Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of mobile markerless augmented reality for cardiovascular system in anatomy and physiology courses in physiotherapy education

This paper focuses on the development of markerless Augmented Reality (AR) using ARCore platform, where interactive three-dimensional (3D) content was designed and developed based on the learning outcome syllabus to enhance the visualization and understanding of the anatomy and physiology for cardiovascular system topic. Currently, learning method is based on 2D images and slides, plastic models and cadavers have to deal with students’ experience issues such as lack of interactive, uneasy feeling with dead body and cadavers storing and donation. Therefore, more advances using technology such as Augmented Reality (AR) in learning method are needed to overcome the current gap and enhance the students’ learning. Thus, this study aims to develop markerless AR specifically focus on the cardiovascular system for undergraduate physiotherapy program at UniKL, RCMP. In this study, we describe a method used to create markerless AR content using 3D data from MRI images and 3D unity as an authoring tool. We present three processes, where the first design consideration based on author’s previous works derived from systematic search strategy were outlined, the second 3D model was developed using a real object and subsequently converted to an AR asset that can be linked to a unique markerless using ARCore platform and the third AR content creation using 3D unity authoring tool. This application provides a better visualization for the anatomical parts to support for an innovative and flexible learning process. We have successfully analyzed the design consideration using a systematic search strategy and developed the markerless AR specifically for cardiovascular system in anatomy and physiology courses. This study has contributed to knowledge in design and development of AR used in physiotherapy education. Therefore, this will be a step forward to an exploration of design-based research for an AR benefit in experienced-learning approach application.
Lane-Finding Based on Structure Analysis of Lane and Computer Vision

Advanced driver-assisting systems (ADAS) performs a crucial role for the safety and ease of transportation. Finding of lane markings is an essential step of ADAS. For the protection of automated vehicles, the precise results of lane detection are compulsory. In this study, we present an innovative approach based on structural analysis of lane and Computer Vision Technique such as Hough Transform (HT) for the detection of lane markers. Firstly, a stage of preprocessing is applied to remove the pavement area that develops the lane markings background. Next, an edge detection technique is applied to generate the map of the edge and masking is performed to obtain edges in our region of interest (ROI). Then, using HT to find lanes, important lines are analyzed in the area of interest. In end, the concept of vanishing point used to predict lane has been explained. Experimental result states that the proposed system can find boundaries of the lane and exact lane departure alert in the existence of several image artefacts, such as light change, bad lane mark. The proposed technique displays more efficiency.
Impedance Analysis of Battery Bidirectional DC-DC Converter

When a converter is composed of cascaded system with another unknown converter, the optimal design of the converter is especially important owing to stability of the cascaded systems. Battery ‘s model is introduced to analysis. Battery converter’s different work conditions are considered. Battery converter is the front stage when it discharges, and it is the rear one when it charges in a cascade DC system. From the view of impedance, it’s good for the cascaded system stability to reduce output impedance of battery converter when the converter is the first stage. Another, increasing the converter’s input impedance could improve the cascade system stability when the converter becomes the rear one. The battery equivalent circuit model is introduced as a key component of system. Battery bidirectional converter should act as the front or rear stage of cascaded system. Selection rules of LC filter are put forward according to analyzing the inductor and capacitor’s effects on two conditions. The PI controller parameters’ effects on impedance are analyzed to furnish the basis for the PI controller parameters selection. Finally, experiment is carried out to verify the correctness of the analyses.
Analysis and Recovery Monitoring of Meibomian Gland Dysfunction Disease using Hyperspectral Imaging

Meibomian Gland Dysfunction (MGD) is considered one of the main causes of dry eyes syndrome. Early detection of MGD disease, lower the probability of having surgery operations and further complications can be avoided. In this research paper, an analytical study of MGD using Hyperspectral Imaging technique (HSI) is presented. The study considers an early stage of MGD disease and its evolvement during an interval of four months. The changes in the light absorbance while the disease is progressing are recorded. The obtained hypercube data is analyzed to determine the relation between the MGD infected area spectral response and evolvement. The proposed analysis proves the possibility of MGD monitoring using HSI technique. The obtained results can lead to a successful mechanism for early MGD detection.
Temozolomide in Combination With NF-κB Inhibitor Significantly Disrupts the Glioblastoma Multiforme Spheroid Formation

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor, accounting for 50% of all cases. GBM patients have a five-year survival rate of merely 5.6% and a median overall survival of 14.6 months with the “Stupp” regimen, 20.9 months with tumor treatment fields (TTF, OptuneR) in patients who participated in clinical trials, and 11 months for all GBM patients prior to TTF use. Objective: Our group recently developed a brain cancer chip which generates tumor spheroids, and provides large-scale assessments on the response of tumor cells to various concentrations and combinations of drugs. This platform could optimize the use of tumor samples derived from GBM patients to provide valuable insight on the tumor growth and responses to drug therapies. To minimize any sample loss in vitro, we improved our brain cancer chip system by adding an additional laminar flow distribution layer, which reduces sample loss during cell seeding and prevents spheroids from escaping from the microwells. Methods: In this study, we cultured 3D spheroids from GBM cell lines and patient-derived GBM cells in vitro, and investigated the effect of the combination of Temozolomide and nuclear factor-κB inhibitor on tumor growth. Results: Our study revealed that these drugs have synergistic effects in inhibiting spheroid formation when used in combination. Conclusions: These results suggest that the brain cancer chip enables large-scale, inexpensive and sample-effective drug screening to 3D cancer tumors in vitro, and could be applied to related tissue engineering drug screening studies.
Designing Low Cost Yet Robust EEG Acquisition System

With the advent of VLSI technology; the ability to acquire, filter and analyze low power and highly random signals has increased. Signal acquisition systems have become smaller, more accurate, portable and extremely reliable. EEG is a highly sought upon signal acquisition system having range of applications in neurological and psychiatry treatments. Yet this system is costly and often beyond the availability of a common patient. In this paper we propose the design of an EEG system which is low cost, making it affordable for common patients yet robust enough to acquire EEG signals for critical neuro-psychiatric study. Also, we review the current state of research in the field of EEG acquisition systems, signal processing of EEG signal and its applications to design a low-cost EEG system.
Software Defined Networking (SDN) based Secure Integrated Framework against Distributed Denial of Service (DDoS) Attack in Cloud Environment

Cloud computing is nowadays developed as a scalable and cost-effective technique. It facilitates many features in the computing environment like resource pooling, multi-tenancy, virtualization etc. In order to avoid resource maintenance, computing has been done on the service provider's infrastructure. Data security is the main concern in cloud computing because it is stored in data centers provided by service providers. DDoS attacks are rapidly growing in the cloud environment and have become a serious threat to cloud consumers. SDN is used to identify and prevent DDoS attacks in the cloud and it simplifies network complexity. In this paper, we have proposed a method that detects and mitigates DDoS attacks in cloud computing using the features of SDN with high accuracy and low computational cost. The proposed system is able to block the illegitimate users and allow only legitimate users to access the cloud services. The results are calculated by simulation experiments.
Performance Analysis of Single Phase Multilevel Inverter Using dsPIC30F2010 Microcontroller

This paper presents a single phase multilevel inverter (MLI) topology for 7, 9, 11, 13 and 15 level. MATLAB simulation of proposed topology is carried out by taking R and RL load for different levels. Total Harmonic Distortion (THD) in the output voltage of MLI is evaluated and presented in the paper. To validate the proposed topology hardware implementation is also done using commercially available microcontroller (dsPIC30F2010). The proposed topology provides less THD so becomes an attractive solution for PV based inverters and can be suitable for stand-alone applications like PV fed water pump used in agriculture, multi-story buildings or industries.
Modeling the Population Growth of the Co-cultured Blood Cells Exposed by Microbubble-mediated Ultrasound

Ultrasound-based drug delivery inspires rigorous experimental studies on the cell experiments involving microbubble-mediated ultrasound. Yet few tools can be found to accelerate the cellular exposure experiments on the heterogeneous-population-based bioeffects under various conditions. In this study, we propose a 2D model which can be used to simulate the population evolution of the co-cultured cells after microbubble-mediated ultrasound is induced. Two update rules based on the experimental data were proposed and incorporated in the simulation procedures. The bioeffects found in the previous experiment, such as cell cycle arrest, are taken into account in the model to simulate the population growth in a co-cultured setting. This model may provide a predictive tool for the multi-cell type responses to ultrasound-induced perforation and facilitate the future experiment design.
A Proposal of smart hospital management using hybrid Cloud, IoT, ML, and AI

There has been a rapid shift in the medical industry from the service point of view. More importance is being given to patient care and customer satisfaction than ever before. The need to keep the customers happy with the hospital's service has increased rapidly and one way they can improve a patient's experience, even more is if they integrate cloud, IoT, ML, and AI into their system. This would help the medical sector to achieve customization which would enable them to address the needs of their customers more efficiently and offering personalized solutions. In this paper, we are proposing a novel model which focuses on a smart hospital information management system that runs by using hybrid cloud, IoT, ML, and AI. This system would be beneficial not only from the hospitals perspective but also from the patient's side as well. Patients and doctors unique ID would make the entire process a lot more efficient and easier. The advances happening in the field of AI and ML due to cloud-based computing is extremely beneficial for the medical industry. By integrating these components along with IoT it is possible for multi-specialty hospitals and super specialty hospital to be able to set up a smart hospital information management system.
A Link Fault Tolerant Routing Algorithm for Mesh of Tree Based Network-on-Chips

