Inverse Problem: Comparison between Linear Back-Projection Algorithm and Filtered Back-Projection Algorithm in Soft-Field Tomography

An image reconstruction algorithm in process tomography is very important in ensure that the reconstructed image is satisfied.  The back-projection algorithm is the most popular algorithm applied in process tomography including a linear back-projection (LBP) algorithm and a filtered back-projection (FBP) algorithm.  The objectives of this paper are: (1) to compare the result from LBP and FBP algorithm in soft-field tomography, and (2) to investigate the effect of FBP algorithm to a reconstructed image in soft-field tomography. Also, a comparison between hard-field tomography (ultrasonic tomography) and soft-field tomography (electrical resistance tomography, ERT) had been discussed in this paper.  The ERT had been conducted based on a simulation using COMSOL Multiphysics live link with MATLAB software.  As a result, a nature behavior of soft-field gave a main factor of inaccurate position of reconstructed image using FBP algorithm.  When it multiplied with the concentration profile from LBP result, a “hill†surface sensitivity distribution and a color scale shifted in FBP gave inaccurate result. Therefore, it is believed that it gave a reason of why the FBP algorithm is not the concern on reconstructing image in soft-field tomography.Â

2 mhz electrical resistance tomography for static liquid- solid profile measurement

Tomography is a technique used to reconstruct cross-sectional image of a pipeline for flow monitoring applications. There are several types of tomography system such as X-ray tomography, ultrasonic tomography, and electrical resistance tomography (ERT). ERT has many advantages compared to other types of tomography such as low cost, robust and no radiation. Thus, it becomes particularly suitable for industrial applications. However, it has been observed that the conventional practice of ERT through invasive sensing technique has exposed the ERT metal sensor to corrosion and limited its application because of inaccurate measurement of the data. Consequently, non-invasive ERT has also been introduced in low frequency (in kHz) applied to the ERT system. The low frequency ERT makes use of the phase-sensitive demodulation (PSD) approach and is a complicated technique to implement. Hence, the goal of this research is to design and develop a non-invasive ERT system with a high frequency (2 MHz) source. A total impedance of coupling capacitances (between metal electrode and conductive medium) series with resistance (conductive medium) for each pair of electrodes was assumed in the research. Based on the mathematical equation of the total impedance, the real part is the resistance (conductive medium) must be larger than the imaginary part (capacitances), so that it can easily detect the concentration profile of the conductive medium. Therefore, the minimum frequency needed to ensure that the real part is bigger than the imaginary one is 2 MHz. Simultaneously, the independent and flexible sixteen ERT electrodes designed for the system make it easier to replace and troubleshoot any problems with the sensor. In addition, the experiment was carried out on a two-phase static liquid–solid regime for a linear back-projection algorithm using online configuration, with MATLAB as a software platform. It was also able to detect and visualize the non-homogenous system of the two-phase regime. Later, the reconstructed image was improved using a global threshold technique through offline configuration. The experiment results indicate that it could detect obstacles in a vertical pipe with minimum 12 mm in diameter and 4.5 cm in height

Elucidating the toll-like receptor 4 involvement in striatum and cerebellum of swiss albino adult mice on motor and sickness behaviours

Alcohol addiction is one of the possible factors in stimulating brain microglia activation and leading to neuroinflammation through toll-like receptors (TLR) which are present in microglia. In fact, alcohol addiction ultimately causes motor deficits through neuroinflammation. However, the underlying mechanisms of neuroinflammation inducing motor behaviour through activation of TLR4 receptors have not yet been elucidated. TLR are always found to be associated or involved in the induction of neuroinflammation in neurodegenerative diseases. TLR4 is stimulated by TLR4 Agonist, Lipopolysaccharide (LPS), and the TLR4-LPS interaction has been found to result in physiological and behavioural changes including retardation of motor activity in the mouse model. Therefore, the present study aimed to investigate the locomotor behaviour, gene expression of serotonin receptors (HTR1A and HTR2A), dopamine receptors (Dopamine D1 receptor and Dopamine D2 receptor) and glutamate transporters (EAAT1 and EAAT4) in the striatum and cerebellum following treatment with TLR4 agonist. The animals were divided into three groups; (1) Control (n=12), (2) LPS treatment (0.83mg/kg) (n=12) 6 h and (3) LPS treatment (0.83mg/kg) (n=12) 24 h. After treatment, locomotor behaviour was analysed in open field test, wooden beam test and hanging test at 6 and 24 h post-LPS administration. Following behaviour test, animal’s brains were harvested and striatum and cerebellum isolated for gene expression studies. Results showed that there were locomotor deficits at 6 h but not in 24 h. The gene expression studies suggested that there were significant changes in serotonin receptors (HTR1A and HTR2A), dopamine receptors (Dopamine D1 receptor and Dopamine D2 receptor) and glutamate (EAAT1 and EAAT4) transporters in the striatum and cerebellum along with motor deficits. In conclusion,TLR4 possibly causes motor deficits through regulation of glutamate transporter EAAT1 in striatum and cerebellum

Cardiac arrhythmia classification using self organizing MAP (SOM) - based ensemble model

Many clinical decision support systems have been using data mining techniques for prediction and diagnosis of various diseases with good accuracy. This is due to its ability to distinguish various patterns of data from its background, and make conclusions about the categories of the patterns. A large number of such systems have been widely used in the diagnosis of heart diseases. One of the heart diseases in concern is cardiac arrhythmia. Most systems used in diagnosing cardiac arrhythmia uses data mining techniques, like Artificial Neural Networks, particularly in the form of a single classifier. In this project, a Self Organizing Map (SOM) - Based Ensemble model is proposed for the classification of cardiac arrhythmia disease dataset. An ensemble is a model that applies multiple learning models and combining the outputs or predictions to solve a particular problem. An ensemble is stated to predict or classify datasets more accurately than some single classifier models. The ensemble consists of three SOM classifiers trained with different number of dimension. For the ensemble, a voting technique is used to average the prediction of each single SOM classifier to obtain the final prediction. The results displayed show that the SOM ensemble model has higher classification accuracy than that of single SOM classifiers. Ensemble learning eliminates errors of single classifiers by averaging the prediction of each classifier, thus resulting in a more accurate output

Contrast modification for pre-enhancement process in multicontrast rubeosis iridis images

Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image into three groups; low, medium and high contrast. Increment, decrement and maintenance of the images’ original contrast were further operated by noise reduction and multi-contrast manipulation to attain the best contrast value in each category for increased compatibility prior subsequent enhancement. As a result, this study proved that there have three rules for the contrast modification method. Firstly, the histogram equalization (HE) filter and increasing the image contrast by 50% will achieve the optimum value for the low contrast category. Experimental revealed that HE filters successfully increase the luminance value before undergoing the contrast modification method. Secondly, reducing the 50% of the image contrast to achieve the optimum value for the high contrast category. Finally, the image contrast was maintained for the middle contrast category to optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the outputs were then calculated, yielding an average of 18.25 and 28.87, respectively

Performance comparison of SVM and ANN for aerobic granular sludge

To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP

H∞ Controller with Graphical LMI Region Profile for Gantry Crane System

This paper presents investigations into the development of H∞ controller with pole clustering based on LMI techniques to control the payload positioning of INTECO 3D crane system with very minimal swing. The linear model of INTECO 3D crane system is obtained using the system identification process. Using LMI approach, the regional pole placement known as LMI region combined with design objective in H∞ controller guarantee a fast input tracking capability, precise payload positioning and very minimal sway motion. A graphical profile of the transient response of crane system with respect to pole placement is very useful in giving more flexibility to the researcher in choosing a specific LMI region. The results of the response with the controllers are presented in time domains. The performances of control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specification. Finally, the control techniques is discussed and presented

Simulation study on electrical resistance tomography using metal wall for bubble detection

Industrial process pipelines are mostly known to be constructed from metal which is a conducting material. Bubbles or gas detection are crucial in facilitating the bubble columns performance. By employing the Electrical Resistance Tomography (ERT) technique, a simulation study using COMSOL has been conducted to investigate the effect of excitation strategy, bubble sizes and locations towards the metal wall system. As for the current excitation strategy, conducting boundary protocol has to be applied when it comes to metallic vessel to overcome the grounding effect. Bubbles with a greater size than 2 mm and especially the one that is located near the wall boundary are much easier to detect. Further potential improvements to the current design and image reconstruction of the ERT system are desirable to improve the detection of small and centred bubble

Estimation of pH and MLSS using Neural Network

The main challenges to achieving a reliable model which can predict well the process are the nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent approaches revolved as better alternative in predicting the system. Typical measured variables for effluent quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN) modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feed- forward neural network techniques as nonlinear function approximators have demonstrated the capability of predicting nonlinear behaviour of the system. The data for the period of two years and nine months sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The ANFIS model may serves as a valuable prediction tool for the plant

Forward problem solving for non-invasive electrical resistance tomography system

This paper aims to provide a forward problem solving for non-invasive electrical resistance tomography. A finite element model (FEM) using COMSOL Multiphysics is implemented for generating the sensitivity map for ERT system. Later, a masking data for a better sensitivity map was done to optimize the map. As a result, the sensitivity map can be used later for reconstructing the image of the medium of interest