DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty

This paper proposes a new algorithm based on sparse signal recovery for estimating the direction of arrival (DOA) of multiple sources. The problem model we build is about the sample covariance matrix fitting by unknown source powers. We enhance the sparsity by the double-threshold sigmoid penalty function which can approximate the l0 norm accurately. Our method can distinguish closely spaced sources and does not need the knowledge of the number of the sources. In addition, our method can also perform well in low SNR. Besides, our method can handle more sources accurately than other methods. Simulations are done to certify the great performance of the proposed method

eXplainable Artificial Intelligence (XAI) for the Measurement of Br(K + → π + ν ν̄ ) with NA62 Experiment at CERN

In this thesis a Neural Net (NN) code is first presented from scratch and applied to the Kaon-Pion matching in the rare Kaon decay (K+ → π+νν¯ ) analysis of NA62 at CERN. The NN code showed increased efficiency in Kaon decay identification with respect to the standard algorithm based on statistical analysis. It is designed and trained on K+ → π+π+π− decay channel to optimize the statistical significance of K+ - π+ matching by amplifying the association between parent Kaons and downstream Pions over accidental beam particles (“Pileup”) and final state Pions. Essential enhancement and evaluation processes using state-of-the-art techniques of XAI (eXplainable Artificial Intelligence) are presented in the context of choosing the optimal NN-discriminant that fits in the framework of πνν analysis in NA62 based on necessary physics-related metrics. Another XAI application of an innovative Calorimetric “Virtual Bubble Chamber” technique, called NNODA (Neural Net Object Detection Approach), for NA62’s LKr (Liquid Krypton Calorimeter) is constructed to analyze images of clusters using DL (Deep Learning) Computer Vision (CV) techniques. The idea is to use color tags on the cluster timing to veto random activities and unwanted decay products (mainly π0 background) allowing an unusual and flexible event selection time window of ±10 ns around the arrival time of the charged single particle in the final state. NNODA efficiently increased signal acceptance by controlling random cuts. Additionally, practical data science skills in Robotics are presented, by training algorithms that would help a drone to identify and locate endeffectors in unusual environments. Then, An AI-based vision system is proposed for an embedded device and presented in its full facets, and specifically uses DL CV in image classification and object detection. These XAI tools and others have been successfully transferred to NA62’s most precise measurement of Br (K+ → π+νν¯ ) in a cross-disciplinary fashion

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods