The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

Mateusz Saków - One of the best experts on this subject based on the ideXlab platform.

  • Novel robust disturbance observer.
    ISA transactions, 2020
    Co-Authors: Mateusz Saków
    Abstract:

    Abstract In this paper, a predictive disturbance observer (DOB) design is presented. The proposed DOB does not require a direct model of the process without the transport delay for Prediction purposes. The primary advantage of the proposed DOB design is its simplicity due to usage of the Prediction Block (PB), which approximates the ideal anticipation object. Furthermore, the entire DOB becomes robust to external disturbances because the DOB is based on the approximation of the anticipation object. The response accuracy of the proposed predictive DOB is directly dependent on the inverse model parameters and Prediction coefficients in the PB. Notwithstanding the simplification, convenient implementation, identification, and optimization structure, the proposed solution allows for direct implementation of process models describing discontinuous nonlinear time-invariant systems.

  • Time Constant and Model-Free Signal Prediction in Communication Channel of Teleoperation System
    Advances in Intelligent Systems and Computing, 2019
    Co-Authors: Mateusz Saków, Arkadiusz Parus, M. Pajor, Karol Miądlicki
    Abstract:

    In the paper a sensor-less control scheme for a bilateral teleoperation system with a force-feedback based on a model-free Prediction in the communication channel by Prediction Blocks was presented. The Prediction Block was designed to minimize the effect of the transport delay in the communication channel of bilateral teleoperation system. The single Prediction Block has been a phase shifter with a specific behavior. The specific behavior of the Prediction Block is a strongly linear phase diagram in a useful frequency spectrum which allows the system to predict the manipulator motion and the force with a close to constant time shift. Another important feature is a gain of the Block which is close to a unity when system operates in the useful frequency spectrum. The solution is an alternative to complex and mostly non-linear methods presented in the literature. The effectiveness of the method has been verified on the hydraulic manipulator test stand under control of many operators.

  • Sensor-Less Bilateral Teleoperation System Based on Non Linear Inverse Modelling with Signal Prediction
    Advances in Intelligent Systems and Computing, 2019
    Co-Authors: Mateusz Saków, Arkadiusz Parus, M. Pajor, Karol Miądlicki
    Abstract:

    In the paper a sensor-less control scheme for a bilateral teleoperation system with a force-feedback based on a Prediction of an input and an output of a non-linear inverse model by Prediction Blocks was presented. As a part of the paper a method of a time constant estimation of the Prediction Block was also presented. The Prediction method of an input and an output of an inverse model was designed to minimize the effect of the transport delay and the phase shift of sensors, actuators and mechanical objects. The solution is an alternative to complex non-linear models like artificial neural networks, which requires complex stability analysis and control systems with a high computing power. The effectiveness of the method has been verified on the hydraulic manipulator test stand.

  • Signal Prediction in Bilateral Teleoperation with Force-Feedback
    Dynamical Systems in Applications, 2018
    Co-Authors: Mateusz Saków, K. Marchelek, Arkadiusz Parus, M. Pajor, Karol Miądlicki
    Abstract:

    In the paper a sensor-less and self-sensing control scheme for a bilateral teleoperation system with force-feedback based on a Prediction of an input of a non-linear inverse model by Prediction Blocks was presented. As a part of the paper a method of a time constant estimation of the Prediction Block was also proposed. The Prediction method of an input of an inverse model was designed to minimize the effect of the transport delay and the phase shift of sensors, actuators and mechanical objects. The solution is an alternative to complex non-linear models like NARX or artificial neural networks, which requires complex stability analysis, and control systems with high computing powers. The effectiveness of the method has been verified on the hydraulic manipulator’s test stand.

  • Model-free and time-constant Prediction for closed-loop systems with time delay
    Control Engineering Practice, 2018
    Co-Authors: Mateusz Saków, K. Marchelek
    Abstract:

    Abstract This study presents a model-free method of signal Prediction dedicated to closed-loop systems affected by time delay. The proposed Prediction method leads to better Prediction results than the Smith Predictor for relatively small delays. Moreover, the solution is dedicated to complex systems, which are susceptible to differences between the system and its model. The theoretical analysis, experiments, and comparisons performed in this study confirmed these features. This paper presents simplifications that were conducted to obtain the Prediction Block directly from the Smith Predictor control scheme. The Prediction Block, under specific conditions, allows for signal Prediction with a constant time value. The Prediction Block was compared with an ideal anticipatory object. Furthermore, the Smith Predictor control scheme was compared with the proposed method. In this case, the transport-delay value in the closed-loop system is the only information required for proper Prediction. An adapted Nyquist criterion that can prove the asymptotic stability of the control scheme was proposed. The theoretical part of the paper was supported by experiments performed on an experimental test stand with a hydraulic manipulator. In the experiments, the Prediction Block improved the position tracking of the system. The Prediction control scheme has already demonstrated widespread potential for applications in multiple control approaches and experiments. The solution is dedicated to complex systems that are susceptible to differences between the system and its model.

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

  • 1Sequential Error Concealment for Video/Images by Sparse Linear Prediction
    2016
    Co-Authors: Jan Ostergaard, Soren Holdt Jensen, Antonio M Peinado, Senior Member
    Abstract:

    Abstract—In this paper we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environment are considered. Images are modelled by means of linear Prediction and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also pro-posed. Both techniques provide high quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM. Index Terms—Error concealment, Block-coded images/video, convex optimization, missing data imputation, sparse represen-tation I

  • sequential error concealment for video images by sparse linear Prediction
    IEEE Transactions on Multimedia, 2013
    Co-Authors: Jan Koloda, Jan Ostergaard, Soren Holdt Jensen, Victoria Sanchez, Antonio M Peinado
    Abstract:

    In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear Prediction, and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

  • Sequential Error Concealment for Video/Images by Sparse Linear Prediction
    IEEE Transactions on Multimedia, 2013
    Co-Authors: Jan Koloda, Jan Ostergaard, Soren Holdt Jensen, Victoria Sanchez, Antonio M Peinado
    Abstract:

    In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear Prediction, and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

Jan Koloda - One of the best experts on this subject based on the ideXlab platform.

  • sequential error concealment for video images by sparse linear Prediction
    IEEE Transactions on Multimedia, 2013
    Co-Authors: Jan Koloda, Jan Ostergaard, Soren Holdt Jensen, Victoria Sanchez, Antonio M Peinado
    Abstract:

    In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear Prediction, and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

  • Sequential Error Concealment for Video/Images by Sparse Linear Prediction
    IEEE Transactions on Multimedia, 2013
    Co-Authors: Jan Koloda, Jan Ostergaard, Soren Holdt Jensen, Victoria Sanchez, Antonio M Peinado
    Abstract:

    In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear Prediction, and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

Jan Ostergaard - One of the best experts on this subject based on the ideXlab platform.

  • 1Sequential Error Concealment for Video/Images by Sparse Linear Prediction
    2016
    Co-Authors: Jan Ostergaard, Soren Holdt Jensen, Antonio M Peinado, Senior Member
    Abstract:

    Abstract—In this paper we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environment are considered. Images are modelled by means of linear Prediction and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also pro-posed. Both techniques provide high quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM. Index Terms—Error concealment, Block-coded images/video, convex optimization, missing data imputation, sparse represen-tation I

  • sequential error concealment for video images by sparse linear Prediction
    IEEE Transactions on Multimedia, 2013
    Co-Authors: Jan Koloda, Jan Ostergaard, Soren Holdt Jensen, Victoria Sanchez, Antonio M Peinado
    Abstract:

    In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear Prediction, and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

  • Sequential Error Concealment for Video/Images by Sparse Linear Prediction
    IEEE Transactions on Multimedia, 2013
    Co-Authors: Jan Koloda, Jan Ostergaard, Soren Holdt Jensen, Victoria Sanchez, Antonio M Peinado
    Abstract:

    In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear Prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear Prediction, and missing macroBlocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

Davide Quaglia - One of the best experts on this subject based on the ideXlab platform.

  • new sorting based lossless motion estimation algorithms and a partial distortion elimination performance analysis
    IEEE Transactions on Circuits and Systems for Video Technology, 2005
    Co-Authors: Bartolomeo Montrucchio, Davide Quaglia
    Abstract:

    In video encoding, Block motion estimation represents a CPU-intensive task. For this reason, many fast algorithms have been developed to improve searching and matching phases. A milestone within the lossless approach is partial distortion elimination (PDE/SpiralPDE) in which distortion is the difference between the Block to be coded and the candidate Prediction Block. In this paper, (i) we analyze distortion behavior from local information using the Taylor series expansion and show that our general analysis includes other previous similar approaches. (ii) Then, we propose two full-search (lossless), fast-matching, Block motion estimation algorithms, based on the PDE idea. The proposed algorithms, called fast full search with sorting by distortion (FFSSD) and fast full search with sorting by gradient (FFSSG), sort the contributions to distortion and the gradient values, respectively, in order to quickly discard invalid Blocks. Experimental results show that the proposed algorithms outperform other existing full search algorithms, reducing by up to 20% the total CPU encoding time (with respect to SpiralPDE), while the computation strictly required by the motion estimation is reduced by about 30%. (iii) Finally, we experimentally find an operational lower bound (based on standard test sequences) for the average number of checked pixels in the PDE approach, which measures the performance of the searching and matching phases. In particular, SpiralPDE achieves performances very close to the searching phase bound, while there is still a remarkable margin on the matching phase. We then show that our algorithms, aimed at improving the performances of the matching phase, achieve interesting results, significantly approaching this margin.

  • On New Sorting-Based Lossless Motion Estimation Algorithms
    WSEAS TRANSACTIONS on COMMUNICATIONS archive, 2004
    Co-Authors: Davide Quaglia, Massimo Perga, Bartolomeo Montrucchio, Paolo Montuschi
    Abstract:

    Block motion estimation represents a cpu-intensive task in video encoding and many fast algorithms have been developed to improve the searching and matching phases. A milestone within the lossless approach is partial distortion elimination (PDE/SpiralPDE) in which distortion is the difference between the Block to be coded and the candidate Prediction Block. In this paper we show that contributions to distortion can be reliably estimated using the Taylor series expansion. The approximation method is then used to derive eight new PDE-based algorithms in which the matching order depends on the magnitude of the estimated distortion contributions. Exhaustive comparisons using several, widely different, video sequences show that these algorithms reduce the total encoding time by up to 20% with respect to SpiralPDE, while the computation for motion estimation is reduced by about 30%. The proposed algorithms are also compared with other PDE-based lossless approaches known in literature and there is a significant gain over all of them