Dependence of exchange anisotropy and coercivity on the Fe–oxide structure in oxygen-passivated Fe nanoparticles

Ultrafine Fe particles have been prepared by the inert gas condensation method and subsequently oxygen passivated. The as-obtained particles consist in an Fe core surrounded by an amorphous Fe-oxide surface layer. The antiferromagnetic character of the Fe-oxide surface induces an exchange anisotropy in the ferromagnetic Fe core when the system is field cooled. Samples have been heat treated in vacuum at different temperatures. Structural changes of the Fe–O layer have been monitored by x-ray diffraction and transmission electron microscopy. Magnetic properties as coercivity, hysteresis loop shift, and evolution of magnetization with temperature have been analyzed for different oxide crystallization stages. A decrease of the exchange anisotropy strength is reported as the structural disorder of the surface oxide layer is decreased with thermal treatment

Dependence of exchange anisotropy and coercivity on the Fe–oxide structure in oxygen-passivated Fe nanoparticles

Ultrafine Fe particles have been prepared by the inert gas condensation method and subsequently oxygen passivated. The as-obtained particles consist in an Fe core surrounded by an amorphous Fe-oxide surface layer. The antiferromagnetic character of the Fe-oxide surface induces an exchange anisotropy in the ferromagnetic Fe core when the system is field cooled. Samples have been heat treated in vacuum at different temperatures. Structural changes of the Fe–O layer have been monitored by x-ray diffraction and transmission electron microscopy. Magnetic properties as coercivity, hysteresis loop shift, and evolution of magnetization with temperature have been analyzed for different oxide crystallization stages. A decrease of the exchange anisotropy strength is reported as the structural disorder of the surface oxide layer is decreased with thermal treatment

Some fast higher order ar estimation techniques applied to parametric wiener filtering

Some Speech Enhancement algorithms based on the iterative Wiener filtering Method due to L1m-Oppenheim [2] are presented. In the original Lim-Oppenheim algorithm, speech AR estimation is carried out using classic second-order analysis, but our algorithms consider a more robust AR modelling. Two different strategies of speech AR estimation are presented and both estimators are trying to see as less amount of noise as possible. First one uses a previous One-Sided Autocorrelation computation, that is a pole-preserving function, and the actual SNR m the second-order LPC analysis is increased. Second one combines advantages of Higher-Order Statistics [1] with a linear combination of AR coefficients, belonging to two consecutive overlapped frames, to assess a less disturbed speech estimation.Peer ReviewedPostprint (published version

Spatiotemporal Characteristics of the Largest HIV-1 CRF02_AG Outbreak in Spain: Evidence for Onward Transmissions

Background and Aim: The circulating recombinant form 02_AG (CRF02_AG) is the predominant clade among the human immunodeficiency virus type-1 (HIV-1) non-Bs with a prevalence of 5.97% (95% Confidence Interval-CI: 5.41–6.57%) across Spain. Our aim was to estimate the levels of regional clustering for CRF02_AG and the spatiotemporal characteristics of the largest CRF02_AG subepidemic in Spain.Methods: We studied 396 CRF02_AG sequences obtained from HIV-1 diagnosed patients during 2000–2014 from 10 autonomous communities of Spain. Phylogenetic analysis was performed on the 391 CRF02_AG sequences along with all globally sampled CRF02_AG sequences (N = 3,302) as references. Phylodynamic and phylogeographic analysis was performed to the largest CRF02_AG monophyletic cluster by a Bayesian method in BEAST v1.8.0 and by reconstructing ancestral states using the criterion of parsimony in Mesquite v3.4, respectively.Results: The HIV-1 CRF02_AG prevalence differed across Spanish autonomous communities we sampled from (p < 0.001). Phylogenetic analysis revealed that 52.7% of the CRF02_AG sequences formed 56 monophyletic clusters, with a range of 2–79 sequences. The CRF02_AG regional dispersal differed across Spain (p = 0.003), as suggested by monophyletic clustering. For the largest monophyletic cluster (subepidemic) (N = 79), 49.4% of the clustered sequences originated from Madrid, while most sequences (51.9%) had been obtained from men having sex with men (MSM). Molecular clock analysis suggested that the origin (tMRCA) of the CRF02_AG subepidemic was in 2002 (median estimate; 95% Highest Posterior Density-HPD interval: 1999–2004). Additionally, we found significant clustering within the CRF02_AG subepidemic according to the ethnic origin.Conclusion: CRF02_AG has been introduced as a result of multiple introductions in Spain, following regional dispersal in several cases. We showed that CRF02_AG transmissions were mostly due to regional dispersal in Spain. The hot-spot for the largest CRF02_AG regional subepidemic in Spain was in Madrid associated with MSM transmission risk group. The existence of subepidemics suggest that several spillovers occurred from Madrid to other areas. CRF02_AG sequences from Hispanics were clustered in a separate subclade suggesting no linkage between the local and Hispanic subepidemics

Deep neural networks for i-vector language identification of short utterances in cars

This paper is focused on the application of the Language Identification (LID) technology for intelligent vehicles. We cope with short sentences or words spoken in moving cars in four languages: English, Spanish, German, and Finnish. As the response time of the LID system is crucial for user acceptance in this particular task, speech signals of different durations with total average of 3.8s are analyzed. In this paper, the authors propose the use of Deep Neural Networks (DNN) to model effectively the i-vector space of languages. Both raw i-vectors and session variability compensated i-vectors are evaluated as input vectors to DNNs. The performance of the proposed DNN architecture is compared with both conventional GMM-UBM and i-vector/LDA systems considering the effect of durations of signals. It is shown that the signals with durations between 2 and 3s meet the requirements of this application, i.e., high accuracy and fast decision, in which the proposed DNN architecture outperforms GMM-UBM and i-vector/LDA systems by 37% and 28%, respectively.Peer ReviewedPostprint (published version

Multiple multilabeling applied to HMM-based noisy speech recognition

The performance of existing speech recognition systems degrades rapidly in the presence of background noise when training and testing cannot be done under the same ambient conditions. The aim of this paper is to propose the application of a simple multilabeling method, instead of the standard vector quantization -so called labeling-, as the front end for a speech recognizer based on the Vector Quantization (VQ) and Hidden Markov Models (HMM) approaches in order to increase its robustness to noise. Furthermore, not only cepstrum but also other features such as energy and dynamic parameters are evaluated and quantized independently in the multilabeling stage to represent more accurately characteristics of speech. The result of this process is a multiple multilabeling. Experimental results in the presence of additive white noise clearly demonstrate its good performance in isolated word recognition in noisy environments.Postprint (published version

Speech recognition and enhancement using some robust HOS-based AR estimation techniques

Peer ReviewedPostprint (published version