The number of Intellectual Property (IP) modules in a System-on-Chip (SoC) is increasing rapidly. As the number of IP modules on a chip increases, the complexity in the communication between them also increases and the advanced applications requiring large bandwidths and scalability has led to the emergence of Network-on-Chips (NoCs). NoCs provide scalability and efficient communication between large number of IPs and so the components of NoCs are prone to faults. This requires the need for fault-tolerant NoCs. This paper proposes an algorithm for routing the packets in case of a link fault in Mesh-of-Tree (MoT) topology. The algorithm will ensure that the packets will reach the destination in the shortest possible path. We also compare the results obtained with that of the deterministic routing approach.
Trap Analysis Based on Low-Frequency Noise for SiC Power MOSFETs Under Repetitive Short-Circuit Stress

In this paper, the degradation behavior of the electrical characteristics was investigated, and trap analysis based on low-frequency noise (LFN) was carried out for the commercial 1.2-kV /30-A silicon carbide (SiC) power MOSFETs under repetitive short-circuit (SC) stress. The experiment results show that the on-state resistance (<inline-formula> <tex-math notation="LaTeX">${R} _{\mathrm{ dson}}$ </tex-math></inline-formula>) and threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{\mathrm{ th}}$ </tex-math></inline-formula>) increase significantly. Meanwhile, the drain-source current (<inline-formula> <tex-math notation="LaTeX">${I} _{\mathrm{ ds}}$ </tex-math></inline-formula>) decreases obviously with the increase of the SC cycles. Furthermore, the gate-source leakage current (<inline-formula> <tex-math notation="LaTeX">${I} _{\mathrm{ gss}}$ </tex-math></inline-formula>) of the SiC power MOSFETs increase greatly and the blocking characteristics deteriorated after 1000 SC cycles. The positive shift was observed on the gate-capacitance versus gate-voltage (<inline-formula> <tex-math notation="LaTeX">${C} _{\mathrm{ g}}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">${V} _{\mathrm{ g}}$ </tex-math></inline-formula>) curve, which shows that the damage region could be in channel along the SiC/SiO<sub>2</sub> interface after repetitive SC stress. In order to obtain the trap information, trap characterization was performed by using LFN method, and the LFN results show that the trap density increases with the SC cycles. The physical mechanism could be attributed to electrically active traps generated at SiC/SiO<sub>2</sub> interface and oxide layer due to the peak ionization rate, the perpendicular electrical field and high temperature during SC stress. The study may be useful to provide reference for converters design and fault protection of SiC power MOSFETs.
Ferrite Permanent Magnets in Electrical Machines: Opportunities and Challenges of a Non-Rare-Earth Alternative

Rare-earth permanent magnets (RE-PMs) are widely used in high-efficiency and high-torque density electrical machines. However, in recent years, the exclusive and unstable supply of rare-earth elements (REEs) is a major concern to substitute RE-based electrical machines by less or even no RE alternatives. Ferrite (Fe) PMs as the least expensive PMs show promising opportunities for designing low-cost non-RE electrical machines. This article reviews some relevant works on Fe-based machines, which generally have been conducted to replace RE-based machines. Fe-based machines, including PM brushless, PM-assisted synchronous reluctance, PM flux switching, and PM Vernier machines, are introduced and their main design challenges, such as low PM energy product and vulnerability to irreversible demagnetization, are addressed. In addition, a comparison of RE- and Fe-based machines reveals that when both cost and efficiency are the major concerns, Fe-based machines can be a good successor to RE-based machines.
On the Almost Sure Convergence Rate for A Series Expansion of Fractional Brownian Motion

Fractional Brownian motions (fBM) and related processes are widely used in financial modeling to capture the complicated dependence structure of the volatility. In this paper, we analyze an infinite series representation of fBM proposed in (Dzhaparidze and Van Zanten 2004) and establish an almost sure convergence rate of the series representation. The rate is also shown to be optimal. We then demonstrate how the strong convergence rate result can be applied to construct simulation algorithms with path-by-path error guarantees.
Time Optimal Motion Planning and Admittance Control for Cooperative Grasping

Cooperative grasping refers to the situation when an object is manipulated by multiple robots and the grasp is achieved by the unilateral contact between the robots and the object. This is different from the cooperation of multiple robots where each robot rigidly grasps the object. Motion planning of cooperative grasping tasks involves active force control of the interaction wrench in order to ensure stable grasp. This becomes particularly challenging when aiming at time optimal motions. It is crucial that the trajectories are continuous up to third-order, in order to satisfy velocity, acceleration, and jerk as well as torque limits of the robots. A solution approach is presented for the time optimal path following of two robots performing cooperative grasping tasks. The time optimal path is determined with a dynamic programing method. An admittance control scheme in task space is proposed and used to generate the contact wrench. The method is applicable to grasping of general objects that are in surface contact with the robot.
Finite-Element Analysis of Pulsed High-Field Magnets With Precise Thermal and Structural Calculations

The design of high-performance pulsed high-field magnets strongly depends on accurate thermal and structural analyses. In this article, a 2-D multi-physical simulation method for pulsed magnets based on the ANSYS is presented. The skin effect of the coil in a transient magnetic field and the magneto-resistance (MR) effect are considered using ANSYS-parameter design language (APDL). The induced voltage is calculated by the magnetic-penetration method to realize the transient field coupling between the coil and circuit. The MR effect is considered as an additional equivalent heat source. Contact pairs are used to simulate discontinuous boundary and axial compression. The simulated pulse waveform agrees well with the experimental results. Simulations show that the skin effect and MR will increase the maximum temperature by 40%. The contact state between layers in the axial direction is analyzed in detail, which increases the accuracy of stress calculation compared to the existing 1-D programs. The design accuracy of a 90 T magnet is improved effectively using the proposed method.
ECG arrhythmia Discrimination using SVM and Nonlinear and Non-stationary Decomposition

ECG signals represent the all heart's electrical activity. Consequently, it performs a key function in the diagnosis of cardiac disorder and arrhythmia detection. Based on small variations in the ECG's amplitude, length, and morphology, Computer-assisted diagnosis has to turn out to be a recognized method to classifying the heartbeats of one-of-a-kind types of arrhythmia. Due to the nature of the ECG signal, a classification method was created based on the techniques of time-frequency decomposition. Discrete Wavelet Transform (DWT) was used to acquire various frequency components where Multiresolution Analysis (MRA) was implied. Based on these frequency components (MARs), the features vector was calculated using four statistical parameters. Average Power (AP), Dispersion Coefficient (CD), Sample Entropy (SE) and Singular Values (SV) were calculated from 9 RAMs as statistical parameters. SVM was then presented to use the features vector and discriminate ten distinct heartbeats of arrhythmia downloaded from the MIT-BIH database in the Physionet. Confusion matrix, Sensitivity (SEN), specificity (SPE), precision (PRE) was used and calculated to assess the efficiency of the suggested technique and compare it with the past algorithms. The performance of the suggested was discovered to be better than the current techniques, and the accuracy was 99.84
Learning to Walk a Tripod Mobile Robot Using Nonlinear Soft Vibration Actuators With Entropy Adaptive Reinforcement Learning

Soft mobile robots have shown great potential in unstructured and confined environments by taking advantage of their excellent adaptability and high dexterity. However, there are several issues to be addressed, such as actuating speeds and controllability, in soft robots. In this letter, a new vibration actuator is proposed using the nonlinear stiffness characteristic of a hyperelastic material, which creates continuous vibration of the actuator. By integrating three proposed actuators, we also present an advanced soft mobile robot with high degrees of freedom of movement. However, since the dynamic model of the soft mobile robot is generally hard to obtain(intractable), it is difficult to design a controller for the robot. In this regard, we present a method to train a controller, using a novel reinforcement learning (RL) algorithm called adaptive soft actor-critic (ASAC). ASAC gradually reduces a parameter called an entropy temperature, which regulates the entropy of the control policy. In this way, the proposed method can narrow down the search space during training, and reduce the duration of demanding data collection processes in real-world experiments. For the verification of the robustness and the controllability of our robot and the RL algorithm, experiments for zig-zagging path tracking and obstacle avoidance were conducted, and the robot successfully finished the missions with only an hour of training time.
A Method for SSVEP Recognition Based on Weighted Canonical Correlation Analysis

As a multivariate statistical method, canonical correlation analysis (CCA) has been one of the most common methods for recognizing steady-state visual evoked potential (SSVEP) in the field of brain-computer interface (BCI). Normally CCA-based methods do not distinguish the different visual feeling of the subject made by the stimuli with different frequencies. To address this issue, this paper proposed a novel method called weighted canonical correlation analysis (WCCA). To recognize the SSVEP frequency, WCCA first employs the standard CCA to obtain its canonical correlation coefficients with each of the reference signals, which correspond to each stimulus frequency. Then each coefficient is modified with a weight, which is associated with each stimulus frequency and obtained by a training procedure. Different from the SSVEP recognition method based on the standard CCA, WCCA uses the weighted coefficients, rather than the original coefficients, to recognize the SSVEP frequency of the testing EEG data. WCCA may be regarded as a generalized CCA, since WCCA is just CCA when each weight value is one. Therefore, by optimizing the weight values, WCCA may outperform CCA. The effectiveness of WCCA was verified by the experiment results.