Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.     

Microfluidic device with dual-channel fluorescence acquisition for quantification/identification of cancer cells

This paper presents an optofluidic device for cell discrimination with two independent interrogation regions. Pumping light is coupled to the device, and cell fluorescence is extracted from the two interrogation zones by using optical fibers embedded in the optofluidic chip. To test the reliability of this device, AU-565 cells—expressing EpCAM and HER2 receptors—and RAMOS cells were mixed in a controlled manner, confined inside a hydrodynamic focused flow in the microfluidic chip and detected individually so that they could be discriminated as positive (signal reception from fluorescently labeled antibodies from the AU-565 cells) or negative events (RAMOS cells). A correlation analysis of the two signals reduces the influence of noise on the overall data.     

Special factors,periodicity, and an application to Sturmian words

Let w be a finite word and n the least non-negative integer such that w has no right special factor of length and its right factor of length n is unrepeated. We prove that if all the factors of another word v up to the length n + 1 are also factors of w, thenv itself is a factor ofw. A similar result for ultimately periodic infinite words is established. As a consequence, some ‘uniqueness conditions’ for ultimately periodic words are obtained as well as an upper bound for the rational exponents of the factors of uniformly recurrent non-periodic infinite words. A general formula is derived for the ‘critical exponent’ of a power-free Sturmian word. In particular, we effectively compute the ‘critical exponent’ of any Sturmian sequence whose slope has a periodic development in a continued fraction. Received: 6 May 1999 / 21 February 2000     

Generalization in the XCSF classifier system: analysis, improvement, and extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.     

Simulating Emergent Properties of Coordination in Maude: the Collective Sort Case

Recent coordination languages and models are moving towards the application of techniques coming from the research context of complex systems: adaptivity and self-organization are exploited in order to tackle the openness, dynamism and unpredictability of today's distributed systems. In this area, systems are to be described using stochastic models, and simulation is a valuable tool both for analysis and design. Accordingly, in this work we focused on modelling and simulating emergent properties of coordination techniques.We first develop a framework acting as a general-purpose engine for simulating stochastic transition systems, built as a library for the Maude term rewriting system. We then evaluate this tool to a coordination problem called collective sort, where autonomous agents move tuples across different tuple spaces according to local criteria, and resulting in the emergence of the complete clustering property.     

First-Void Urine Microbiome in Women with Chlamydia trachomatis Infection

Background: Chlamydia trachomatis (CT) is the agent of the most common bacterial sexually transmitted infection worldwide. Until now, little information is available about the microbial composition of urine samples during CT urethritis. Therefore, in this study, we characterized the microbiome and metabolome profiles of first-void urines in a cohort of women with CT urethral infection attending an STI clinic. Methods: Based on CT positivity by nucleic acid amplification techniques on urine samples, the enrolled women were divided into two groups, i.e., “CT-negative” (n = 21) and “CT-positive” (n = 11). Urine samples were employed for (i) the microbiome profile analysis by means of 16s rRNA gene sequencing and (ii) the metabolome analysis by ¹H-NMR. Results: Irrespective of CT infection, the microbiome of first-void urines was mainly dominated by Lactobacillus, L. iners and L. crispatus being the most represented species. CT-positive samples were characterized by reduced microbial biodiversity compared to the controls. Moreover, a significant reduction of the Mycoplasmataceae family—in particular, of the Ureaplasma parvum species—was observed during CT infection. The Chlamydia genus was positively correlated with urine hippurate and lactulose. Conclusions: These data can help elucidate the pathogenesis of chlamydial urogenital infections, as well as to set up innovative diagnostic and therapeutic approaches.