OHNO Shuichi

写真a

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Title

Professor

Laboratory location

Sugimoto Campus

Research Areas 【 display / non-display

Communication/Network engineering

Research Interests 【 display / non-display

digital communication, signal processing, data analysis, machine learning

Current Career 【 display / non-display

  • Osaka City University   Graduate School of Engineering   Physical Electronics and Informatics Course   Professor  

 

Published Papers 【 display / non-display

  • State space realizations robust to overloading for discrete-time LTI systems

    Ohno Shuichi, Yoshimura Yuichi

    SIGNAL PROCESSING  156   12 - 20 2019.03  [Refereed]

    DOI

  • Min-Max Design of Error Feedback Quantizers Without Overloading

    Ohno Shuichi, Ishihara Yuma, Nagahara Masaaki

    Institute of Electrical and Electronics Engineers Inc. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS  65 ( 4 ) 1395 - 1405 2018.04  [Refereed]

     View Summary

    In this paper, we design a no-overloading error feedback quantizer based on a ΔΣ modulator, composed of an error feedback filter and a static quantizer. To guarantee no-overloading in the quantizer, we impose an l∞ norm constraint on the feedback signal in the quantizer. Then, for a prescribed l∞ norm constraint on the error at the system output induced by the quantizer, we design the error feedback filter that requires the minimum number of bits that achieves the constraint. Next, for a fixed number of bits for the quantizer, we investigate the achievable minimum l∞ norm of the error at the system output with the no-overloading quantizer. Numerical examples are provided to validate our analysis and synthesis.

    DOI

  • An iterative LMI algorithm for quantization noise reduction in Delta Sigma modulators

    Tariq M. Rizwan, Ohno Shuichi

    SIGNAL PROCESSING  144   163 - 168 2018.03  [Refereed]

    DOI

  • Mean Squared Error Analysis of Quantizers With Error Feedback

    Ohno Shuichi, Shiraki Teruyuki, Tariq M. Rizwan, Nagahara Masaaki

    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON SIGNAL PROCESSING  65 ( 22 ) 5970 - 5981 2017.11  [Refereed]

     View Summary

    Quantization is a fundamental process in digital signal processing, Delta Sigma Smodulators are often utilized for quantization, which can be easily implemented with static uniform quantizers and error feedback filters. In this paper, we analyze the mean squared quantization error of the quantizer with error feedback including the Delta Sigma modulators. First, we study the quantizer with an ideal optimal error feedback filter that minimizes the mean squared error (MSE) of quantization. We show that the amplitude response of the optimal error feedback filter can be parameterized by one parameter. This parameterization enables us to find the optimal error feedback filter numerically. Second, the relationship between the number of bits used for the quantizer and the achievable MSE is clarified by using the optimal error feedback filter. This makes it possible to investigate the efficiency of the quantizer with the optimal error feedback filter in terms ofMSE. Then, ideal optimal error feedback filters are approximated by practically implementable filters using the Yule-Walker method and the linear matrix inequality-basedmethod. Numerical examples are provided for demonstrating our analysis and synthesis.

    DOI

  • Optimization of Noise Shaping Filter for Quantizer With Error Feedback

    Ohno Shuichi, Tariq M. Rizwan

    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS  64 ( 4 ) 918 - 930 2017.04  [Refereed]

     View Summary

    Noise shaping filters for quantizers with error feedback are designed to mitigate the effects of quantization errors. In this paper, we prove that if the transfer function from the quantization error to the signal-of-interest has minimum phase and there is no constraint on the feedback signal, then the scaled inverse of the transfer function is the optimal noise shaping filter. Next, we design a noise shaping filter to minimize the variance or the l(2) norm of the error in the signal-ofinterest under a constraint on the variance or the l(2) norm of the feedback signal, which can be expressed as bilinear matrix inequalities (BMIs). Although the BMIs are not convex, we prove that the minimization of the error variance under the constraint on the variance of the feedback signal can be cast into a convex optimization problem. This enables us to design optimal noise shaping filters using numerical methods. We formulate our design problem as a convex optimization problem using extended linear matrix inequalities to obtain noise shaping filters, except for the special case. Examples are provided to demonstrate the effectiveness of the designed noise shaping filters.

    DOI

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