16,058 research outputs found
Distributed classifier based on genetically engineered bacterial cell cultures
We describe a conceptual design of a distributed classifier formed by a
population of genetically engineered microbial cells. The central idea is to
create a complex classifier from a population of weak or simple classifiers. We
create a master population of cells with randomized synthetic biosensor
circuits that have a broad range of sensitivities towards chemical signals of
interest that form the input vectors subject to classification. The randomized
sensitivities are achieved by constructing a library of synthetic gene circuits
with randomized control sequences (e.g. ribosome-binding sites) in the front
element. The training procedure consists in re-shaping of the master population
in such a way that it collectively responds to the "positive" patterns of input
signals by producing above-threshold output (e.g. fluorescent signal), and
below-threshold output in case of the "negative" patterns. The population
re-shaping is achieved by presenting sequential examples and pruning the
population using either graded selection/counterselection or by
fluorescence-activated cell sorting (FACS). We demonstrate the feasibility of
experimental implementation of such system computationally using a realistic
model of the synthetic sensing gene circuits.Comment: 31 pages, 9 figure
Visual identification by signature tracking
We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics
The Use of Phonetic Motor Invariants Can Improve Automatic Phoneme Discrimination
affiliation: Castellini, C (Reprint Author), Univ Genoa, LIRA Lab, Genoa, Italy. Castellini, Claudio; Metta, Giorgio; Tavella, Michele, Univ Genoa, LIRA Lab, Genoa, Italy. Badino, Leonardo; Metta, Giorgio; Sandini, Giulio; Fadiga, Luciano, Italian Inst Technol, Genoa, Italy. Grimaldi, Mirko, Salento Univ, CRIL, Lecce, Italy. Fadiga, Luciano, Univ Ferrara, DSBTA, I-44100 Ferrara, Italy.
article-number: e24055
keywords-plus: SPEECH-PERCEPTION; RECOGNITION
research-areas: Science & Technology - Other Topics
web-of-science-categories: Multidisciplinary Sciences
author-email: [email protected]
funding-acknowledgement: European Commission [NEST-5010, FP7-IST-250026]
funding-text: The authors acknowledge the support of the European Commission project CONTACT (grant agreement NEST-5010) and SIEMPRE (grant agreement number FP7-IST-250026). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
number-of-cited-references: 31
times-cited: 0
journal-iso: PLoS One
doc-delivery-number: 817OO
unique-id: ISI:000294683900024We investigate the use of phonetic motor invariants (MIs), that is, recurring kinematic patterns of the human phonetic articulators, to improve automatic phoneme discrimination. Using a multi-subject database of synchronized speech and lips/tongue trajectories, we first identify MIs commonly associated with bilabial and dental consonants, and use them to simultaneously segment speech and motor signals. We then build a simple neural network-based regression schema (called Audio-Motor Map, AMM) mapping audio features of these segments to the corresponding MIs. Extensive experimental results show that (a) a small set of features extracted from the MIs, as originally gathered from articulatory sensors, are dramatically more effective than a large, state-of-the-art set of audio features, in automatically discriminating bilabials from dentals; (b) the same features, extracted from AMM-reconstructed MIs, are as effective as or better than the audio features, when testing across speakers and coarticulating phonemes; and dramatically better as noise is added to the speech signal. These results seem to support some of the claims of the motor theory of speech perception and add experimental evidence of the actual usefulness of MIs in the more general framework of automated speech recognition
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