The Experts below are selected from a list of 3975 Experts worldwide ranked by ideXlab platform
Francesco Zerbetto - One of the best experts on this subject based on the ideXlab platform.
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Stability, dynamics, and lubrication of MoS2 platelets and nanotubes.
Langmuir, 2012Co-Authors: Marco Dallavalle, Nadja Sändig, Francesco ZerbettoAbstract:A model is introduced to investigate structure, stability, dynamics, and properties of MoS2. The tribological behavior of the material is obtained from the Autocorrelation Function, ACF, of the forces, using the Green–Kubo equation, and by the classical Amontons’ laws. In the idealized system, i.e. without defects, junctions, vacancies, asperities, and impurities, both models find a superlubrication regime, in agreement with some experiments. In nanotubes, NTs, friction is an order of magnitude lower than in the layered systems. The calculations also show that there is a substantial stabilization, per atom, for the formation of multiwall NTs with at least four walls.
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Stability, dynamics, and lubrication of MoS2 platelets and nanotubes.
Langmuir : the ACS journal of surfaces and colloids, 2012Co-Authors: Marco Dallavalle, Nadja Sändig, Francesco ZerbettoAbstract:A model is introduced to investigate structure, stability, dynamics, and properties of MoS2. The tribological behavior of the material is obtained from the Autocorrelation Function, ACF, of the for...
M. Renfors - One of the best experts on this subject based on the ideXlab platform.
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A Fast Unambiguous Acquisition Algorithm for BOC-Modulated Signals
IEEE Transactions on Vehicular Technology, 2013Co-Authors: F. Benedetto, G. Giunta, E. S. Lohan, M. RenforsAbstract:This paper proposes a fast unambiguous acquisition technique for binary-offset-carrier (BOC)-modulated signals. We remove the ambiguities (side peaks) of the BOC Autocorrelation Function (ACF), exploiting a reduced-complexity (real and symmetric) filter composed of only seven nonzero samples. The proposed scheme is applicable to both generic sine- and cosine-phased BOC signals. Theoretical and simulation results show that the proposed method removes the ambiguities in the acquisition problem without requiring an auxiliary signal in the receiver.
Yoichi Ando - One of the best experts on this subject based on the ideXlab platform.
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Application to Sound Design
Neurally Based Measurement and Evaluation of Environmental Noise, 2015Co-Authors: Yoshiharu Soeta, Yoichi AndoAbstract:In previous chapters, noise characteristics and annoyance to noise are evaluated by the auditory system model that includes factors extracted from Autocorrelation Function (ACF) and interaural cross-correlation Function (IACF). In this chapter, other applications to sound design are discussed based on the ACF and IACF factors. They are subjective diffuseness of music signals, listening level of music through headphones in noise environments, subject preference for birdsongs, and urban soundscape design.
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Spatial Primary Sensations of Noise
Neurally Based Measurement and Evaluation of Environmental Noise, 2015Co-Authors: Yoshiharu Soeta, Yoichi AndoAbstract:Spatial sensations include the localization, the subjective diffuseness (envelopment), and the apparent source width in the sound field. These are described by the multiple spatial factors extracted from the interaural cross-correlation Function (IACF) for the signal arriving at two ears. As for the localization in the median plane, temporal factors extracted from the Autocorrelation Function (ACF) are also useful because the information arriving at two ears is same.
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Noise Measurement Method Based on the Model
Neurally Based Measurement and Evaluation of Environmental Noise, 2015Co-Authors: Yoshiharu Soeta, Yoichi AndoAbstract:To evaluate environmental noise, we need to use methods based on Functioning of our auditory system. In this chapter, basic concept of noise measurement is described. First, the correlation between two variables, correlation Function as a Function of time, such as Autocorrelation Function (ACF) and cross-correlation Function (CCF), and factors extracted from the ACF and CCF are described. ACF and CCF are related monaural and binaural criteria, respectively. Second, system of noise measurement based on the ACF and IACF factors is described.
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Temporal Primary Sensations of Noise
Neurally Based Measurement and Evaluation of Environmental Noise, 2015Co-Authors: Yoshiharu Soeta, Yoichi AndoAbstract:The basic, perceived attributes of sound can be divided into those qualities that distinguish different sounds independent of location (temporal sensations) and those related to a sound’s perceived location in space (spatial sensations). Temporal sensations include pitch, loudness, timbre, and duration. They can be described in terms of temporal factors extracted from the Autocorrelation Function (ACF). The ACF has the same information as the power density spectrum of the signal under analysis. From the ACF, however, significant factors may be extracted, which are related to temporal sensations.
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Analyses of Temporal Factors of a Source Signal
Opera House Acoustics Based on Subjective Preference Theory, 2015Co-Authors: Yoichi AndoAbstract:Sound signals proceed along auditory pathways and are perceived in a time sequence while the brain simultaneously interprets the meaning of signals. Thus, a great deal of attention is paid here to analyze the signal in the time domain. This chapter mainly treats physical aspects of the running Autocorrelation Function (ACF) of the signal, which contains the envelope and its finer structures as well as the power at its starting time.
Sabah Manfi Redha - One of the best experts on this subject based on the ideXlab platform.
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The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 10. Drawing of Autocorrelation Function and partial correlation for Males and Females primary stage students
2018Co-Authors: Sabah Manfi RedhaAbstract:The instability of the time series is recognized, and to be more accurate, we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (10).
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The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation Function and partial correlation for females primary stage students
2018Co-Authors: Sabah Manfi RedhaAbstract:The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.
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The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 2. Drawing of Autocorrelation Function and partial correlation for males primary stage
2018Co-Authors: Sabah Manfi RedhaAbstract:We get to notice the instability of the time series, and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure 2.
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The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 4. Drawing of Autocorrelation Function and partial correlation for males primary stage students
2018Co-Authors: Sabah Manfi RedhaAbstract:We get to notice the stability of the time series, and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure (4).
Xiaoyi Bao - One of the best experts on this subject based on the ideXlab platform.
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Autocorrelation Function of the principal State of polarization vector for systems having PMD
IEEE Photonics Technology Letters, 2004Co-Authors: Saeed Hadjifaradji, Liang Chen, David S. Waddy, Xiaoyi BaoAbstract:The Autocorrelation Function (ACF) for the principal state of polarization (PSP) vector is reported. It is shown that the PSP vector ACF and the magnitude of the polarization-mode dispersion vector, i.e., the differential group delay (DGD) ACF are not independent. The PSP vector correlation bandwidth is verified to be narrower than that of the DGD.
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Principal state vector Autocorrelation in a fiber optic system having both polarization-mode dispersion and polarization-dependent loss
Applications of Photonic Technology 6, 2003Co-Authors: Liang Chen, Saeed Hadjifaradji, David S. Waddy, Xiaoyi BaoAbstract:A combination of polarization mode dispersion (PMD) and polarization dependent loss (PDL) in a fiber optic system will lead to a complex principal state vector. We report the Autocorrelation Function (ACF) for such a complex principal state vector. It is shown that the correlation bandwidth decreases with the increasing PDL.
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Directional Autocorrelation Function of the polarization-mode dispersion vector
Applications of Photonic Technology 6, 2003Co-Authors: Saeed Hadjifaradji, Liang Chen, David S. Waddy, Xiaoyi BaoAbstract:The Autocorrelation Function (ACF) for the direction of the principal state of polarization (PSP) vector is reported. The analytical results are compared with the simulation results. It is shown that the magnitude and the directional ACF of the PSP vector are not independent. The directional correlation bandwidth for the PSP vector is verified to be narrower than that of the PSP magnitude.
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Polarization-dependent loss Autocorrelation in the presence of combined polarization-mode dispersion and polarization-dependent losses in optical fibers
Applications of Photonic Technology 6, 2003Co-Authors: Liang Chen, Saeed Hadjifaradji, David S. Waddy, Xiaoyi BaoAbstract:We report the Autocorrelation Function (ACF) of polarization dependent loss (PDL) in optical systems having polarization mode dispersion (PMD). A characteristic PDL ACF is derived. It is shown that the PDL Autocorrelation bandwidth is proportional to the inverse of the mean PMD and the proportionality constant is a Function of mean PDL. Monte Carlo simulations of the Autocorrelation Functions confirm the analytical results.
