207 research outputs found

    Consonant gemination in Italian: the affricate and fricative case

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    Consonant gemination in Italian affricates and fricatives was investigated, completing the overall study of gemination of Italian consonants. Results of the analysis of other consonant categories, i.e. stops, nasals, and liquids, showed that closure duration for stops and consonant duration for nasals and liquids, form the most salient acoustic cues to gemination. Frequency and energy domain parameters were not significantly affected by gemination in a systematic way for all consonant classes. Results on fricatives and affricates confirmed the above findings, i.e., that the primary acoustic correlate of gemination is durational in nature and corresponds to a lengthened consonant duration for fricative geminates and a lengthened closure duration for affricate geminates. An inverse correlation between consonant and pre-consonant vowel durations was present for both consonant categories, and also for both singleton and geminate word sets when considered separately. This effect was reinforced for combined sets, confirming the hypothesis that a durational compensation between different phonemes may serve to preserve rhythmical structures. Classification tests of single vs. geminate consonants using the durational acoustic cues as classification parameters confirmed their validity, and highlighted peculiarities of the two consonant classes. In particular, a relatively poor classification performance was observed for affricates, which led to refining the analysis by considering dental vs. non-dental affricates in two different sets. Results support the hypothesis that dental affricates, in Italian, may not appear in intervocalic position as singletons but only in their geminate form.Comment: Submitted to Speech Communication. arXiv admin note: substantial text overlap with arXiv:2005.0696

    MoMo: a group mobility model for future generation mobile wireless networks

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    Existing group mobility models were not designed to meet the requirements for accurate simulation of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date informa- tion on the position of each node in the network, combined with a simple and flexible approach to group mobility modeling. A new model for group mobility in wireless networks, named MoMo, is proposed in this paper, based on the combination of a memory-based individual mobility model with a flexible group behavior model. MoMo is capable of accurately describing all mobility scenarios, from individual mobility, in which nodes move inde- pendently one from the other, to tight group mobility, where mobility patterns of different nodes are strictly correlated. A new set of intrinsic properties for a mobility model is proposed and adopted in the analysis and comparison of MoMo with existing models. Next, MoMo is compared with existing group mobility models in a typical 5G network scenario, in which a set of mobile nodes cooperate in the realization of a distributed MIMO link. Results show that MoMo leads to accurate, robust and flexible modeling of mobility of groups of nodes in discrete event simulators, making it suitable for the performance evaluation of networking protocols and resource allocation algorithms in the wide range of network scenarios expected to characterize 5G networks.Comment: 25 pages, 17 figure

    Multiple Regimes in Cross-Region Growth Regressions with Spatial Dependence: A Parametric and a Semi-parametric Approach

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    This paper studies the distribution dynamics of development across European regions over the period 1975-2000. Regional development is measured in terms of both per capita GDP (Y/P) and its components: labour productivity and employment ratio (that in turn can be decomposed in terms of activity and unemployment rate). The Core/Periphery pattern in the European Union is firstly investigated and a comparative analysis in terms of income, productivity, employment and unemployment rates of the two partitions is carried out. Moreover, for each variable as well as for each partition, a nonparametric beta convergence analysis is applied. Synthetically, the results confirm the lack of regional convergence in per capita incomes, the presence of a negative quasi-linear relationship between growth rates and initial levels of labour productivity and a U-shaped relationship between growth rates and initial levels of unemployment rates. As it is well known, however, b-convergence analysis does not allow any test of multiple equilibria, such as “emerging twin peaks”, in the growth process. Equilibrium multiplicity can be properly assessed by using nonparametric techniques of analysis of the cross-regional distribution. In particular, a way to quantify the intra-distribution dynamics is the multivariate kernel, which estimates the joint density of regional income, productivity and (un)employment distribution at time t0 and t0+t. The results of this analysis suggest that over the period considered the regional growth pattern in Europe has followed a polarisation process rather than a convergence path. This appears particularly true in the case of per capita incomes and unemployment rates. Finally, in order to “explain” polarisation, conditional multivariate kernels are estimated. In particular, the role of spatial contiguity and regional sectoral specialisation is investigated.

    Cooperative sensing of spectrum opportunities

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    Reliability and availability of sensing information gathered from local spectrum sensing (LSS) by a single Cognitive Radio is strongly affected by the propagation conditions, period of sensing, and geographical position of the device. For this reason, cooperative spectrum sensing (CSS) was largely proposed in order to improve LSS performance by using cooperation between Secondary Users (SUs). The goal of this chapter is to provide a general analysis on CSS for cognitive radio networks (CRNs). Firstly, the theoretical system model for centralized CSS is introduced, together with a preliminary discussion on several fusion rules and operative modes. Moreover, three main aspects of CSS that substantially differentiate the theoretical model from realistic application scenarios are analyzed: (i) the presence of spatiotemporal correlation between decisions by different SUs; (ii) the possible mobility of SUs; and (iii) the nonideality of the control channel between the SUs and the Fusion Center (FC). For each aspect, a possible practical solution for network organization is presented, showing that, in particular for the first two aspects, cluster-based CSS, in which sensing SUs are properly chosen, could mitigate the impact of such realistic assumptions

    Cognitive routing models

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    This paper investigates the effect of introducing cognitive mechanisms in the routing module of a wireless network. A routing cost function that incorporates measurements of both internal network status and instantaneous behavior of external world is described. The proposed cost function is analyzed by simulation in the framework of IEEE 802.1.5.4a-like low data rate and low cost networks for mixed indoor/outdoor communications. The analysis focuses on the impact of MUI modeling on network performance. Results indicate that MUI-awareness, as provided by the proposed cognitive cost function, may improve network performance in terms of network lifetime. Based on this analysis, a mechanism for learning from previous routing decisions and adapting the routing cost function to MUI conditions is introduced

    Frame potential of Brownian SYK model of Majorana and Dirac fermions

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    We consider the Brownian SYK, i.e. a system of NN Majorana (Dirac) fermions with a white-noise qq-body interaction term. We focus on the dynamics of the Frame potentials, a measure of the scrambling and chaos, given by the moments of the overlap between two independent realisations of the model. By means of a Keldysh path-integral formalism, we compute its early and late-time value. We show that, for q>2q>2, the late time path integral saddle point correctly reproduces the saturation to the value of the Haar frame potential. On the contrary, for q=2q=2, the model is quadratic and consistently we observe saturation to the Haar value in the restricted space of Gaussian states (Gaussian Haar). The latter is characterised by larger system size corrections that we correctly capture by counting the Goldstone modes of the Keldysh saddle point

    Purification Timescales in Monitored Fermions

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    We investigate the crucial role played by a global symmetry in the purification timescales and the phase transitions of monitored free fermionic systems separating a mixed and a pure phase. Concretely, we study Majorana and Dirac circuits with Z2\mathbb{Z}_2 and U(1) symmetries, respectively. In the first case, we demonstrate the mixed phase of LL sites has a purification timescale that scales as τP∌Lln⁥L\tau_P\sim L \ln L . At 1â‰Ștâ‰ȘτP1\ll t\ll \tau_P the system attains a finite residual entropy, that we use to unveil the critical properties of the purification transition. In contrast, free fermions with U(1) manifest a sublinear purification timescale at any measurement rate and an apparent Berezinskii-Kosterlitz-Thouless criticality. We find the mixed phase is characterized by τP∌Lα(p)\tau_P\sim L^{\alpha(p)}, with a continuously varying exponent α(p)<1\alpha(p)<1.Comment: 7 pgase, 3 figures, 3 pages of supplementary materia

    A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning

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    The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms
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