Positioning by Multicell Fingerprinting in Urban NB-IoT Networks.

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.

Lexical and syntactic gemination in Italian consonants-Does a geminate Italian consonant consist of a repeated or a strengthened consonant?

Two types of consonant gemination characterize Italian: lexical and syntactic. Italian lexical gemination is contrastive, so that two words may differ by only one geminated consonant. In contrast, syntactic gemination occurs across word boundaries and affects the initial consonant of a word in specific contexts, such as the presence of a monosyllabic morpheme before the word. This study investigates the acoustic correlates of Italian lexical and syntactic gemination, asking if the correlates for the two types are similar in the case of stop consonants. Results confirmed previous studies showing that duration is a prominent gemination cue, with a lengthened consonant closure and a shortened pre-consonant vowel for both types. Results also revealed the presence, in about 10%-12% of instances, of a double stop-release burst, providing strong support for the biphonematic nature of Italian geminated stop consonants. Moreover, the timing of these bursts suggests a different planning process for lexical vs syntactic geminates. The second burst, when present, is accommodated within the closure interval in syntactic geminates, while lexical geminates are lengthened by the extra burst. This suggests that syntactic gemination occurs during a post-lexical phase of production planning, after timing has already been established.

A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning.

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.

The GEMMA speech database: VCV and VCCV words for the acoustic analysis of consonants and lexical gemination in Italian.

The GEMMA database consists of recordings of disyllabic words: vowel-consonant-vowel (VCV) for nongeminate cases and vowel-consonant-consonant-vowel (VCCV) for geminate cases. The consonants in the words are stops /b/, /d/, /g/, /p/, /t/, /k/, affricates /ts/, /dz/, /ʧ/, /ʤ/, fricatives /f/, /v/, /s/, /z/ (singleton only) and /ʃ/ (geminate only), nasals /m/, /n/ and /ɲ/ (geminate only), and liquids /l/, /r/ and / λ / (geminate only). The database also includes recordings for glides (/j/, /w/). The vowels in the words are /a, i, u/; words are symmetric with respect to vowel. Six native adult speakers of Standard Italian, raised and living in Rome, Italy, three female and three male, uttered the speech materials in three different recording sessions; three repetitions for each word per speaker were therefore collected. The dataset also includes the durations of vowel and consonant segments for all cases where the consonant can be singleton vs. geminate (see [1] and [2]).

The LaMIT database: A read speech corpus for acoustic studies of the Italian language toward lexical access based on the detection of landmarks and other acoustic cues to features.

The LaMIT database consists in recordings of 100 Italian sentences. The sentences in the database were designed so to include all phonemes of the Italian language, and also take into account the typical frequency of each phoneme in written Italian. Four native adult speakers of Standard Italian, raised and living in Rome, Italy, two female and two male, pronounced the sentences in two different recording sessions; two repetitions for each sentence per speaker were therefore collected, for a total of 800 recordings. The database was specifically created for application in the LaMIT project, that focuses on the application to the Italian language of the Lexical Access model proposed by Ken Stevens for American English. The model relies on the detection of specific acoustic discontinuities called landmarks and other acoustic cues to features that characterize each phoneme. Each recording was thus processed to generate a set of labeling files that identify both predicted landmarks and other cues, and actual landmarks/cues. The labeling files, compiled according to the labeling syntax used in the Praat speech processing software, are also made available as part of the LAMIT database.