Relevant bibliographies by topics / Estimation scalable de l'incertitude

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

Academic literature on the topic 'Estimation scalable de l'incertitude'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Estimation scalable de l'incertitude"

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Perret, Christian, P. Marchand, Arnaud Belleville, et al. "La variabilité en fonction du temps des relations hauteur débit. Sa prise en compte dans l'estimation des incertitudes des données hydrométriques par une méthode tabulée." La Houille Blanche, no. 4 (August 2018): 65–72. http://dx.doi.org/10.1051/lhb/2018043.

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La démarche engagée depuis plusieurs années par la communauté des hydromètres pour quantifier les incertitudes associées au processus d'élaboration des données de débit mériterait de trouver des applications opérationnelles. La présente étude incite notamment les gestionnaires des stations hydrométriques à mieux valoriser les jaugeages effectués en systématisant la démarche de précision du modèle de courbe de tarage. Les auteurs proposent ensuite une démarche simplifiée de quantification de l'incertitude associée à une valeur de débit prédite par une courbe de tarage à partir du calcul de l'éc
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Jiang, Zhuqing, Likuo Wei, Ganmin Zeng, et al. "Bitrate Estimation for Spatial Scalable Videos." IEEE Transactions on Broadcasting 67, no. 2 (2021): 549–55. http://dx.doi.org/10.1109/tbc.2021.3064278.

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Wang, Xianglu. "Gaussian graphical model estimation with measurement error." JUSTC 53, no. 11 (2023): 1105. http://dx.doi.org/10.52396/justc-2022-0108.

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It is well known that regression methods designed for clean data will lead to erroneous results if directly applied to corrupted data. Despite the recent methodological and algorithmic advances in Gaussian graphical model estimation, how to achieve efficient and scalable estimation under contaminated covariates is unclear. Here a new methodology called convex conditioned innovative scalable efficient estimation (COCOISEE) for Gaussian graphical model under both additive and multiplicative measurement errors is developed. It combines the strengths of the innovative scalable efficient estimation
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Surya A, Kavya Sri Vaipava S, Ashika Deulin J, and Janani S. "Cognitive Brain Age Estimation." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 648–52. https://doi.org/10.47392/irjaeh.2025.0089.

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Cognitive brain age estimation combines computational techniques with health sciences to assess cognitive health. Traditional methods like neuroimaging and clinical evaluations are costly and not scalable. This research proposes a machine learning-based system for cognitive age estimation using non-invasive data, including speech patterns, behavioral metrics, and lifestyle factors. The system follows a modular architecture with data collection, preprocessing, feature extraction, and predictive modelling. By analyzing behavioral logs, speech characteristics, and lifestyle metrics, it generates
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Cicala, Marco, Egidio D’Amato, Immacolata Notaro, and Massimiliano Mattei. "Scalable Distributed State Estimation in UTM Context." Sensors 20, no. 9 (2020): 2682. http://dx.doi.org/10.3390/s20092682.

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This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of,
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Li, Cheng, Sanvesh Srivastava, and David B. Dunson. "Simple, scalable and accurate posterior interval estimation." Biometrika 104, no. 3 (2017): 665–80. http://dx.doi.org/10.1093/biomet/asx033.

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Summary Standard posterior sampling algorithms, such as Markov chain Monte Carlo procedures, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarantees and show that the credible intervals from our algorithm asymptotically approximate those fr
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Emerson, Joseph, Robert Alicki, and Karol Życzkowski. "Scalable noise estimation with random unitary operators." Journal of Optics B: Quantum and Semiclassical Optics 7, no. 10 (2005): S347—S352. http://dx.doi.org/10.1088/1464-4266/7/10/021.

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马, 跃. "Scalable Model Averaging Estimation with Missing Responses." Advances in Applied Mathematics 13, no. 05 (2024): 2520–29. http://dx.doi.org/10.12677/aam.2024.135240.

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Chen, Dong, Hua You Su, Wen Mei, Li Xuan Wang, and Chun Yuan Zhang. "Scalable Parallel Motion Estimation on Muti-GPU System." Applied Mechanics and Materials 347-350 (August 2013): 3708–14. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3708.

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With NVIDIA’s parallel computing architecture CUDA, using GPU to speed up compute-intensive applications has become a research focus in recent years. In this paper, we proposed a scalable method for multi-GPU system to accelerate motion estimation algorithm, which is the most time consuming process in video encoding. Based on the analysis of data dependency and multi-GPU architecture, a parallel computing model and a communication model are designed. We tested our parallel algorithm and analyzed the performance with 10 standard video sequences in different resolutions using 4 NVIDIA GTX460 GPU
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Galante, Franco, Giovanni Neglia, and Emilio Leonardi. "Scalable Decentralized Algorithms for Online Personalized Mean Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 16699–707. https://doi.org/10.1609/aaai.v39i16.33835.

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In numerous settings, agents lack sufficient data to learn a model directly. Collaborating with other agents may help, but introduces a bias-variance trade-off when local data distributions differ. A key challenge is for each agent to identify clients with similar distributions while learning the model, a problem that remains largely unresolved. This study focuses on a particular instance of the overarching problem, where each agent collects samples from a real-valued distribution over time to estimate its mean. Existing algorithms face impractical per-agent space and time complexities (linear
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Dissertations / Theses on the topic "Estimation scalable de l'incertitude"

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Candela, Rosa. "Robust and scalable probabilistic machine learning methods with applications to the airline industry." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS078.

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Dans le secteur aérien, la prévision des prix joue un rôle important tant pour les clients que pour les agences de voyage. Dans cette thèse, nous présentons quelques approches pratiques pour aider les voyageurs à faire face à l'incertitude dans l'évolution du prix des billets et nous proposons un cadre basé sur des données pour surveiller les performances des modèles de prévision de séries chronologiques. La descente de gradient stochastique (SGD) représente la méthode d'optimisation plus utilisée dans le domaine de l'apprentissage automatique et cela est également vrai pour les systèmes distr
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Rossi, Simone. "Improving Scalability and Inference in Probabilistic Deep Models." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS042.

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Au cours de la dernière décennie, l'apprentissage profond a atteint un niveau de maturité suffisant pour devenir le choix privilégié pour résoudre les problèmes liés à l'apprentissage automatique ou pour aider les processus de prise de décision.En même temps, l'apprentissage profond n'a généralement pas la capacité de quantifier avec précision l'incertitude de ses prédictions, ce qui rend ces modèles moins adaptés aux applications critiques en matière de risque.Une solution possible pour résoudre ce problème est d'utiliser une formulation bayésienne ; cependant, bien que cette solution soit él
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Pinson, Pierre. "Estimation de l'incertitude des prédictions de production éolienne." Phd thesis, École Nationale Supérieure des Mines de Paris, 2006. http://pastel.archives-ouvertes.fr/pastel-00002187.

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L'énergie éolienne connaît un développement considérable en Europe. Pourtant, le caractère intermittent de cette énergie renouvelable introduit des difficultés pour la gestion du réseau électrique. De plus, dans le cadre de la dérégulation des marchés de l'électricité, l'énergie éolienne est pénalisée par rapport aux moyens de production contrôlables. La prédiction de la production éolienne à des horizons de 2-3 jours aide l'intégration de cette énergie. Ces prédictions consistent en une seule valeur par horizon, qui correspond à la production la plus probable. Cette information n'est pas suff
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Lu, Ruijin. "Scalable Estimation and Testing for Complex, High-Dimensional Data." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93223.

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With modern high-throughput technologies, scientists can now collect high-dimensional data of various forms, including brain images, medical spectrum curves, engineering signals, etc. These data provide a rich source of information on disease development, cell evolvement, engineering systems, and many other scientific phenomena. To achieve a clearer understanding of the underlying mechanism, one needs a fast and reliable analytical approach to extract useful information from the wealth of data. The goal of this dissertation is to develop novel methods that enable scalable estimation, testing,
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Blier, Mylène. "Estimation temporelle avec interruption: les effets de localisation et de durée d'interruptions sont-ils sensibles à l'incertitude ?" Thesis, Université Laval, 2009. http://www.theses.ulaval.ca/2009/26367/26367.pdf.

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Blier, Mylène. "Estimation temporelle avec interruption : les effets de localisation et de durée d'interruption sont-ils sensibles à l'incertitude ?" Doctoral thesis, Université Laval, 2009. http://hdl.handle.net/20.500.11794/21201.

