This book covers a broad range of filter theories, algorithms, and numerical examples. The representative linear and nonlinear filters such as the Kalman filter, the steady-state Kalman filter, the H infinity filter, the extended Kalman filter, the Gaussian sum filter, the statistically linearized Kalman filter, the unscented Kalman filter, the Gaussian filter, the cubature Kalman filter are first visited. Then, the non-Gaussian filters such as the ensemble Kalman filter and the particle filters based on the sequential Bayesian filter and the sequential importance resampling are described, together with their recent advances. Moreover, the information matrix in the nonlinear filtering, the nonlinear smoother based on the Markov Chain Monte Carlo, the continuous-discrete filters, factorized filters, and nonlinear filters based on stochastic approximation method are detailed. 1 Review of the Kalman Filter and Related Filters 2 Information Matrix in Nonlinear Filtering 3 Extended Kalman Filter and Gaussian Sum Filter 4 Statistically Linearized Kalman Filter 5 The Unscented Kalman Filter 6 General Gaussian Filters and Applications 7 The Ensemble Kalman Filter 8 Particle Filter 9 Nonlinear Smoother with Markov Chain Monte Carlo 10 Continuous-Discrete Filters 11 Factorized Filters 12 Nonlinear Filters Based on Stochastic Approximation Method
Statistics plays an important role in pharmacology and related subjects such as toxicology and drug discovery and development. Improper statistical tool selection for analyzing the data obtained from studies may result in wrongful interpretation of the performance or safety of drugs. This book communicates statistical tools in simple language. The examples used are similar to those that scientists encounter regularly in their research area. The authors provide cognitive clues for selection of appropriate tools to analyze the data obtained from the studies and explain how to interpret the result of the statistical analysis.
Fukushima Accident: 10 Years After evaluates the post-Fukushima accident situation with up-to-date information, emphasizing radionuclide impacts on the terrestrial and marine environments, and comparing them to the pre-Fukushima accident levels of radionuclides in the environment. This is based on scientific results, as well as knowledge gathered from literature to provide current information on the present status, summarize 10 years of data on the Fukushima accident, and describe the present situation in the local, regional, and global time and space scales. It provides data on radioactivity released into the atmosphere and the ocean, the distribution of radionuclides in the world atmosphere and oceans, and their impact on the total environment, including assessments of radiation doses in Japanese and world populations from consumption of terrestrial food and seafood. It goes on to describe future aspects of the radioactive contamination of these environments and the health implications. This book informs environmental scientists, academics, and researchers in environmental science and nuclear energy as well as postgraduate students in the field of environmental science, radioactivity, and nuclear energy, on the present situation of radioactive contamination of Japan and in the world. - Covers the Fukushima radioactivity impact on humans and the environment from the accident to the present - Provides full information on radiation doses to Japanese citizens and biota, as well as to the world population, 10 years after the Fukushima accident - Details transport of radionuclides in terrestrial and ocean environments, describing how to apply this information to ocean global circulation models and quantify radionuclide contamination of coastal regions - Assesses future trends in radioactive contamination of the Fukushima site
Pre-Earthquake signals are advanced warnings of a larger seismic event. A better understanding of these processes can help to predict the characteristics of the subsequent mainshock. Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies presents the latest research on earthquake forecasting and prediction based on observations and physical modeling in China, Greece, Italy, France, Japan, Russia, Taiwan, and the United States. Volume highlights include: Describes the earthquake processes and the observed physical signals that precede them Explores the relationship between pre-earthquake activity and the characteristics of subsequent seismic events Encompasses physical, atmospheric, geochemical, and historical characteristics of pre-earthquakes Illustrates thermal infrared, seismo–ionospheric, and other satellite and ground-based pre-earthquake anomalies Applies these multidisciplinary data to earthquake forecasting and prediction Written for seismologists, geophysicists, geochemists, physical scientists, students and others, Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies offers an essential resource for understanding the dynamics of pre-earthquake phenomena from an international and multidisciplinary perspective.
This is essential for those who wish to know more about Takeshi Mitarai, one of the founders of Canon. This book is a translation of "Yume ga Kakenuketa: Mitarai Takeshi and Canon" published by Gendai Sozosha in 1983. This is the story of Canon’s first president. Despite numerous trials and tribulations, he never lets go of his ideals. The trajectory of Takeshi Mitarai and Canon unfolds through dramas.
This book covers a broad range of filter theories, algorithms, and numerical examples. The representative linear and nonlinear filters such as the Kalman filter, the steady-state Kalman filter, the H infinity filter, the extended Kalman filter, the Gaussian sum filter, the statistically linearized Kalman filter, the unscented Kalman filter, the Gaussian filter, the cubature Kalman filter are first visited. Then, the non-Gaussian filters such as the ensemble Kalman filter and the particle filters based on the sequential Bayesian filter and the sequential importance resampling are described, together with their recent advances. Moreover, the information matrix in the nonlinear filtering, the nonlinear smoother based on the Markov Chain Monte Carlo, the continuous-discrete filters, factorized filters, and nonlinear filters based on stochastic approximation method are detailed. 1 Review of the Kalman Filter and Related Filters 2 Information Matrix in Nonlinear Filtering 3 Extended Kalman Filter and Gaussian Sum Filter 4 Statistically Linearized Kalman Filter 5 The Unscented Kalman Filter 6 General Gaussian Filters and Applications 7 The Ensemble Kalman Filter 8 Particle Filter 9 Nonlinear Smoother with Markov Chain Monte Carlo 10 Continuous-Discrete Filters 11 Factorized Filters 12 Nonlinear Filters Based on Stochastic Approximation Method
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