The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
P Serra - One of the best experts on this subject based on the ideXlab platform.
-
Recursive tracking algorithm for a predictable time-varying parameter of a time series
Mathematical Methods of Statistics, 2015Co-Authors: E. Belitser, P SerraAbstract:We propose a recursive algorithm for tracking a multi-dimensional time-varying parameter of a time series, which is also allowed to be a predictable process with respect to the underlying time series. The algorithm is driven by a Gain Function. For an arbitrary time series model and a Gain Function satisfying some conditions, we derive a general uniform non-asymptotic accuracy bound for the tracking algorithm in terms of chosen step size for the algorithm and the oscillations of the parameter of interest. We outline how appropriate Gain Functions can be constructed and give several examples of different variability settings for the parameter process for which our general result can be applied, leading to different convergence rates in different asymptotic regimes. The proposed approach covers many frameworks and models where stochastic approximation algorithms comprise the main inference tool for the data analysis.We treat in some detail a couple of specificmodels.
E. Belitser - One of the best experts on this subject based on the ideXlab platform.
-
Recursive tracking algorithm for a predictable time-varying parameter of a time series
Mathematical Methods of Statistics, 2015Co-Authors: E. Belitser, P SerraAbstract:We propose a recursive algorithm for tracking a multi-dimensional time-varying parameter of a time series, which is also allowed to be a predictable process with respect to the underlying time series. The algorithm is driven by a Gain Function. For an arbitrary time series model and a Gain Function satisfying some conditions, we derive a general uniform non-asymptotic accuracy bound for the tracking algorithm in terms of chosen step size for the algorithm and the oscillations of the parameter of interest. We outline how appropriate Gain Functions can be constructed and give several examples of different variability settings for the parameter process for which our general result can be applied, leading to different convergence rates in different asymptotic regimes. The proposed approach covers many frameworks and models where stochastic approximation algorithms comprise the main inference tool for the data analysis.We treat in some detail a couple of specificmodels.
-
Online tracking of a drifting parameter of a time series
arXiv: Statistics Theory, 2013Co-Authors: E. Belitser, De Pj Paulo Andrade SerraAbstract:We propose an online algorithm for tracking a multivariate time-varying parameter of a time series. The algorithm is driven by a Gain Function. Under assumptions on the Gain Function, we derive uniform error bounds on the tracking algorithm in terms of chosen step size for the algorithm and on the variation of the parameter of interest. We give examples of a number of different variational setups for the parameter where our result can be applied, and we also outline how appropriate Gain Functions can be constructed. We treat in some detail the tracking of time varying parameters of an AR(d) model as a particular application of our method.
Binboga Siddik Yarman - One of the best experts on this subject based on the ideXlab platform.
-
On numerical design technique of wideband microwave amplifiers based on GaN small-signal device model
Analog Integrated Circuits and Signal Processing, 2014Co-Authors: Ramazan Köprü, Hakan Kuntman, Binboga Siddik YarmanAbstract:This work presents an application of Normalized Gain Function (NGF) method to the design of linear wideband microwave amplifiers based on small-signal model of a device. NGF has been originally developed to be used together with an S-parameter (*.s2p) file, whereas this work enables the NGF to be able to work with explicit S-parameter formulae derived from the small-signal model of the device. This approach provides the designer to be able to use simple set of S-parameter equations instead of S-parameter file of the device. Representation of the device simply by several model equations not only eliminates the need of carrying large number of data but also provides the capability of equation-based easy, realistic and equispaced S-parameter data generation in any desired resolution in frequency axis without requiring interpolation. NGF is defined as the ratio of T and |S_21|^2, i.e. T_N = T/|S_21|^2, Gain Function of the amplifier to be designed and transistor forward Gain Function, respectively. Synthesis of output/input matching networks (OMN/IMN) of the amplifier requires two target Gain Functions in terms of T_N, to be used in two sequential non-linear optimization procedures, respectively. An amplifier with a flat Gain of ~10 dB operating in 0.8–2.35 GHz is designed using a small-signal model of an experimental GaN-HEMT. Theoretical amplifier performance obtained in Matlab is shown to be in excellent agreement with the simulated performance in MWO (Microwave Office, AWR Inc.). A prototype low-power amplifier having a ~10 to 12 dB Gain, operating in (0.9–1.5 GHz) is also produced and measured which yielded good performance results.
