Ultra-low power IoT applications: from transducers to wireless protocols

This dissertation aims to explore Internet of Things (IoT) sensor nodes in various application scenarios with different design requirements. The research provides a comprehensive exploration of all the IoT layers composing an advanced device, from transducers to on-board processing, through low power hardware schemes and wireless protocols for wide area networks. Nowadays, spreading and massive utilization of wireless sensor nodes pushes research and industries to overcome the main limitations of such constrained devices, aiming to make them easily deployable at a lower cost. Significant challenges involve the battery lifetime that directly affects the device operativity and the wireless communication bandwidth. Factors that commonly contrast the system scalability and the energy per bit, as well as the maximum coverage. This thesis aims to serve as a reference and guideline document for future IoT projects, where results are structured following a conventional development pipeline. They usually consider communication standards and sensing as project requirements and low power operation as a necessity. A detailed overview of five leading IoT wireless protocols, together with custom solutions to overcome the throughput limitations and decrease the power consumption, are some of the topic discussed. Low power hardware engineering in multiple applications is also introduced, especially focusing on improving the trade-off between energy, functionality, and on-board processing capabilities. To enhance these features and to provide a bottom-top overview of an IoT sensor node, an innovative and low-cost transducer for structural health monitoring is presented. Lastly, the high-performance computing at the extreme edge of the IoT framework is addressed, with special attention to image processing algorithms running on state of the art RISC-V architecture. As a specific deployment scenario, an OpenCV-based stack, together with a convolutional neural network, is assessed on the octa-core PULP SoC

Learning Dynamics from Data Using Optimal Transport Techniques and Applications

Optimal Transport has been studied widely in recent years, the concept of Wasserstein distance brings a lot of applications in computational mathematics, machine learning, engineering, even finance areas. Meanwhile, people are gradually realizing that as the amount of data as well as the needs of utilizing data increase vastly, data-driven models have great potentials in real-world applications. In this thesis, we apply the theories of OT and design data-driven algorithms to form and compute various OT problems. We also build a framework to learn inverse OT problem. Furthermore, we develop OT and deep learning based models to solve stochastic differential equations, optimal control, mean field games related problems, all in data-driven settings. In Chapter 2, we provide necessary mathematical concepts and results that form the basis of this thesis. It contains brief surveys of optimal transport, stochastic differential equations, Fokker-Planck equations, deep learning, optimal controls and mean field games. Chapter 3 to Chapter 5 present several scalable algorithms to handle optimal transport problems within different settings. Specifically, Chapter 3 shows a new saddle scheme and learning strategy for computing the Wasserstein geodesic, as well as the Wasserstein distance and OT map between two probability distributions in high dimensions. We parametrize the map and Lagrange multipliers as neural networks. We demonstrate the performance of our algorithms through a series of experiments with both synthetic and realistic data. Chapter 4 presents a scalable algorithm for computing the Monge map between two probability distributions since computing the Monge maps remains challenging, in spite of the rapid developments of the numerical methods for optimal transport problems. Similarly, we formulate the problem as a mini-max problem and solve it via deep learning. The performance of our algorithms is demonstrated through a series of experiments with both synthetic and realistic data. In Chapter 5 we study OT problem in an inverse view, which we also call Inverse OT (IOT) problem. IOT also refers to the problem of learning the cost function for OT from observed transport plan or its samples. We derive an unconstrained convex optimization formulation of the inverse OT problem. We provide a comprehensive characterization of the properties of inverse OT, including uniqueness of solutions. We also develop two numerical algorithms, one is a fast matrix scaling method based on the Sinkhorn-Knopp algorithm for discrete OT, and the other one is a learning based algorithm that parameterizes the cost function as a deep neural network for continuous OT. Our numerical results demonstrate promising efficiency and accuracy advantages of the proposed algorithms over existing state-of-the-art methods. In Chapter 6 we propose a novel method using the weak form of Fokker Planck Equation (FPE) --- a partial differential equation --- to describe the density evolution of data in a sampled form, which is then combined with Wasserstein generative adversarial network (WGAN) in the training process. In such a sample-based framework we are able to learn the nonlinear dynamics from aggregate data without explicitly solving FPE. We demonstrate our approach in the context of a series of synthetic and real-world data sets. Chapter 7 introduces the application of OT and neural networks in optimal density control. Particularly, we parametrize the control strategy via neural networks, and provide an algorithm to learn the strategy that can drive samples following one distribution to new locations following target distribution. We demonstrate our method in both synthetic and realistic experiments, where we also consider perturbation fields. Finally Chapter 8 presents applications of mean field game in generative modeling and finance area. With more details, we build a GAN framework upon mean field game to generate desired distribution starting with white noise, we also investigate its connection to OT. Moreover, we apply mean field game theories to study the equilibrium trading price in stock markets, we demonstrate the theoretical result by conducting experiments on real trading data.Ph.D

Основи прикладної теорії цифрових автоматів

Подано перший модуль (семестр) двосеместрового курсу “Комп’ютерна логіка”, передбачений освітніми програмами та навчальними планами для студентів першого (бакалаврського) рівня освіти за спеціальностями “Інженерія програмного забезпечення”, “Комп’ютерна інженерія”. Висвітлено прикладні аспекти теорії цифрових автоматів: аналіз та синтез комбінаційних схем, схем з пам’яттю, методи синтезу керуючих цифрових автоматів; запропоновано методики синтезу типових вузлів цифрової обчислювальної техніки (на прикладах розв’язування задач). Для студентів, аспірантів, викладачів, а також для фахівців, які працюють у галузі проектування цифрових систем

[Research Pertaining to Physics, Space Sciences, Computer Systems, Information Processing, and Control Systems]

Research project reports pertaining to physics, space sciences, computer systems, information processing, and control system

NASA Tech Briefs, March 1996

Topics: Computer-Aided Design and Engineering; Electronic Components and Cicuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information; Books and Reports

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

ECOS 2012

The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion