The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Gerard Czajkowski - One of the best experts on this subject based on the ideXlab platform.
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Electro-Optical Properties of Quantum Dots with an Asymmetric Confinement
2020Co-Authors: Gerard CzajkowskiAbstract:We show how to compute the optical response of a Quantum Dot with asymmetric parabolic confinement exposed to a constant electric field applied in the growth direction. The method uses the microscopic calculation of QD eigenfunctions and the macroscopic Real Density matrix approach to compute the electroabsorption. Results are computed for In0.64Al0.36As/Al0.3Ga0.7As Quantum Dots. We obtain asymmetrical Stark shift of the electroabsorption maximum and the dependence on QDs dimensions is also displayed. New effects in absorption line shapes are observed. Fair agreement with experiments is obtained.
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Nonlinear optical properties and self-Kerr effect of Rydberg excitons
Physical Review B, 2019Co-Authors: Sylwia Zielińska-raczyńska, Gerard Czajkowski, Karol Karpiński, David ZiemkiewiczAbstract:We show how to compute the nonlinear optical functions (absorption, reflection, and transmission) for a medium with Rydberg excitons, including the effect of the coherence between the electron-hole pair and the electromagnetic field. Using the Real Density Matrix Approach the analytical expressions for nonlinear optical functions are obtained and numerical calculations for Cu$_2$0 crystal are performed. We report a good agreement with recently published experimental data. Propagation of the electromagnetic waves in Rydberg excitons media with nonliner effect is also discussed and the possibility of obtainig self-phase modulation due to Kerr nonlinearity is investigated.
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Magneto-optical properties of Rydberg excitons: Center-of-mass quantization approach
Physical Review B, 2017Co-Authors: Sylwia Zielińska-raczyńska, David Ziemkiewicz, Gerard CzajkowskiAbstract:We show how to compute the magnetooptical functions (absorption, reflection, and transmission) when Rydberg Exciton-Polaritons appear, including the effect of the coherence between the electron-hole pair and the electromagnetic field, and the polaritonic effect. Using the Real Density Matrix Approach the analytical expressions for magnetooptical functions are obtained and numerical calculations for Cu$_2$0 crystal are performed. The influence of the strength of applied external magnetic field on the resonance displacement of excitonic spectra is discussed. We report a good agreement with recently published experimental data.
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Electro-optical properties of Rydberg excitons
Physical Review B, 2016Co-Authors: Sylwia Zielińska-raczyńska, David Ziemkiewicz, Gerard CzajkowskiAbstract:We show how to compute the electrooptical functions (absorption, reflection, and transmission) when Rydberg Excitons appear, including the effect of the coherence between the electron-hole pair and the electromagnetic field. With the use of Real Density Matrix Approach numerical calculations applied for Cu$_2$0 crystal are performed. We also examine in detail and explain the dependence of the resonance displacement on the state number and applied electric field strength. We report a good agreement with recently published experimental data.
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Quantum confined stark effect in wide parabolic quantum wells: Real Density matrix approach
European Physical Journal B, 2015Co-Authors: Sylwia Zielińska-raczyńska, Gerard Czajkowski, David ZiemkiewiczAbstract:We show how to compute the optical functions of wide parabolic quantum wells (WPQWs) exposed to uniform electric F applied in the growth direction, in the excitonic energy region. The effect of the coherence between the electron-hole pair and the electromagnetic field of the propagating wave including the electron-hole screened Coulomb potential is adopted, and the valence band structure is taken into account in the cylindrical approximation. The role of the interaction potential and of the applied electric field, which mix the energy states according to different quantum numbers and create symmetry forbidden transitions, is stressed. We use the Real Density matrix approach (RDMA) and an effective e-h potential, which enable to derive analytical expressions for the WPQWs electrooptical functions. Choosing the susceptibility, we performed numerical calculations appropriate to a GaAs/GaAlAs WPQWs. We have obtained a red shift of the absorption maxima (quantum confined Stark effect), asymmetric upon the change of the direction of the applied field (F → −F), parabolic for the ground state and strongly dependent on the confinement parameters (the QWs sizes), changes in the oscillator strengths, and new peaks related to the states with different parity for electron and hole.
Yuan Yuan - One of the best experts on this subject based on the ideXlab platform.
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Deep Metric Learning for Crowdedness Regression
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Qi Wang, Yuan YuanAbstract:Cross-scene regression tasks, such as congestion level detection and crowd counting, are useful but challenging. There are two main problems, which limit the performance of existing algorithms. The first one is that no appropriate congestion-related feature can reflect the Real Density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks, since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information, such as a Density map, to guide the learning, which increases the effort of labeling. Another problem is that most existing methods are composed of several steps, for example, feature extraction and regression. Since the steps in the pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning-based regression method is proposed to extract Density related features, and learn better distance measurement simultaneously. The proposed networks trained end-to-end for better optimization can be used for crowdedness regression tasks, including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method.
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Deep Metric Learning for Crowdedness Regression
IEEE Transactions on Circuits and Systems for Video Technology, 2017Co-Authors: Qi Wang, Jia Wan, Yuan YuanAbstract:Cross-scene regression tasks such as congestion level detection and crowd counting are useful but challenging. There are two main problems which limit the performance of existing algorithms. The first one is that no appropriate congestion related feature can reflect the Real Density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information such as Density map to guide the learning which increases the effort of labeling. Another one is that most existing methods are composed of several steps, for example feature extraction and regression. Since the steps in pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning based regression method is proposed to extract Density related features, and learn better distance measurement simultaneously. The proposed network trained endto- end for better optimization can be used for crowdedness regression tasks including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method.
Maria Odila Hilário Cioffi - One of the best experts on this subject based on the ideXlab platform.
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Survey on chemical, physical, and thermal prediction behaviors for sequential chemical treatments used to obtain cellulose from Imperata Brasiliensis
Journal of Thermal Analysis and Calorimetry, 2020Co-Authors: Kelly Cristina Coelho Carvalho Benini, Heitor L. Ornaghi, Paulo Henrique Fernandes Pereira, Leandro José Maschio, Herman Jacobus Cornelis Voorwald, Maria Odila Hilário CioffiAbstract:The effects of chemical treatment sequences on the chemical, physical, and mainly the thermal properties of Imperata Brasiliensis grass in the process used to obtain cellulose fibers were analyzed. The thermal properties were extensively investigated by a thermogravimetric analysis, and a thermal behavior prediction was carried out using kinetic parameters and simulation. Thermal simulations using statistical tools enable thermal predictions for any material under different conditions. However, they are currently not widely reported in the literature for untreated and treated natural fibers. We used an alkaline treatment and alkaline treatment followed by one, two, or three bleaching steps with hydrogen peroxide (H_2O_2) (24% v/v). After each chemical treatment, changes in chemical composition due to the removal of amorphous constituents were observed and confirmed by the analysis of properties such as coloration, Density, porosity, crystallinity, and thermal decomposition. The alkaline treatment followed by one step of bleaching was the most effective and viable chemical treatment sequence to obtain cellulose. The changes in coloration from dark brown to light yellow were accompanied by increases in Real Density (65%), crystallinity (69%), and thermal stability (27.4%) upon one step of bleaching. In general, the subsequent bleaching steps provided similar values. The predicted thermal degradation profiles were compared with experimental data in order to validate the proposed degradation mechanisms and models. The obtained kinetic parameters adequately described the mass loss histories of the studied natural fibers, even when extremely simplified kinetic schemes were used. The degradation mechanisms consisted of diffusion followed by autocatalytic reactions for all studied fibers.
Qi Wang - One of the best experts on this subject based on the ideXlab platform.