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Lors d'études portant sur la production d'intervalles temporels avec interruption, deux principaux effets sont généralement retrouvés lorsque la localisation et la durée de l'interruption varient: les productions temporelles sont plus courtes 1) lorsque l'interruption arrive tôt dans l'intervalle de temps et 2) lorsque la durée de l'interruption est plus longue. L'effet de localisation s'explique principalement par un partage attentionnel lors de la période qui précède l'interruption, mais peut aussi refléter en partie l'effet de processus préparatoires précédant l'interruption. L'effet de dur
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Kang, Seong-Ryong. "Performance analysis and network path characterization for scalable internet streaming." Texas A&M University, 2008. http://hdl.handle.net/1969.1/85912.

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Delivering high-quality of video to end users over the best-effort Internet is a challenging task since quality of streaming video is highly subject to network conditions. A fundamental issue in this area is how real-time applications cope with network dynamics and adapt their operational behavior to offer a favorable streaming environment to end users. As an effort towards providing such streaming environment, the first half of this work focuses on analyzing the performance of video streaming in best-effort networks and developing a new streaming framework that effectively utilizes unequal im
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Rahmani, Mahmood. "Urban Travel Time Estimation from Sparse GPS Data : An Efficient and Scalable Approach." Doctoral thesis, KTH, Transportplanering, ekonomi och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167798.

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The use of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place, with significant number of mobile sensors moving around covering expansive areas of the road network. Many travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Vehicles in commercial fleets are now routinely equipped with GPS. Travel time is important information for various actors of a transport system, ranging from city planning, to day to day traffic management, to indiv
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Shriram, Alok Kaur Jasleen. "Efficient techniques for end-to-end bandwidth estimation performance evaluations and scalable deployment /." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2248.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2009.<br>Title from electronic title page (viewed Jun. 26, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.
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Simsa, Jiri. "Systematic and Scalable Testing of Concurrent Programs." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/285.

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The challenge this thesis addresses is to speed up the development of concurrent programs by increasing the efficiency with which concurrent programs can be tested and consequently evolved. The goal of this thesis is to generate methods and tools that help software engineers increase confidence in the correct operation of their programs. To achieve this goal, this thesis advocates testing of concurrent software using a systematic approach capable of enumerating possible executions of a concurrent program. The practicality of the systematic testing approach is demonstrated by presenting a novel
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Book chapters on the topic "Estimation scalable de l'incertitude"

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Zhang, Ying-Jun Angela, Congmin Fan, and Xiaojun Yuan. "Scalable Channel Estimation." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15884-2_3.

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Pelikan, Martin, Kumara Sastry, and David E. Goldberg. "Multiobjective Estimation of Distribution Algorithms." In Scalable Optimization via Probabilistic Modeling. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_10.

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Izumi, Taisuke, and Hironobu Kanzaki. "Scalable Estimation of Network Average Degree." In Lecture Notes in Computer Science. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03089-0_32.

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Sastry, Kumara, Martin Pelikan, and David E. Goldberg. "Efficiency Enhancement of Estimation of Distribution Algorithms." In Scalable Optimization via Probabilistic Modeling. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_7.

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Ocenasek, Jiri, Erick Cantú-Paz, Martin Pelikan, and Josef Schwarz. "Design of Parallel Estimation of Distribution Algorithms." In Scalable Optimization via Probabilistic Modeling. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_8.

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Chakrabarti, Indrajit, Kota Naga Srinivasarao Batta, and Sumit Kumar Chatterjee. "Introduction to Scalable Image and Video Coding." In Motion Estimation for Video Coding. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14376-7_7.

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Bosman, Peter A. N., and Dirk Thierens. "Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms." In Scalable Optimization via Probabilistic Modeling. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_5.

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Baglietto, P., M. Maresca, A. Migliaro, and M. Migliardi. "A VLSI scalable processor array for motion estimation." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_247.

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Lee, Seongsoo. "Energy-Scalable Motion Estimation for Low-Power Multimedia Applications." In Interactive Multimedia on Next Generation Networks. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-40012-7_33.

