Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Improving acoustic vehicle classification by information fusion

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac

fMRI activation detection with EEG priors

The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio

Process Monitoring and Uncertainty Quantification for Laser Powder Bed Fusion Additive Manufacturing

Metal Additive manufacturing (AM) such as Laser Powder-Bed Fusion (LPBF) processes offer new opportunities for building parts with geometries and features that other traditional processes cannot match. At the same time, LPBF imposes new challenges on practitioners. These challenges include high complexity of simulating the AM process, anisotropic mechanical properties, need for new monitoring methods. Part of this Dissertation develops a new method for layerwise anomaly detection during for LPBF. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of a procedure utilizing spatial statistics and machine learning. Another parts of this Dissertation solves problems for efficient use of computer simulation models. Simulation models are vital for accelerated development of LPBF because we can integrate multiple computer simulation models at different scales to optimize the process prior to the part fabrication. This integration of computer models often happens in a hierarchical fashion and final model predicts the behavior of the most important Quantity of Interest (QoI). Once all the models are coupled, a system of models is created for which a formal Uncertainty Quantification (UQ) is needed to calibrate the unknown model parameters and analyze the discrepancy between the models and the real-world in order to identify regions of missing physics. This dissertation presents a framework for UQ of LPBF models with the following features: (1) models have multiple outputs instead of a single output, (2) models are coupled using the input and output variables that they share, and (3) models can have partially unobservable outputs for which no experimental data are present. This work proposes using Gaussian process (GP) and Bayesian networks (BN) as the main tool for handling UQ for a system of computer models with the aforementioned properties. For each of our methodologies, we present a case study of a specific alloy system. Experimental data are captured by additively manufacturing parts and single tracks to evaluate the proposed method. Our results show that the combination of GP and BN is a powerful and flexible tool to answer UQ problems for LPBF

Spectral unmixing of Multispectral Lidar signals

In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design