Motion blur removal from photographs by Cho, Taeg Sang

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Corporate bodies and chemical bonds : an STS analysis of natural gas development in the United States by Wylie, Sara Ann

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Control, estimation, and planning algorithms for aggressive flight using onboard sensing by Bry, Adam Parker

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Sensitivity of Economic Performance of the Nuclear Fuel Cycle to Simulation Modeling Assumptions by Bonnet, Nicephore, Kazimi, Mujid S.

“…The shape of the optimum solution is injected into an algorithm based on a complete model of the fuel cycle. An iterative process yields the value of b assuring depletion and satisfactory loading factors. …”
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Multidelity methods for multidisciplinary system design by March, Andrew I. (Andrew Irving)

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Expanding and optimizing fuel management and data analysis capabilities of MCODE-FM in support of MIT research reactor (MITR-II) LEU conversion by Horelik, Nicholas E. (Nicholas Edward)

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The Role of the state in large-scale hydropower development perspectives from Chile, Ecuador, and Perú by Zambrano-Barragán, Patricio Xavier

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Molecular design of interfacial modifications to alter adsorption/desorption equilibria at fluid-adjoining interfaces by Musolino, Nicholas J

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Decision support tool for dynamic workforce scheduling in manufacturing environments \ by Malik, Radhika, M. Eng. Massachusetts Institute of Technology

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High-temperature defect engineering for silicon solar cells : predictive process simulation and synchrotron-based microcharacterization by Fenning, David P

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Probabilistic graphical models : distributed inference and learning models with small feedback vertex sets by Liu, Ying, Ph. D. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

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Materials characterization and structural design of ceramic micro turbomachinery by Chen, Kuo-Shen, 1966-

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Model-code separation architectures for compression based on message-passing by Huang, Ying-zong

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Game for learning by Low, Wei Rong

“…An Agile model is also adopted to allow iterative testing of our game and to get audience feedback during development. …”
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Geometry estimation by deep neural network by Mei, Jianhan

“…To resolve the inconsistency between the matched correspondences and clustering results, we propose a semi-supervised iterative training scheme together with a dual-margins loss. …”
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Software-aided disaster risk management with Geolytics.AI by Bhatia, Ritik

“…The design and development process followed an Iterative Development Model – once a service was developed, it was given to beta testers to gather feedback and work on improvements until a Minimum Viable Product was developed. …”
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Remote sensing and verification of forest growth by Leong, Jing Wen

“…A traditional pixel-based classification was compared to an object-based classification approach whereby the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters and the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices were combined. …”
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An adaptive dropout based deep metric learning algorithm by Tan, Ronald Tay Siang

“…ADroDML can theoretically learn the retention rates in an optimal way and update them iteratively to achieve better performance. Experiments on the given dataset were made and found that the algorithm was able to produce a substantial accuracy score, comparable to the results produced in the research paper proposing ADroDML [1]. …”
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Dielectric and hybrid metal-dielectric nanoantennas for fluorescence enhancement by Dmitriev, Pavel

“…Since the resulting structure geometry has many degrees of freedom, brute force scanning of parameters was not a feasible way of achieving an optimal design, so more elaborate iterative approaches were developed. Having achieved an optimal design, fabrication was the next hurdle to overcome, because of the complexity of integrating several distinct nanostructures on the same substrate. …”
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Jointly optimized ensemble deep random vector functional link network for semi-supervised classification by Shi, Qiushi, Suganthan, Ponnuthurai Nagaratnam, Del Ser, Javier

“…The JOSedRVFL network uses an iterative procedure to compute the output weights and consequently predicts the class labels of the unlabeled training data during the training process. …”
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