Дисертації з теми "Bayesian recovery"
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Tan, Xing. "Bayesian sparse signal recovery." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0041176.
Повний текст джерелаKarseras, Evripidis. "Hierarchical Bayesian models for sparse signal recovery and sampling." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32102.
Повний текст джерелаEchavarria, Gregory Maria Angelica. "Predictive Data-Derived Bayesian Statistic-Transport Model and Simulator of Sunken Oil Mass." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/471.
Повний текст джерелаTang, Man. "Bayesian population dynamics modeling to guide population restoration and recovery of endangered mussels in the Clinch River, Tennessee and Virginia." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/49598.
Повний текст джерелаPassive integrated transponder (PIT) tags were applied in my fieldwork to monitor the translocation efficiency of E. capsaeformis and Actinonaias pectorosa at Cleveland Islands (CRM 270.8). Hierarchical Bayesian models were developed to address the individual variability and sex-related differences in growth. In model selection, the model considering individual variability and sex-related differences (if a species has sexual dimorphism) yielded the lowest DIC value. The results from the best model showed that the mean asymptotic length and mean growth rate of female E. capsaeformis were 45.34 mm and 0.279, which were higher than values estimated for males (42.09 mm and 0.216). The mean asymptotic length and mean growth rate for A. pectorosa were 104.2 mm and 0.063, respectively.
To test for the existence of individual and sex-related variability in survival and recapture rates, Bayesian models were developed to address the variability in the analysis of the mark-recapture data of E. capsaeformis and A. pectorosa. DIC was used to compare different models. The median survival rates of male E. capsaeformis, female E. capsaeformis and A. pectorosa were high (>87%, >74% and >91%), indicating that the habitat at Cleveland Islands was suitable for these two mussel species within this survey duration. In addition, the median recapture rates for E. capsaeformis and A. pectorosa were >93% and >96%, indicating that the PIT tag technique provided an efficient monitoring approach. According to model comparison results, the non-hierarchical model or the model with sex--related differences (if a species is sexually dimorphic) in survival rate was suggested for analyzing mark-recapture data when sample sizes are small.
Master of Science
Cave, Vanessa M. "Statistical models for the long-term monitoring of songbird populations : a Bayesian analysis of constant effort sites and ring-recovery data." Thesis, St Andrews, 2010. http://hdl.handle.net/10023/885.
Повний текст джерелаDine, James. "A habitat suitability model for Ricord's iguana in the Dominican Republic." Connect to resource online, 2009. http://hdl.handle.net/1805/1889.
Повний текст джерелаTitle from screen (viewed on August 27, 2009). Department of Geography, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Jan Ramer, Aniruddha Banergee, Jeffery Wilson. Includes vita. Includes bibliographical references (leaves 47-52).
Sugimoto, Tatsuhiro. "Anelastic Strain Recovery Method for In-situ Stress Measurements: A novel analysis procedure based on Bayesian statistical modeling and application to active fault drilling." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263637.
Повний текст джерелаChen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.
Повний текст джерелаDoctor of Philosophy
The development of automation systems and robotics brought the modern world unrivaled affluence and convenience. However, the current automated tasks are mainly simple repetitive motions. Tasks that require more artificial capability with advanced visual cognition are still an unsolved problem for automation. Many of the high-level cognition-based tasks require the accurate visual perception of the environment and dynamic objects from the data received from the optical sensor. The capability to represent, identify and interpret complex visual data for understanding the geometric structure of the world is 3D perception. To better tackle the existing 3D perception challenges, this dissertation proposed a set of generative learning-based frameworks on sparse tensor data for various high-dimensional robotics perception applications: underwater point cloud filtering, image restoration, deformation detection, and localization. Underwater point cloud data is relevant for many applications such as environmental monitoring or geological exploration. The data collected with sonar sensors are however subjected to different types of noise, including holes, noise measurements, and outliers. In the first chapter, we propose a generative model for point cloud data recovery using Variational Bayesian (VB) based sparse tensor factorization methods to tackle these three defects simultaneously. In the second part of the dissertation, we propose an image restoration technique to tackle missing data, which is essential for many perception applications. An efficient generative chaotic RNN framework has been introduced for recovering the sparse tensor from a single corrupted image for various types of missing data. In the last chapter, a multi-level CNN for high-dimension tensor feature extraction for underwater vehicle localization has been proposed.
SEDDA, GIULIA. "The interplay between movement and perception: how interaction can influence sensorimotor performance and neuromotor recovery." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1011732.
Повний текст джерелаQuer, Giorgio. "Optimization of Cognitive Wireless Networks using Compressive Sensing and Probabilistic Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421992.
Повний текст джерелаLa combinazione delle informazioni nelle reti di sensori wireless è una soluzione promettente per aumentare l'efficienza delle techiche di raccolta dati. Nella prima parte di questa tesi viene affrontato il problema della ricostruzione di segnali distribuiti tramite la raccolta di un piccolo numero di campioni al punto di raccolta dati (DCP). Viene sfruttato il metodo dell'analisi delle componenti principali (PCA) per ricostruire al DCP le caratteristiche statistiche del segnale di interesse. Questa informazione viene utilizzata al DCP per determinare la matrice richiesta dalle tecniche di recupero che sfruttano algoritmi di ottimizzazione convessa (Compressive Sensing, CS) per ricostruire l'intero segnale da una sua versione campionata. Per integrare questo modello di monitoraggio in un framework di compressione e recupero del segnale, viene applicata la logica del paradigma 'cognitive': prima si osserva la rete; poi dall'osservazione si derivano le statistiche di interesse, che vengono applicate per il recupero del segnale; si sfruttano queste informazioni statistiche per prenderere decisioni e infine si rendono effettive queste decisioni con un controllo in retroazione. Il framework di compressione e recupero con controllo in retroazione è chiamato "Sensing, Compression and Recovery through ONline Estimation" (SCoRe1). L'intero framework è stato implementato in una architettura per WSN detta WSN-control, accessibile da Internet. Le scelte nella progettazione del protocollo sono state giustificate da un'analisi teorica con un approccio di tipo Bayesiano. Nella seconda parte della tesi il paradigma cognitive viene utilizzato per l'ottimizzazione di reti locali wireless (WLAN). L'architetture della rete cognitive viene integrata nello stack protocollare della rete wireless. Nello specifico, vengono utilizzati dei modelli grafici probabilistici per modellare lo stack protocollare: le relazioni probabilistiche tra alcuni parametri di diversi livelli vengono studiate con il modello delle reti Bayesiane (BN). In questo modo, è possibile utilizzare queste informazioni provenienti da diversi livelli per ottimizzare le prestazioni della rete, utilizzando un approccio di tipo cross-layer. Ad esempio, queste informazioni sono utilizzate per predire il throughput a livello di trasporto in una rete wireless di tipo single-hop, o per prevedere il verificarsi di eventi di congestione in una rete wireless di tipo multi-hop. L'approccio seguito nei due argomenti principali che compongono questa tesi è il seguente: (i) viene applicato il paradigma cognitive per ricostruire specifiche caratteristiche probabilistiche della rete, (ii) queste informazioni vengono utilizzate per progettare nuove tecniche protocollari, (iii) queste tecniche vengono analizzate teoricamente e confrontate con altre tecniche esistenti, e (iv) le prestazioni vengono simulate, confrontate con quelle di altre tecniche e valutate in scenari di rete realistici.
Antelo, Junior Ernesto Willams Molina. "Estimação conjunta de atraso de tempo subamostral e eco de referência para sinais de ultrassom." Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2616.
Повний текст джерелаEm ensaios não destrutivos por ultrassom, o sinal obtido a partir de um sistema de aquisição de dados real podem estar contaminados por ruído e os ecos podem ter atrasos de tempo subamostrais. Em alguns casos, esses aspectos podem comprometer a informação obtida de um sinal por um sistema de aquisição. Para lidar com essas situações, podem ser utilizadas técnicas de estimativa de atraso temporal (Time Delay Estimation ou TDE) e também técnicas de reconstrução de sinais, para realizar aproximações e obter mais informações sobre o conjunto de dados. As técnicas de TDE podem ser utilizadas com diversas finalidades na defectoscopia, como por exemplo, para a localização precisa de defeitos em peças, no monitoramento da taxa de corrosão em peças, na medição da espessura de um determinado material e etc. Já os métodos de reconstrução de dados possuem uma vasta gama de aplicação, como nos NDT, no imageamento médico, em telecomunicações e etc. Em geral, a maioria das técnicas de estimativa de atraso temporal requerem um modelo de sinal com precisão elevada, caso contrário, a localização dessa estimativa pode ter sua qualidade reduzida. Neste trabalho, é proposto um esquema alternado que estima de forma conjunta, uma referência de eco e atrasos de tempo para vários ecos a partir de medições ruidosas. Além disso, reinterpretando as técnicas utilizadas a partir de uma perspectiva probabilística, estendem-se suas funcionalidades através de uma aplicação conjunta de um estimador de máxima verossimilhança (Maximum Likelihood Estimation ou MLE) e um estimador máximo a posteriori (MAP). Finalmente, através de simulações, resultados são apresentados para demonstrar a superioridade do método proposto em relação aos métodos convencionais.
