Active sequential hypothesis testing

Consider a decision maker who is responsible to dynamically collect observations so as to enhance his information about an underlying phenomena of interest in a speedy manner while accounting for the penalty of wrong declaration. Due to the sequential nature of the problem, the decision maker relies on his current information state to adaptively select the most ``informative'' sensing action among the available ones. In this paper, using results in dynamic programming, lower bounds for the optimal total cost are established. The lower bounds characterize the fundamental limits on the maximum achievable information acquisition rate and the optimal reliability. Moreover, upper bounds are obtained via an analysis of two heuristic policies for dynamic selection of actions. It is shown that the first proposed heuristic achieves asymptotic optimality, where the notion of asymptotic optimality, due to Chernoff, implies that the relative difference between the total cost achieved by the proposed policy and the optimal total cost approaches zero as the penalty of wrong declaration (hence the number of collected samples) increases. The second heuristic is shown to achieve asymptotic optimality only in a limited setting such as the problem of a noisy dynamic search. However, by considering the dependency on the number of hypotheses, under a technical condition, this second heuristic is shown to achieve a nonzero information acquisition rate, establishing a lower bound for the maximum achievable rate and error exponent. In the case of a noisy dynamic search with size-independent noise, the obtained nonzero rate and error exponent are shown to be maximum.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1144 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Deep convolutional regression modelling for forest parameter retrieval

Accurate forest monitoring is crucial as forests are major global carbon sinks. Additionally, accurate prediction of forest parameters, such as forest biomass and stem volume (SV), has economic importance. Therefore, the development of regression models for forest parameter retrieval is essential. Existing forest parameter estimation methods use regression models that establish pixel-wise relationships between ground reference data and corresponding pixels in remote sensing (RS) images. However, these models often overlook spatial contextual relationships among neighbouring pixels, limiting the potential for improved forest monitoring. The emergence of deep convolutional neural networks (CNNs) provides opportunities for enhanced forest parameter retrieval through their convolutional filters that allow for contextual modelling. However, utilising deep CNNs for regression presents its challenges. One significant challenge is that the training of CNNs typically requires continuous data layers for both predictor and response variables. While RS data is continuous, the ground reference data is sparse and scattered across large areas due to the challenges and costs associated with in situ data collection. This thesis tackles challenges related to using CNNs for regression by introducing novel deep learning-based solutions across diverse forest types and parameters. To address the sparsity of available reference data, RS-derived prediction maps can be used as auxiliary data to train the CNN-based regression models. This is addressed through two different approaches. Although these prediction maps offer greater spatial coverage than the original ground reference data, they do not ensure spatially continuous prediction target data. This work proposes a novel methodology that enables CNN-based regression models to handle this diversity. Efficient CNN architectures for the regression task are developed by investigating relevant learning objectives, including a new frequency-aware one. To enable large-scale and cost-effective regression modelling of forests, this thesis suggests utilising C-band synthetic aperture radar SAR data as regressor input. Results demonstrate the substantial potential of C-band SAR-based convolutional regression models for forest parameter retrieval

Developing A Toolbox To Probe Reaction Dynamics With Strong Field Ionization And Non-Linear Attosecond Spectroscopy

Electronic motions which happen in 10 to 100 of attoseconds are the heart of all processes in nature. Therefore monitoring and extracting details in this fundamental level will provide new prospect to the areas as information technology, basic energy science, medicine and life sciences. The challenge being, develop a tool to reach such a fast time scale for real time observation in atomic level. In this thesis work we have address this matter using two interesting approaches related to the laser matter interaction: strong field ionization and nonlinear attosecond spectroscopy. The first part is based on the studies related to the strong field ionization probe. Strong field ionization probe was verified to be sensitive to the sign of magnetic quantum number which evident the capability of probing atomic orientation. The next part is based on non-linear attosecond spectroscopy. With the use 1 kHz laser and the loose focusing geometry we were able to produce attosecond pulse trains with a sufficient flux to perform two photon double ionization. Further, we were also able to extract ion-electron coincidence measurements of the double ionization event of XUV-pump-XUV-probe system for the first time. The extended studies will be carried out with the combination of our newly developed 3D detector to this current setup which will facilitate the triple coincidence capabilities

On Team Decision Problems with Nonclassical Information Structures

In this paper, we consider sequential dynamic team decision problems with nonclassical information structures. First, we address the problem from the point of view of a "manager" who seeks to derive the optimal strategy of the team in a centralized process. We derive structural results that yield an information state for the team which does not depend on the control strategy, and thus it can lead to a dynamic programming decomposition where the optimization problem is over the space of the team's decisions. We, then, derive structural results for each team member that yield an information state which does not depend on their control strategy, and thus it can lead to a dynamic programming decomposition where the optimization problem for each team member is over the space of their decisions. Finally, we show that the control strategy of each team member is the same as the one derived by the manager. We present an illustrative example of a dynamic team with a delayed sharing information structure.Comment: 16 page

Detection and Estimation Theory

Contains research objectives and reports on two research projects.Joint Services Electronics Programs (U. S. Army, U.S. Navy, and U.S. Air Force) under Contract DA 28-043-AMC-02536(E)U. S. Navy Purchasing Office Contract N00140-67-C-021

Semiannual progress report no. 1, 16 November 1964 - 30 June 1965

Summary reports of research in bioelectronics, electron streams and interactions, plasmas, quantum and optical electronics, radiation and propagation, and solid-state electronic