Tyagi, Amit Kumar. Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality. IGI Global, 2021.

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Tyagi, Amit Kumar. Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality. IGI Global, 2021.

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Tyagi, Amit, and Shamila Mohammed. Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality. IGI Global, 2020.

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Stoneley, Sarah, and Simon Rinald. Sensory loss. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0047.

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Sensory disturbance can either be a complete loss (anaesthesia) or a reduction (hypoaesthesia) in the ability to perceive the sensory input. Dysaesthesia is an abnormal increase in the perception of normal sensory stimuli. Hyperalgesia is an increased sensitivity to normally painful stimuli, and allodynia is the perception of usually innocuous stimuli as painful. A complete loss of sensation is likely to be due to a central nervous system problem, while a tingling/paraesthesia (large fibre) or burning/temperature (small fibre) sensation is likely due to an acquired peripheral nervous system pr
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Strayer. Lose Weight by Decreasing Sensory Input: A Revolutionary Mind-Body Approach. Dorrance Publishing Co., Inc., 2004.

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Thoonsen, Monique, and Carmen Lamp. Sensory Solutions in the Classroom. Jessica Kingsley Publishers, 2022. https://doi.org/10.5040/9781805014836.

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Every teacher knows them - the students who are continuously balancing on their chair legs or who prefer to hide in their hoodies all day long. These students are using all kinds of tricks to be able to stay focused, as they are under- or overresponsive to sensory input and trying to restore their balance. Children who struggle with processing sensory input can experience a wide range of symptoms, including hypersensitivity to sound, sight and touch, poor fine motor skills and easy distractibility. Using this accessible, science-based guide, school staff can support these students by understan
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Book chapters on the topic "Sensory input"

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Wells-Jensen, Sheri. "Cognition, Sensory Input, and Linguistics." In Xenolinguistics. Routledge, 2023. http://dx.doi.org/10.4324/9781003352174-13.

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Stein, Wolfgang. "Sensory Input to Central Pattern Generators." In Encyclopedia of Computational Neuroscience. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_465.

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Johansson, Roland S. "Sensory Input and Control of Grip." In Novartis Foundation Symposia. John Wiley & Sons, Ltd., 2007. http://dx.doi.org/10.1002/9780470515563.ch4.

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Stein, Wolfgang. "Sensory Input to Central Pattern Generators." In Encyclopedia of Computational Neuroscience. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_465-3.

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Stein, Wolfgang. "Sensory Input to Central Pattern Generators." In Encyclopedia of Computational Neuroscience. Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4614-7320-6_465-4.

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Strösslin, Thomas, Christophe Krebser, Angelo Arleo, and Wulfram Gerstner. "Combining Multimodal Sensory Input for Spatial Learning." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_15.

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Bullock, Theodore H. "The Comparative Neurology of Expectation: Stimulus Acquisition and Neurobiology of Anticipated and Unanticipated Input." In Sensory Biology of Aquatic Animals. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3714-3_10.

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Bereiter, D. A., E. J. DeMaria, W. C. Engeland, and D. S. Gann. "Endocrine Responses to Multiple Sensory Input Related to Injury." In Advances in Experimental Medicine and Biology. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-2064-5_20.

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Clark, Lauren. "Sensory Awareness – Understanding Your Unique Brain Response to Sensory Input from the World Around You." In Das menschliche Büro - The human(e) office. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33519-9_9.

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Katori, Yuichi. "Brain-Inspired Reservoir Computing Models." In Photonic Neural Networks with Spatiotemporal Dynamics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5072-0_13.

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AbstractThis chapter presents an overview of brain-inspired reservoir computing models for sensory-motor information processing in the brain. These models are based on the idea that the brain processes information using a large population of interconnected neurons, where the dynamics of the system can amplify, transform, and integrate incoming signals. We discuss the reservoir predictive coding model, which uses predictive coding to explain how the brain generates expectations regarding sensory input and processes incoming signals. This model incorporates a reservoir of randomly connected neur
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Conference papers on the topic "Sensory input"

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Bounou, Oumayma, Jean Ponce, and Justin Carpentier. "Learning System Dynamics from Sensory Input under Optimal Control Principles." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886191.

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Furtner, Lukas, Isabell Viedt, and Leon Urbas. "A combined approach to optimization of soft sensor architecture and physical sensor configuration." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.103294.

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In the chemical industry, soft sensors are deployed to reduce equipment cost or allow for a continuous measurement of process variables. Soft sensors monitor parameters not via physical sensors but infer them from other process variables. On the one hand, the precision of a soft sensors is affected by its architecture, the choice of parametric equations like balances and thermodynamic or kinetic dependencies in the soft sensor model. On the other hand, uncertainty that is inherent to the input variable values propagates through the soft sensor model and impacts the output uncertainty. The latt
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Morcos, Michael, Edward Bachelder, Martine Godfroy-Cooper, Spencer Fishman, and Umberto Saetti. "Full-Body Haptic and Spatial Audio Cueing Algorithms for Augmented Pilot Perception." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1179.

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This paper illustrates the development, implementation, and testing of full-body haptic and spatial audio cueing algorithms for augmented pilot perception. Cueing algorithms are developed for roll-axis compensatory tracking tasks where the pilot acts on the displayed error between a desired input and the comparable vehicle output motion to produce a control action. The error is displayed to the pilot using multiple cueing modalities: visual, haptic, audio, and combinations of these. For the visual and combined visual haptic/audio modalities, visual cues are also considered in degraded visual e
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Theisen, M. F., G. M. H. Meesters, and A. M. Schweidtmann. "Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.185977.

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Data-driven soft sensors help in process operations by providing real-time estimates of otherwise hard to measure process quantities, e.g., viscosities or product concentrations. Currently, soft sensors need to be developed individually per plant. Using transfer learning, machine learning based soft sensors could be re-used and fine-tuned across plants and applications. However, transferring data-driven soft sensor models is in practice often not possible, because the fixed input structure of standard soft sensor models prohibits transfer if, e.g., the sensor information is not identical in al
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Evans, Richard, Matko Bošnjak, Lars Buesing, et al. "Making Sense of Raw Input (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/799.

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How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed the raw sensory input had already been parsed using a set of discrete categori
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Jeon, Soo. "State Estimation for Kinematic Model Over Lossy Network." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4297.

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The major benefit of the kinematic Kalman filter (KKF), i.e., the state estimation based on kinematic model is that it is immune to parameter variations and unknown disturbances regardless of the operating conditions. In carrying out complex motion tasks such as the coordinated manipulation among multiple machines, some of the motion variables measured by sensors may only be available through the communication layer, which requires to formulate the optimal state estimator subject to lossy network. In contrast to standard dynamic systems, the kinematic model used in the KKF relies on sensory da
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Hill, Chris, Casey Lee Hunt, Sammie Crowder, Brett Fiedler, Emily B. Moore, and Ann Eisenberg. "Investigating Sensory Extensions as Input for Interactive Simulations." In TEI '23: Seventeenth International Conference on Tangible, Embedded, and Embodied Interaction. ACM, 2023. http://dx.doi.org/10.1145/3569009.3573108.

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Wurdemann, Helge A., Evangelos Georgiou, Lei Cui, and Jian S. Dai. "SLAM Using 3D Reconstruction via a Visual RGB and RGB-D Sensory Input." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47735.

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This paper investigates simultaneous localization and mapping (SLAM) problem by exploiting the Microsoft Kinect™ sensor array and an autonomous mobile robot capable of self-localization. The combination of them covers the major features of SLAM including mapping, sensing, locating, and modeling. The Kinect™ sensor array provides a dual camera output of RGB, using a CMOS camera, and RGB-D, using a depth camera. The sensors will be mounted on the KCLBOT, an autonomous nonholonomic two wheel maneuverable mobile robot. The mobile robot platform has the ability to self-localize and preform navigati
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Kruijff, Ernst, Gerold Wesche, Kai Riege, Gernot Goebbels, Martijn Kunstman, and Dieter Schmalstieg. "Tactylus, a pen-input device exploring audiotactile sensory binding." In the ACM symposium. ACM Press, 2006. http://dx.doi.org/10.1145/1180495.1180557.

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Wakatabe, Ryo, Yasuo Kuniyoshi, and Gordon Cheng. "O (logn) algorithm for forward kinematics under asynchronous sensory input." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989291.

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Reports on the topic "Sensory input"

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Parker, Michael, Alex Stott, Brian Quinn, Bruce Elder, Tate Meehan, and Sally Shoop. Joint Chilean and US mobility testing in extreme environments. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42362.

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Vehicle mobility in cold and challenging terrains is of interest to both the US and Chilean Armies. Mobility in winter conditions is highly vehicle dependent with autonomous vehicles experiencing additional challenges over manned vehicles. They lack the ability to make informed decisions based on what they are “seeing” and instead need to rely on input from sensors on the vehicle, or from Unmanned Aerial Systems (UAS) or satellite data collections. This work focuses on onboard vehicle Controller Area Network (CAN) Bus sensors, driver input sensors, and some externally mounted sensors to assist
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Madsen, Jens, Nikhil Kuppa, and Lucas Parra. The Brain, Body, and Behaviour Dataset - Neural Engineering Lab, CCNY. Fcp-indi, 2025. https://doi.org/10.15387/fcp_indi.retro.bbbd.

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When humans engage with video, their brain and body interact in response to sensory input. To investigate these interactions, we recorded and are releasing a dataset from N=178 participants across five experiments featuring short online educational videos. This dataset comprises approximately 110 hours of multimodal data including electrocardiogram (ECG), heart rate, respiration, breathing rate, pupil size, electrooculogram (EOG), gaze position, saccades, blinks, fixations, head movement, and electroencephalogram (EEG). Participants viewed 3-6 videos (mean total duration: 28±5 min) to test att
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Jones, Scott B., Shmuel P. Friedman, and Gregory Communar. Novel streaming potential and thermal sensor techniques for monitoring water and nutrient fluxes in the vadose zone. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7597910.bard.

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The “Novel streaming potential (SP) and thermal sensor techniques for monitoring water and nutrient fluxes in the vadose zone” project ended Oct. 30, 2015, after an extension to complete travel and intellectual exchange of ideas and sensors. A significant component of this project was the development and testing of the Penta-needle Heat Pulse Probe (PHPP) in addition to testing of the streaming potential concept, both aimed at soil water flux determination. The PHPP was successfully completed and shown to provide soil water flux estimates down to 1 cm day⁻¹ with altered heat input and timing a
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Kuznetsov, Victor, Vladislav Litvinenko, Egor Bykov, and Vadim Lukin. A program for determining the area of the object entering the IR sensor grid, as well as determining the dynamic characteristics. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/bykov.0415.15042021.

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Currently, to evaluate the dynamic characteristics of objects, quite a large number of devices are used in the form of chronographs, which consist of various optical, thermal and laser sensors. Among the problems of these devices, the following can be distinguished: the lack of recording of the received data; the inaccessibility of taking into account the trajectory of the object flying in the sensor area, as well as taking into consideration the trajectory of the object during the approach to the device frame. The signal received from the infrared sensors is recorded in a separate document in
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Beshouri, Greg, and Bob Goffin. PR-309-15209-R01 Evaluation of NSCR Specific Models for Use in CEPM. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011554.

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This 2015 NSCR project continues NSCR research started in 2009 and continued in 2011 under ERLE 2c and combines it with OBD research started in 2008 and continued in 2011. The 2009 NSCR research concluded that downstream measurement of lambda, O2 and NOx are useful for understanding performance of the entire package and evaluating compliance status and diagnosing system problems. However, that research also concluded that advanced signal conditioning and algorithms are required for unambiguous diagnostics. It also concluded system diagnostics was complex and beyond the capabilities of typical
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McMurtrey, Michael, Kunal Mondal, Joseph Bass, Kiyo Fujimoto, and Austin Biaggne. Report on plasma jet printer for sensor fabrication with process parameters optimized by simulation input. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1668670.

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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real tim
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Baker, John L., James L. Olds, and Joel L. Davis. A Novel Approach to Large Scale Brain Network Models: An Algorithmic Model for Place Cell Emergence With Robotic Sensor Input. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425321.

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Meiri, Noam, Michael D. Denbow, and Cynthia J. Denbow. Epigenetic Adaptation: The Regulatory Mechanisms of Hypothalamic Plasticity that Determine Stress-Response Set Point. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7593396.bard.

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Our hypothesis was that postnatal stress exposure or sensory input alters brain activity, which induces acetylation and/or methylation on lysine residues of histone 3 and alters methylation levels in the promoter regions of stress-related genes, ultimately resulting in long-lasting changes in the stress-response set point. Therefore, the objectives of the proposal were: 1. To identify the levels of total histone 3 acetylation and different levels of methylation on lysine 9 and/or 14 during both heat and feed stress and challenge. 2. To evaluate the methylation and acetylation levels of histone
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