bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parameter Analysis on Backprojection for Super-Resolution (SR) Reconstruction Algorithm
Authors: Ng S M , Yazid H , Mustafa N , Mashor MY 
Published: November 2021

Abstract
In this paper, an image SR reconstruction scheme by using k-Singular Value Decomposition (k-SVD) with Orthogonal Matching Pursuit (OMP) as sparse coding method was proposed to obtain the High-Resolution (HR) image. The system conducted in this paper consists of two parts: image SR reconstruction algorithm and also the backprojection process. Since this paper is focused on analysing the effect of parameters in backprojection on the performance of final images produced at the end of the process, therefore, the flow of backprojection is discussed. Generally, the backprojection algorithm is added to the approach to improve the image quality by sharpening the edges of the HR image. However, the parameters used in backprojection could be the factor that caused decrease in the image quality. Thus, the main idea of this paper is to analyse these parameters throughout the flow of backprojection algorithm. The parameters include the predefined 2D filter and interpolation method. Based on the results, it can be concluded that the use of averaging filter (hsize = 3), Gaussian filter (hsize = 5, 7 and 9; σ = 1) and bicubic interpolation method or also known as the cubic kernel can be adopted to the backprojection algorithm since these parameters achieved the highest RMSE, PSNR and SSIM values of 13.87, 25.29dB and 0.83 respectively. The analyses done in this paper has brought a clear understanding on the backprojection algorithm and further implementation and analysis in backprojection such as by using cascaded filters for averaging and Gaussian filters can be considered in the future.
Simulation of Single Channel Magnetic Induction Tomography for Meningitis Detection By Using COMSOL Multiphysics
Authors: Ahmed Aiman Abdulrahman , Zakaria Zulkarnay , Ali Marwah Hamood , Noor Anas Mohd , Abdul Halim Siti Fatimah Binti , Norali Ahmad Nasrul , Pusppanathan Jaysuman , Rahim Abdul Ruzairi 
Published: November 2021

Abstract
Meningitis is a inflammation of the meninges and the most common central nervous system (CNS) due to bacterial infection. Numbers of children who have bacterial meningitis are still high in recent 15 years regardless of the availability of newer antibiotics and preventive strategies. This research focuses on simulation using COMSOL Multiphysics on the design and development of magnetic induction tomography (MIT) system that emphasizes on a single channel rotatable of brain tissue imaging. The purpose of this simulation is to test the capability of the developed MIT system in detecting the change in conductivity and to identify the suitable transmitter-receiver pair and the optimum frequency based on phase shift measurement technique for detecting the conductivity property distribution of brain tissues. The obtained result verified that the performance of the square coil with 12 number of turns (5Tx-12Rx) with 10MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.
A Review on Edge Detection on Osteogenesis Imperfecta (OI) Image using Fuzzy Logic
Authors: Ahmad Zaki Muhammad Zainul Arif , Mat Som Mohd Hanafi , Yazid Haniza , Basaruddin Khairul Salleh , Basah Shafriza Nisha , Megat Ali Megat Syahirul Amin 
Published: November 2021

Abstract
Osteogenesis Imperfecta (OI) is a bone disorder that causes bone to be brittle and easy to fracture. The patient suffered from this disease will have poor quality of life. Simulation on the bone fracture risk would help medical doctors to make decision in their diagnosis. Detection of edges from the OI images is very important as it helps radiologist to segmentize cortical and cancellous bone to make a good 3D bone model for analysis. The purpose of this paper is to review the fundamentals of fuzzy logic in edge detection of OI bone as it is yet to be implemented. Several fuzzy logic concepts are reviewed by previous studies which include fuzziness, membership functions and fuzzy sets regarding digital images. The OI images were produced by modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, or Computed Tomography (CT). In summary, researchers from the reviewed papers concluded that fuzzy logic can be implemented to detect edges in noisy clinical images.
Performance Analysis Between Feature Extraction and Fusion in Familiar and Unfamiliar Typing Biometric Authentication
Authors: Amalina I , Saidatul A , Fook C Y , Navea R F 
Published: November 2021

