Bibliografías temáticas / Dynamic visual signal

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Literatura académica sobre el tema "Dynamic visual signal"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 28 de enero de 2023

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Artículos de revistas sobre el tema "Dynamic visual signal"

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Hirata, Yutaka. "Neural signal processing for dynamic visual stabilization." Brain & Neural Networks 11, no. 4 (2004): 176–92. http://dx.doi.org/10.3902/jnns.11.176.

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Nittrouer, Susan, and Joanna H. Lowenstein. "Beyond Recognition: Visual Contributions to Verbal Working Memory." Journal of Speech, Language, and Hearing Research 65, no. 1 (January 12, 2022): 253–73. http://dx.doi.org/10.1044/2021_jslhr-21-00177.

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Purpose: It is well recognized that adding the visual to the acoustic speech signal improves recognition when the acoustic signal is degraded, but how that visual signal affects postrecognition processes is not so well understood. This study was designed to further elucidate the relationships among auditory and visual codes in working memory, a postrecognition process. Design: In a main experiment, 80 young adults with normal hearing were tested using an immediate serial recall paradigm. Three types of signals were presented (unprocessed speech, vocoded speech, and environmental sounds) in thr
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Lajoie, Guillaume, and Lai-Sang Young. "Dynamic Signal Tracking in a Simple V1 Spiking Model." Neural Computation 28, no. 9 (September 2016): 1985–2010. http://dx.doi.org/10.1162/neco_a_00868.

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This work is part of an effort to understand the neural basis for our visual system’s ability, or failure, to accurately track moving visual signals. We consider here a ring model of spiking neurons, intended as a simplified computational model of a single hypercolumn of the primary visual cortex of primates. Signals that consist of edges with time-varying orientations localized in space are considered. Our model is calibrated to produce spontaneous and driven firing rates roughly consistent with experiments, and our two main findings, for which we offer dynamical explanation on the level of n
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Cezário, Rodrigo Roucourt, Vinicius Marques Lopez, Stanislav Gorb, and Rhainer Guillermo-Ferreira. "Dynamic iridescent signals of male copperwing damselflies coupled with wing-clapping displays: the perspective of different receivers." Biological Journal of the Linnean Society 134, no. 1 (June 2, 2021): 229–39. http://dx.doi.org/10.1093/biolinnean/blab068.

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Abstract Dynamic signals are a widespread phenomenon in several taxa, usually associated with intraspecific communication. In contrast, dynamic iridescent signals are detectable only at specific angles of illumination; hence, the animal can hide the signal to avoid detection when necessary. This structural coloration is mostly dependent on the illumination, the contrast against the background and the vision of the receiver. Complex behavioural displays can be coupled with structural coloration to create dynamic visual signals that enhance these functions. Here, we address whether iridescence o
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Pomante, A., L. P. J. Selen, and W. P. Medendorp. "Perception of the dynamic visual vertical during sinusoidal linear motion." Journal of Neurophysiology 118, no. 4 (October 1, 2017): 2499–506. http://dx.doi.org/10.1152/jn.00439.2017.

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The vestibular system provides information for spatial orientation. However, this information is ambiguous: because the otoliths sense the gravitoinertial force, they cannot distinguish gravitational and inertial components. As a consequence, prolonged linear acceleration of the head can be interpreted as tilt, referred to as the somatogravic effect. Previous modeling work suggests that the brain disambiguates the otolith signal according to the rules of Bayesian inference, combining noisy canal cues with the a priori assumption that prolonged linear accelerations are unlikely. Within this mod
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Gielen, CCAM, and JAM van Gisbergen. "The Visual Guidance of Saccades and Fast Aiming Movements." Physiology 5, no. 2 (April 1, 1990): 58–63. http://dx.doi.org/10.1152/physiologyonline.1990.5.2.58.

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Sensory signals are used to create an internal representation of the environment. There is good evidence that the same internal representation of target position in space is shared by different motor systems. A central problem is how the command signal is transformed from a spatial code at the sensory side into a dynamic activation signal appropriately matched to the properties of the effector system.
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J. B. Meyers, S. E. Prussia, and C. J. Karwoski. "Signal Detection Theory for Optimizing Dynamic Visual Inspection Performance." Applied Engineering in Agriculture 6, no. 4 (1990): 412–17. http://dx.doi.org/10.13031/2013.26406.

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López, María T., Antonio Fernández-Caballero, Miguel A. Fernández, José Mira, and Ana E. Delgado. "Visual surveillance by dynamic visual attention method." Pattern Recognition 39, no. 11 (November 2006): 2194–211. http://dx.doi.org/10.1016/j.patcog.2006.04.018.

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Ligon, Russell A., and Kevin J. McGraw. "Social costs enforce honesty of a dynamic signal of motivation." Proceedings of the Royal Society B: Biological Sciences 283, no. 1841 (October 26, 2016): 20161873. http://dx.doi.org/10.1098/rspb.2016.1873.

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Understanding the processes that promote signal reliability may provide important insights into the evolution of diverse signalling strategies among species. The signals that animals use to communicate must comprise mechanisms that prohibit or punish dishonesty, and social costs of dishonesty have been demonstrated for several fixed morphological signals (e.g. colour badges of birds and wasps). The costs maintaining the honesty of dynamic signals, which are more flexible and potentially cheatable, are unknown. Using an experimental manipulation of the dynamic visual signals used by male veiled
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Soundirarajan, Mirra, Mohammad Hossein Babini, Sue Sim, Visvamba Nathan, and Hamidreza Namazi. "Decoding of the Relationship between Brain and Facial Muscle Activities in Response to Dynamic Visual Stimuli." Fluctuation and Noise Letters 19, no. 04 (June 23, 2020): 2050041. http://dx.doi.org/10.1142/s0219477520500418.

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In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of
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Tesis sobre el tema "Dynamic visual signal"

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Júnior, Alcebíades Dal Col. "Visual analytics via graph signal processing." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22102018-112358/.

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The classical wavelet transform has been widely used in image and signal processing, where a signal is decomposed into a combination of basis signals. By analyzing the individual contribution of the basis signals, one can infer properties of the original signal. This dissertation presents an overview of the extension of the classical signal processing theory to graph domains. Specifically, we review the graph Fourier transform and graph wavelet transforms both of which based on the spectral graph theory, and explore their properties through illustrative examples. The main features of the spect
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How, Martin John, and martin how@anu edu au. "The fiddler crab claw-waving display: an analysis of the structure and function of a movement-based visual signal." The Australian National University. Research School of Biological Sciences, 2004. http://thesis.anu.edu.au./public/adt-ANU20081001.111333.

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Communication is an essential component of animal social systems and a diverse suite of signals can be found in the natural environment. An area of animal communication that, for technical reasons, we know very little about is the field of ‘movement-based’ or ‘dynamic’ visual signals. In this thesis, I make use of recent advances in measurement and analysis techniques, including digital video and image motion processing tools, to improve our understanding of how movement-based signals are adjusted according to signalling context. I measured and characterised the flamboyant claw-waving displays
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Huang, Jian. "Dynamic Image Precompensation for Improving Visual Performance of Computer Users with Ocular Aberrations." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/902.

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With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular
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Varatharajah, Alexander. "EEG signal dynamics in unrestricted natural visual search." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/39777.

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EEG signal dynamics in unrestricted natural visual search: By Alexander Varatharajah. Visual search is a skill that if not mastered, would leave you considerably unfit for Darwin’s idea of survival. Prior knowledge of the cognitive processes involved in visual search stemmed from modelling and behavioural studies collecting subjects’ reaction times, their eye movements, or EEG target signatures under fixed-gaze. To further understand these concepts, progression has to be made with the collection of data. Recent co-registration of EEG and Eye-Tracking has further enhanced this understanding by
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Rawashdeh, Samir Ahmed. "VISUAL ATTITUDE PROPAGATION FOR SMALL SATELLITES." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/30.

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As electronics become smaller and more capable, it has become possible to conduct meaningful and sophisticated satellite missions in a small form factor. However, the capability of small satellites and the range of possible applications are limited by the capabilities of several technologies, including attitude determination and control systems. This dissertation evaluates the use of image-based visual attitude propagation as a compliment or alternative to other attitude determination technologies that are suitable for miniature satellites. The concept lies in using miniature cameras to track
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Yavuz, Esin. "Source separation analysis of visual cortical dynamics revealed by voltage sensitive dye imaging." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00836931.

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Ce travail consiste en une analyse statistique de la séparation des sources des enregistrements d'imagerie VSD afin de 1) séparer le signal neuronal des artefacts, et 2) d'étudier la dynamique des motifs spatio-temporels de l'activité créés par les populations neuronales qui partagent des statistiques communes. À cette fin, nous avons effectué des enregistrements en imagerie VSD des zones 17 et 18 du cortex du chat anesthésié et paralysé, en réponse à des réseaux sinusoïdaux de luminance en mouvement et en plein champ. Nous avons développé une méthode d'analyse pour l'imagerie VSD qui sépare l
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How, Martin John. "The fiddler crab claw-waving display: an analysis of the structure and function of a movement-based visual signal." Phd thesis, 2007. http://hdl.handle.net/1885/49333.

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Communication is an essential component of animal social systems and a diverse suite of signals can be found in the natural environment. An area of animal communication that, for technical reasons, we know very little about is the field of ‘movement-based’ or ‘dynamic’ visual signals. In this thesis, I make use of recent advances in measurement and analysis techniques, including digital video and image motion processing tools, to improve our understanding of how movement-based signals are adjusted according to signalling context. I measured and characterised the flamboyant claw-waving displays
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Karakaya, Mahmut. "Collaborative Solutions to Visual Sensor Networks." 2011. http://trace.tennessee.edu/utk_graddiss/1087.

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Visual sensor networks (VSNs) merge computer vision, image processing and wireless sensor network disciplines to solve problems in multi-camera applications in large surveillance areas. Although potentially powerful, VSNs also present unique challenges that could hinder their practical deployment because of the unique camera features including the extremely higher data rate, the directional sensing characteristics, and the existence of visual occlusions. In this dissertation, we first present a collaborative approach for target localization in VSNs. Traditionally; the problem is solved by loca
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Libros sobre el tema "Dynamic visual signal"

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Stokes, Mark, and John Duncan. Dynamic Brain States for Preparatory Attention and Working Memory. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.032.

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This chapter considers how dynamic brain states continuously fine-tune processing to accommodate changes in behavioural context and task goals. First, the authors review the extant literature suggesting that content-specific patterns of preparatory activity bias competitive processing in visual cortex to favour behaviourally relevant input. Next, they consider how higher-level brain areas might provide a top-down attentional signal for modulating baseline visual activity. Extensive evidence suggests that working memory representations in prefrontal cortex are especially important for generatin
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O’Hanlon, James C., Thomas E. White, and Kate D. L. Umbers. Visual communication. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797500.003.0011.

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The diverse ecological niches that insects occupy have led to the immense variation we observe in the structure of their compound eyes and the visual signals that insects can produce. The modular structure of the compound eye, through which insects receive visual information, is a highly adaptable structure capable of impressive feats of image resolution, colour perception, and motion detection, in a range of varying light environments. Additionally, the insect exoskeleton, through which insects produce visual signals and cues, is a dynamic canvas producing a diversity of shapes, textures, pig
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Babo-Rebelo, Mariana, and Catherine Tallon-Baudry. Interoceptive signals, brain dynamics, and subjectivity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198811930.003.0003.

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The self has long been hypothesized to be rooted in the neural monitoring of bodily signals. We propose here to focus on visceral inputs, which present some key characteristics. Inputs from the heart or the gastrointestinal tract are continuously produced, and can reach multiple cortical targets. In addition, cardiac inputs elicit a neural response at each heartbeat that can be recorded non-invasively in humans, even in the absence of measurable changes in bodily state. We review the recent experimental evidence that neural responses to heartbeats are related to the self, in situations where t
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Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Data Preprocessing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0006.

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Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test th
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Bucy, Erik P., and Patrick Stewart. The Personalization of Campaigns: Nonverbal Cues in Presidential Debates. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.52.

