Relevant bibliographies by topics / Sensory processing

Academic literature on the topic 'Sensory processing'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Sensory processing"

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Abbas, Jabbar, Amin Al-Habaibeh, and Dai Zhong Su. "Sensor Fusion for Condition Monitoring System of End Milling Operations." Key Engineering Materials 450 (November 2010): 267–70. http://dx.doi.org/10.4028/www.scientific.net/kem.450.267.

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This paper describes the utilisation of multi sensor fusion model using force, vibration, acoustic emission, strain and sound sensors for monitoring tool wear in end milling operations. The paper applies the ASPS approach (Automated Sensor and Signal Processing Selection) method for signal processing and sensor selection [1]. The sensory signals were processed using different signal processing methods to create a wide range of Sensory Characteristic Features (SCFs). The sensitivity of these SCFs to tool wear is investigated. The results indicate that the sensor fusion system is capable of detecting machining faults in comparison to a single sensor using the suggested approach.
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Guo, Yixuan, and Gaoyang Liang. "Perceptual Feedback Mechanism Sensor Technology in e-Commerce IoT Application Research." Journal of Sensors 2021 (September 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/3840103.

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With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e-commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load-balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology.
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Mountstephen, Mary. "Sensory processing." Primary Teacher Update 2011, no. 3 (December 2011): 34–35. http://dx.doi.org/10.12968/prtu.2011.1.3.34.

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Lynch, Sharon A., and Cynthia G. Simpson. "Sensory Processing." Young Exceptional Children 7, no. 4 (July 2004): 2–9. http://dx.doi.org/10.1177/109625060400700401.

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Ishikawa, Masatoshi. "Active Sensor System Using Parallel Processing Circuits." Journal of Robotics and Mechatronics 5, no. 1 (February 20, 1993): 31–37. http://dx.doi.org/10.20965/jrm.1993.p0031.

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In this paper, an active sensor system using parallel processing circuits is proposed and its characteristics are discussed. From the perspective of a model of active touch sensory processing mechanism, the system uses information of efferent copy and internal actuator model in order to generate active motions from the local pattern information detected by local pattern sensors, such as tactile sensors. In addition, an experimental system and its basic experimental results are described. The experimental system is a sensor system for active perception of the shape of two-dimensional objects by tracing the edge of the objects. The system realizes both high speed processing of the local pattern and real-time control of the actuator.
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Listou Grimen, Hanne, and Åge Diseth. "Sensory Processing Sensitivity." Perceptual and Motor Skills 123, no. 3 (October 2, 2016): 637–53. http://dx.doi.org/10.1177/0031512516666114.

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Aron, Elaine N., Arthur Aron, and Jadzia Jagiellowicz. "Sensory Processing Sensitivity." Personality and Social Psychology Review 16, no. 3 (January 30, 2012): 262–82. http://dx.doi.org/10.1177/1088868311434213.

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Maulidi, Halima. "Sensory Processing Challenges." Journal of Developmental & Behavioral Pediatrics 36, no. 6 (2015): 433. http://dx.doi.org/10.1097/dbp.0000000000000185.

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FLANAGAN, JOANNE. "Sensory Processing Disorder." Pediatric News 43, no. 8 (August 2009): 22. http://dx.doi.org/10.1016/s0031-398x(09)70239-5.

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Grimen, Hanne Listou, and Åge Diseth. "Sensory Processing Sensitivity." Comprehensive Psychology 5 (June 2016): 216522281666007. http://dx.doi.org/10.1177/2165222816660077.

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Dissertations / Theses on the topic "Sensory processing"

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Chillingworth, Naomi Lisa. "Cyclooxygenases and sensory processing." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402352.

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Wright, Craig, and n/a. "Sensory Processing in Dyslexic Children." Griffith University. School of Psychology, 2005. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20061018.153411.