Abstract (parágrafo único): In non-destructive testing (NDT) with ultrasound, the signal obtained from a real data acquisition system may be contaminated by noise and the echoes may have sub-sample time delays. In some cases, these aspects may compromise the information obtained from a signal by an acquisition system. To deal with these situations, Time Delay Estimation (TDE) techniques and signal reconstruction techniques can be used to perform approximations and also to obtain more information about the data set. TDE techniques can be used for a number of purposes in the defectoscopy, for example, for accurate location of defects in parts, monitoring the corrosion rate in pieces, measuring the thickness of a given material, and so on. Data reconstruction methods have a wide range of applications, such as NDT, medical imaging, telecommunications and so on. In general, most time delay estimation techniques require a high precision signal model, otherwise the location of this estimate may have reduced quality. In this work, an alternative scheme is proposed that jointly estimates an echo model and time delays for several echoes from noisy measurements. In addition, by reinterpreting the utilized techniques from a probabilistic perspective, its functionalities are extended through a joint application of a maximum likelihood estimator (MLE) and a maximum a posteriori (MAP) estimator. Finally, through simulations, results are presented to demonstrate the superiority of the proposed method over conventional methods.
Al-Rabah, Abdullatif R. "Bayesian Recovery of Clipped OFDM Signals: A Receiver-based Approach." Thesis, 2013. http://hdl.handle.net/10754/291094.
Повний текст джерелаKhanna, Saurabh. "Bayesian Techniques for Joint Sparse Signal Recovery: Theory and Algorithms." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5292.
Повний текст джерела"Bayesian Framework for Sparse Vector Recovery and Parameter Bounds with Application to Compressive Sensing." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55639.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Engineering 2019
Masood, Mudassir. "Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications." Diss., 2015. http://hdl.handle.net/10754/553050.
Повний текст джерелаSana, Furrukh. "Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences." Diss., 2016. http://hdl.handle.net/10754/621865.
Повний текст джерелаPrasanna, Dheeraj. "Structured Sparse Signal Recovery for mmWave Channel Estimation: Intra-vector Correlation and Modulo Compressed Sensing." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5215.
Повний текст джерелаDine, James. "A Habitat Suitability Model for Ricord’s Iguana in the Dominican Republic." Thesis, 2009. http://hdl.handle.net/1805/1889.
Повний текст джерелаThe West Indian iguanas of the genus Cyclura are the most endangered group of lizards in the world (Burton & Bloxam, 2002). The Ricord’s iguana, Cyclura ricordii, is listed as critically endangered by the International Union for Conservation of Nature (IUCN) (Ramer, 2004). This species is endemic to the island of Hispaniola (Figure 1), and can only be found in limited geographic areas (Burton & Bloxam, 2002). The range of this species is estimated to be only 60% of historical levels, with most areas being affected by some level of disturbance (Ottenwalder, 1996). The most recent population estimation is between 2,000 and 4,000 individuals (Burton & Bloxam, 2002). Information on potentially suitable habitat can help the conservation efforts for Ricord’s iguana. However, intensive ground surveys are not always feasible or cost effective, and cannot easily provide continuous coverage over a large area. This paper presents results from a pilot study that evaluated variables extracted from satellite imagery and digitally mapped data layers to map the probability of suitable Ricord’s iguana habitat. Bayesian methods were used to determine the probability that each pixel in the study areas is suitable habitat for Ricord’s iguanas by evaluating relevant environmental attributes. This model predicts the probability that an area is suitable habitat based on the values of the environmental attributes including landscape biophysical characteristics, terrain data, and bioclimatic variables.