PREDICTION OF RESPIRATORY MOTION

Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier

An evaluation of selected estimation methods for the processing of differential absorption lidar data

This work examines the application of selected estimation methods to path integrated direct detection CO₂ lidar data, with the objective of improving the precision in the estimates of the log power, and log power ratios. Particular emphasis is given to the optimal estimation techniques of Kalman filtering theory, and to the consequent requirements for system and measurement model identification. A dual wavelength system was designed and constructed, employing two hybridised TEA lasers, a co-axial transceiver, and direct detection.Over a period of several months, a database of differential absorption measurements was accumulated, each consisting of 10,000 dual wavelength lidar returns. Various wavelength pairs were used, including those recommended for the monitoring of H₂O, CO₂, NH₃ and C₂H₄. A subset of this database is used to evaluate the above mentioned estimation methods. The results are compared with simulated data files in which it was possible to control precisely process models which are believed to form an approximation to the real processes latent in the actual lidar data

Acute stress state classification based on electrodermal activity modeling

Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single- subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios

Scalable Bayesian Time Series Modelling for Streaming Data

Ph. D. ThesisUbiquitous cheap processing power and reduced storage costs have led to increased deployment of connected devices used to collect and store information about their surroundings. Examples include environmental sensors used to measure pollution levels and temperature, or vibration sensors deployed on machinery to detect faults. This data is often streamed in real time to cloud services and used to make decisions such as when to perform maintenance on critical machinery, and monitor systems, such as how interventions to reduce pollution are performing. The data recorded at these sensors is unbounded, heterogeneous and often inaccurate, recorded with different sampling frequencies, and often on irregular time grids. Connection problems or hardware faults can cause information to be missing for days at a time. Additionally, multiple co-located sensors can report different readings for the same process. A exible class of dynamic models can be used to ameliorate these issues and used to smooth and interpolate the data. Irregularly observed time series can be conveniently modelled using state space models with a continuous time latent-state represented by di usion processes. In order to model the wide array of different environmental sensors the observation distributions of these dynamic models are exible, in all cases particle filtering methods can be used for inference and in some cases the exact Kalman filter can be used. The models along with a binary composition operator form a semigroup, making model composition and reuse straightforward. Heteroskedastic time series are accounted for by using a factor structure to model a full-rank time-dependent system noise matrix for the dynamic models which can account for changes in variance and the correlation structure between each time series in a multivariate model. Finally, to model multiple nearby sensors a dynamic model is used to model a time-dependent common mean and a time-invariant Gaussian process can account for the spatial variation between the sensors. Functional programming in Scala is used to implement these time series models. Functional programming provides a unified principled API (application programming interface) for interacting with different collection types using higher order functions. This, combined with the type-class pattern, makes it possible to write inference algorithms once and deploy them locally using serial collections and later on unbounded time series data using libraries such as Akka streams using techniques from functional reactive programming

Stochastic chaos and thermodynamic phase transitions : theory and Bayesian estimation algorithms

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 177-200).The chaotic behavior of dynamical systems underlies the foundations of statistical mechanics through ergodic theory. This putative connection is made more concrete in Part I of this thesis, where we show how to quantify certain chaotic properties of a system that are of relevance to statistical mechanics and kinetic theory. We consider the motion of a particle trapped in a double-well potential coupled to a noisy environment. By use of the classic Langevin and Fokker-Planck equations, we investigate Kramers' escape rate problem. We show that there is a deep analogy between kinetic rate theory and stochastic chaos, for which we propose a novel definition. In Part II, we develop techniques based on Volterra series modeling and Bayesian non-linear filtering to distinguish between dynamic noise and measurement noise. We quantify how much of the system's ergodic behavior can be attributed to intrinsic deterministic dynamical properties vis-a-vis inevitable extrinsic noise perturbations.by Zhi-De Deng.M.Eng.and S.B

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Essays on empirical time series modeling with causality and structural change

In this dissertation, three related issues of building empirical time series models for financial markets are investigated with respect to contemporaneous causality, dynamics, and structural change. In the first essay, nation-wide industry information transmission among stock returns of ten sectors in the U.S. economy is examined through the Directed Acyclical Graph (DAG) for contemporaneous causality and Bernanke decomposition for dynamics. The evidence shows that the information technology sector is the most root cause sector. Test results show that DAG from ex ante forecast innovations is consistent with the DAG fro m ex post fit innovations. This supports innovation accounting based on DAGs using ex post innovations. In the second essay, the contemporaneous/dynamic behaviors of real estate and stock returns are investigated. Selected macroeconomic variables are included in the model to explain recent movements of both returns. During 1971-2004, there was a single structural break in October 1980. A distinct difference in contemporaneous causal structure before and after the break is found. DAG results show that REITs take the role of a causal parent after the break. Innovation accounting shows significantly positive responses of real estate returns due to an initial shock in default risk but insignificant responses of stock returns. Also, a shock in short run interest rates affects real estate returns negatively with significance but does not affect stock returns. In the third essay, a structural change in the volatility of five Asian and U.S. stock markets is examined during the post-liberalization period (1990-2005) in the Asian financial markets, using the Sup LM test. Four Asian financial markets (Hong Kong, Japan, Korea, and Singapore) experienced structural changes. However, test results do not support the existence of structural change in volatility for Thailand and U.S. Also, results show that the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) persistent coefficient increases, but the Autoregressive Conditional heteroskedasticity (ARCH) impact coefficient, implying short run adjustment, decreases in Asian markets. In conclusion, when the econometric model is set up, it is necessary to consider contemporaneous causality and possible structural breaks (changes). The dissertation emphasizes causal inference and structural consistency in econometric modeling. It highlights their importance in discovering contemporaneous/dynamic causal relationships among variables. These characteristics will likely be helpful in generating accurate forecasts