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Nonverbal cues are important elements of persuasive communication whose influence in political debates are receiving renewed attention. Recent advances in political debate research have been driven by biologically grounded explanations of behavior that draw on evolutionary theory and view televised debates as contests for social dominance. The application of biobehavioral coding to televised presidential debates opens new vistas for investigating this time-honored campaign tradition by introducing a systematic and readily replicated analytical framework for documenting the unspoken signals tha
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Sandler, Daniela. Counterpreservation. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501703164.001.0001.

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In Berlin, decrepit structures do not always denote urban blight. Decayed buildings are incorporated into everyday life as residences, exhibition spaces, shops, offices, and as leisure space. As nodes of public dialogue, they serve as platforms for dissenting views about the future and past of Berlin. This book introduces the concept of counter-preservation as a way to understand this intentional appropriation of decrepitude. The embrace of decay is a sign of Berlin's iconoclastic rebelliousness, but it has also been incorporated into the mainstream economy of tourism and development as part o
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Capítulos de libros sobre el tema "Dynamic visual signal"

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Narwaria, Manish, Matthieu Perreira Da Silva, and Patrick Le Callet. "High Dynamic Range Visual Quality of Experience Measurement: Challenges and Perspectives." In Visual Signal Quality Assessment, 129–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10368-6_5.

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Yamada, T., K. Ukai, and S. Ishikawa. "Error signal detection and dynamics of accommodation." In Accommodation and Vergence Mechanisms in the Visual System, 213–23. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-7586-8_19.

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Cannas, Barbara, Sara Carcangiu, Alessandra Fanni, Ivan Lupelli, Fulvio Militello, Augusto Montisci, Fabio Pisano, Giuliana Sias, and Nick Walkden. "Convolutional Neural Networks for the Identification of Filaments from Fast Visual Imaging Cameras in Tokamak Reactors." In Neural Advances in Processing Nonlinear Dynamic Signals, 167–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95098-3_15.

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Niebur, Ernst, and Christof Koch. "Models for the Neuronal Implementation of Selective Visual Attention Based on the Temporal Structure of Neural Signals." In Oscillatory Event-Related Brain Dynamics, 295–309. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-1307-4_21.

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Oswald, Julie N., Christine Erbe, William L. Gannon, Shyam Madhusudhana, and Jeanette A. Thomas. "Detection and Classification Methods for Animal Sounds." In Exploring Animal Behavior Through Sound: Volume 1, 269–317. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_8.

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AbstractClassification of the acoustic repertoires of animals into sound types is a useful tool for taxonomic studies, behavioral studies, and for documenting the occurrence of animals. Classification of acoustic repertoires enables the identification of species, age, gender, and individual identity, correlations between sound types and behavior, the identification of changes in vocal behavior over time or in response to anthropogenic noise, comparisons between the repertoires of populations living in different geographic regions and environments, and the development of software tools for automated signal processing. Techniques for classification have evolved over time as technical capabilities have expanded. Initially, researchers applied qualitative methods, such as listening and visually discerning sounds in spectrograms. Advances in computer technology and the development of software for the automatic detection and classification of sounds have allowed bioacousticians to quickly find sounds in recordings, thus significantly reducing analysis time and enabling the analysis of larger datasets. In this chapter, we present software algorithms for automated signal detection (based on energy, Teager–Kaiser energy, spectral entropy, matched filtering, and spectrogram cross-correlation) as well as for signal classification (e.g., parametric clustering, principal component analysis, discriminant function analysis, classification trees, artificial neural networks, random forests, Gaussian mixture models, support vector machines, dynamic time-warping, and hidden Markov models). Methods for evaluating the performance of automated tools are presented (i.e., receiver operating characteristics and precision-recall) and challenges with classifying animal sounds are discussed.
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Brandstetter, Andrea, Najoua Bolakhrif, Christian Schiffer, Timo Dickscheid, Hartmut Mohlberg, and Katrin Amunts. "Deep Learning-Supported Cytoarchitectonic Mapping of the Human Lateral Geniculate Body in the BigBrain." In Lecture Notes in Computer Science, 22–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_2.

