14 accepted papers
Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces. The extension allows for handling more complex…
False discovery rate (FDR) control is a popular approach for maintaining the integrity of statistical analyses, especially in high-dimensional data settings, where multiple comparisons increase the risk of false positives. FDR control has been extensively researched for real-valued data. However, th…
In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR). In these applications, strong dependencies often exist among the variables (e.g., stock re…
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the double duty of dimension reduction and variable selection. Sparse PCA algorithms are usually expressed as a trade-off be…
We address the problem of robust network localization in realistic mixed LOS/NLOS environments. We make use of the fact that the bias of range measurement errors is not only non-negative but also sparse when LOS dominates, which has been long overlooked in the existing literature. To exploit these t…
The problem of deciding whether a given set of data points forms one cluster or two clusters is investigated from a robust hypothesis testing perspective. It is assumed that a clustering algorithm exists that for both cases calculates cluster assignments and estimates of the corresponding probabilit…
Conventional approaches to matrix completion are sensitive to outliers and impulsive noise. This paper develops robust and computationally efficient M-estimation based matrix completion algorithms. By appropriately arranging the observed entries, and then applying alternating minimization, the robus…
This paper is meant to share our experience on signal processing hands-on opportunities within the formal engineering education at Technische Universität Darmstadt. It is our strong belief that undergraduate students should be offered hands-on opportunities from the very beginning of their studies u…
The Bayesian information criterion is generic in the sense that it does not include information about the specific model selection problem at hand. Nevertheless, it has been widely used to estimate the number of data clusters in cluster analysis. We have recently derived a Bayesian cluster enumerati…
We propose an improved version of the non-negative independent component analysis algorithm that uses a multiplicative update rule (M-NICA). We examine a challenging NICA application in a noise-embedded multi-speaker voice activity detection (VAD) setup. We present a novel approach that includes spa…
Solving a linear inverse problem may include difficulties such as the presence of outliers and a mixing matrix with a large condition number. In such cases a regularized robust estimator is needed. We propose a new-type regularized robust estimator that is simultaneously highly robust against outlie…
The detection of abrupt changes in signals that are observed by wireless sensor networks (WSN), is an important research area with potential applications, e.g., in fault detection, prediction of natural catastrophic events, and speech segmentation. We consider the distributed robust detection of cha…
A novel algorithm for distributed labeling of speech sources is proposed. We consider a wireless sensor network comprising devices that are equipped with multiple microphones, which can “hear” a number of speech signals. The labeling task is performed in a decentralized fashion with a new two-step a…
Today's wireless sensor networks provide the possibility to monitor physical environments via small low-cost wireless devices. Given the large amount of sensed data, efficient and robust classification becomes a critical task in many applications. Typically, the devices must operate under stringent…