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This study tested the prediction that a group of dyslexic children (n = 70) would be less sensitive to auditory and visual temporal stimuli than a control group (n = 52). In the auditory domain, detection thresholds for 2 Hz FM, 2 Hz AM and 20 Hz AM were assessed. The modulations in these stimuli are detected on the basis of temporal cues. In contrast, the modulations in the control stimulus 240 Hz FM modulate too rapidly to be detected with temporal cues. The dyslexic group were significantly less sensitive than the control group to the temporal and non-temporal measures at initial testing (Phase 1) and again nine months later (Phase 4). These data demonstrated that the auditory deficit in the dyslexic group was more general in nature than had previously been suggested. In the visual domain, sensitivity to global coherent motion was assessed. The dyslexic group were significantly less sensitive than the control group on this measure at both phases of the study. Despite the overall between group differences, the magnitude of the effects were low to moderate. There was also substantial overlap between the performance of the two groups on the sensory processing measures. A deviance analysis was conducted to determine the proportion of dyslexic individuals who had sensory processing deficits. When data from each phase was examined separately, the incidence of sensory processing deficits in the dyslexic group was comparable to previous studies. However, when the data from both phases was combined, only 5-18% of the dyslexic group had impairments on any of the sensory tasks that were stable across time. Nevertheless, these results do not preclude sensory processing making a contribution to reading difficulties in some children. When the relationship between sensory processing thresholds and reading ability was considered, sensitivity to auditory and visual temporal measures accounted for significant unique variance in phonological processing, orthographic coding and overall reading skill, even after accounting for IQ and vigilance. This study was also tested the prediction that visual attention can explain the link between visual temporal processing and reading. Vidyasagar (1999) proposed that the magnocellular (M) system, which processes temporal stimuli (e.g., motion), is also important for efficient functioning of an attentional spotlight. This spotlight is proposed to arise in parietal cortex (a major endpoint of the M system), and is involved in highlighting areas for detailed visual processing when performing visual tasks, such as visual search or reading. It was predicted that only those dyslexic participants with motion detection impairments would also be impaired on a serial search task that required the attentional spotlight. On average, the dyslexic group had significantly slower serial search than the control group. However, the magnitude of effect was small and a deviance analysis demonstrated that only 8.5% of the dyslexic group had stable impairments relative to the control group. Furthermore, only one of the six dyslexic participants with a visual attention impairment had a co-existing deficit in detecting coherent motion. Thus, visual attention deficits of this type appear to exist independently of coherent motion deficits. This study also provided important evidence on the reliability of measurement for the sensory processing tasks. The data showed that the test-retest reliability of the sensory measures was only moderate over a nine month period. Test-retest for other cognitive measures over the same time frame was high - including that for an orthographic coding task, which had similar procedure and task demands to the sensory measures. The results also demonstrated that a high proportion of participants in both groups performed inconsistently across time (i.e., they had a threshold indicative of a deficit at one phase and performance within normal limits at the other). Up to 32% of the dyslexic group and 19% of the control group had inconsistent performance on the sensory measures across time. The importance of developing more reliable methods of estimating sensory sensitivity is discussed, as is the need for normative data on sensory processing tasks in order to more accurately make decisions about the incidence of sensory deficits. In summary, this study provided evidence for a relationship between sensory processing and reading. However, the current data demonstrated that sensory processing deficits are not characteristic of all dyslexic individuals. Future research should focus on explaining why only a sub-group of dyslexics have sensory deficits, and also why some control participants have deficits.
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Murdin, L. J. "Audiovestibular sensory processing in migraine." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1331899/.

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Migraine can be conceptualised as a disorder of sensory processing, manifest by such symptoms as headache (pain), phonophobia and photophobia. Current models of migraine pathophysiology incorporate a significant role for the brainstem. Vestibular migraine (VM) is a subtype of the disorder in which significant brainstem dysfunction has been documented. The condition is known to have a significant effect on mental health. This study was designed to investigate disturbances in audiovestibular brainstem function in vestibular migraine in a four part study: 1. Otoacoustic emission suppression by contralateral noise, a test of auditory efferent pathway function, was measured in a group of 33 VM patients and compared with 31 healthy controls. Regression analysis showed a higher rate of abnormality amongst the VM group (p=0.03). 2. Vestibular evoked myogenic potentials were recorded in a group of 30 VM patients and compared with 35 healthy controls. Recordings showed a higher rate of abnormal responses in the VM group than amongst controls (p=0.008). 3. The potential for vestibular stimuli to act as migraine triggers was investigated by observing the effect of vestibular testing or a control condition on 148 individuals. Vestibular stimulation was associated with a significant increase in the probability of developing a migraine attack over the following 24 hour period (p=0.01). 4. Psychological symptoms of depression and anxiety were assessed using questionnaires 39 patients with VM and compared with a control group of 44 patients with dizziness of other causes. Although the VM group had a significantly higher load of symptoms of depression and anxiety, regression modelling showed that this effect was largely accounted for by an excess of dizziness symptoms. In conclusion, this study documents a number of audiovestibular sensory processing abnormalities using a variety of techniques. Vestibular migraine has a significant effect on psychological wellbeing, largely via the associated balance symptoms.
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Wright, Craig. "Sensory Processing in Dyslexic Children." Thesis, Griffith University, 2005. http://hdl.handle.net/10072/366474.

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This study tested the prediction that a group of dyslexic children (n = 70) would be less sensitive to auditory and visual temporal stimuli than a control group (n = 52). In the auditory domain, detection thresholds for 2 Hz FM, 2 Hz AM and 20 Hz AM were assessed. The modulations in these stimuli are detected on the basis of temporal cues. In contrast, the modulations in the control stimulus 240 Hz FM modulate too rapidly to be detected with temporal cues. The dyslexic group were significantly less sensitive than the control group to the temporal and non-temporal measures at initial testing (Phase 1) and again nine months later (Phase 4). These data demonstrated that the auditory deficit in the dyslexic group was more general in nature than had previously been suggested. In the visual domain, sensitivity to global coherent motion was assessed. The dyslexic group were significantly less sensitive than the control group on this measure at both phases of the study. Despite the overall between group differences, the magnitude of the effects were low to moderate. There was also substantial overlap between the performance of the two groups on the sensory processing measures. A deviance analysis was conducted to determine the proportion of dyslexic individuals who had sensory processing deficits. When data from each phase was examined separately, the incidence of sensory processing deficits in the dyslexic group was comparable to previous studies. However, when the data from both phases was combined, only 5-18% of the dyslexic group had impairments on any of the sensory tasks that were stable across time. Nevertheless, these results do not preclude sensory processing making a contribution to reading difficulties in some children. When the relationship between sensory processing thresholds and reading ability was considered, sensitivity to auditory and visual temporal measures accounted for significant unique variance in phonological processing, orthographic coding and overall reading skill, even after accounting for IQ and vigilance. This study was also tested the prediction that visual attention can explain the link between visual temporal processing and reading. Vidyasagar (1999) proposed that the magnocellular (M) system, which processes temporal stimuli (e.g., motion), is also important for efficient functioning of an attentional spotlight. This spotlight is proposed to arise in parietal cortex (a major endpoint of the M system), and is involved in highlighting areas for detailed visual processing when performing visual tasks, such as visual search or reading. It was predicted that only those dyslexic participants with motion detection impairments would also be impaired on a serial search task that required the attentional spotlight. On average, the dyslexic group had significantly slower serial search than the control group. However, the magnitude of effect was small and a deviance analysis demonstrated that only 8.5% of the dyslexic group had stable impairments relative to the control group. Furthermore, only one of the six dyslexic participants with a visual attention impairment had a co-existing deficit in detecting coherent motion. Thus, visual attention deficits of this type appear to exist independently of coherent motion deficits. This study also provided important evidence on the reliability of measurement for the sensory processing tasks. The data showed that the test-retest reliability of the sensory measures was only moderate over a nine month period. Test-retest for other cognitive measures over the same time frame was high - including that for an orthographic coding task, which had similar procedure and task demands to the sensory measures. The results also demonstrated that a high proportion of participants in both groups performed inconsistently across time (i.e., they had a threshold indicative of a deficit at one phase and performance within normal limits at the other). Up to 32% of the dyslexic group and 19% of the control group had inconsistent performance on the sensory measures across time. The importance of developing more reliable methods of estimating sensory sensitivity is discussed, as is the need for normative data on sensory processing tasks in order to more accurately make decisions about the incidence of sensory deficits. In summary, this study provided evidence for a relationship between sensory processing and reading. However, the current data demonstrated that sensory processing deficits are not characteristic of all dyslexic individuals. Future research should focus on explaining why only a sub-group of dyslexics have sensory deficits, and also why some control participants have deficits.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Psychology
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Ferré, Hernandez Isabelle. "Sensory Processing Sensitivity : En valideringsstudie." Thesis, Stockholms universitet, Psykologiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-169862.

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Sensory processing sensitivity is believed to be a personality trait in up to 20% of individuals, including other species than humans. The trait is associated with higher levels of unpleasant arousal, a higher sensitivity to sensory input, empathy and a deeper level of informational processing in the brain. Sensory processing sensitivity is measured using the Highly Sensitive Person Scale (HSPS), which has been evaluated in several languages. Aron & Aron (1997) who first created the scale found that it was unidimensional, however further research suggests that it consists rather of two or three dimensions. In this study (N= 1024) a Swedish version of the HSPS is evaluated through exploratory and confirmatory factor analysis, and results support earlier findings of the scale being multidimensional. Regressions between the dimensions of SPS and outcome variables Managerial Support, Creativity and Percieved Stress show that one of SPS dimensions is a strong predictor for percieved stress, and another dimension is a strong predictor for creativity.
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Clapp, S. A. "Attention and sensory processing for balance." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597695.