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AbstractThe human lateral geniculate body (LGB) with its six sickle shaped layers (lam) represents the principal thalamic relay nucleus for the visual system. Cytoarchitectonic analysis serves as the groundtruth for multimodal approaches and studies exploring its function. This technique, however, requires experienced knowledge about human neuroanatomy and is costly in terms of time. Here we mapped the six layers of the LGB manually in serial, histological sections of the BigBrain, a high-resolution model of the human brain, whereby their extent was manually labeled in every 30th section in both hemispheres. These maps were then used to train a deep learning algorithm in order to predict the borders on sections in-between these sections. These delineations needed to be performed in 1 µm scans of the tissue sections, for which no exact cross-section alignment is available. Due to the size and number of analyzed sections, this requires to employ high-performance computing. Based on the serial section delineations, high-resolution 3D reconstruction was performed at 20 µm isotropic resolution of the BigBrain model. The 3D reconstruction shows the shape of the human LGB and its sublayers for the first time at cellular precision. It represents a use case to study other complex structures, to visualize their shape and relationship to neighboring structures. Finally, our results could provide reference data of the LGB for modeling and simulation to investigate the dynamics of signal transduction in the visual system.
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Maharaj, Gyanpriya, Godfrey Bourne, and Abdullah Ansari. "A Review of Floral Color Signals and Their Heliconiid Butterfly Receivers." In Arthropods [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98666.

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Signals vary in type and function. However, regardless of the signal, effective transmission and receiver detection are needed to exist for communication. This chapter focuses on a review of visual color signals used by plants to attract pollinators. Signal detection work has intensely focused on epigamic signals; therefore, this review adds to the body of knowledge on nonsexual signal communication. In this review, we investigate visual signals as it relates to pollinators. We focus specifically on visual color signals used by Angiosperms flowers, both static and dynamic, and look at their Heliconiid pollinators as these butterflies provide a perfect organism for studies on floral signal use and pollinators’ behavior. We noted that many of these butterflies have three specifically distinct rhodopsins used to identify food and oviposition sites and some have more due to selective pressures of conspecific and mate identification as such they have served as the focal organisms of numerous genetic and ecological studies as they use color signaling in all aspects of their lives. This review further shows that although their color preferences related to feeding, ovipositing, and mate selection have been demonstrated in countless studies, there are gaps in invertebrate literature, as research on the relationships among signal use, evolution, dynamic signals, effects of signals changes on decision making and thus behavior have not been carried out to a large extent.
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Shiell, Derek J., Louis H. Terry, Petar S. Aleksic, and Aggelos K. Katsaggelos. "Audio-Visual and Visual-Only Speech and Speaker Recognition." In Visual Speech Recognition, 1–38. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-186-5.ch001.

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The information imbedded in the visual dynamics of speech has the potential to improve the performance of speech and speaker recognition systems. The information carried in the visual speech signal compliments the information in the acoustic speech signal, which is particularly beneficial in adverse acoustic environments. Non-invasive methods using low-cost sensors can be used to obtain acoustic and visual biometric signals, such as a person’s voice and lip movement, with little user cooperation. These types of unobtrusive biometric systems are warranted to promote widespread adoption of biometric technology in today’s society. In this chapter, the authors describe the main components and theory of audio-visual and visual-only speech and speaker recognition systems. Audio-visual corpora are described and a number of speech and speaker recognition systems are reviewed. Finally, various open issues about the system design and implementation, and present future research and development directions in this area are discussed.
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Plastino, A., and M. T. Martin. "Generalized Information Measures and the Analysis of Brain Electrical Signals." In Nonextensive Entropy. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195159769.003.0020.

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The traditional way of analyzing brain electrical activity, on the basis of electroencephalogram (EEG) records, relies mainly on visual inspection and years of training. Although it is quite useful, of course, one has to acknowledge its subjective nature that hardly allows for a systematic protocol. In order to overcome this undesirable feature, a quantitative EEG analysis has been developed over the years that introduces objective measures. These reflect not only characteristics of the brain activity itself, but also clues concerning the underlying associated neural dynamics. The processing of information by the brain is reflected in dynamical changes of the electrical activity in (i) time, (ii) frequency, and (iii) space. Therefore, the concomitant studies require methods capable of describing the qualitative variation of the signal in both time and frequency. In the present work we introduce new information tools based on the wavelet transform for the assessment of EEG data. In particular, different complexity measures are utilized…. The traditional electroencephalogram (EEG) tracing is now interpreted in much the same way as it was 50 years ago. More channels are used now and much more is known about clinical implication of the waves, but the basic EEG display and quantification of it are quite similar to those of its predecessors. The clinical interpretation of EEG records is made by a complex process of visual pattern recognition and the association with external and evident characteristics of clinical symptomatology. Analysis of EEG signals always involves the queries of quantification, i.e., the ability to state objective data in numerical and/or graphic form that simplify the analysis of long EEG time series. Without such measures, EEG appraisal remains subjective and can hardly lead to logical systematization [36]. Spectral decomposition of the EEG by computing the Fourier transform has been used since the very early days of electroencephalography. The rhythmic nature of many EEG activities lends itself naturally to this analysis. Fourier transform allows separation of various rhythms and estimation of their frequencies independently of each other, a difficult task to perform visually if several rhythmic activities occur simultaneously. Spectral analysis can also quantify the amount of activity in a frequency band.
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Afef, Ouelhazi, Rudy Lussiez, and Molotchnikoff Stephane. "Cortical Plasticity under Ketamine: From Synapse to Map." In Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104787.

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Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.
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Actas de conferencias sobre el tema "Dynamic visual signal"

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Feng, Tian, and Charith Abhayaratne. "Visual Saliency Guided High Dynamic Range Image Compression." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553456.

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Rice, Iain, and David Lowe. "Topographic visual analytics of multibeam dynamic SONAR data." In 2014 Sensor Signal Processing for Defence (SSPD). IEEE, 2014. http://dx.doi.org/10.1109/sspd.2014.6943326.

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Lv, Guoyun, Shuixian Hu, Yangyu Fan, and Min Qi. "Visual emotion recognition based on dynamic models." In 2013 IEEE International Conference on Signal Processing, Communications and Computing. IEEE, 2013. http://dx.doi.org/10.1109/icspcc.2013.6664137.

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Yagi, Nobuyuki, Ryoichi Yajima, Kazumasa Enami, Kazuo Fukui, Nobuyuki Sasaki, Kouji Hoshino, Kazuhiro Harukawa, and Masaru Kogure. "Real-Time Video Signal Processing System For Dynamic Images." In 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, edited by William A. Pearlman. SPIE, 1989. http://dx.doi.org/10.1117/12.970096.

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Sagel, Alexander, and Hao Shen. "Dynamic Variational Autoencoders for Visual Process Modeling." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053660.

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Dal Col, Alcebiades, and Luis Gustavo Nonato. "Visual Analytics via Graph Signal Processing." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8295.

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Resumen
This dissertation presents an overview of the extension of the classical signal processing theory to graph domains. Furthermore, we introduce in this dissertation a novel method for visual analysis of dynamic networks, which relies on the graph wavelet theory. Our method enables the automatic analysis of a signal defined on the nodes of a network. We use a fast approximation of the graph wavelet transform to derive a set of wavelet coefficients, which are then used to identify activity patterns on large networks, including their temporal recurrence. The wavelet coefficients naturally encode sp
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Siracusa, Michael R., and John W. Fisher. "Dynamic Dependency Tests for Audio-Visual Speaker Association." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366271.

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Chen, Duan-Yu, Hsiao-Rong Tyan, Sheng-Wen Shih, and Hong-Yuan Mark Liao. "Dynamic Visual Saliency Modeling for Video Semantics." In 2008 Fourth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2008. http://dx.doi.org/10.1109/iih-msp.2008.306.

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Marcheret, Etienne, Vit Libal, and Gerasimos Potamianos. "Dynamic Stream Weight Modeling for Audio-Visual Speech Recognition." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367227.

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Zhao, Jinlong, Zhiyu Xiang, Lei Liu, and Zhike Zhang. "DMVO: A Multi-motion Visual Odometry for Dynamic Environments." In 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2021. http://dx.doi.org/10.1109/wcsp52459.2021.9613336.

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