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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...60 Hits 1 – 20 of 1.183 1 Measuring the Reading-Attention Relationship: Functional Differences in Working Memory Activity During Single Word Decoding in Children With and Without Reading Disorder Sinha, Niki. - : Brock University, 2022 BASE Show details 2 Memory as the political art in Plato's Statesman. Craig, Catherine, 1995-. - 2022 BASE Show details 3 Bilingualism, Math Anxiety, and Math Performance Yu, Tianle. - : The Ohio State University, 2022 BASE Show details 4 Voicing trauma: ungraspable idea and comprehensible presentation In Arnold Schoenberg's String Trio, Op. 45 and A survivor from Warsaw Jacobs, Ruth. - : The University of Edinburgh, 2022 BASE Show details 5 Temporal cognition: subjective time and its connection with memory in frontotemporal dementia Liu, Lulu. - : The University of Sydney, 2022. : Faculty of Science, School of Psychology, 2022 BASE Show details 6 The adaptable mind : what neuroplasticity and neural reuse tell us about language and cognition Zerilli, John. - New York, NY : Oxford University Press, 2021 BLLDB UB Frankfurt Linguistik Show details 7 Cognitive and communication interventions : neuroscience applications for speech-language pathologists Burns, Martha S.. - San Diego : Plural Publishing, 2021 BLLDB UB Frankfurt Linguistik Show details 8 An investigation of human error in software development Nagaria, Bhaveet. - : Brunel University London, 2021 BASE Show details 9 The Mental Representation of Visual Information Robitaille, Joel. - : Brock University, 2021 BASE Show details 10 Deep Learning Methods for Human Behavior Recognition Lu, Jia. - : Auckland University of Technology, 2021 BASE Show details 11 Motoric and Language Systems Associated with Note-taking: Going Beyond Handwriting Speed Tyson, Elena Aurora Salazar. - 2021 BASE Show details 12 Gesturing at Encoding Enhances Episodic Memory Recall for Older Adults. Simhairi, Voula Sadie. - 2021 BASE Show details 13 Discursive forms. Memory and identity Brîndușa-Mariana, Amălăncei; Raluca, Bălăiță; Elena, Bonta. - : HAL CCSD, 2021. : Editura Alma Mater, 2021 In: ISSN: 2065-3204 ; Interstudia ; Conférence internationale Formes discursives. Mémoire et identité(s) ; https://hal.univ-lorraine.fr/hal-03541794 ; Conférence internationale Formes discursives. Mémoire et identité(s), Apr 2021, Bacău, Roumanie. Interstudia, 31, 2021 ; http://interstudia.ub.ro/issue/34 (2021) BASE Show details 14 SANKAT PRADATA AUR JANATA. ... Tomar, Neetusingh. - : figshare, 2021 BASE Show details 15 SANKAT PRADATA AUR JANATA. ... Tomar, Neetusingh. - : figshare, 2021 BASE Show details 16 Motoric and Language Systems Associated with Note-taking: Going Beyond Handwriting Speed ... Tyson, Elena Aurora Salazar. - : Columbia University, 2021 BASE Show details 17 A procura do Eu na identidade familiar: autobiografia (indireta) no documentário “Além do horizonte” Fontoura, Ana Priscila Laurindo Pereira. - 2021 BASE Show details 18 The Aristocratic Body and the Memory Economy of Church Reform, 900-1300 C.E. Sapp, Jonathan Taylor. - 2021 BASE Show details 19 Human Gait Phase Recognition in Embedded Sensor System Liu, Zhenbang. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021 Abstract: Gait analysis can improve our understanding of gait to improve medical diagnosis or treatment in clinical assessment. Studying the gait cycle in an embedded sensor system is essential for the detection of any abnormal walking pattern. This project aims to investigate several methods for gait phase recognition on embedded systems based on Hidden Markov Model (HMM) and Long short term memory (LSTM). This project proposes three methods, single HMM, multiple HMMs, and LSTM models, to identify the phase number in one gait. Single HMM has been constructed with the unit of gait via HMM learning. The corresponding phase number in the hidden state sequence can be selected for the observations via HMM decoding. Multiple HMMs have been constructed with the unit of phase instead of gait via HMM learning. The HMM evaluation can select the corresponding phase number in the hidden state sequence with the largest log- likelihood. Frame blocking and windowing function is also applied to evaluate these two methods. Estimation, validation, and forecast are implemented in the LSTM method as a benchmark. After comparing and evaluating the three methods for phase inference in terms of execution time, accuracy, and limitations, the method with multiple HMMs can provide satisfactory accuracy of gait phase number recognition in a relatively short time. It can be concluded that the multiple HMMs method may be more suitable for application in this phase inference scenario on the embedded sensor processing systems if the timing requirement is not so stringent. ; Gånganalys kan förbättra vår förståelse för gång för att förbättra medicinsk diagnos eller behandling vid klinisk bedömning. Att studera gångcykeln i ett inbyggt sensorsystem är avgörande för detektering av onormalt gångmönster. Detta projekt syftar till att undersöka flera metoder för gångfasinferens på inbäddade system baserat på Hidden Markov Model (HMM) och Long short term memory (LSTM). I detta projekt har tre metoder, enstaka HMM, flera HMM och LSTM-modeller, föreslagits för att identifiera fasnumret i en gång. Enstaka HMM har konstruerats med gångenheten via HMM-lärande. Motsvarande fasnummer i den dolda tillståndssekvensen kan väljas för observationerna via HMM-avkodning. Flera HMM har konstruerats med fasenheten istället för gång via HMM-lärande. Motsvarande fasnummer i den dolda tillståndssekvensen kan väljas med störst logsannolikhet via HMM-utvärdering. Frame Blocking och Windowing-funktionen används också för att utvärdera dessa två metoder. Uppskattning, validering och prognos implementeras i LSTM-metoden som ett riktmärke. Efter att ha jämfört och utvärderat de tre metoderna för fasinferens när det gäller exekveringstid, noggrannhet och begränsningar kan metoden med flera HMM: er uppnå tillfredsställande noggrannhet för fasnummerigenkänning på relativt kort tid. Vi kan dra slutsatsen att den flera HMM-metoden kan vara mer lämplig för tillämpning i detta fasinferensscenario på de inbyggda sensorbehandlingssystemen om tidskravet inte är så strikt. Keyword: Computer and Information Sciences; Data- och informationsvetenskap; Dold Markov modell; Gait Phases; gångfaser; Hidden Markov Model; långtidsminne; Long Short Term Memory; State Inference; Tillståndsinferens URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-304889 BASE Hide details 20 Dynamic gesture classification of American Sign Language using deep learning Vaghasiya, Devina. - : Laurentian University of Sudbury, 2021 BASE Show details Page: 1 2 3 4 5...60

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