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Corona, Julio Camejo, Hector Gonzalez, and Carlos Morell. "Scalable Generalized Multitarget Linear Regression With Output Dependence Estimation." In Progress in Artificial Intelligence and Pattern Recognition. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89691-1_7.

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Conference papers on the topic "Estimation scalable de l'incertitude"

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D’Ingillo, Rocco, Andrea D’Amico, Renato Ambrosone, Stefano Straullu, Francesco Aquilino, and Vittorio Curri. "Enhancing Optical Multiplex Section QoT Estimation Using Scalable Gray-box DNN." In 2024 IEEE Photonics Conference (IPC). IEEE, 2024. https://doi.org/10.1109/ipc60965.2024.10799669.

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Coutinho, Fábio D. L., Hugerles S. Silva, and Arnaldo S. R. Oliveira. "A Bandwidth-Scalable CNN-based Channel Estimation Algorithm for 5G Systems." In 2025 IEEE Radio and Wireless Symposium (RWS). IEEE, 2025. https://doi.org/10.1109/rws62086.2025.10904836.

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Jiang, Nan, Wenxuan Zhu, Xu Han, Weiqiang Huang, and Yumeng Sun. "Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation." In 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2025. https://doi.org/10.1109/cisce65916.2025.11065138.

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Hu, Shulin, Cao Zeng, Minti Liu, and Guisheng Liao. "Learning Interpretable Phase Difference Mapping for Scalable DOA Estimation via Deep Learning." In 2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/icccworkshops62562.2024.10693687.

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Leduc, Arnaud, Anthony Cioppa, Silvio Giancola, Bernard Ghanem, and Marc Van Droogenbroeck. "SoccerNet-Depth: a Scalable Dataset for Monocular Depth Estimation in Sports Videos." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00333.

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Braspenning, Ralph A. C., Gerard de Haan, and Christian Hentschel. "Complexity scalable motion estimation." In Electronic Imaging 2002, edited by C. C. Jay Kuo. SPIE, 2002. http://dx.doi.org/10.1117/12.453085.

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Zhai, Guangtao, Qian Chen, Xiaokang Yang, and Wenjun Zhang. "Scalable visual sensitivity profile estimation." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517749.

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Cohen, Edith, Daniel Delling, Fabian Fuchs, Andrew V. Goldberg, Moises Goldszmidt, and Renato F. Werneck. "Scalable similarity estimation in social networks." In the first ACM conference. ACM Press, 2013. http://dx.doi.org/10.1145/2512938.2512944.

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Looks, Moshe. "Scalable estimation-of-distribution program evolution." In the 9th annual conference. ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277072.

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Konieczny, Jacek, and Adam Luczak. "Motion estimation algorithm for scalable hardware implementation." In 2009 Picture Coding Symposium (PCS). IEEE, 2009. http://dx.doi.org/10.1109/pcs.2009.5167455.

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Reports on the topic "Estimation scalable de l'incertitude"

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Hunter, Margaret, Jijo K. Mathew, Ed Cox, Matthew Blackwell, and Darcy M. Bullock. Estimation of Connected Vehicle Penetration Rate on Indiana Roadways. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317343.

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Over 400 billion passenger vehicle trajectory waypoints are collected each month in the United States. This data creates many new opportunities for agencies to assess operational characteristics of roadways for more agile management of resources. This study compared traffic counts obtained from 24 Indiana Department of Transportation traffic counts stations with counts derived by the vehicle trajectories during the same periods. These stations were geographically distributed throughout Indiana with 13 locations on interstates and 11 locations on state or US roads. A Wednesday and a Saturday in
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McMartin, I., M. S. Gauthier, and A. V. Page. Updated post-glacial marine limits along western Hudson Bay, central mainland Nunavut and northern Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330940.

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A digital compilation of updated postglacial marine limits was completed in the coastal regions of central mainland Nunavut and northern Manitoba between Churchill and Queen Maud Gulf. The compilation builds on and updates previous mapping of the marine limits at an unprecedented scale, making use of high-resolution digital elevation models, new field-based observations of the marine limit and digital compilations of supporting datasets (i.e. marine deltas and marine sediments). The updated mapping also permits a first-hand, knowledgedriven interpolation of a continuous limit of marine inundat
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