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Deep Metric Learning for Crowdedness Regression
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Qi Wang, Yuan YuanAbstract:Cross-scene regression tasks, such as congestion level detection and crowd counting, are useful but challenging. There are two main problems, which limit the performance of existing algorithms. The first one is that no appropriate congestion-related feature can reflect the Real Density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks, since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information, such as a Density map, to guide the learning, which increases the effort of labeling. Another problem is that most existing methods are composed of several steps, for example, feature extraction and regression. Since the steps in the pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning-based regression method is proposed to extract Density related features, and learn better distance measurement simultaneously. The proposed networks trained end-to-end for better optimization can be used for crowdedness regression tasks, including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method.
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Deep Metric Learning for Crowdedness Regression
IEEE Transactions on Circuits and Systems for Video Technology, 2017Co-Authors: Qi Wang, Jia Wan, Yuan YuanAbstract:Cross-scene regression tasks such as congestion level detection and crowd counting are useful but challenging. There are two main problems which limit the performance of existing algorithms. The first one is that no appropriate congestion related feature can reflect the Real Density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information such as Density map to guide the learning which increases the effort of labeling. Another one is that most existing methods are composed of several steps, for example feature extraction and regression. Since the steps in pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning based regression method is proposed to extract Density related features, and learn better distance measurement simultaneously. The proposed network trained endto- end for better optimization can be used for crowdedness regression tasks including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method.
Zahra Dehghani - One of the best experts on this subject based on the ideXlab platform.
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INTERNATIONAL JOURNA L OF ENGINEERING SCI ENCES & RESEARCH TECHNOLOGY Optical and Physical Properties of SiO2 Nanoparticles and Tetra Ortho Silicate Doped in Polyurethane Foams
2020Co-Authors: Rasoul Malekfar, Marzieh Nadafan, Zahra DehghaniAbstract:In this article optical and physical property of th e composition of polyurethane open cell (PUOC) with two different concentrations of SiO2 nanoparticles (1 a nd 2wt. %) will be reported. Tetra ortho silicate ( TEOS) as an organic agent with different concen trations (0.05, 0.1, 0.15 and 0.2 Vol./Vol.) was ad ded to polyurethane composition. Optical microscopy imaging, watering uptake, FTIR and Raman spectroscopy of the synthesized samples were measured. The cell size of samples by adding SiO2 NPs and TEOS was SiO2 was recognized as a best specimen for absorbing water. By focusing on the recorded Raman spectra, it is revealed that PUOC/1wt. % SiO2 and PUOC/200 phase separation, DPS, and the hydrogen bonding index, R, in samples were evaluated in terms of their FTIR spectroscopy data. Two samples, PUOC/1wt. % SiO2 and PUOC/800 among the synthesized samples. By adding SiO2 NPs and TEOS in increased. This is similar to the behavior of Real Density in SiO2 NPs into PUOC but by adding TEOS into PUOC, the Real Density of samples were decreased. The tot al porosity, open porosity and closed porosity of t samples were calculated. By adding SiO2 NPs and TEOS into PUOC, the open porosity of samples was increased.
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High Loading of SiO2 Nanoparticles to Investigate Optical and Mechanical Properties of Polyurethane Open Cell
Advanced Materials Research, 2013Co-Authors: Marzieh Nadafan, Rasoul Malekfar, Ali Izadi-darbandi, Zahra DehghaniAbstract:In this research the composition of polyurethane open cell (PUOC) with two concentrations of SiO2 nanoparticles (1 and 2wt. %) have been prepared. Optical microscopy imaging, watering uptake, FTIR and Raman spectroscopy of the synthesized samples were measured. The optical microscopy imaging of samples showed differences in the appearance of matrix by applying of different amount of SiO2 nanoparticles. Variations of the water uptake of specimens were related to the function of SiO2 nanoparticles (NPs) and their concentrations. The degree of phase separation and the hydrogen bonding index in samples were evaluated in terms of their FTIR spectroscopy data. The apparent and Real densities of foams were measured and then total porosity, open porosity and close porosity of samples were calculated. According to creating voids in polyurethane, the apparent and Real Density has different behavior by adding of SiO2 nanoparticles (NPs). The open porosity of samples is increased by adding the amount of nanoparticles but the close and total porosity are decreased.
