Hajalukujen tunnistaminen ja poistaminen RFID-porteilla

A modern UHF RFID system can reach a read range of over ten meters. Long read range and difficulties to create uniform interrogation zone lead to stray reads. Interrogation zone can be adjusted by controlling the transmit power of RFID reader, selecting antennas and RFID tags carefully for the purpose, and with different signal quality validation methods. It is common that industry processes prevent using optimal RFID tag size and location for best read reliability. In these situations reader transmit power is increased to gain better read reliability. This extends the interrogation zone over the designed and leads to stray reads. At UHF band the interrogation zone is not sharp edged. This is due to orientation differences between tags, tag to tag differences, multipathing and interference caused by multipathing. In this thesis, different methods to identify RFID-tags moving along specified routes in the far field on the specific interrogation zone and discard other read events as stray events, are studied. Algorithm to detect lay around tags is proposed.Modernin UHF RFID-järjestelmän lukuetäisyys on parhaimmillaan yli kymmenen metriä. Suuri lukuetäisyys ja hankaluudet lukualueen tarkassa rajaamisessa johtavat hajalukuihin. Lukualuetta voidaan säätää RFID-lukijan lähetystehoa muuttamalla, antenni- ja RFID-tunnistevalinnoilla, sekä erilaisilla signaalin laatuun liittyvillä menetelmillä. Teollisuuden prosessit eivät aina mahdollista tunnisteen sijoittamista lukuvarmuuden kannalta parhaaseen paikkaan. Tällaisissa tapauksissa joudutaan usein kasvattamaan RFID-lukijan lähetystehoa lukuvarmuuden parantamiseksi. Tämä taas kasvattaa lukualuetta suunniteltua suuremmaksi, mikä johtaa hajalukuihin. UHF taajuuksilla lukualue ei ole tarkkareunainen, johtuen RFID-tunnisteiden asentoeroista, tunnisteiden yksilökohtaisista eroista sekä monikanavakuulumisesta ja siihen liittyvästä interferenssistä. Tässä tutkimuksessa on etsitty ja kokeiltu menetelmiä, joilla voidaan tunnistaa määritellyllä lukualueella lukijan kaukokentässä haluttuja reittejä liikkuvat RFID-tunnisteet ja hylätä muut lukutapahtumat hajalukuina. Tutkimuksessa esitetään uusi algoritmi hajalukujen tunnistamiseksi

Associations Between IFI44L Gene Variants and Rates of Respiratory Tract Infections During Early Childhood

Background. Genetic heterogeneity in type I interferon (IFN)-related gene IFI44L may account for variable susceptibility to respiratory tract infections (RTIs) in children. Methods. In 2 prospective, population-based birth cohorts, the STEPS Study and the FinnBrain Birth Cohort Study, IFI44L genotypes for rs273259 and rs1333969 were determined in relation to the development of RTIs until 1 or 2 years of age, respectively. At age 3 months, whole-blood transcriptional profiles were analyzed and nasal samples were tested for respiratory viruses in a subset of children. Results. In the STEPS Study (n=1135), IFI44L minor/minor gene variants were associated with lower rates of acute otitis media episodes (adjusted incidence rate ratio, 0.77 [95% confidence interval, .61-.96] for rs273259 and 0.74 [.55-.99] for rs1333969) and courses of antibiotics for RTIs (0.76 [.62-.95] and 0.73 [.56-.97], respectively. In the FinnBrain cohort (n=971), IFI44L variants were associated with lower rates of RTIs and courses of antibiotics for RTIs. In respiratory virus-positive 3-month-old children, IFI44L gene variants were associated with decreased expression levels of IFI44L and several other IFN-related genes. Conclusions. Variant forms of IFI44L gene were protective against early-childhood RTIs or acute otitis media, and they attenuated IFN pathway activation by respiratory viruses.Peer reviewe

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research