-
Design and implementation of wideband microwave amplifiers based on Normalized Gain Function
2014 IEEE Benjamin Franklin Symposium on Microwave and Antenna Sub-systems for Radar Telecommunications and Biomedical Applications (BenMAS), 2014Co-Authors: Ramazan Köprü, Sedat Kilinc, Ahmet Aksen, Binboga Siddik YarmanAbstract:In this work, we introduce the design and implementation of wideband microwave amplifiers based on “Normalized Gain Function (NGF)” method. Normalized Gain Function is defined as the ratio of desired shape or frequency response of the Gain Function of the amplifier to be designed and shape of the transistor forward Gain Function. Synthesis of input/output matching networks (IMN/OMN) of the amplifier require target Gain curves as the Functions of normalized Gain Function to be tracked in two sequential nonlinear optimization processes. A prototype low power amplifier circuit is produced and measured to show the usability of the design approach.
-
2W wideband microwave PA design for 824-2170 MHz band using Normalized Gain Function method
2013 8th International Conference on Electrical and Electronics Engineering (ELECO), 2013Co-Authors: Ramazan Köprü, Hakan Kuntman, Binboga Siddik YarmanAbstract:In this work, we present the design of a 2W linear wideband microwave PA (power amplifier) targeted to operate in 824-2170 MHz mobile frequency range covering GSM850, EGSM, DCS, PCS and WCDMA. The design is basically based on the NGF (Normalized Gain Function) method which is very recently introduced into the literature. NGF is defined as the ratio of T and |S21|2, i.e. TNGF=T/|S21|2, shape of the Gain Function of the amplifier to be designed and the shape of the transistor forward Gain Function, respectively. Synthesis of input/output matching networks (IMN/OMN) of the amplifier requires target Gain Functions, which are mathematically generated in terms of TNGF. The particular transistor used in the design is FP31QF, a 2W HFET from TriQuint Semiconductor. Theoretical PA performance obtained in Matlab is shown to be in a very high agreement with the simulated performance in MWO (Microwave Office) of AWR Inc.
Philipos C. Loizou - One of the best experts on this subject based on the ideXlab platform.
-
Impact of SNR and Gain-Function over- and under-estimation on speech intelligibility
Speech communication, 2012Co-Authors: Fei Chen, Philipos C. LoizouAbstract:Most noise reduction algorithms rely on obtaining reliable estimates of the SNR of each frequency bin. For that reason, much work has been done in analyzing the behavior and performance of SNR estimation algorithms in the context of improving speech quality and reducing speech distortions (e.g., musical noise). Comparatively little work has been reported, however, regarding the analysis and investigation of the effect of errors in SNR estimation on speech intelligibility. It is not known, for instance, whether it is the errors in SNR overestimation, errors in SNR underestimation, or both that are harmful to speech intelligibility. Errors in SNR estimation produce concomitant errors in the computation of the Gain (suppression) Function, and the impact of Gain estimation errors on speech intelligibility is unclear. The present study assesses the effect of SNR estimation errors on Gain Function estimation via sensitivity analysis. Intelligibility listening studies were conducted to validate the sensitivity analysis. Results indicated that speech intelligibility is severely compromised when SNR and Gain over-estimation errors are introduced in spectral components with negative SNR. A theoretical upper bound on the Gain Function is derived that can be used to constrain the values of the Gain Function so as to ensure that SNR overestimation errors are minimized. Speech enhancement algorithms that can limit the values of the Gain Function to fall within this upper bound can improve speech intelligibility.
-
estimators of the magnitude squared spectrum and methods for incorporating snr uncertainty
IEEE Transactions on Audio Speech and Language Processing, 2011Co-Authors: Philipos C. LoizouAbstract:Statistical estimators of the magnitude-squared spectrum are derived based on the assumption that the magnitude-squared spectrum of the noisy speech signal can be computed as the sum of the (clean) signal and noise magnitude-squared spectra. Maximum a posterior (MAP) and minimum mean square error (MMSE) estimators are derived based on a Gaussian statistical model. The Gain Function of the MAP estimator was found to be identical to the Gain Function used in the ideal binary mask (IdBM) that is widely used in computational auditory scene analysis (CASA). As such, it was binary and assumed the value of 1 if the local signal-to-noise ratio (SNR) exceeded 0 dB, and assumed the value of 0 otherwise. By modeling the local instantaneous SNR as an F-distributed random variable, soft masking methods were derived incorporating SNR uncertainty. The soft masking method, in particular, which weighted the noisy magnitude-squared spectrum by the a priori probability that the local SNR exceeds 0 dB was shown to be identical to the Wiener Gain Function. Results indicated that the proposed estimators yielded significantly better speech quality than the conventional minimum mean square error spectral power estimators, in terms of yielding lower residual noise and lower speech distortion.
Antti Oulasvirta - One of the best experts on this subject based on the ideXlab platform.
-
CHI - AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020Co-Authors: Byungjoo Lee, Mathieu Nancel, Sunjun Kim, Antti OulasvirtaAbstract:A well-designed control-to-display Gain Function can improve pointing performance with indirect pointing devices like trackpads. However, the design of Gain Functions is challenging and mostly based on trial and error. AutoGain is a novel method to individualize a Gain Function for indirect pointing devices in contexts where cursor trajectories can be tracked. It gradually improves pointing efficiency by using a novel submovement-level tracking+optimization technique that minimizes aiming error (undershooting/overshooting) for each submovement. We first show that AutoGain can produce, from scratch, Gain Functions with performance comparable to commercial designs, in less than a half-hour of active use. Second, we demonstrate AutoGain's applicability to emerging input devices (here, a Leap Motion controller) with no reference Gain Functions. Third, a one-month longitudinal study of normal computer use with AutoGain showed performance improvements from participants' default Functions.
-
AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization.
arXiv: Human-Computer Interaction, 2020Co-Authors: Byungjoo Lee, Mathieu Nancel, Sunjun Kim, Antti OulasvirtaAbstract:A well-designed control-to-display Gain Function can improve pointing performance with indirect pointing devices like trackpads. However, the design of Gain Functions is challenging and mostly based on trial and error. AutoGain is a novel method to individualize a Gain Function for indirect pointing devices in contexts where cursor trajectories can be tracked. It gradually improves pointing efficiency by using a novel submovement-level tracking+optimization technique that minimizes aiming error (undershooting/overshooting) for each submovement. We first show that AutoGain can produce, from scratch, Gain Functions with performance comparable to commercial designs, in less than a half-hour of active use. Second, we demonstrate AutoGain's applicability to emerging input devices (here, a Leap Motion controller) with no reference Gain Functions. Third, a one-month longitudinal study of normal computer use with AutoGain showed performance improvements from participants' default Functions.
-
AutoGain: Adapting Gain Functions by Optimizing Submovement Efficiency.
arXiv: Human-Computer Interaction, 2016Co-Authors: Byungjoo Lee, Mathieu Nancel, Antti OulasvirtaAbstract:A well-designed control-to-display (CD) Gain Function can improve pointing performance with an indirect pointing device such as a trackpad. However, the design of Gain Functions has been challenging and mostly based on trial and error. AutoGain is an unobtrusive method to obtain a Gain Function for an indirect pointing device in contexts where cursor trajectories can be tracked. It gradually improves pointing efficiency by using a novel submovement-level tracking+optimization technique. In a study, we show that AutoGain can produce Gain Functions with performance comparable to commercial designs in less than a half hour of active use. This is attributable to reductions in aiming error (undershooting/overshooting) for each submovement. Our second study shows that AutoGain can be used to obtain Gain Functions for emerging input devices (here, a Leap Motion controller) for which no good Gain Function may exist yet. Finally, we discuss deployment in a real interactive system.
