Hardware Design of Laser Optical Tomography System for Detection of Bubbles Column

Fadzil, Naizatul Shima Mohd; Rahim, Ruzairi Abdul; Karis, Mohd Safirin; Muji, Siti Zarina Mohd; Sahib, Mohd Fadzli Abdul; Mansor, Mohd Saiful Badri; Ayob, Nor Muzakkir Nor; Jumaah, Mohd Fahajumi; Zawahir, Mohd Zikrillah

doi:10.11113/jt.v64.2135

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Here, the minimum size of gas that could be detected by the system was 20 mm. However, in 2013, Mohd Fadzil et al [18] proposed optical tomography for a vertical bubble column (130 mm in outer diameter and 1.2 m in length). Here, sixteen laser pointers and sixteen photodiode model EPD-660-5 were chosen as transmitters and receivers, respectively.…”

Section: Optical Tomographymentioning

confidence: 98%

Non-invasive process tomography in chemical mixtures – A review

Wahab

Rahim

Rahiman

et al. 2015

Sensors and Actuators B: Chemical

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Section: Optical Tomographymentioning

confidence: 98%

Non-invasive process tomography in chemical mixtures – A review

Wahab

Rahim

Rahiman

et al. 2015

Sensors and Actuators B: Chemical

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“…For instance, in [ 13 , 14 ], the authors developed a laser tomography system to detect bubbles inside a vertical column pipeline. Sixteen laser pointer-photodetector couples are mounted in a jig around the circumference of the pipe.…”

Section: Introductionmentioning

confidence: 99%

Optical Multi-Parameter Measuring System for Fluid and Air Bubble Recognition

Bello

Bodo

Merlo

2023

Sensors

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Detection of air bubbles in fluidic channels plays a fundamental role in all that medical equipment where liquids flow inside patients’ blood vessels or bodies. In this work, we propose a multi-parameter sensing system for simultaneous recognition of the fluid, on the basis of its refractive index and of the air bubble transit. The selected optofluidic platform has been designed and studied to be integrated into automatic pumps for the administration of commercial liquid. The sensor includes a laser beam that crosses twice a plastic cuvette, provided with a back mirror, and a position-sensitive detector. The identification of fluids is carried out by measuring the displacement of the output beam on the detector active surface and the detection of single air bubbles can be performed with the same instrumental scheme, exploiting a specific signal analysis. When a bubble, traveling along the cuvette, crosses the readout light beam, radiation is strongly scattered and a characteristic fingerprint shape of the photo-detected signals versus time is clearly observed. Experimental testing proves that air bubbles can be successfully detected and counted. Their traveling speed can be estimated while simultaneously monitoring the refractive index of the fluid.

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“…Tomography systems that use computers to reconstruct the images are called Computed Tomography (CT). This is because the tomography systems developed are using computers to reconstruct the images [2][3][4][5][6][7] . The hardware designs developed are call Data Acquisition Systems (DAS) that can be connected to the computers for data processing of image reconstruction.…”

Section: 10 Introductionmentioning

confidence: 99%

Fixed-point Sensitivity Maps for Image Reconstruction in Tomography

et al. 2015

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This study proposes new fixed-point sensitivity maps for image reconstruction in optical tomography systems by using a linear back projection (LBP) algorithm. The projection selected is based on fan beam orientation with 16 pairs of transmitters and receivers. Many optical tomography systems previously published, focused on microcontroller implementations, which have limited processing speed for real time systems to be used in critical applications such as underwater gas transmission pipeline. To gain benefits from parallel processing in digital hardware implementations such as using FPGA or ASIC, slight modification must be done in the reconstructed images mechanism. The normalized sensitivity maps for image reconstruction are recreated without using floating numbers to optimize the digital design implementations. By multiplying the sensitivity maps matrix with 128, the new sensitivity maps matrices are developed and rounding processes are performed to eliminate floating numbers. The error was determined using Normalized Mean Square Error (NMSE) and the results show that the new fixed-point sensitivity maps produced comparable image reconstruction quality for optical tomography systems with NMSE values of 1.88 × 10-7 and 0.02 for phantoms (a) and (b) respectively.

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Hardware Design of Laser Optical Tomography System for Detection of Bubbles Column

Cited by 6 publications

References 10 publications

Non-invasive process tomography in chemical mixtures – A review

Non-invasive process tomography in chemical mixtures – A review

Optical Multi-Parameter Measuring System for Fluid and Air Bubble Recognition

Fixed-point Sensitivity Maps for Image Reconstruction in Tomography

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