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You searched for subject:(Self calibrating). Showing records 1 – 3 of 3 total matches.

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Loughborough University

1. Mohd Azmin, Farraen. Algorithms and architectures for self-calibration of engines.

Degree: PhD, 2016, Loughborough University

Engine Management Systems (EMS) is getting more complicated each year with new functions being introduced due to tighter emission regulations of both air quality and CO2. This directly a ects the calibration process of a powertrain because the number of vehicle parameters has increased about 10 times in the last 10 years. Self-calibrating feature such as proposed in this thesis has the potential to increase the e ciency of calibrating a complex EMS. The feature is intended to adapt itself to the engine behaviour and performance by continuously updating its calibration maps as the engine is being operated. This process will reduce the needs for new calibration data and additional ne-tuning when an EMS is being carried over to a di erent vehicle. The self-calibrating feature automatically adjusts the air path calibration maps of an engine. It adjusts the air path setpoint maps in real-time for steady state operating conditions.

Subjects/Keywords: Calibration; Model-based; Air-path; SOBOL sequence; Automation; Self-calibrating; Statistical modelling; Design of experiments

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APA (6th Edition):

Mohd Azmin, F. (2016). Algorithms and architectures for self-calibration of engines. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/21347

Chicago Manual of Style (16th Edition):

Mohd Azmin, Farraen. “Algorithms and architectures for self-calibration of engines.” 2016. Doctoral Dissertation, Loughborough University. Accessed October 30, 2020. http://hdl.handle.net/2134/21347.

MLA Handbook (7th Edition):

Mohd Azmin, Farraen. “Algorithms and architectures for self-calibration of engines.” 2016. Web. 30 Oct 2020.

Vancouver:

Mohd Azmin F. Algorithms and architectures for self-calibration of engines. [Internet] [Doctoral dissertation]. Loughborough University; 2016. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/2134/21347.

Council of Science Editors:

Mohd Azmin F. Algorithms and architectures for self-calibration of engines. [Doctoral Dissertation]. Loughborough University; 2016. Available from: http://hdl.handle.net/2134/21347

2. Prucksakorn, Tanapol. Autonomous Learning of Motion Parallax for Active Depth Perception.

Degree: Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学

Supervisor: Professor Nak Young Chong

School of Information Science

Master

Subjects/Keywords: Autonomous Learning; Motion Parallax; Active Depth Perception; Self-Calibrating; Action-Perception Cycle

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APA (6th Edition):

Prucksakorn, T. (n.d.). Autonomous Learning of Motion Parallax for Active Depth Perception. (Thesis). Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10119/12921

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Prucksakorn, Tanapol. “Autonomous Learning of Motion Parallax for Active Depth Perception.” Thesis, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Accessed October 30, 2020. http://hdl.handle.net/10119/12921.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Prucksakorn, Tanapol. “Autonomous Learning of Motion Parallax for Active Depth Perception.” Web. 30 Oct 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Prucksakorn T. Autonomous Learning of Motion Parallax for Active Depth Perception. [Internet] [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; [cited 2020 Oct 30]. Available from: http://hdl.handle.net/10119/12921.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Prucksakorn T. Autonomous Learning of Motion Parallax for Active Depth Perception. [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; Available from: http://hdl.handle.net/10119/12921

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.


University of Illinois – Urbana-Champaign

3. Sharif, Behzad. Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction.

Degree: PhD, 1200, 2010, University of Illinois – Urbana-Champaign

In this dissertation, we address several inverse problems associated with multi-channel sampling and reconstruction that pertain to parallel magnetic resonance imaging (pMRI). The first part of this dissertation addresses adaptive design of spatio-temporal acquisition and reconstruction in model-based pMRI wherein the signal model is a sparse support. We develop a highly-accelerated real-time dynamic MRI technique, dubbed PARADISE, which incorporates a physiologically-driven sparse support model in the joint spatial domain and temporal frequency dimension. The imaging scheme gains its acceleration from: (i) sparsity of the support model; and (ii) the redundancy in data acquired by the parallel receiver coils. The PARADISE adaptation procedure ensures that maximally compressed MR data is acquired by optimally exploiting the degrees of freedom in the joint k-t sampling space, thereby enabling high accelerations and quality in the cine reconstruction stage. We propose and verify the efficacy of a geometric multi-channel sampling design algorithm that does not require explicit knowledge of the channel characteristics. Accompanied by a customized pulse sequence, the fast semi-blind acquisition design technique enables streamlined implementation of the method in a clinical setting. Moreover, the unified multi-channel sampling framework explicitly accounts for speed limitations of gradient encoding, provides performance guarantees on achievable image quality both in terms of noise gain and aliasing distortion, and allows for analysis of the method's robustness to model mismatch. We present in-vivo results demonstrating the feasibility of the PARADISE scheme  – and its distinctive features and effectiveness  – for high resolution non-gated cardiac imaging during a short breath-hold. The second part of the dissertation addresses the problems of blind and nonblind perfect inversion of multi-channel multi-rate systems. Driven by applications in multi-sensor imaging systems such as pMRI, we focus on systems wherein each channel is subsampled relative to the Nyquist rate but the overall multi-channel system is oversampled. We address the feasibility of perfect reconstruction (PR) using short finite impulse response (FIR) synthesis filters given an oversampled but otherwise general FIR analysis filter bank (FB). We provide prescriptions for the shortest filter length of the synthesis bank that would guarantee PR and, in addition, study the requirements for achieving near-optimal noise performance. Next, we address the problem of multi-channel perfect interpolation (PI) by building upon the developed framework for the multi-channel PR problem. We present the theory and algorithms for identifying a FIR multi-input multi-output interpolation bank that achieves PI both with and without the knowledge of the channel characteristics. The theory developed for the latter case, called the blind PI problem, is in turn used to develop a self-calibrating algorithm, dubbed ACSIOM, for blind identification of the interpolation FB with… Advisors/Committee Members: Bresler, Yoram (advisor), Bresler, Yoram (Committee Chair), Liang, Zhi-Pei (committee member), Kamalabadi, Farzad (committee member), Sutton, Bradley P. (committee member), Do, Minh N. (committee member).

Subjects/Keywords: magnetic resonance; Magnetic resonance imaging (MRI); parallel magnetic resonance imaging (MRI); parallel imaging; dynamic magnetic resonance imaging (MRI); cardiac magnetic resonance imaging MRI; real-time magnetic resonance imaging MRI; image formation; nongated; model-based; patient-adaptive; k-t sampling; sensitivity encoding; multi-channel; filter banks; multi-rate systems; minimum filter length; frame theory; dual frame; oversampled; subsampled; generic; perfect reconstruction; perfect interpolation; multi-channel interpolation; distortion free; distortion optimal; aliasing free; self-calibrating; auto-calibrated; image reconstruction; blind identification; blind reconstruction; self calibration; Papoulis sampling; multi-channel sampling; aliasing error; geometric factor; equivalence class; lattice sampling; dual lattice; time-sequential; nonbandlimited; adaptive acquisition; sparse sampling; compressive sampling; spatio-temporal acquisition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sharif, B. (2010). Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/17056

Chicago Manual of Style (16th Edition):

Sharif, Behzad. “Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction.” 2010. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 30, 2020. http://hdl.handle.net/2142/17056.

MLA Handbook (7th Edition):

Sharif, Behzad. “Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction.” 2010. Web. 30 Oct 2020.

Vancouver:

Sharif B. Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2010. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/2142/17056.

Council of Science Editors:

Sharif B. Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2010. Available from: http://hdl.handle.net/2142/17056

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