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This thesis investigates the role of attention in sensory processing for the maintenance of balance. A review of relevant literature suggests that a method for quantifying the efficiency of sensory processing for balance performance that can result from concurrent attentional demands, the conventional centre-of-pressure (CoP) measures. An approach originally used by Jeka and Lackner (1994) to investigate processing of somatosensory input for balance is adopted. The application of this new method to the investigation of information processing used to examine the temporal relationship between tactile sensory input (shear forces detected at the fingertip) and subsequent corrective postural responses. An initial investigation extended the findings of Jeka and Lackner to normal stance (their cross-correlation findings had been restricted to unstable stance). Results from a further study showed that measures obtained using this procedure were affected by concurrent performance of a cognitive task. In a study involving a prosthetic-limb user, cross-correlation measures indicated that sensory information for balance could be mediated via the artificial limb. A detailed single case study is provided in the fourth experimental chapter, involving a patient whose balance deteriorates under cognitive load. A dual-task paradigm was employed to explore the source of the interference. Cross-correlation methods provided evidence of slowed sensory-motor processing.
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Ray, Nicola. "Visual sensory processing skills in dyslexia." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531804.

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Dotson, Deborah, Michelle Johnson, and Christy Isbell. "Treating Children With Sensory Processing Disorders." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/etsu-works/8281.

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Muro, Catherine Ann. "Sensory Processing Disorders and ADHD Subtypes." Master's thesis, Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/138931.

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Occupational Therapy
M.S.
The purpose of the study was to explore sensory processing patterns with children ages 5 to 12 years who are diagnosed with two subtypes of ADHD, inattention and hyperactive- impulsive and with children who do not have ADHD. The study delineated children with ADHD from a control group of children without ADHD and how sensory processing issues affect the population with ADHD. The participants were parents or caregivers of children aged 5 to 12 years diagnosed with ADHD and parents or caregivers of children aged 5 to 12 years without a diagnosis of ADHD. The participants totaled 45 with 26 participants in the ADHD group and 19 participants in the non ADHD group. Parents or caregivers completed the SSP Caregiver Questionnaire, the Sensory Processing Measure [SPM], and the Connors Parent Rating Scale-Revised: Short Form. The independent t-test was the statistical procedure used to determine whether the means of the ADHD and non-ADHD groups were statistically different from each other. A Pearson correlation was calculated to measure the degree of association between the children with ADHD and non-ADHD with the types of sensory processing patterns. Finding suggested that children who score high on these ADHD scales have more sensory processing difficulties. Children with hyperactivity ADHD scored a significantly higher mean then children with inattention on an auditory subtest and on a under responsive subtest. Children with inattentive ADHD scored a significantly higher mean than children with hyperactivity on a touch subtest.
Temple University--Theses
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Walmsley, Lauren. "Sensory processing in the mouse circadian system." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/sensory-processing-in-the-mouse-circadian-system(bd32ea60-48a8-46d4-b5db-dd83d0326d87).html.

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In order to anticipate the predictable changes in the environment associated with the earth’s rotation, most organisms possess intrinsic biological clocks. To be useful, such clocks require a reliable signal of ‘time’ from the external world. In mammals, light provides the principle source of such information; conveyed to the suprachiasmatic nucleus circadian pacemaker (SCN) either directly from the retina or indirectly via other visual structures such as the thalamic intergeniculate leaflet (IGL). Nonetheless, while the basic pathways supplying sensory information to the clock are well understood, the sensory signals they convey or how these are processed within the circadian system are not. One established view is that circadian entrainment relies on measuring the total amount of environmental illumination. In line with that view, the dense bilateral retinal input to the SCN allows for the possibility that individual neurons could average signals from across the whole visual scene. Here I test this possibility by examining responses to monocular and binocular visual stimuli in the SCN of anaesthetised mice. In fact, these experiments reveal that SCN cells provide information about (at most) irradiance within just one visual hemisphere. As a result, overall light-evoked activity across the SCN is substantially greater when light is distributed evenly across the visual scene when the same amount of light is non-uniformly distributed. Surprisingly then, acute electrophysiological responses of the SCN population do not reflect the total amount of environmental illumination. Another untested suggestion has been that the circadian system might use changes in the spectral composition of light to estimate time of day. Hence, during ‘twilight’, there is a relative enrichment of shortwavelength light, which is detectable as a change in colour to the dichromatic visual system of most mammals. Here I used a ‘silent substitution’ approach to selectively manipulate mouse cone photoreception, revealing a subset of SCN neurons that exhibit spectrally-opponent (blue-yellow) visual responses and are capable of reliably tracking sun position across the day-night transition. I then confirm the importance of this colour discrimination mechanism for circadian entrainment by demonstrating a reliable change in mouse body temperature rhythms when exposed to simulated natural photoperiods with and without simultaneous changes in colour. This identification of chromatic influences on circadian entrainment then raises important new questions such as which SCN cell types process colour signals and do these properties originate in the retina or arise via input from other visual regions? Advances in mouse genetics now offer powerful ways to address these questions. Our original method for studying colour discrimination required transgenic mice with red-shifted cone sensitivity – presenting a barrier to applying this approach alongside other genetic tools. To circumvent this issue I validated a modified approach for manipulating wildtype cone photoreception. Using this approach alongside optogenetic cell-identification I then demonstrate that the thalamic inputs to the SCN are unlikely to provide a major source of chromatic information. To further probe IGL-contributions to SCN visual responses, I next used electrical microstimulation to show that the thalamus provides inhibitory input to both colour and brightness sensitive SCN cells. Using local pharmacological inhibition I then show that thalamic inputs supress specific features of the SCN light response originating with the contralateral retina, including colour discrimination. These data thus provide new insight into the ways that arousal signals reaching the visual thalamus could modulate sensory processing in the SCN. Together then, the work described in this thesis provides important new insight into sensory control of the circadian system and the underlying neural mechanisms.
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Books on the topic "Sensory processing"

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M, Guthrie D., ed. Higher order sensory processing. Manchester: Manchester University Press, 1989.

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Urban, Laszlo, ed. Cellular Mechanisms of Sensory Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78762-1.

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Collin, Shaun P., and N. Justin Marshall, eds. Sensory Processing in Aquatic Environments. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/b97656.

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Sensory Processing Disorder Answer Book. Naperville: Sourcebooks, Inc., 2008.

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name, No. Sensory processing in aquatic environments. New York, NY: Springer, 2003.

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P, Collin Shaun, and Marshall N. Justin, eds. Sensory processing in aquatic environments. New York: Springer, 2003.

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Kollmeier, Birger, Georg Klump, Volker Hohmann, Ulrike Langemann, Manfred Mauermann, Stefan Uppenkamp, and Jesko Verhey, eds. Hearing – From Sensory Processing to Perception. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73009-5.

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B, Laughlin Simon, ed. Principles of sensory coding and processing. Cambridge: Company of Biologists, 1989.

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International Symposium on Hearing (14th 2006 Cloppenburg, Germany). Hearing - from sensory processing to perception. Berlin: Springer, 2007.

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International Symposium on Hearing (14th 2006 Cloppenburg, Germany). Hearing - from sensory processing to perception. Berlin: Springer, 2007.

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Book chapters on the topic "Sensory processing"

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Powers, Kristen M. "Sensory Processing." In Encyclopedia of Autism Spectrum Disorders, 2795–99. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_1201.

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Sharma, Ajay, Helen Cockerill, and Lucy Sanctuary. "Sensory processing." In Mary Sheridan's From Birth to Five Years, 120–23. 5th ed. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003057154-21.

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Blanche, Erna Imperatore, and Stefanie C. Bodison. "Sensory Processing." In An Evidence-Based Guide to Combining Interventions with Sensory Integration in Pediatric Practice, 39–45. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003050810-4.

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Powers, Kristen M. "Sensory Processing." In Encyclopedia of Autism Spectrum Disorders, 4261–65. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_1201.

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Blackwell, Angela Labrie, Anna Wallisch, Lauren M. Little, and Winnie Dunn. "Sensory Processing." In Learners on the Autism Spectrum, 55–72. 3rd ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003373193-6.

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Blackwell, Angela Labrie, Lauren M. Little, and Winnie Dunn. "Sensory Processing." In Introducing Autism, 167–88. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003524663-9.

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Brown, Ted. "Sensory Processing Measure." In Encyclopedia of Autism Spectrum Disorders, 1–10. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-6435-8_1894-3.

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Glennon, Tara J. "Sensory Processing Assessment." In Encyclopedia of Autism Spectrum Disorders, 2799–800. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_1202.

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Brown, Ted. "Sensory Processing Measure." In Encyclopedia of Autism Spectrum Disorders, 2800–2808. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_1894.

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Glennon, Tara J. "Sensory Processing Assessment." In Encyclopedia of Autism Spectrum Disorders, 4265–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_1202.

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Conference papers on the topic "Sensory processing"

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Ewing, R. L., and M. D. Eyster. "Context Processing of Sensory Image Data." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.155.

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Cai, Qifeng, Ming Li, Ming He, and Ru Huang. "Integration of Multimode Sensory and Data Processing in Single-Transistor Sensors." In 2022 International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA). IEEE, 2022. http://dx.doi.org/10.1109/vlsi-tsa54299.2022.9770982.

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Yoon, Taehyung, Taehyun Cha, and Jaeshin Lee. "Correlation of Children's Playfulness and Sensory Processing." In Healthcare and Nursing 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.47.79.

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Dorosheva, Elena. "SENSORY PROCESSING SENSITIVITY AND SELF-REGULATION PROCESSES." In XIX INTERNATIONAL INTERDISCIPLINARY CONGRESS NEUROSCIENCE FOR MEDICINE AND PSYCHOLOGY. LCC MAKS Press, 2023. http://dx.doi.org/10.29003/m3226.sudak.ns2023-19/108-109.

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Lymberopoulos, D., A. S. Ogale, A. Savvides, and Y. Aloimonos. "A sensory grammar for inferring behaviors in sensor networks." In The Fifth International Conference on Information Processing in Sensor Networks. IEEE, 2006. http://dx.doi.org/10.1109/ipsn.2006.243781.

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Costa-López, Borja, Nicolás Ruiz-Robledillo, Rosario Ferrer-Cascales, Natalia Albaladejo-Blázquez, and Miriam Sánchez-SanSegundo. "RELATIONSHIP BETWEEN SENSORY PROCESSING SENSITIVITY AND MENTAL HEALTH." In The 3rd International Electronic Conference on Environmental Research and Public Health —Public Health Issues in the Context of the COVID-19 Pandemic. Basel, Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/ecerph-3-09064.

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Rahman, M. K. Nahan, and P. V. Sruthi. "Real time compressed sensory data processing framework to integrate wireless sensory networks with mobile cloud." In 2015 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2015. http://dx.doi.org/10.1109/get.2015.7453835.

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Reformat, Marek Z., Ronald R. Yager, and Majid RobatJazi. "Multi-level Processing of Sensory Data with Evidence Theory." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491576.

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Lankow, Benjamin S., and Mark S. Goldman. "Competing inhibition-stabilized networks in sensory and memory processing." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645209.

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Takeuchi, Eijiro, and Takashi Tsubouchi. "Sensory Data Processing Middlewares for Service Mobile Robot Applications." In 2006 SICE-ICASE International Joint Conference. IEEE, 2006. http://dx.doi.org/10.1109/sice.2006.315366.

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Reports on the topic "Sensory processing"

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Varshney, Pramod K. Noise Enhanced Sensory Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada567093.

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Gazzaniga, Michael S. Multimodal Interactions in Sensory-Motor Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada255780.

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Hughes, H. C., P. A. Reuter-Lorenz, R. Fendrich, G. Nozawa, and M. S. Gazzaniga. Multimodal Interactions in Sensory-Motor Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada229111.

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Kohn, Adam, Elyse Sussman, and Odelia Schwartz. Linking Man and Machine Through Adaptive Sensory Processing. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada626013.

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Tommerdahl, Mark. Cortical-Cortical Interactions and Sensory Information Processing in Autism. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada586811.

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Tommerdahl, Mark. Cortical-Cortical Interactions and Sensory Information Processing in Autism. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada549257.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada251567.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada259120.

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Horst, John, Anthony Barbera, Craig Schlenoff, and David W. Aha. Identifying sensory processing requirements for an on-road driving application of 4DRCS. Gaithersburg, MD: National Institute of Standards and Technology, 2006. http://dx.doi.org/10.6028/nist.ir.7167.

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Agmon, Eran. A Computational Model of Adaptive Sensory Processing in the Electroreception of Mormyrid Electric Fish. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.291.

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