The Experts below are selected from a list of 153621 Experts worldwide ranked by ideXlab platform

M Schlechtweg - One of the best experts on this subject based on the ideXlab platform.

  • a systematic State Space Approach to large signal transistor modeling
    IEEE Transactions on Microwave Theory and Techniques, 2007
    Co-Authors: M Seelmanneggebert, T Merkle, F Van Raay, R Quay, M Schlechtweg
    Abstract:

    A State-Space Approach to large-signal (LS) modeling of high-speed transistors is presented and used as a general framework for various model descriptions of the dispersive features frequently observed for HEMTs at low frequency. Ensuring unrestricted LS-small-signal (SS) model compatibility, the Approach allows to construct LS models from multibias SS S-parameter measurements. A general transformation between State-Space models is derived, which are equivalent in the SS limit, but nonequivalent under LS stimuli. This transformation has the potential to compensate deviations observed by comparing model predictions with LS measurements and to find an optimum State linear LS model without any change of the SS behavior

  • A Systematic State–Space Approach to Large-Signal Transistor Modeling
    IEEE Transactions on Microwave Theory and Techniques, 2007
    Co-Authors: Matthias Seelmann-eggebert, T Merkle, F Van Raay, R Quay, M Schlechtweg
    Abstract:

    A State-Space Approach to large-signal (LS) modeling of high-speed transistors is presented and used as a general framework for various model descriptions of the dispersive features frequently observed for HEMTs at low frequency. Ensuring unrestricted LS-small-signal (SS) model compatibility, the Approach allows to construct LS models from multibias SS S-parameter measurements. A general transformation between State-Space models is derived, which are equivalent in the SS limit, but nonequivalent under LS stimuli. This transformation has the potential to compensate deviations observed by comparing model predictions with LS measurements and to find an optimum State linear LS model without any change of the SS behavior

U B Desai - One of the best experts on this subject based on the ideXlab platform.

  • a State Space Approach to orthogonal digital filters
    IEEE Transactions on Circuits and Systems, 1991
    Co-Authors: U B Desai
    Abstract:

    An algorithm for designing multi-input multi-output (MIMO) orthogonal digital filters is developed using a State-Space Approach. The algorithm consists of three parts: (i) orthogonal embedding, (ii) transformation of the embedded orthogonal transition matrix to the alpha -extended upper Hessenberg form; and (iii) factorization of this new form into Givens (planar) rotations. Appropriately interconnecting the rotors leads to the pipelined orthogonal filter structure. As a consequence of the Approach, for the single-input, single-output (SISO) case, an essentially orthogonal structure is obtained for the inverse filter; only one of the Givens rotors is replaced by a hyperbolic rotor. >

  • ICASSP - A State-Space Approach to orthogonal digital filters for VLSI implementation
    International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: U B Desai
    Abstract:

    An algorithm for designing orthogonal digital filters using a purely State-Space Approach is developed. The algorithm consists of three parts: (i) orthogonal embedding, (ii) transformation of the embedded orthogonal transition matrix to the extended upper Hessenberg form, and (iii) factorization of this new form into into (2n+1) Givens rotations. Appropriately interconnecting the rotors leads to the pipelined orthogonal filter structure. As a consequence of this Approach, an essentially orthogonal structure is obtained for the inverse filter, and only one Givens rotor gets replaced by a hyperbolic rotor. >

Alex B. Gershman - One of the best experts on this subject based on the ideXlab platform.

  • A State-Space Approach to Robust Multiuser Detection
    IEEE Transactions on Wireless Communications, 2007
    Co-Authors: Amr El-keyi, Thiagalingam Kirubarajan, Alex B. Gershman
    Abstract:

    In this paper, we develop a State-Space Approach to the blind multiuser detection problem with robustness against mismatches in the desired user signature and the time-varying number of users in the channel. The solution is obtained adaptively using a second-order extended Kalman filter (EKF) and requires only O(L2) operations per iteration, where L is the dimension of the subSpace containing the signatures of all the users. We also present a State-Space Approach to the decision directed multiuser detection problem and an algorithm for switching between robust blind and decision directed detection. The proposed switching algorithm is based on using the normalized innovation square (NIS) of the blind detector to test for its convergence and the NIS of the decision directed detector to detect nonstationarities. Thus, it combines the advantages of both these detection schemes and can achieve an output signal- to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training, even in the presence of mismatches in the desired user signature. Therefore, it is well suited to practical nonstationary environments where users repeatedly enter and leave the system making the cost of retraining un affordable.

  • A State-Space Approach to robust multiuser detection
    Computational Advances in Multi-Sensor Adaptive Processing 2005 1st IEEE International Workshop on, 2005
    Co-Authors: Amr El-keyi, Thiagalingam Kirubarajan, Alex B. Gershman
    Abstract:

    In this paper, we develop a State-Space Approach to the blind multiuser detection problem with robustness against arbitrary mismatches in the desired user signature. A State-Space Approach to the decision directed detection problem and an algorithm for switching between the two detection techniques are also presented. The proposed switching algorithm can achieve an output signal-to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training even in the presence of desired user signature mismatches

Emery N Brown - One of the best experts on this subject based on the ideXlab platform.

  • a State Space Approach to multimodal integration of simultaneously recorded eeg and fmri
    International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Patrick L Purdon, Camilo Lamus, Matti Hamalainen, Emery N Brown
    Abstract:

    We develop a State Space Approach to multimodal integration of simultaneously recorded EEG and fMRI. The EEG is represented with a distributed current source model using realistic MRI-based forward models, whose temporal evolution is governed by a linear State Space model. The fMRI signal is similarly modeled by a linear State Space model describing the hemodynamic response to underlying EEG current activity. We explore the feasibility of high dimensional dynamic estimation of simultaneous EEG/fMRI using simulation studies of the alpha wave.

  • ICASSP - A State Space Approach to multimodal integration of simultaneously recorded EEG and fMRI
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Patrick L Purdon, Camilo Lamus, Matti Hamalainen, Emery N Brown
    Abstract:

    We develop a State Space Approach to multimodal integration of simultaneously recorded EEG and fMRI. The EEG is represented with a distributed current source model using realistic MRI-based forward models, whose temporal evolution is governed by a linear State Space model. The fMRI signal is similarly modeled by a linear State Space model describing the hemodynamic response to underlying EEG current activity. We explore the feasibility of high dimensional dynamic estimation of simultaneous EEG/fMRI using simulation studies of the alpha wave.

Amr El-keyi - One of the best experts on this subject based on the ideXlab platform.

  • A State-Space Approach to Robust Multiuser Detection
    IEEE Transactions on Wireless Communications, 2007
    Co-Authors: Amr El-keyi, Thiagalingam Kirubarajan, Alex B. Gershman
    Abstract:

    In this paper, we develop a State-Space Approach to the blind multiuser detection problem with robustness against mismatches in the desired user signature and the time-varying number of users in the channel. The solution is obtained adaptively using a second-order extended Kalman filter (EKF) and requires only O(L2) operations per iteration, where L is the dimension of the subSpace containing the signatures of all the users. We also present a State-Space Approach to the decision directed multiuser detection problem and an algorithm for switching between robust blind and decision directed detection. The proposed switching algorithm is based on using the normalized innovation square (NIS) of the blind detector to test for its convergence and the NIS of the decision directed detector to detect nonstationarities. Thus, it combines the advantages of both these detection schemes and can achieve an output signal- to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training, even in the presence of mismatches in the desired user signature. Therefore, it is well suited to practical nonstationary environments where users repeatedly enter and leave the system making the cost of retraining un affordable.

  • A State-Space Approach to robust multiuser detection
    Computational Advances in Multi-Sensor Adaptive Processing 2005 1st IEEE International Workshop on, 2005
    Co-Authors: Amr El-keyi, Thiagalingam Kirubarajan, Alex B. Gershman
    Abstract:

    In this paper, we develop a State-Space Approach to the blind multiuser detection problem with robustness against arbitrary mismatches in the desired user signature. A State-Space Approach to the decision directed detection problem and an algorithm for switching between the two detection techniques are also presented. The proposed switching algorithm can achieve an output signal-to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training even in the presence of desired user signature mismatches