Slow-light and evanescent modes at interfaces in photonic crystal waveguides: optimal extraction from experimental near-field measurements

We develop a systematic approach for simultaneous extraction of the dispersion relations and profiles of multiple modes in periodic waveguides though a special global optimization procedure applied to near-field electric field measurements in the waveguide plane. We apply this method to perform in-depth analysis of experimental data on wave propagation close to an interface between waveguide sections with different dispersion characteristics, and we successfully identify several modes contributing to the experimentally measured fields. We find clear evidence that when the group velocity is reduced across the interface, evanescent modes that facilitate the excitation of propagating slow-light waves appear, confirming previous theoretical predictions. (C) 2011 Optical Society of AmericaPublisher PDFPeer reviewe

Collective flow and hydrodynamics in large and small systems at the LHC

In this article, we briefly review the recent progress on collective flow and hydrodynamics in large and small systems at the Large Hadron Collider (LHC), which includes the following topics: extracting the QGP viscosity from the flow data, initial state fluctuations and final state correlations in 2.76 A TeV Pb--Pb collisions, correlations and collective flow in high energy p--Pb and p--p collisions.Comment: 43 pages, 15 figures, Invited Review for Nuclear Science and Technique

Optimized complex power quality classifier using one vs. rest support vector machine

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

A coordinated optical and X-ray spectroscopic campaign on HD179949: searching for planet-induced chromospheric and coronal activity

HD179949 is an F8V star, orbited by a close-in giant planet with a period of ~3 days. Previous studies suggested that the planet enhances the magnetic activity of the parent star, producing a chromospheric hot spot which rotates in phase with the planet orbit. However, this phenomenon is intermittent since it was observed in several but not all seasons. A long-term monitoring of the magnetic activity of HD179949 is required to study the amplitude and time scales of star-planet interactions. In 2009 we performed a simultaneous optical and X-ray spectroscopic campaign to monitor the magnetic activity of HD179949 during ~5 orbital periods and ~2 stellar rotations. We analyzed the CaII H&K lines as a proxy for chromospheric activity, and we studied the X-ray emission in search of flux modulations and to determine basic properties of the coronal plasma. A detailed analysis of the flux in the cores of the CaII H&K lines and a similar study of the X-ray photometry shows evidence of source variability, including one flare. The analysis of the the time series of chromospheric data indicates a modulation with a ~11 days period, compatible with the stellar rotation period at high latitudes. Instead, the X-ray light curve suggests a signal with a period of ~4 days, consistent with the presence of two active regions on opposite hemispheres. The observed variability can be explained, most likely, as due to rotational modulation and to intrinsic evolution of chromospheric and coronal activity. There is no clear signature related to the orbital motion of the planet, but the possibility that just a fraction of the chromospheric and coronal variability is modulated with the orbital period of the planet, or the stellar-planet beat period, cannot be excluded. We conclude that any effect due to the presence of the planet is difficult to disentangle

Variations in the Cyclotron Resonant Scattering Features during 2011 outburst of 4U 0115+63

We study the variations in the Cyclotron Resonant Scattering Feature (CRSF) during 2011 outburst of the high mass X-ray binary 4U 0115+63 using observations performed with Suzaku, RXTE, Swift and INTEGRAL satellites. The wide-band spectral data with low energy coverage allowed us to characterize the broadband continuum and detect the CRSFs. We find that the broadband continuum is adequately described by a combination of a low temperature (kT ~ 0.8 keV) blackbody and a power-law with high energy cutoff (Ecut ~ 5.4 keV) without the need for a broad Gaussian at ~ 10 keV as used in some earlier studies. Though winds from the companion can affect the emission from the neutron star at low energies (< 3 keV), the blackbody component shows a significant presence in our continuum model. We report evidence for the possible presence of two independent sets of CRSFs with fundamentals at ~ 11 keV and ~ 15 keV. These two sets of CRSFs could arise from spatially distinct emitting regions. We also find evidence for variations in the line equivalent widths, with the 11 keV CRSF weakening and the 15 keV line strengthening with decreasing luminosity. Finally, we propose that the reason for the earlier observed anti-correlation of line energy with luminosity could be due to modelling of these two independent line sets (~ 11 keV and ~ 15 keV) as a single CRSF.Comment: 12 pages, 8 figures (4 in colour), 6 tables. Accepted for publication in MNRAS. Typos corrected, Figure 8 changed and some changes to draf

Combined ion and atom trap for low temperature ion-atom physics

We report an experimental apparatus and technique which simultaneously traps ions and cold atoms with spatial overlap. Such an apparatus is motivated by the study of ion-atom processes at temperatures ranging from hot to ultra-cold. This area is a largely unexplored domain of physics with cold trapped atoms. In this article we discuss the general design considerations for combining these two traps and present our experimental setup. The ion trap and atom traps are characterized independently of each other. The simultaneous operation of both is then described and experimental signatures of the effect of the ions and cold-atoms on each other are presented. In conclusion the use of such an instrument for several problems in physics and chemistry is briefly discussed.Comment: 24 pages, 13 figures. Figures Fixe

Improving acoustic vehicle classification by information fusion

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac

Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acoustic from the unknown competing speech sources relying on localization of the early images of the speakers by sparse approximation of the spatial spectra of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech recovery and recognition.Comment: 31 page

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável