Next Article in Journal
Feasibility Analysis of a LoRa-Based WSN Using Public Transport
Next Article in Special Issue
An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
Previous Article in Journal
Design and Development of a Web Application for Matching Drug Addiction Treatment Services with Substance Users
Previous Article in Special Issue
New Approximation Methods Based on Fuzzy Transform for Solving SODEs: II
Open AccessArticle

A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration

Department of Engineering, Design and Physical Sciences, Brunel University London, Kingston Lane, Uxbridge, London UB8 3PH, UK
Appl. Syst. Innov. 2018, 1(4), 48; https://doi.org/10.3390/asi1040048
Received: 18 October 2018 / Revised: 25 November 2018 / Accepted: 25 November 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a fuzzy inference system (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility, and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules, and one output. The FIS is responsible for monitoring the restoration results, grading their validity, and choosing the one that yields to a better grade. These grades are produced autonomously by analyzing results of a Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 μm wire probe were compared with the restorations. Main aims are therefore: (a) Provide unsupervised deblurring for device independent EB measurement; (b) improve the reliability of the process; and (c) apply deblurring without knowing the probe size. These further facilitate the deployment and manufacturing of EB probes as well as facilitate accurate and probe-independent EB characterization. This paper’s findings also makes restoration of previously collected EB measurements easier where the probe sizes are not known nor recorded. View Full-Text
Keywords: fuzzy inference system; fuzzy logics; linear motion blur; fuzzy deblurring; electron beam calibration; signal and image processing fuzzy inference system; fuzzy logics; linear motion blur; fuzzy deblurring; electron beam calibration; signal and image processing
Show Figures

Figure 1

MDPI and ACS Style

Hosseinzadeh, S. A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration. Appl. Syst. Innov. 2018, 1, 48.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop