No abstract

Crowdsourced data in science might be severely error-prone due to the inexperience of annotators participating in the project. In this work, we present a procedure to detect specific structures in an image given tags provided by multiple annotators and collected through a crowdsourcing methodology. The procedure consists of two stages based on the Expectation-Maximization (EM) algorithm, one for clustering and the other one for detection, and it gracefully combines data coming from annotators with unknown reliability in an unsupervised manner. An online implementation of the approach is also presented that is well suited to crowdsourced streaming data. Comprehensive experimental results with real data from the MalariaSpot project are also included.

Cell-free (CF) structures are expected to be a game changer for beyond-5G wireless networks. With every user potentially communicating with every base station, cooperation at a central processing point is poised to provide much higher spectral efficiencies. At the same time, the growing interest in unmanned aerial vehicles (UAVs) makes CF-UAV networks an appealing scenario. This paper investigates the uplink of a CF network where UAVs serve as flying base stations. The optimization of the UAV locations is shown to markedly increase the minimum local-average signal-to-interference-plus-noise ratio, which in turn increases the spectral efficiency. The improvements are associated to pilot contamination and to geometry.

Decision-making procedures when a set of individual binary labels is processed to produce a unique joint decision can be approached modeling the individual labels as multivariate independent Bernoulli random variables. This probabilistic model allows an unsupervised solution using EM-based algorithms, which basically estimate the distribution model parameters and take a joint decision using a Maximum a Posteriori criterion. These methods usually assume that individual decision agents are conditionally independent, an assumption that might not hold in practical setups. Therefore, in this work we formulate and solve the decision-making problem using an EM-based approach but assuming correlated decision agents. Improved performance is obtained on synthetic and real datasets, compared to classical and state-of-the-art algorithms.

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.