Abstract
The brain signals recorded by EEG devices are largely developed in for biometric authentication purposes. Those signals are very informative and reliable to be classified using signal processing. In this paper, the feature extraction and feature fusion are further studied to observe their performance towards the typing tasks. The signals are pre-processed to eliminate the unwanted noise present in the signals. The feature extraction method such as Welch’s method, Burg’s method and Yule Walk’s method are applied to extract the mean, median, standard deviation and variance in the data. Nonlinear feature such as fuzzy entropy is also been extracted. The extracted features are further classified by using k-Nearest Neighbour (k-NN), Random Forest (RF) and Ensemble Bagged Tree (EBT). The performance of feature extraction and feature fusion through concatenation are recorded and compared. For comparison, the feature fusion shows a better performance accuracy rather than feature extraction. The highest percentage accuracy was produced by Burg’s method for frontal-parietal lobes feature fusion which is 95.94% using Ensemble Bagged Tree (EBT).
Astrocytoma Brain Tumor Automated Classification Procedure Based on Ki67 Expression
Authors: Mohd Hisam N S , Mashor M Y , Akmal F , Jaafar H 
Published: November 2021

AbstractIt is known to be a very challenging task for pathologists to perform astrocytoma biopsy analysis especially with the high demand for an accurate and precise analysis. An inaccurate diagnosis might affect the treatment decisions for the patient. Therefore, this study aims to develop an automated procedure for classification astrocytoma brain tumor. The procedure consists of four stages, namely image pre-processing, image segmentation, feature extraction and selection, and classification of Ki67 expression. Initially, the image’s quality was enhanced by using global stretching and unsharp masking filter. Then, the adaptive color thresholding technique was used to segment the Ki67 expression. The process was followed by extracting the features related to the positive and negative Ki67 cells. ReliefF feature selection algorithm was used to select the best features in defining the positive and negative cells. In the classification stage, two classifiers, namely Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were used to differentiate between the positive and negative cells. From 81 samples images, both classifiers yielded promising results, with an accuracy of 98.5% for SVM and 99.6% for MLP respectively.
Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)
Authors: Lubis A J , Mohd Nasir N F , Zakaria Z , Jusoh M , Azizan M M , Zakaria M Z , Kazim M N F M , Rahman S 
Published: November 2021

Abstract
Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution’s internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose.
Investigation of Channel Reduction Based on Brain Lobes in EEG-based Authentication System
Authors: Rosli F A , Saidatul A , Markom M A , Mohamaddan S 
Published: November 2021

Abstract
Biometric authentication is recently used for verification someone’s identity according to their physiological and behavioural characteristics. The most popular biometric techniques are fingerprints, facial and voices recognition. However, these techniques have the disadvantage in which they can easily be imitated and mimicked by hackers to access a device or a system. Therefore, this study proposed electroencephalogram (EEG) as a biometric technique to encounter this problem. The wavelet packet decomposition is explored in this study for the feature extraction method. The wavelet packet decomposition feature is represented, root mean squared (RMS) wavelet features to extract a piece of meaningful information from the original EEG signal. These features were applied to classify between 15 subjects by using Support Vector Machine (SVM). The channel reduction was conducted to investigate the brain lobe effectiveness during the paradigms of familiar and unfamiliar EEG signals which the channel reduction is based on the brain lobes (temporal, occipital, parietal, and frontal). As a result, the above 14 channels obtained the best performance of the system which is 97.44% of correct recognition rate (CRR). The analysis of the paradigms among familiar only, unfamiliar only, and both familiar and unfamiliar was conducted to evaluate the contribution of the paradigms. The results show that 14 channels obtained the best familiar paradigms while the other contributed by unfamiliar. The result is promising because the CRR computed above 90%, however further analysis of channel reduction has to be work to obtain specific channel to develop the small number of channel for comfort and convenience biometric sensor which is suitable for future authentication.
Modelling of Retinal Images for Analysis of Diabetic Retinopathy Severity Levels
Authors: Qaid M , Basah S N , Yazid H , Safar M J A , Mat Som M H , Lim C C 
Published: November 2021

Abstract
Synthetic data by various algorithms that resemble actual data in terms of statistical features. Computer-aided medical applications have been extensively applied to model specific scenarios, such as medical imaging of retinal images for diabetic retinopathy (DR) detection. The available data and annotated medical data are typically rare and costly due to the difficulties of conducting medical screening and rely on highly trained doctors to review and diagnose. The modelling of retinal images for DR analysis is essential since it will provide a model to guide and test DR detection algorithms. This paper aims to model normal retina and non-proliferative diabetic retinopathy (NPDR) stages (mild, moderate, and severe) data models with the variation of dynamic models. The Digital Retinal Images for Vessel Extraction (DRIVE), The Standard Diabetic Retinopathy Database, Calibration Level 1 (DIARETDB1), and E-OPHTHA datasets are analyzed to obtain the specification of the human retina and DR lesions. In the data modelling phases, the model includes the bright and dark retinal lesions with the variation of dynamic parameters. 4100 synthetic images are used where 200 normal images and 3900 NPDR images to test the performance of DR detection algorithms over the full range of parameters.
SAR Performance of Rectangular Microstrip Antenna for Breast Cancer Hyperthermia Treatment with Different Period of Treatment Procedure
Authors: Hassan M M , Lias K , Buniyamin N , Naimullah B S S , Jobli A T 
Published: November 2021

Abstract
Cancer treatment using hyperthermia techniques recently become the interest among researchers in investigating and improving certain deficiencies of the treatment since this treatment has the potential to denaturate cancer into necrotic tissue. Hyperthermia uses high heat from 41°C to 45°C at a certain period of time. It is difficult to control the focus position distance of heat distribution on the treated tissue. Therefore, this paper presents the rectangular microstrip as hyperthermia applicator, which deliver the heat on the targeted treated breast cancer tissue with different period of time in order to obtain sufficient heat or SAR distribution. Sim4LifeLight software simulator is used to design, simulate and generate the specific absorption rate (SAR) distribution on the treated tissue. Three frequencies of 434MHz, 915MHz and 2450MHz are used to be compared. Based on the results, 2450MHz shows better performance than the other two frequencies. However, there is a certain limitation, such as skin burn and unwanted hotspots, that need to be further improved. The cancer is sufficiently heating at different operating frequencies at different periods of procedures.
The Response of EEG Signal on Brain Relaxation Meditation Using Different Type of Zikr
Authors: Abd Razak N N , Sidek K A , Alaa K Y D , Mohd Nasir N F 
Published: November 2021

Abstract
Depression and stress are increasingly prevalent in today’s society, owing to people’s hectic, competitive, and demanding lifestyles. These illnesses had become very common, particularly among young and middle-aged people, and suicidal ideation had been identified as one of the leading causes of death by the World Health Organization (WHO). Nature sound (sound of downpours or beach) has been linked to depression and anxiety in neurology research, and it has been shown to be an alternative to alleviate anxiety. The electroencephalogram (EEG) waveform has been discovered to possess the potential in identifying information from the brain signal as well as data from the past via Bluetooth communication. The waveform use in the study are the result of a few experiments. In this study, EEG data were collected from eight subjects, four males and four females, in between the age of 20 to 30 years old and in good health, using the BrainLink device. The participants were asked to listen to two playlists of zikr, Allah, Ya Allah, and SubhanAllah, during the experiments. To smooth the signal, the Butterworth filter was used. Later, the extracted features were Alpha, Beta, and Delta waves, which were segmented based on the filtered signal. To complete the decision-making stage, the average value of amplitude differences and the p-value test were performed in the final phase. Based on the results of the experiments, it is clear that zikr is dominant in Delta wave. In terms of data hypothesis analysis, the results of both experiment were differentiated to determine which brain signal was dominant, and p-value testing was performed. Furthermore, this research is an alternative to current methods because it suggest that zikr has the tendency to alter a person’s brain state to be either in a relaxed or calm mode regardless of the type of zikr recitation. As an outcome, the study recommends the relationship of the reaction of EEG signal on brain relaxation with different types of zikr in order to facilitate stress and anxiety patients to achieve relax or calm condition.
Electrical Capacitance Tomography (ECT) Electrode Size Simulation Study for Cultured Cell
Authors: Zulkiflli N A , Shahrulnizahani M D , Hor X F , Phang F A , Rahmat M F , Khang A W Y , Zakaria Z , Mohamed R , Leow P L , Pusppanathan J 
Published: November 2021

Abstract
Cell sensing and monitoring using capacitive sensors are widely used in cell monitoring because of the flexible and uncomplicated design and fabrication. Previous work from many different fields of applications has integrated capacitive sensing technique with tomography to produce cross-sectional images of the internal dielectric distribution. This paper carried an investigation on the capabilities of four 16-channel sensor electrodes with different electrode sizes to detect the change in the dielectric distribution of the cultured cells. All three 16-channel sensor electrodes are designed and simulate on COMSOL 6.3a Multiphysics. The pre-processing results obtained from three finite element models (FEM) of ECT sensor configurations in detecting the cell phantom shows that bigger electrodes size are more sensitive to permittivity distribution.
A Micro-flow Phantom for Superficial Micro-vasculature Imaging
Authors: Bhatti A , Ishii T , Saijo Y 
Published: November 2021

Abstract
Visualization of cutaneous micro-vasculatures is a powerful approach assisting in the diagnosis of skin vascular disorders. These minute structures can be visualized by high-frequency ultrasound (HFUS) using ultrafast Doppler imaging. Ultrasound flow phantoms have been used as assessment tools to evaluate the performance of the ultrasound imaging system, however, to optimize the imaging system for visualization of micro-structures, flow phantom with micro-channels is required which are usually difficult to fabricate. Here, we design a simple approach for micro-flow phantom which is easy to fabricate and cast for detection of micro-circulation in superficial micro-structures. The proposed approach features (i) the micro-channels of 200-micron at the depth of 4 mm (ii) casted in the cryogel mixture of Poly-vinyl alcohol (PVA) and (iii) infused at flow speed of 30 mm/s using infusion pump. Visualization of micro-flow channel in power Doppler image obtained by HFUS ultrafast Doppler imaging reveals that the proposed micro-flow phantom could serve as a viable assessment tool for optimizing the system for in-vivo cutaneous micro-vasculature imaging.
Study on Key Technologies of Power Dispatching Control System under the Background of Cloud Computing
Authors: Zhou Siming , Liu Guanqiang , Qin Hai , Xia Tian 
Published: November 2021

Abstract
In order to adapt to the development and application of power dispatch control systems in the future cloud computing environment, this study proposes to make full use of the elastic characteristics of cloud computing to construct a new power dispatch control system architecture. First, we analyze the shortcomings of the traditional power dispatch control system architecture, from the perspective of architecture complexity, operation and maintenance, universality and disaster tolerance. Secondly, combining the characteristics of the existing dispatch control system, we propose a new power dispatch control system architecture that conforms to the cloud computing platform. Finally, we emphatically analyzed the key technologies for key breakthroughs needed to realize the power dispatching control system architecture. Through the comparison of traditional and new power dispatch control system architectures, it can be considered that the new power dispatching control system architecture has greatly improved reliability, flexibility, safety and economy, and has certain advantages.
Electromechanical Transient Modeling and LVRT Parameter Identification of Large Capacity Full Power Converter Wind Turbines Based on PSASP Program
Authors: Lu Guoqiang , Tao Xiangyu , Chen Chunmeng , Gan Jiatian , Zhao Xufeng 
Published: November 2021

Abstract
Full power converter wind turbine is the main type of wind power, so the simulation calculation needs to establish accurate model parameters. This paper analyzes the model structure of PSASP program according to its low voltage ride through control and physical characteristics, and puts forward the parameter identification method of LVRT characteristics of full power converter wind turbine, and to use the LVRT data of 5. 5MW unit for parameter identification and simulation verification. This paper proposes that the electromechanical transient simulation can ignore the part of the generator model of the full power converter wind turbine, and simulates the grid side converter with the controlled current source. The main characteristics of LVRT are determined by the control system of the converter. In order to do the parameter identification, it is necessary to calculate and analyze the control characteristics of multiple measured data. First, determine the control mode, then determine the control parameters to complete the parameter identification. In this paper, the modeling conditions and model structure of the full power converter wind turbine are confirmed. The correlation between the parameters during the LVRT fault and the parameters during the LVRT recovery period and the LVRT characteristics is analyzed. In this paper, a parameter identification method is proposed to analyze the active current and reactive current during the LVRT fault, which has strong physical significance and operability. Based on the actual LVRT characteristics of 5. 5MW wind turbine, the parameter identification and simulation are carried out to verify the correctness of the method.
Application of space vector pulse width modulation algorithm based on programmable logic array in power system
Authors: Cheng Zhihai , Liu Hui 
Published: November 2021

Abstract
In the traditional power system, the voltage regulation and frequency modulation control method used for boiler induced draft fan is limited in control accuracy. With the improvement of furnace pressure change accuracy, higher requirements are put forward for motor control algorithm. This paper studies permanent magnet synchronous motor (PMSM) as the executive part of induced draft fan servo control system, and adopts FOC control method based on space vector pulse width modulation (SVPWM). Firstly, the simulation model of SVPWM pulse width modulation algorithm is established by MATLAB to provide theoretical support for the subsequent debugging algorithm; When using FPGA to realize SVPWM modulation algorithm, FPGA has the advantages of high reliability and high real-time processing, which provides theoretical support and application guidance for the realization of high-precision motor control of boiler induced draft fan in power system.