Impact of Cortical Bone Thickness on the Parameters of Fast and Slow Ultrasound Wave based on 2-D SimulationWahab, Muhamad Amin Bin Abd; Sudirman, Rubita; Mahmood, Nasrul Humaimi
doi: 10.1088/1742-6596/2622/1/012008pmid: N/A
Quantitative Ultrasound (QUS) has been introduced to measure the quality of human bones using ultrasound and become one of the prevention methods for Osteoporosis diseases. Because of the porous composition inherent in human cancellous bone, the generation of both fast and slow waves occurs, and these waves exhibit a distinct association with the cancellous bone structure, particularly the extent of porosity. Nonetheless, the presence of these waves is also contingent upon the anisotropy of cancellous bone, and it is noteworthy that most human cancellous bones are enveloped by cortical bone, which may influence the parameters of the fast and slow waves. Therefore, the aim of this study is to perform a 2-Dimensional (2-D) simulation utilizing the through transmission (TT) measurement method. The primary focus is to examine the impact of cortical thickness on the parameters of both the fast and slow waves. The cortical thickness will be added to the cancellous bone models and the thickness will be varied. Then, the fast and slow wave parameters will be compared in terms of correlation coefficient to identify which wave is affected more. The result shows that the cortical thickness causes increasing in attenuation and velocity for both fast and slow waves. The increase in attenuation is due to sonometry effects while the different longitudinal velocities of water and bone material may contribute to the behaviors for phase velocity measurements. However, the fast wave shows more correlation with the cortical thickness for attenuation (R2= 0.76) and phase velocity (R2= 0.77) parameters. This is due to fast wave corresponding to the solid structure and increasing cortical thickness also increase the solid structure. Thus, analyzing fast waves against human cancellous bone, cortical bone thickness needs to be considered to ensure accurate measurements.
An Optimized Cosine Similarity-Based Attention Gate for Temporal Sequence Patterns RecognitionAbdullah, Ammar; Abdul-Kadir, Nurul Ashikin; Harun, Fauzan Khairi Che
doi: 10.1088/1742-6596/2622/1/012012pmid: N/A
The attention gate is often employed in various applications, such as recommender systems, to determine the correct context of the input data. Adopting the attention gate has been proven to improve prediction accuracy successfully. However, for a temporal sequence problem such as sensor-based Sign Language Recognition (SLR), it is still challenging to integrate the attention gate into the solution since the processing input is only in the form of a sine waveform. In our work, we propose an optimized cosine similarity-based attention gate to manipulate the sine wave signal while improving the recognition of temporal sequence data. We also evaluate and compare the various distance calculations and determine the best one for the SLR application. Adopting a distance-based attention gate has successfully achieved the recognition accuracy of 99% while reducing the error rate to 5%.
Optical Properties of TiO2 Doped ZnO-PVA NanocompositesIsmardi, Abrar; Nasir, Muhamad; Khoiri, Miftahul; Abdullah, Nor Hakimin; Gunawan, Luthfi Aprilio; Rafsanjani, Mukhammad Fahlevi Ali; Mayundri, Siti Ashila Farikha; Gunawan, Theresia Deviyana
doi: 10.1088/1742-6596/2622/1/012018pmid: N/A
One of the materials with the greatest potential for use in flexible devices is semiconductor nanocomposite material. In this paper, we report the synthesis of ZnO-PVA-TiO2 nanocomposite to be applied as a flexible optical sensor device. The optical property of ZnO-PVA nanocomposite is improved by the addition of TiO2 in this work. The optical characteristics of the ZnO-PVA nanocomposite depend on the concentration of TiO2. Spin coating method was successfully used to synthesize ZnO-PVA-TiO2 with various TiO2 concentrations of 1%, 2%, and 3% w/v, respectively. The absorbance spectrum of ZnO-PVA-TiO2 nanocomposite changes with the addition of TiO2. The absorbance peaks of ZnO-PVA-TiO2 nanocomposites were shifting to a lower wavelength, resulting in increasing the nanocomposite band gap energy. According to this result, future applications of the flexible optical sensor are one of the potential devices based on this study.
Heart condition determination based on MET value to NYHA Classification and abnormal ST segment identificationMd Yassin, Siti Norhayati; Othman, Mohd Afzan; Samad, Whomaira Abdul
doi: 10.1088/1742-6596/2622/1/012007pmid: N/A
Cardiovascular diseases have always been among the top causes of death. Thus, there were variety of research focuses on cardiac stress test and on self-stress test as well. However, the portable device existed for heart monitoring were still insufficient where most device are for fitness monitoring rather than functional capacity. Thus, this paper determines heart condition based on New York Heart Classification (NYHA) for The Rockport Walking Fitness Test (TRWFT). TRWFT gave out parameters to calculate maximum oxygen consumption (VO2max) to find metabolic equivalent (MET) value. The MET values were compared with NYHA functional capacity classes. Hence, the comparison simplified the heart condition to the urgency to visit hospitals. Plus, ST segments of MIT-BIH ECG database were extracted and assessed for abnormality. Accordingly, a mobile application was developed. The prototype is built using Genuino 101 microcontroller, an AD8232 ECG module with leads to sense heart pulse, and a SD card module. Meanwhile, simulation test on the prototype shows that the prototype succeeds in produce the same values with manual calculation and managed to assess ST segments as programmed. In conclusion, a prototype to demonstrate the implementation of TRFWT and prediction of cardiac condition based on MET value is successful.
Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 DetectionSaleh, Basma Jumaa; Omar, Zaid; Bhateja, Vikrant; Izhar, Lila Iznita
doi: 10.1088/1742-6596/2622/1/012002pmid: N/A
During the COVID-19 pandemic, medical imaging techniques like computed tomography (CT) scans have demonstrated effectiveness in combating the rapid spread of the virus. Therefore, it is crucial to conduct research on computerized models for the detection of COVID-19 using CT imaging. A novel processing method has been developed, utilizing radiomic features, to assist in the CT-based diagnosis of COVID-19. Given the lower specificity of traditional features in distinguishing between different causes of pulmonary diseases, the objective of this study is to develop a CT-based radiomics framework for the differentiation of COVID-19 from other lung diseases. The model is designed to focus on outlining COVID-19 lesions, as traditional features often lack specificity in this aspect. The model categorizes images into three classes: COVID-19, non-COVID-19, or normal. It employs enhancement auto-segmentation principles using intensity dark channel prior (IDCP) and deep neural networks (ALS-IDCP-DNN) within a defined range of analysis thresholds. A publicly available dataset comprising COVID-19, normal, and non-COVID-19 classes was utilized to validate the proposed model’s effectiveness. The best performing classification model, Residual Neural Network with 50 layers (Resnet-50), attained an average accuracy, precision, recall, and F1-score of 98.8%, 99%, 98%, and 98% respectively. These results demonstrate the capability of our model to accurately classify COVID-19 images, which could aid radiologists in diagnosing suspected COVID-19 patients. Furthermore, our model’s performance surpasses that of more than 10 current state-of-the-art studies conducted on the same dataset.
Comparison between Romberg test with sensor-based balance assessment using electronic wobble boardWoon, Yvonne Khor Yee; Su, Eileen L. M.; Khor, Kang Xiang; Abdullah, Muhammad Najib Bin; Yeong, Che Fai
doi: 10.1088/1742-6596/2622/1/012009pmid: N/A
The number of fall-related injuries resulting in hospital admissions could place a significant load on hospitals. Prior research revealed that balance impairment is the most reliable indicator of future falls, and postural instability testing is expensive and tedious to be applied in clinical settings. This paper presents an objective based balance assessment method using an electronic wobble board to examine the balance ability among individuals, with minimal specialist intervention. The balancing assessment software modules provide visual feedback such as visual concentration, test procedure teaching, and time counting. Quick objective balance evaluation is facilitated by the software modules, which also can comprise visual feedback and provides instant biofeedback with objective measurements of user’s progress. The balance assessment score obtained from this electronic wobble board was compared against physiotherapists’ ratings using the Romberg test. The results indicated that the sensor-based balance score is comparable with the expert ratings using the Romberg test. Additionally, the sensor-based score provides higher sensitivity in differentiating among similar categories of subjects as compared to the conventional Romberg test.
Design and Analysis of Electrical Characteristics of 14nm SOI-based Trigate Gaussian Channel Junctionless FinFETRamakrishnan, Mathangi; Alias, Nurul Ezaila; Hamzah, Afiq; Tan, Michael Loong Peng; Yusof, Yusmeeraz; Natarajamoorthy, Mathan
doi: 10.1088/1742-6596/2622/1/012020pmid: N/A
Planar MOSFETs are reaching their physical limits. To overcome the limitations and improve channel gate control, FinFET technology, which uses many gate devices, is a superior choice while lowering the size of planar MOSFETs even further. In this paper, 14nm Silicon-On-Insulator-based Trigate Gaussian Channel Junctionless FinFET is presented. The gate length of 14nm is considered along with an Equivalent Oxide Thickness of 1nm, 5nm as fin width, and the work function of the gate metal is 4.75eV. The device architecture has a non-uniform doping profile (Gaussian distribution) across the fin’s thickness. It is devised to address the effects of Random Dopant Fluctuations such as channel mobility degradation in Junctionless FinFET based devices. The impact of fin height (Fh), gate dielectric and spacer dielectric on the Drain Induced Barrier Lowering, Subthreshold Swing, drain current of GC-JLFinFET is analyzed. The results show that the Ion=101.5μA/μm and Ion/Ioff is 3.2×107 are obtained for the proposed device structure compared to the existing structure, which has Ion/Ioff of 1.1x107. Furthermore, the proposed design shows better efficiency in short channel characteristics, namely DIBL=25.3 mV/V, Subthreshold Swing=63.88 mV/dec and Transconductance =3.621×105 S/μm. Thus the Gaussian Channel-based FinFET architecture can provide optimum results for Junctionless-based FinFET devices.
Prefacedoi: 10.1088/1742-6596/2622/1/011001pmid: N/A
This issue of the Journal of Physics: Conference Series (JPCS) presents the proceedings of the highly successful 1st International Conference on Electronic and Computer Engineering (ECE2023). The conference took place virtually from 4th to 5th July 2023, attracting more than 60 participants from both local and international communities.The electronic and computer engineering Division, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, organized the ECE2023 with the aim of creating a platform for researchers from industry and academia worldwide to converge and explore emerging issues and trends in electronics and computer engineering. This conference facilitated the exchange of research findings, valuable experiences, and the establishment of research collaborations on a global scale.ECE2023 focused on four main tracks to address cutting-edge developments in the field:• Biosensors, Biosignals, and Biomedical Imaging• Machine Learning and Pattern Recognition• Nanotechnology and Advanced Materials• VLSI & Computer ArchitectureEminent figures from academia and industry were invited to deliver keynote speeches that provided valuable insights into the research areas covered by the conference.Each submitted paper underwent a rigorous and comprehensive peer-review process to ensure high-quality technical content and effective presentation. This meticulous evaluation guaranteed that only top-tier papers were selected for presentation during the conference. Acknowledging excellence, the ECE2023 conference awarded the best papers and presenters for each of the four tracks. This recognition aimed to encourage further advancements in the field and acknowledge outstanding contributions.The ECE2023 organizing committee extends heartfelt gratitude to all the speakers, reviewers, and participants whose contributions and enthusiasm contributed to the resounding success of this conference. Additionally, special appreciation is extended to the Journal of Physics: Conference Series (JPCS) for providing a robust platform for publishing the conference proceedings. Collaborating with all involved has been an absolute pleasure, and the committee eagerly anticipates future opportunities for continued collaboration and growth in Electronic and Computer Engineering.Lis of Organizing Committee, Technical Members, Editorial Board Members are available in this pdf.
Simulation and Characterization of Carbon Nanotube-based 2:1 Multiplexer Electrical PropertiesYing, Yong Jie; Johari, Zaharah
doi: 10.1088/1742-6596/2622/1/012023pmid: N/A
This paper reports on using simulation to characterize a Carbon Nanotube (CNT) based 2:1 multiplexer (MUX). This study aimed to evaluate the electrical properties, particularly the propagation delay, average power consumption, Power-Delay Product (PDP), and Energy-Delay Product (EDP). Different design approaches namely conventional CMOS, Pass Transistor Logic (PTL) approach, and Gate Diffusion Input (GDI) were adopted. The voltage supply (VDD) and diameter of the CNT are varied to see the effect on the electrical properties. The simulation was carried out using HSPICE. Through simulation, it is found that the GDI approach used the least number of transistors followed by PTL and CMOS. The calculation of the propagation delay exhibits a substantial improvement of more than 95% using the GDI approach. The average power consumption shows a 55.30% and 35.16% reduction when compared to CMOS and PTL respectively. The PDP demonstrates an improvement of more than 95% when compared with conventional CMOS and PTL approaches. The same trend of observation is also achieved for EDP. The variation of the VDD and chirality has a markable effect on the propagation delay and average power consumption. This is a preliminary attempt to evaluate the performance of CNT implementation in MUX. The outcome can become the guideline for engineers working in circuit design using emerging materials for future nanoelectronics applications.
3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour FeaturesAzeez, Ibrahim Izdhan; Chow, Li Sze; Solihin, Mahmud Iwan; Ang, Chun Kit
doi: 10.1088/1742-6596/2622/1/012015pmid: N/A
Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth.